Long-Term Changes in Sea Surface Temperature Off the Coast of Central California and Monterey Bay from 1920 to 2014: Are They Commensurate?

*Laurence C. Breaker*

## **Abstract**

We examine to what extent the waters of Monterey Bay act independently of those along the central California coast. Sea surface temperatures (SSTs) from 1920 to 2014 from the central California coast and Monterey Bay were analyzed for longterm trends. To estimate the trends, singular spectrum analysis and empirical mode decomposition were employed. Between 1920 and 1940, long-term trends inside and outside Monterey Bay revealed rapidly increasing temperatures. After 1940 trends inside the bay indicate that temperatures increased from ~1950 for the next 40 years, peaking around 1990, and then decreased rapidly through 2013. Offshore, temperatures increased to the early 1960s, after which they decreased until 2014. El Nino episodes, the Pacific decadal oscillation (PDO), and increased coastal upwelling contribute to the long-term trends. Also, the impact of regime shifts associated with the PDO may be sustained for decades. Overall, the differences in the trends inside and outside Monterey Bay are significant only during summer where largescale processes dominate offshore, and smaller-scale processes are important in and around the bay. Finally, our results suggest that waters inside the bay, although they co-vary with the waters further offshore, often appear to behave independently based on the long-term trends.

**Keywords:** long-term trends, nonlinear trends, singular spectrum analysis, ensemble empirical mode decomposition, central California coast, Monterey Bay, El Nino, Pacific decadal oscillation, coastal upwelling, regime shifts

## **1. Part I: background and preliminaries**

## **1.1 Introduction**

This study seeks to determine if the waters off the central California coast and Monterey Bay have warmed significantly during the 94-year period from 1920 to 2014 by examining sea surface temperatures (SSTs) inside the bay and outside the bay off the central California coast. The period of observation was terminated in December 2013 due to the unexpected arrival of a massive temperature anomaly

off the coast of central California called the "Blob" in early 2014 [1]. We also seek to determine if warming inside the bay differs from warming outside the bay. This comparison is motivated, in part, by the following question. Is the bay merely an extension of the waters further offshore along the central California coast and thus expected to have similar physical properties, or are there local processes within or near the bay that significantly alter those properties? One of the arguments that favors the similarity of these waters is due to the relatively large entrance of the bay compared to its longest internal dimension (approximately 36 vs. 42 km). Thus, the waters offshore have wide and direct access to the bay. Also, residence times in the bay are not overly long, on the order of 7 days [2]. Thus, there is relatively less time for local waters that enter the bay to be modified before they exit. Early work in the greater Monterey Bay area by Skogsberg [3] and Skogsberg and Phelps [4], for example, tended to favor similarity. However, several of the more recent processoriented studies that have been conducted on finer temporal and spatial scales in or near the bay would favor dissimilarity [5, 6]. In the simplest case, if we were to find significant differences in the long-term trends of temperature inside and outside the bay, we might favor dissimilarity.

To make these determinations, we calculate the long-term trends in SST in both domains. The results depend to a certain degree on how we distinguish between "coastal" waters and waters further offshore and on the methods that are used to estimate these trends. In this study we examine not only the long-term trends but also the nature of the processes that most likely have contributed to the trends.

The coastal ocean off central California and Monterey Bay is strongly affected by the process of coastal upwelling. According to Garcia-Reyes and Largier [7], coastal upwelling off central and northern California occurs from April through June, followed by relaxation of the upwelling-favorable winds from July through September. During the winter months from December through February, extratropical storms occur which contribute to cooler surface temperatures through wind mixing and the transfer of sensible and latent heat. Other times of the year tend to be transitional.

Based on the work of Mendelssohn and Schwing [8] and Garcia-Reyes and Largier [9], coastal upwelling off central California has been shown to have increased over the past several decades. According to Bakun [10] and Snyder et al. [11], coastal upwelling may be expected to increase in the future due to climate change or long-term climate variability.

Coastal upwelling is not the only physical process in the California Current System (CCS) that has a significant impact on SST. Ocean fronts, Ekman pumping, eddies, and squirts and jets all affect temperature. With respect to ocean fronts, the front that separates upwelled waters near the coast from oceanic waters further offshore is a primary example [12]. Positive wind stress curl off the central California coast during the spring and summer leads to Ekman pumping or offshore upwelling due to Ekman divergence. This is an important process that brings colder waters to the surface away from the influence of a coastal boundary. Both cyclonic and anticyclonic eddies are found in the CCS. Cyclonic eddies promote upwelling and are often found south of capes along the California coast. A major anticyclonic eddy is occasionally observed just west of Monterey Bay [13]. Jets and squirts occur off central and northern California characterized by patterns of vigorous circulation, readily observed in satellite images of SST [14].

During the spring and summer, upwelled waters are found inside Monterey Bay that often originate at Pt. Ano Nuevo (**Figure 1**), are advected down the coast, and then enter the bay forming a cyclonic pattern of circulation that has frequently been observed [15, 16]. In addition to upwelled waters that are advected into the bay from further offshore, local upwelling occurs in northern Monterey Bay due to the diurnal sea breeze that is well developed during the summer [5].

**47**

**Figure 1.**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

Now we turn to a review of the previous work that is directly relevant to this study. Inside Monterey Bay, SSTs from the Hopkins Marine Station at the southern end of the bay (**Figure 1**) have been examined on several occasions for possible long-term trends [17–19]. The record begins in 1919 and extends up to the present time. In each case, warming rates of approximately +0.01°C/year were found, based

*A map of the study areas including the offshore domain shown by the dotted box and the inshore domain which is Monterey Bay. The location where the data used to evaluate the SST from HMS is shown in the inset by the gray dot just above (north) of the Hopkins Marine Station (HMS). N26, N12, and N42 refer to National Data* 

Barry et al. [17] found that the annual maximum in SST increased more rapidly than the annual minimum. Further, they observed that changes in the flora and fauna of the intertidal zone in Monterey Bay coincided with well-documented secular warming along the US West Coast. In addition, they indicated that climaterelated faunal changes in California's rocky intertidal community were related to long-term changes in the coastal environment based on the SST data from Hopkins

Sagarin et al. [18] indicated that temperature changes in Monterey Bay tended to be monotonic when the Hopkins record was subdivided into selected time periods between 1920 and 1995. They also concluded that climate warming, based on the Hopkins record and other sources, was responsible for range-related shifts in the

In a previous study, Breaker [19] examined the record at Hopkins for the period from 1920 to 2001 and also estimated the long-term linear trend. He found the slope to be +0.011°C/year, consistent with the values obtained by Barry et al. and Sagarin et al. He further found that the trend was statistically significant at the 95% level of confidence. Breaker examined the seasonal effects on long-term warming and found that warming in July exceeded the yearly rate by ~7%, whereas warming in

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

on linear, least-squares fits to the data.

*Buoy Center (NDBC) buoy locations (***Figure 4***).*

and inferred that these changes were due to climate warming.

intertidal communities along the California coast.

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

#### **Figure 1.**

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

bay, we might favor dissimilarity.

change or long-term climate variability.

readily observed in satellite images of SST [14].

diurnal sea breeze that is well developed during the summer [5].

off the coast of central California called the "Blob" in early 2014 [1]. We also seek to determine if warming inside the bay differs from warming outside the bay. This comparison is motivated, in part, by the following question. Is the bay merely an extension of the waters further offshore along the central California coast and thus expected to have similar physical properties, or are there local processes within or near the bay that significantly alter those properties? One of the arguments that favors the similarity of these waters is due to the relatively large entrance of the bay compared to its longest internal dimension (approximately 36 vs. 42 km). Thus, the waters offshore have wide and direct access to the bay. Also, residence times in the bay are not overly long, on the order of 7 days [2]. Thus, there is relatively less time for local waters that enter the bay to be modified before they exit. Early work in the greater Monterey Bay area by Skogsberg [3] and Skogsberg and Phelps [4], for example, tended to favor similarity. However, several of the more recent processoriented studies that have been conducted on finer temporal and spatial scales in or near the bay would favor dissimilarity [5, 6]. In the simplest case, if we were to find significant differences in the long-term trends of temperature inside and outside the

To make these determinations, we calculate the long-term trends in SST in both domains. The results depend to a certain degree on how we distinguish between "coastal" waters and waters further offshore and on the methods that are used to estimate these trends. In this study we examine not only the long-term trends but also the nature of the processes that most likely have contributed to the trends.

The coastal ocean off central California and Monterey Bay is strongly affected by the process of coastal upwelling. According to Garcia-Reyes and Largier [7], coastal upwelling off central and northern California occurs from April through June, followed by relaxation of the upwelling-favorable winds from July through September. During the winter months from December through February, extratropical storms occur which contribute to cooler surface temperatures through wind mixing and the transfer of sensible and latent heat. Other times of the year tend to be transitional. Based on the work of Mendelssohn and Schwing [8] and Garcia-Reyes and Largier [9], coastal upwelling off central California has been shown to have increased over the past several decades. According to Bakun [10] and Snyder et al. [11], coastal upwelling may be expected to increase in the future due to climate

Coastal upwelling is not the only physical process in the California Current System (CCS) that has a significant impact on SST. Ocean fronts, Ekman pumping, eddies, and squirts and jets all affect temperature. With respect to ocean fronts, the front that separates upwelled waters near the coast from oceanic waters further offshore is a primary example [12]. Positive wind stress curl off the central California coast during the spring and summer leads to Ekman pumping or offshore upwelling due to Ekman divergence. This is an important process that brings colder waters to the surface away from the influence of a coastal boundary. Both cyclonic and anticyclonic eddies are found in the CCS. Cyclonic eddies promote upwelling and are often found south of capes along the California coast. A major anticyclonic eddy is occasionally observed just west of Monterey Bay [13]. Jets and squirts occur off central and northern California characterized by patterns of vigorous circulation,

During the spring and summer, upwelled waters are found inside Monterey Bay that often originate at Pt. Ano Nuevo (**Figure 1**), are advected down the coast, and then enter the bay forming a cyclonic pattern of circulation that has frequently been observed [15, 16]. In addition to upwelled waters that are advected into the bay from further offshore, local upwelling occurs in northern Monterey Bay due to the

**46**

*A map of the study areas including the offshore domain shown by the dotted box and the inshore domain which is Monterey Bay. The location where the data used to evaluate the SST from HMS is shown in the inset by the gray dot just above (north) of the Hopkins Marine Station (HMS). N26, N12, and N42 refer to National Data Buoy Center (NDBC) buoy locations (***Figure 4***).*

Now we turn to a review of the previous work that is directly relevant to this study. Inside Monterey Bay, SSTs from the Hopkins Marine Station at the southern end of the bay (**Figure 1**) have been examined on several occasions for possible long-term trends [17–19]. The record begins in 1919 and extends up to the present time. In each case, warming rates of approximately +0.01°C/year were found, based on linear, least-squares fits to the data.

Barry et al. [17] found that the annual maximum in SST increased more rapidly than the annual minimum. Further, they observed that changes in the flora and fauna of the intertidal zone in Monterey Bay coincided with well-documented secular warming along the US West Coast. In addition, they indicated that climaterelated faunal changes in California's rocky intertidal community were related to long-term changes in the coastal environment based on the SST data from Hopkins and inferred that these changes were due to climate warming.

Sagarin et al. [18] indicated that temperature changes in Monterey Bay tended to be monotonic when the Hopkins record was subdivided into selected time periods between 1920 and 1995. They also concluded that climate warming, based on the Hopkins record and other sources, was responsible for range-related shifts in the intertidal communities along the California coast.

In a previous study, Breaker [19] examined the record at Hopkins for the period from 1920 to 2001 and also estimated the long-term linear trend. He found the slope to be +0.011°C/year, consistent with the values obtained by Barry et al. and Sagarin et al. He further found that the trend was statistically significant at the 95% level of confidence. Breaker examined the seasonal effects on long-term warming and found that warming in July exceeded the yearly rate by ~7%, whereas warming in

January was less than the yearly rate by almost 19%, consistent with the results of Barry et al. [17]. In the same study, Breaker also estimated the relative importance of the various processes that contributed to variability in the data and found that the annual cycle and its first harmonic, El Nino warming episodes, the Pacific decadal oscillation (PDO), and the long-term trend were all significant sources of variability.

The present study departs significantly from the previous study in a number of ways. First, one of the questions from the earlier work that was left unanswered was to what extent did the results from Monterey Bay reflect what was happening on larger scales outside the bay. Second, the record is now almost 15 years longer than the record used by Breaker [19], and significant cooling has occurred during this period. Third, in the previous study, only the long-term *linear* trend was calculated, and, since then, it has become clear that using a linear basis to model long-term changes in the data is a poor choice. Finally, we apply singular spectrum analysis to the data together with empirical mode decomposition to obtain independent estimates of the long-term trends. The methods are complementary and provide a basis for evaluating their quality.

Off the coast of central California between 35°N and 39°N, Garcia-Reyes and Largier [9] examined long-term trends in SST and several related parameters from the existing network of NDBC buoys for the period from 1982 to 2008. During the period of observation, the upwelling-favorable alongshore winds increased and SST decreased, consistent with increased coastal upwelling. Not only was the upwelling stronger, the length of the upwelling season was found to have increased, starting earlier in the spring and ending later in the fall. Relevant to this study, the observed trend in SST was found to be strongest off central California. Further, the observed cooling was limited to coastal waters over the shelf, where upwelling is the dominant process during the spring and summer. Because the record was only 27 years long, it was not possible to determine if the apparent trends were related to decadal or longer-term periodic behavior.

A larger view of the CCS was taken by Field et al. [20] who examined long-term warming from extensive records of SST off California and from other regions of the North Pacific. Much of their data spanned the twentieth century although it did not resolve the region of coastal upwelling off California. For locations where SSTs were examined in the North Pacific, the Pacific decadal oscillation (PDO) index accounted for a significant fraction of the observed variability. Also, near-surface temperature variations throughout the CCS were found to be similar between regions. Their data indicate that from 1900 on, strong negative SST anomalies prevailed during the period leading up to the early 1920s. They resulted from higher atmospheric pressure off California that intensified flow in the California Current and upwelling during this period. During the twentieth century, data from the CCS revealed warming trends with slopes ranging from +0.007°C/year to +0.010°C/year, similar to the warming observed in Monterey Bay over approximately the same period. Also, the negative anomalies in the early years of the twentieth century have contributed significantly to the positive trends that have been observed in records that are long enough to include this period.

Finally, we return to the original question posed in the title of this chapter. Although the word "commensurate" can refer to many different measures of similarity, we take it to mean to what extent do the waters inside Monterey Bay co-vary with the waters outside the bay with respect to their physical properties. During the winter the influence of the poleward flowing Davidson Current dominates the behavior of waters inside and outside the bay, and thus SSTs are generally similar [13]. During the summer when coastal upwelling is a dominant process, the relationship between the waters inside and outside the bay becomes more complicated

**49**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

mer. Our subsequent analyses will shed more light on this question.

from Hopkins per se and are discussed in Section 3.

ferent than the point observations we employ inside the bay.

because smaller-scale processes in and around Monterey Bay become important. Thus, the answer to this question may depend to a significant degree on the season, with higher co-variability to be expected during the winter than during the sum-

The primary source of SST data inside the bay comes from the Hopkins Marine

Station (HMS) located at the southern end of Monterey Bay in Pacific Grove (**Figure 1**). The data have been acquired nominally at 08:00 am PST on a daily basis since January 20, 1919. Because of several issues that have affected data quality over its duration, Breaker et al. [21] reconstructed the record by making time-of-day adjustments for varying data collection times, removed gaps, improved data resolution consistency, and, finally, reconstructed one entire year (1940) that was missing from the record based on regression with daily SSTs from the Farallon Islands. For the purposes of this study, we have extracted the 95+ year period from January 1920 through May 2015 although most of the results we present are limited to the period from 1920 through 2013 to avoid the confounding effects of the major temperature anomaly that arrived in early 2014. The daily observations were then averaged to obtain monthly mean values. Because these data have been acquired at a single location, they represent point observations. Daily observations of SST adjacent to HMS were also collected from August 1, 2006, through January 31, 2007, to ascertain the representativeness of the data

The primary source of data for the 2°× 2° region shown in **Figure 1** along the coast of central California is the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). These data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www.esrl.noaa.gov/psd/*.* Monthly averaged SSTs were acquired for the region from 36 to 38°N, and from 122 to 124°W, for the period from January 1920 through May of 2015. Again, most of the results we present are limited to the period that ends in December 2013. These monthly data represent spatially averaged values and as a result are inherently dif-

**Figure 2** shows the number of observations per month in the 2 × 2 degree study area starting January 1920. Overall, the number of observations remains below 1500 per month until 2004 when a significant increase occurs. The inset shows the period from 1920 through 1970 where the numbers of observations are far smaller. Plots of the monthly averaged SST data from the Hopkins Marine Station, inside Monterey Bay, and from ICOADS, adjacent to the central California coast, outside the bay, are shown in **Figure 3**. Superimposed on the data are smoothed versions shown in red obtained using a *LOWESS* smoothing function. The method is nonparametric and performs robust, locally weighted regression. It fits linear or quadratic basis functions to the data at the center of neighborhoods where the radius of each neighborhood contains a specific percentage of the data points. This type of smoothing function does not lose degrees of freedom at the ends of the record and introduces minimal distortion at these locations. The fraction of data in each neighborhood, and thus the smoothness, is determined by two parameters, the degree of the local polynomial basis function (linear or quadratic), *λ*, and the level

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

**1.2 Data sources**

*1.2.1 Data sources inside the bay*

*1.2.2 Data outside the bay*

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

because smaller-scale processes in and around Monterey Bay become important. Thus, the answer to this question may depend to a significant degree on the season, with higher co-variability to be expected during the winter than during the summer. Our subsequent analyses will shed more light on this question.

#### **1.2 Data sources**

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

January was less than the yearly rate by almost 19%, consistent with the results of Barry et al. [17]. In the same study, Breaker also estimated the relative importance of the various processes that contributed to variability in the data and found that the annual cycle and its first harmonic, El Nino warming episodes, the Pacific decadal oscillation (PDO), and the long-term trend were all significant sources of

The present study departs significantly from the previous study in a number of ways. First, one of the questions from the earlier work that was left unanswered was to what extent did the results from Monterey Bay reflect what was happening on larger scales outside the bay. Second, the record is now almost 15 years longer than the record used by Breaker [19], and significant cooling has occurred during this period. Third, in the previous study, only the long-term *linear* trend was calculated, and, since then, it has become clear that using a linear basis to model long-term changes in the data is a poor choice. Finally, we apply singular spectrum analysis to the data together with empirical mode decomposition to obtain independent estimates of the long-term trends. The methods are complementary and provide a

Off the coast of central California between 35°N and 39°N, Garcia-Reyes and Largier [9] examined long-term trends in SST and several related parameters from the existing network of NDBC buoys for the period from 1982 to 2008. During the period of observation, the upwelling-favorable alongshore winds increased and SST decreased, consistent with increased coastal upwelling. Not only was the upwelling stronger, the length of the upwelling season was found to have increased, starting earlier in the spring and ending later in the fall. Relevant to this study, the observed trend in SST was found to be strongest off central California. Further, the observed cooling was limited to coastal waters over the shelf, where upwelling is the dominant process during the spring and summer. Because the record was only 27 years long, it was not possible to determine if the apparent trends were related to decadal

A larger view of the CCS was taken by Field et al. [20] who examined long-term warming from extensive records of SST off California and from other regions of the North Pacific. Much of their data spanned the twentieth century although it did not resolve the region of coastal upwelling off California. For locations where SSTs were examined in the North Pacific, the Pacific decadal oscillation (PDO) index accounted for a significant fraction of the observed variability. Also, near-surface temperature variations throughout the CCS were found to be similar between regions. Their data indicate that from 1900 on, strong negative SST anomalies prevailed during the period leading up to the early 1920s. They resulted from higher atmospheric pressure off California that intensified flow in the California Current and upwelling during this period. During the twentieth century, data from the CCS revealed warming trends with slopes ranging from +0.007°C/year to +0.010°C/year, similar to the warming observed in Monterey Bay over approximately the same period. Also, the negative anomalies in the early years of the twentieth century have contributed significantly to the positive trends that have been observed in records that are long

Finally, we return to the original question posed in the title of this chapter. Although the word "commensurate" can refer to many different measures of similarity, we take it to mean to what extent do the waters inside Monterey Bay co-vary with the waters outside the bay with respect to their physical properties. During the winter the influence of the poleward flowing Davidson Current dominates the behavior of waters inside and outside the bay, and thus SSTs are generally similar [13]. During the summer when coastal upwelling is a dominant process, the relationship between the waters inside and outside the bay becomes more complicated

**48**

variability.

basis for evaluating their quality.

or longer-term periodic behavior.

enough to include this period.

#### *1.2.1 Data sources inside the bay*

The primary source of SST data inside the bay comes from the Hopkins Marine Station (HMS) located at the southern end of Monterey Bay in Pacific Grove (**Figure 1**). The data have been acquired nominally at 08:00 am PST on a daily basis since January 20, 1919. Because of several issues that have affected data quality over its duration, Breaker et al. [21] reconstructed the record by making time-of-day adjustments for varying data collection times, removed gaps, improved data resolution consistency, and, finally, reconstructed one entire year (1940) that was missing from the record based on regression with daily SSTs from the Farallon Islands. For the purposes of this study, we have extracted the 95+ year period from January 1920 through May 2015 although most of the results we present are limited to the period from 1920 through 2013 to avoid the confounding effects of the major temperature anomaly that arrived in early 2014. The daily observations were then averaged to obtain monthly mean values. Because these data have been acquired at a single location, they represent point observations. Daily observations of SST adjacent to HMS were also collected from August 1, 2006, through January 31, 2007, to ascertain the representativeness of the data from Hopkins per se and are discussed in Section 3.

#### *1.2.2 Data outside the bay*

The primary source of data for the 2°× 2° region shown in **Figure 1** along the coast of central California is the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). These data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www.esrl.noaa.gov/psd/*.* Monthly averaged SSTs were acquired for the region from 36 to 38°N, and from 122 to 124°W, for the period from January 1920 through May of 2015. Again, most of the results we present are limited to the period that ends in December 2013. These monthly data represent spatially averaged values and as a result are inherently different than the point observations we employ inside the bay.

**Figure 2** shows the number of observations per month in the 2 × 2 degree study area starting January 1920. Overall, the number of observations remains below 1500 per month until 2004 when a significant increase occurs. The inset shows the period from 1920 through 1970 where the numbers of observations are far smaller.

Plots of the monthly averaged SST data from the Hopkins Marine Station, inside Monterey Bay, and from ICOADS, adjacent to the central California coast, outside the bay, are shown in **Figure 3**. Superimposed on the data are smoothed versions shown in red obtained using a *LOWESS* smoothing function. The method is nonparametric and performs robust, locally weighted regression. It fits linear or quadratic basis functions to the data at the center of neighborhoods where the radius of each neighborhood contains a specific percentage of the data points. This type of smoothing function does not lose degrees of freedom at the ends of the record and introduces minimal distortion at these locations. The fraction of data in each neighborhood, and thus the smoothness, is determined by two parameters, the degree of the local polynomial basis function (linear or quadratic), *λ*, and the level

#### **Figure 2.**

*The number of observations per month from ICOADS for the offshore domain are plotted by month since 1920. The inset shows the period between 1920 and 1970 where the number of observations per month is relatively small.*

#### **Figure 3.**

*Sea surface temperature from HMS inside Monterey Bay (a), and from ICOADS, off the coast of central California, outside the bay (b). The red curves provide smoothed versions of the data where important features such as major El Nino warning episodes can be more easily identified.*

of smoothing, *α*. In the present study, the degree of the local polynomial has been set to quadratic. The smoothing parameter is specified by the choice of *α*, where 0 ≤ *α* ≤ 1. For *α* equal to 0, no smoothing occurs, and for *α* equal to 1, we obtain

**51**

will influence the trends we calculate.

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

essentially a straight line. The goal is to choose *α* large enough to obtain as much smoothness as possible without distorting underlying patterns in the data [22]. At the level of detail shown in **Figure 3**, the relative importance of the annual cycle is readily apparent. It is also possible to identify major El Nino warming episodes that occurred in 1931–1932, 1940–1941, 1958–1959, 1982–1983, 1986, 1992–1993, and 1997–1998. In this study we usually refer to El Nino warming events or simply El Ninos rather than ENSO which has a broader meaning that includes La Nina cooling events as well. Off central and northern California, it is the warming phase of the El Nino phenomenon that stands out and not the cooling phase. Also, as mentioned earlier, there was a major increase in SST that is readily apparent in the data inside and outside the bay during 2014. We show it here but do not include it in our calculations. According to **Figure 3**, the increase in SST based on the unsmoothed data during 2014 clearly exceeds 3°C, somewhat larger than the

Inside the bay, the SST data are acquired at the shoreline in a somewhat sheltered

offshore, slightly north of HMS in southern Monterey Bay (**Figure 1**). The data were acquired over a period of almost 6 months from August 4, 2007, through January 31, 2008. This data was acquired specifically to evaluate the daily observations of SST that have been collected at HMS (M. Denny, personal communication). The two data sets are almost identical with a mean difference of less than 0.1°C over the 6-month period of observation. Based on an earlier comparison of SST data between HMS and Santa Cruz, located at the north end of Monterey Bay (**Figure 1**), Breaker [19] concluded that the data from HMS was generally representative of SST data collected elsewhere in the bay, consistent with the results presented here. The offshore data used in this study have been acquired over a spatial domain that is regional in scale (**Figure 1**). The number of observations per month has increased significantly since 1920 (**Figure 2**). This, in turn, changes the uncertainty in estimating the monthly means over the length of record. This problem is not unique in climate research. The globally averaged record of surface air temperature

Spatial biases in the SST observations acquired over the offshore domain lead to biases in the monthly mean values that are calculated. This arises for at least two reasons. First, the observations that enter into the monthly means are not uniformly spaced over the region since they come from a variety of sources. Second, even if the observations were acquired uniformly, the underlying ocean itself is not thermally homogeneous. The second factor becomes particularly apparent during the spring and summer when coastal upwelling occurs. During this period SSTs may increase by 5°C or more between the coast and the offshore boundary of the study domain. These biases are significantly reduced between November and February during the Davidson Current period when the waters tend to become more isothermal. Overall, spatial biases are to be expected, they vary with the season, and they

To examine the spatial bias problem we have drawn from Garcia-Reyes and Largier [9] who examined SST and other data for the period from 1982 through 2008

used extensively in climate studies suffers from the same problem.

area next to the Hopkins Marine Station. Next to the coast where the circulation is expected to be weaker, residence times could be longer, leading to increased temperatures, locally. To examine this question which has arisen before, we compared the SST data from HMS with SSTs acquired at an adjacent offshore location. Specifically, daily SSTs at HMS were compared with daily SSTs acquired just

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

reported value of 2.5°C [1].

**1.3 Data representativeness**

#### *Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

essentially a straight line. The goal is to choose *α* large enough to obtain as much smoothness as possible without distorting underlying patterns in the data [22].

At the level of detail shown in **Figure 3**, the relative importance of the annual cycle is readily apparent. It is also possible to identify major El Nino warming episodes that occurred in 1931–1932, 1940–1941, 1958–1959, 1982–1983, 1986, 1992–1993, and 1997–1998. In this study we usually refer to El Nino warming events or simply El Ninos rather than ENSO which has a broader meaning that includes La Nina cooling events as well. Off central and northern California, it is the warming phase of the El Nino phenomenon that stands out and not the cooling phase.

Also, as mentioned earlier, there was a major increase in SST that is readily apparent in the data inside and outside the bay during 2014. We show it here but do not include it in our calculations. According to **Figure 3**, the increase in SST based on the unsmoothed data during 2014 clearly exceeds 3°C, somewhat larger than the reported value of 2.5°C [1].

#### **1.3 Data representativeness**

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

of smoothing, *α*. In the present study, the degree of the local polynomial has been set to quadratic. The smoothing parameter is specified by the choice of *α*, where 0 ≤ *α* ≤ 1. For *α* equal to 0, no smoothing occurs, and for *α* equal to 1, we obtain

*Sea surface temperature from HMS inside Monterey Bay (a), and from ICOADS, off the coast of central California, outside the bay (b). The red curves provide smoothed versions of the data where important features* 

*such as major El Nino warning episodes can be more easily identified.*

*The number of observations per month from ICOADS for the offshore domain are plotted by month since 1920. The inset shows the period between 1920 and 1970 where the number of observations per month is relatively* 

**50**

**Figure 3.**

**Figure 2.**

*small.*

Inside the bay, the SST data are acquired at the shoreline in a somewhat sheltered area next to the Hopkins Marine Station. Next to the coast where the circulation is expected to be weaker, residence times could be longer, leading to increased temperatures, locally. To examine this question which has arisen before, we compared the SST data from HMS with SSTs acquired at an adjacent offshore location. Specifically, daily SSTs at HMS were compared with daily SSTs acquired just offshore, slightly north of HMS in southern Monterey Bay (**Figure 1**). The data were acquired over a period of almost 6 months from August 4, 2007, through January 31, 2008. This data was acquired specifically to evaluate the daily observations of SST that have been collected at HMS (M. Denny, personal communication). The two data sets are almost identical with a mean difference of less than 0.1°C over the 6-month period of observation. Based on an earlier comparison of SST data between HMS and Santa Cruz, located at the north end of Monterey Bay (**Figure 1**), Breaker [19] concluded that the data from HMS was generally representative of SST data collected elsewhere in the bay, consistent with the results presented here.

The offshore data used in this study have been acquired over a spatial domain that is regional in scale (**Figure 1**). The number of observations per month has increased significantly since 1920 (**Figure 2**). This, in turn, changes the uncertainty in estimating the monthly means over the length of record. This problem is not unique in climate research. The globally averaged record of surface air temperature used extensively in climate studies suffers from the same problem.

Spatial biases in the SST observations acquired over the offshore domain lead to biases in the monthly mean values that are calculated. This arises for at least two reasons. First, the observations that enter into the monthly means are not uniformly spaced over the region since they come from a variety of sources. Second, even if the observations were acquired uniformly, the underlying ocean itself is not thermally homogeneous. The second factor becomes particularly apparent during the spring and summer when coastal upwelling occurs. During this period SSTs may increase by 5°C or more between the coast and the offshore boundary of the study domain. These biases are significantly reduced between November and February during the Davidson Current period when the waters tend to become more isothermal. Overall, spatial biases are to be expected, they vary with the season, and they will influence the trends we calculate.

To examine the spatial bias problem we have drawn from Garcia-Reyes and Largier [9] who examined SST and other data for the period from 1982 through 2008 from eleven NDBC Environmental Data Buoys along the California coast. Three of those buoys (N26, N12, and N42) are located in our offshore study area (**Figure 1**). They calculated the linear trends in SST for the upwelling season which they defined as the period from March through July for all buoys. We have taken our offshore data, with the buoy data specifically removed, and calculated the linear trend for the same time period and upwelling season (**Figure 4**). For buoys N26, N12, and N42, Garcia-Reyes and Largier obtained slopes of −0.032, −0.034, and −0.021°C/year, respectively, with a mean of −0.0290°C/year, taken over the three buoys. Based on our data, we obtained a slope of −0.024°C/year, slightly less than, but still relatively close to, the value obtained by Garcia-Reyes and Largier. If the non-buoy data were randomly spaced over the domain, we would find this result encouraging in that it suggests a certain degree of spatial homogeneity in the data. If, however, the nonbuoy data were concentrated closer to the coast, as we suspect, then all we have shown is that the buoy and non-buoy data tend to be consistent.

## **1.4 Long-term trends**

## *1.4.1 Background*

Although trends may appear to be intuitively obvious, precise definitions are hard to find. According to Fuller [23], the term "trend" only acquires meaning when a specific procedure is used for its estimation. For time series, trends have been labeled as deterministic when the trend per se is modeled explicitly [24]. A trend implies non-stationarity of the first order, and so its removal may transform the data into a stationary process. Linear trends are often fitted to the data, but as we shall see in the present study, the long-term changes we observe are not linear. In this regard, two studies are relevant. Jevrejeva et al. [25] analyzed sea level data for long-term trends that were nonlinear in nature using a variation of singular spectrum analysis (SSA) called Monte Carlo SSA. This method provides error estimates associated with the trend as well. Breaker et al. [26] examined sea surface temperature data from the coast of Ecuador for long-term trends that were inherently nonlinear using ensemble

#### **Figure 4.**

*Mean seasonal SST for the offshore domain for the upwelling period from March through July plotted on a yearly basis for 1982 through 2008. A least-squares linear fit to this data yields a slope of −0.024°C/year.*

**53**

cerning SSA, see [31–35].

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

empirical mode decomposition. This method performed well in extracting the longterm trends regardless of the degree to which the data tended to be nonlinear. In our experience, when the data are trend-dominant, different methods of estimating the trend often yield similar results, but when the trend is not a dominant feature in the data, then different methods may yield significantly different results. One common definition of the trend is that of a smoothly varying function whose first derivative does not change sign [27]. Although this definition may be intuitively appealing, in practice, it is often too restrictive. Although formal definitions of the trend are scarce, Wu et al. [28] recently proposed the following: "The trend is an intrinsically fitted monotonic function or a function in which there can be, at most, one extremum within a given data span." For rather extensive reviews of methods for estimating trends, see Esterby [27] and Alexandrov et al. [29].

To estimate long-term trends in the data, we use two methods of spectral decomposition, singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD). We find that SSA and EEMD are often complementary in the trends they produce, and, when they are, it increases our confidence that meaningful trends have indeed been extracted from the data. Both methods decompose the data into a set of independent modes. To this extent, the methods are similar. However, the methodologies per se are entirely different. We give a brief introduction to each

Singular spectrum analysis (SSA) decomposes a time series into a set of independent modes, similar in many respects to principal component analysis. The method is well-suited for analyzing data that are nonstationary and/or nonlinear due to the adaptive nature of the basis functions employed. A lagged covariance matrix (a Toeplitz matrix, in this case) is formed from the time series that is decomposed into eigenvalues, eigenvectors, and principal components. Reconstructed components can be calculated from the eigenvectors and principal components that represent partial time series whose sum over all modes reproduces the original time series. These components, modes, or eigentriples as they are variously called are often amenable to physical interpretation. The number of modes that is selected is called the window length and determines the resolution of the decomposition. The mode or modes that correspond to the trend can often be selected by inspection when the trend contains a significant portion of the variance, but when the trend is relatively weak, the problem becomes more difficult. Alexandrov [30] provides a new approach for extracting the trend when it is not a dominant feature in the data. In this study, the trend, although not the most dominant feature in the data, is robust enough that we do not resort to the method of Alexandrov in order to isolate it. Finally, we note that because the method of decomposition in SSA is global in nature, problems can arise at the boundaries of the data. For further details con-

Empirical mode decomposition (EMD) is a method of decomposing a time series into a sequence of empirically orthogonal intrinsic mode function (IMF) components and a residual. In EMD, the number of modes is determined by the data, whereas in SSA, the number of modes is a free parameter that must be specified by the user. Like SSA, EMD is data adaptive and well-suited for the analysis of nonstationary and nonlinear time series. The IMF components are often physically meaningful because the characteristic scales in each case are determined by the data itself. As in SSA, selected modes may require grouping in order to extract a physical basis. Each IMF represents a mode of oscillation with time-dependent amplitude

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

method here and the appropriate references.

*1.4.2 Methods*

#### *Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

empirical mode decomposition. This method performed well in extracting the longterm trends regardless of the degree to which the data tended to be nonlinear.

In our experience, when the data are trend-dominant, different methods of estimating the trend often yield similar results, but when the trend is not a dominant feature in the data, then different methods may yield significantly different results. One common definition of the trend is that of a smoothly varying function whose first derivative does not change sign [27]. Although this definition may be intuitively appealing, in practice, it is often too restrictive. Although formal definitions of the trend are scarce, Wu et al. [28] recently proposed the following: "The trend is an intrinsically fitted monotonic function or a function in which there can be, at most, one extremum within a given data span." For rather extensive reviews of methods for estimating trends, see Esterby [27] and Alexandrov et al. [29].

#### *1.4.2 Methods*

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

shown is that the buoy and non-buoy data tend to be consistent.

**1.4 Long-term trends**

*1.4.1 Background*

from eleven NDBC Environmental Data Buoys along the California coast. Three of those buoys (N26, N12, and N42) are located in our offshore study area (**Figure 1**). They calculated the linear trends in SST for the upwelling season which they defined as the period from March through July for all buoys. We have taken our offshore data, with the buoy data specifically removed, and calculated the linear trend for the same time period and upwelling season (**Figure 4**). For buoys N26, N12, and N42, Garcia-Reyes and Largier obtained slopes of −0.032, −0.034, and −0.021°C/year, respectively, with a mean of −0.0290°C/year, taken over the three buoys. Based on our data, we obtained a slope of −0.024°C/year, slightly less than, but still relatively close to, the value obtained by Garcia-Reyes and Largier. If the non-buoy data were randomly spaced over the domain, we would find this result encouraging in that it suggests a certain degree of spatial homogeneity in the data. If, however, the nonbuoy data were concentrated closer to the coast, as we suspect, then all we have

Although trends may appear to be intuitively obvious, precise definitions are hard to find. According to Fuller [23], the term "trend" only acquires meaning when a specific procedure is used for its estimation. For time series, trends have been labeled as deterministic when the trend per se is modeled explicitly [24]. A trend implies non-stationarity of the first order, and so its removal may transform the data into a stationary process. Linear trends are often fitted to the data, but as we shall see in the present study, the long-term changes we observe are not linear. In this regard, two studies are relevant. Jevrejeva et al. [25] analyzed sea level data for long-term trends that were nonlinear in nature using a variation of singular spectrum analysis (SSA) called Monte Carlo SSA. This method provides error estimates associated with the trend as well. Breaker et al. [26] examined sea surface temperature data from the coast of Ecuador for long-term trends that were inherently nonlinear using ensemble

*Mean seasonal SST for the offshore domain for the upwelling period from March through July plotted on a yearly basis for 1982 through 2008. A least-squares linear fit to this data yields a slope of −0.024°C/year.*

**52**

**Figure 4.**

To estimate long-term trends in the data, we use two methods of spectral decomposition, singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD). We find that SSA and EEMD are often complementary in the trends they produce, and, when they are, it increases our confidence that meaningful trends have indeed been extracted from the data. Both methods decompose the data into a set of independent modes. To this extent, the methods are similar. However, the methodologies per se are entirely different. We give a brief introduction to each method here and the appropriate references.

Singular spectrum analysis (SSA) decomposes a time series into a set of independent modes, similar in many respects to principal component analysis. The method is well-suited for analyzing data that are nonstationary and/or nonlinear due to the adaptive nature of the basis functions employed. A lagged covariance matrix (a Toeplitz matrix, in this case) is formed from the time series that is decomposed into eigenvalues, eigenvectors, and principal components. Reconstructed components can be calculated from the eigenvectors and principal components that represent partial time series whose sum over all modes reproduces the original time series. These components, modes, or eigentriples as they are variously called are often amenable to physical interpretation. The number of modes that is selected is called the window length and determines the resolution of the decomposition. The mode or modes that correspond to the trend can often be selected by inspection when the trend contains a significant portion of the variance, but when the trend is relatively weak, the problem becomes more difficult. Alexandrov [30] provides a new approach for extracting the trend when it is not a dominant feature in the data. In this study, the trend, although not the most dominant feature in the data, is robust enough that we do not resort to the method of Alexandrov in order to isolate it. Finally, we note that because the method of decomposition in SSA is global in nature, problems can arise at the boundaries of the data. For further details concerning SSA, see [31–35].

Empirical mode decomposition (EMD) is a method of decomposing a time series into a sequence of empirically orthogonal intrinsic mode function (IMF) components and a residual. In EMD, the number of modes is determined by the data, whereas in SSA, the number of modes is a free parameter that must be specified by the user. Like SSA, EMD is data adaptive and well-suited for the analysis of nonstationary and nonlinear time series. The IMF components are often physically meaningful because the characteristic scales in each case are determined by the data itself. As in SSA, selected modes may require grouping in order to extract a physical basis. Each IMF represents a mode of oscillation with time-dependent amplitude

and frequencies that lie within a specific band of frequencies, the center of which defines the mean period of that mode. The process of extracting the individual modes or essential scales from the data is called sifting and is performed many times to produce a single IMF. In practice, extracting the trend may require that not only the residual but one or several of the highest adjacent modes be grouped in order to reconstruct it.

One problem in the application of EMD in the past was that mode mixing often occurred when a time series included intermittently occurring signals with widely separated time scales. To address this problem, EMD now includes a noise-assisted component in its calculation. Wu and Huang [36] developed the technique that is now called "ensemble EMD," or EEMD, which defines the true IMF as the mean of an ensemble of IMFs and, in the process, preserves the physical uniqueness of the decomposition. An ensemble member consists of the signal plus white noise of finite amplitude. The magnitude of the white noise that should be added is given by the ratio of the standard deviation of the first IMF to the standard deviation of the data itself and is called *Nstd*. Although this is the recommended value, in many cases, if slightly different values above or below the recommended value are used, the resulting IMF patterns remain stable although the patterns can differ slightly. Typically, the number of realizations or ensemble size is several hundred in order to generate the ensemble*.* The ensemble size that we have used in this study is 300. Thus, in EEMD there are two free parameters that must be specified, the level of white noise to be added and the ensemble size. The problem of end effects is discussed in Wu and Huang [36]. In the latest version, the IMFs themselves are extended after they are calculated which helps to reduce problems at the boundaries. The problem is further reduced in the noise-assisted version, i.e., in EEMD, because the slopes of the IMFs tend to be more uniformly distributed in the ensemble. For more information concerning EMD and EEMD, see [36–40].

## **2. Part II: analyses, results, and discussion**

### **2.1 Analyses**

### *2.1.1 Application of SSA and EEMD*

In applying SSA to the data inside and outside the bay, the decomposition was accomplished using a window length, *L*, of 240 months inside the bay, and *L* = 316 months in the offshore domain (outside the bay). Although we started with *L* = 240 months in the second case, due to the occasionally encountered problem of mode mixing (the partial presence of one mode in an adjacent mode), we increased *L* in steps to its present value in order to suppress it. In SSA terminology this issue is referred to as the separability problem and is addressed in detail in Golyandina et al. [32]. **Figure 5** shows the first 12 reconstructed modes for *L* = 316 outside the bay. The first two modes (Ro1 and Ro2) correspond to the annual cycle, and modes 6 and 7 (Ro6 and Ro7) correspond to the first harmonic. Harmonic components in the data often appear as matched pairs in the reconstructed modes where the amplitudes and phases are the same or similar [41], making it easier to identify them. Finally, we note that mode 5 is trend-like in nature.

Inside the bay, the corresponding SSA decomposition indicates that modes 1 and 2 correspond to the annual cycle and modes 9 and 10 to its first harmonic (not shown). In this case, mode 3 is trend-like and reveals two maxima, one in the early 1940s and the second around 1990, reminiscent of the maxima that we associate with the Pacific decadal oscillation (PDO). At this point, we tentatively associate

**55**

**Figure 5.**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

this mode and mode 5 from the decomposition outside the bay with the underlying

*The first 12 reconstructed modes are shown from the SSA decomposition of the data (L = 316) outside the bay. The first two modes show the annual cycle, and modes 6 and 7 show the first harmonic of the annual cycle.* 

In decomposing the data inside the bay based on EEMD, the noise parameter, *Nstd*, was determined to be 0.33 and the ensemble size was 300. The EEMD decomposition produced 10 modes (imf1 − imf10) where imf10 is often referred to as the residual. In a similar decomposition of the data outside the bay, the value for *Nstd* was 0.31 with the same ensemble size, and, again, 10 modes were produced. The results from inside the bay (black) for all 10 modes are shown in **Figure 6**, together with the three highest modes (imf8, imf9, imf10—blue) from outside the Bay. The results obtained inside and outside the Bay are generally similar except for the

trends, awaiting the results from the EEMD decompositions.

*Reconstructed mode 5 shows the long-term trend.*

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

#### **Figure 5.**

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

ble. For more information concerning EMD and EEMD, see [36–40].

In applying SSA to the data inside and outside the bay, the decomposition was accomplished using a window length, *L*, of 240 months inside the bay, and *L* = 316 months in the offshore domain (outside the bay). Although we started with *L* = 240 months in the second case, due to the occasionally encountered problem of mode mixing (the partial presence of one mode in an adjacent mode), we increased *L* in steps to its present value in order to suppress it. In SSA terminology this issue is referred to as the separability problem and is addressed in detail in Golyandina et al. [32]. **Figure 5** shows the first 12 reconstructed modes for *L* = 316 outside the bay. The first two modes (Ro1 and Ro2) correspond to the annual cycle, and modes 6 and 7 (Ro6 and Ro7) correspond to the first harmonic. Harmonic components in the data often appear as matched pairs in the reconstructed modes where the amplitudes and phases are the same or similar [41], making it easier to identify

Inside the bay, the corresponding SSA decomposition indicates that modes 1 and 2 correspond to the annual cycle and modes 9 and 10 to its first harmonic (not shown). In this case, mode 3 is trend-like and reveals two maxima, one in the early 1940s and the second around 1990, reminiscent of the maxima that we associate with the Pacific decadal oscillation (PDO). At this point, we tentatively associate

**2. Part II: analyses, results, and discussion**

them. Finally, we note that mode 5 is trend-like in nature.

*2.1.1 Application of SSA and EEMD*

order to reconstruct it.

and frequencies that lie within a specific band of frequencies, the center of which defines the mean period of that mode. The process of extracting the individual modes or essential scales from the data is called sifting and is performed many times to produce a single IMF. In practice, extracting the trend may require that not only the residual but one or several of the highest adjacent modes be grouped in

One problem in the application of EMD in the past was that mode mixing often occurred when a time series included intermittently occurring signals with widely separated time scales. To address this problem, EMD now includes a noise-assisted component in its calculation. Wu and Huang [36] developed the technique that is now called "ensemble EMD," or EEMD, which defines the true IMF as the mean of an ensemble of IMFs and, in the process, preserves the physical uniqueness of the decomposition. An ensemble member consists of the signal plus white noise of finite amplitude. The magnitude of the white noise that should be added is given by the ratio of the standard deviation of the first IMF to the standard deviation of the data itself and is called *Nstd*. Although this is the recommended value, in many cases, if slightly different values above or below the recommended value are used, the resulting IMF patterns remain stable although the patterns can differ slightly. Typically, the number of realizations or ensemble size is several hundred in order to generate the ensemble*.* The ensemble size that we have used in this study is 300. Thus, in EEMD there are two free parameters that must be specified, the level of white noise to be added and the ensemble size. The problem of end effects is discussed in Wu and Huang [36]. In the latest version, the IMFs themselves are extended after they are calculated which helps to reduce problems at the boundaries. The problem is further reduced in the noise-assisted version, i.e., in EEMD, because the slopes of the IMFs tend to be more uniformly distributed in the ensem-

**54**

**2.1 Analyses**

*The first 12 reconstructed modes are shown from the SSA decomposition of the data (L = 316) outside the bay. The first two modes show the annual cycle, and modes 6 and 7 show the first harmonic of the annual cycle. Reconstructed mode 5 shows the long-term trend.*

this mode and mode 5 from the decomposition outside the bay with the underlying trends, awaiting the results from the EEMD decompositions.

In decomposing the data inside the bay based on EEMD, the noise parameter, *Nstd*, was determined to be 0.33 and the ensemble size was 300. The EEMD decomposition produced 10 modes (imf1 − imf10) where imf10 is often referred to as the residual. In a similar decomposition of the data outside the bay, the value for *Nstd* was 0.31 with the same ensemble size, and, again, 10 modes were produced. The results from inside the bay (black) for all 10 modes are shown in **Figure 6**, together with the three highest modes (imf8, imf9, imf10—blue) from outside the Bay. The results obtained inside and outside the Bay are generally similar except for the

highest three modes, and thus the reason why they are included in the same figure. Also, it is these modes that most likely contribute to the long-term trends.

Next, we calculate and plot the variances for each mode inside and outside the bay from the EEMD decompositions (**Figure 7**). If we can identify the oceanic processes in accordance with the modal decompositions, then we may be able to estimate their relative importance. The estimated center frequencies for each mode (i.e., band) and location are given in **Table 1**. The center frequencies are not fixed because they are determined by the data and so vary slightly between locations [42]. The variances tend to be similar overall between locations but there are several notable differences. The variances associated with the first modes are similar

**Figure 6.**

*EEMD decompositions of SST from inside and outside Monterey Bay each generated 10 modes. All 10 modes are shown for inside the bay (black) and the highest 3 modes (imf8 − imf10) are shown for outside the bay (blue) as well. See the text for details.*

**57**

**Figure 7.**

**Table 1.**

*outside the bay (blue).*

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

and represent approximately 15% of the total variances. The primary periods are 3 months outside and 3.8 months inside the bay with most of the variability distributed over a band that extends from roughly 2– 6 months. A significant portion of the variability in this range should be related to coastal upwelling and upwellingrelated processes such as offshore Ekman transport and the evolution of ocean fronts, eddies, jets, and squirts [43]. The second modes correspond primarily to the annual cycle and in both cases represent over 50% of the total variance. Modes 3 and 4 are transitional in nature in that they generally fall between the periods we associate with annual and interannual variability (**Figure 6**). By mode 5, interannual variability is dominant with the major El Ninos of 1957–1958, 1982–1983, 1992–1993, and 1997–1998 making their appearances at both locations. Modes 6–8 show a gradual transition from what are distinctly El Nino events (mode 6) to

*Approximate center frequencies in cycles per year and the corresponding periods in months or years (in* 

9 — — Insufficient data 10 — — Insufficient data

*Variances by mode obtained from the EEMD decompositions for observations acquired inside the bay (red) and* 

**IMF mode number Inside bay Outside bay Comments**

3 0.55 (1.8 years) 0.55 (1.8 years) Depends on smoothing

1 3.13 (3.8 months) 3.95 (3.0 months) 2 1.0 (1.0 year) 1.0 (1.0 year)

 0.31 (3.2 years) 0.328 (3.0 years) 0.17 (5.9 years) 0.16 (6.3 years) 0.082 (12.2 years) 0.06 (16.7 years) 0.047 (21.3 years) 0.036 (27.8 years) 0.012 (83.3 years) 0.012 (83.3 years)

*parentheses) for each mode from the EEMD decompositions.*

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

#### **Figure 7.**

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

Also, it is these modes that most likely contribute to the long-term trends.

highest three modes, and thus the reason why they are included in the same figure.

Next, we calculate and plot the variances for each mode inside and outside the bay from the EEMD decompositions (**Figure 7**). If we can identify the oceanic processes in accordance with the modal decompositions, then we may be able to estimate their relative importance. The estimated center frequencies for each mode (i.e., band) and location are given in **Table 1**. The center frequencies are not fixed because they are determined by the data and so vary slightly between locations [42]. The variances tend to be similar overall between locations but there are several notable differences. The variances associated with the first modes are similar

*EEMD decompositions of SST from inside and outside Monterey Bay each generated 10 modes. All 10 modes are shown for inside the bay (black) and the highest 3 modes (imf8 − imf10) are shown for outside the bay (blue)* 

**56**

**Figure 6.**

*as well. See the text for details.*

*Variances by mode obtained from the EEMD decompositions for observations acquired inside the bay (red) and outside the bay (blue).*


#### **Table 1.**

*Approximate center frequencies in cycles per year and the corresponding periods in months or years (in parentheses) for each mode from the EEMD decompositions.*

and represent approximately 15% of the total variances. The primary periods are 3 months outside and 3.8 months inside the bay with most of the variability distributed over a band that extends from roughly 2– 6 months. A significant portion of the variability in this range should be related to coastal upwelling and upwellingrelated processes such as offshore Ekman transport and the evolution of ocean fronts, eddies, jets, and squirts [43]. The second modes correspond primarily to the annual cycle and in both cases represent over 50% of the total variance. Modes 3 and 4 are transitional in nature in that they generally fall between the periods we associate with annual and interannual variability (**Figure 6**). By mode 5, interannual variability is dominant with the major El Ninos of 1957–1958, 1982–1983, 1992–1993, and 1997–1998 making their appearances at both locations. Modes 6–8 show a gradual transition from what are distinctly El Nino events (mode 6) to

a signature that we associate with the Pacific decadal oscillation (PDO) in mode 8, in each case. The PDO index [44] characteristically has a maximum circa 1940 and a second maximum circa 1990 that are separated by a period of approximately 50 years (**Figure 12**). Of particular note, the variance associated with the PDO in mode 8 is far greater inside the bay than it is outside the bay (by at least 6 decibels which corresponds to a factor of 2). We discuss this curious result in more detail in the final section of the text. Mode 9 in both cases carries very little variance but could be related to the long-term trend. The residual long-term trends are shown in mode 10. Finally, the variance associated with the residual long-term trend outside the bay far exceeds that of the trend inside bay as shown (**Figure 6**).

#### *2.1.2 Trend identification*

The highest and the next two highest modes from EEMD inside and outside the bay are shown in **Figure 8a** and **b**. In more detail, the highest mode (imf10), the sum of modes 9 and 10 (imf9 + imf10), and the sum of modes 8, 9, and 10 (imf8 + imf9 + imf10) are shown inside (**Figure 8a**) and outside (**Figure 8b**) the bay. First, we note that the results of adding modes 9 and 10 together (i.e., imf8 + imf9—red) makes little difference, as expected, based on the small variance carried by mode 9. It is not until we include mode 8 (i.e., imf8 + imf9 + imf10 green) that we introduce more structure into the trends. We note that the added structure is primarily due to the inclusion of the PDO in our tentative definition of the long-term trend.

#### **Figure 8.**

*In the upper panel (a), the highest mode (imf10—blue), the sum of the highest and next highest modes (imf9 + imf10—red), and the sum of highest, next highest, and third from the highest modes (imf8 + imf9 + imf10—green) are shown from the EEMD decomposition of the data from outside the bay. In the lower panel (b), the same combinations of modes from the EEMD decomposition of the data from inside the bay are shown.*

**59**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

The SSA results, however, provide a slightly different story. In deciding which mode or combination of modes corresponds to the long-term trend, the similarity between the tentatively identified SSA trends and the EEMD-derived trends obtained by combining modes 8, 9, and 10 becomes apparent. The trends from SSA and EEMD inside the bay are shown in **Figure 9a** and those trends outside the bay in **Figure 9b**. A high degree of similarity between SSA and EEMD is quite apparent

Differences in the trends outside the bay can be explained, at least in part, by two factors. First, it was necessary to employ a larger value for the window length in the SSA decomposition outside the bay (*L* = 316 vs. 240 months) because of mode mixing that resulted in greater smoothing and thus a reduction in amplitude. The second problem relates to end effects. In our experience, SSA does not perform as well as EEMD when it comes to resolving structure near the beginning and end of the modal time histories. According to Mann [45], the problem of how to smooth data with trends near boundaries is particularly problematic. As stated earlier, SSA can exhibit problems at the boundaries of the reconstructed modes, but the boundary issues associated with EEMD, in our experience, are far smaller. Also, because the methods we have used are completely independent, there is no reason to expect that the combinations of modes which we choose to call the trend in one case should

In summary, we favor the trends obtained from EEMD primarily because the problem of mode mixing that arose in the SSA decomposition outside the bay led to an unrealistic reduction in the amplitude of the reconstructed mode. Secondly, the end effects from EEMD are expected to be smaller than they are from SSA, and that has been our experience. It is important to note, however, that SSA has been most helpful in guiding our selection of the modes from EEMD to include or group in arriving at

Based on the EEMD results, the trends inside and outside the bay are shown together in **Figure 9c**. They are clearly nonlinear and would be poorly approximated using a linear basis. During the 1920s the trends in SST are similar, but by 1930, the trends start to diverge with SSTs increasing more rapidly offshore than inside the bay. This pattern of divergence continues until about 1960 when SSTs inside the bay start to increase more rapidly and SSTs offshore start to decrease. Opposing trends in SST since 1960 continue for approximately the next 30 years. By about 1990, however, the trend inside the bay changes from increasing to decreasing temperatures, consistent with strongly decreasing temperatures offshore. Without the arrival of the warm anomaly in early 2014, both trends would most likely have continued to reflect decreasing temperatures up through at least 2014

Uncertainty in climate science has been referred to as a "monster," a "wicked problem," or "the 800 pound gorilla who is sitting in the next chair" [46]. Mathematically, estimating uncertainty is considered to be an ill-posed problem because different

In our work, the question of uncertainty in estimating the trends shown in **Figure 10** naturally arises. As stated by Moore et al. [47] regarding nonlinear trends, we expect the confidence interval of a nonlinear trend to be significantly smaller than it would be for a least-squares fit to the same data, since the data are

methods of estimating this quantity often produce different results.

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

be the same or even similar in the other.

our definition of the long-term trends.

and perhaps for several years longer.

*2.1.3 The trends per se*

*2.1.4 Uncertainty*

inside the bay but to a lesser extent outside the bay.

#### *Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

The SSA results, however, provide a slightly different story. In deciding which mode or combination of modes corresponds to the long-term trend, the similarity between the tentatively identified SSA trends and the EEMD-derived trends obtained by combining modes 8, 9, and 10 becomes apparent. The trends from SSA and EEMD inside the bay are shown in **Figure 9a** and those trends outside the bay in **Figure 9b**. A high degree of similarity between SSA and EEMD is quite apparent inside the bay but to a lesser extent outside the bay.

Differences in the trends outside the bay can be explained, at least in part, by two factors. First, it was necessary to employ a larger value for the window length in the SSA decomposition outside the bay (*L* = 316 vs. 240 months) because of mode mixing that resulted in greater smoothing and thus a reduction in amplitude. The second problem relates to end effects. In our experience, SSA does not perform as well as EEMD when it comes to resolving structure near the beginning and end of the modal time histories. According to Mann [45], the problem of how to smooth data with trends near boundaries is particularly problematic. As stated earlier, SSA can exhibit problems at the boundaries of the reconstructed modes, but the boundary issues associated with EEMD, in our experience, are far smaller. Also, because the methods we have used are completely independent, there is no reason to expect that the combinations of modes which we choose to call the trend in one case should be the same or even similar in the other.

In summary, we favor the trends obtained from EEMD primarily because the problem of mode mixing that arose in the SSA decomposition outside the bay led to an unrealistic reduction in the amplitude of the reconstructed mode. Secondly, the end effects from EEMD are expected to be smaller than they are from SSA, and that has been our experience. It is important to note, however, that SSA has been most helpful in guiding our selection of the modes from EEMD to include or group in arriving at our definition of the long-term trends.

#### *2.1.3 The trends per se*

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

the bay far exceeds that of the trend inside bay as shown (**Figure 6**).

*In the upper panel (a), the highest mode (imf10—blue), the sum of the highest and next highest modes (imf9 + imf10—red), and the sum of highest, next highest, and third from the highest modes (imf8 + imf9 + imf10—green) are shown from the EEMD decomposition of the data from outside the bay. In the lower panel (b), the same combinations of modes from the EEMD decomposition of the data from inside* 

*2.1.2 Trend identification*

the long-term trend.

a signature that we associate with the Pacific decadal oscillation (PDO) in mode 8, in each case. The PDO index [44] characteristically has a maximum circa 1940 and a second maximum circa 1990 that are separated by a period of approximately 50 years (**Figure 12**). Of particular note, the variance associated with the PDO in mode 8 is far greater inside the bay than it is outside the bay (by at least 6 decibels which corresponds to a factor of 2). We discuss this curious result in more detail in the final section of the text. Mode 9 in both cases carries very little variance but could be related to the long-term trend. The residual long-term trends are shown in mode 10. Finally, the variance associated with the residual long-term trend outside

The highest and the next two highest modes from EEMD inside and outside the bay are shown in **Figure 8a** and **b**. In more detail, the highest mode (imf10), the sum of modes 9 and 10 (imf9 + imf10), and the sum of modes 8, 9, and 10 (imf8 + imf9 + imf10) are shown inside (**Figure 8a**) and outside (**Figure 8b**) the bay. First, we note that the results of adding modes 9 and 10 together (i.e., imf8 + imf9—red) makes little difference, as expected, based on the small variance carried by mode 9. It is not until we include mode 8 (i.e., imf8 + imf9 + imf10 green) that we introduce more structure into the trends. We note that the added structure is primarily due to the inclusion of the PDO in our tentative definition of

**58**

**Figure 8.**

*the bay are shown.*

Based on the EEMD results, the trends inside and outside the bay are shown together in **Figure 9c**. They are clearly nonlinear and would be poorly approximated using a linear basis. During the 1920s the trends in SST are similar, but by 1930, the trends start to diverge with SSTs increasing more rapidly offshore than inside the bay. This pattern of divergence continues until about 1960 when SSTs inside the bay start to increase more rapidly and SSTs offshore start to decrease. Opposing trends in SST since 1960 continue for approximately the next 30 years. By about 1990, however, the trend inside the bay changes from increasing to decreasing temperatures, consistent with strongly decreasing temperatures offshore. Without the arrival of the warm anomaly in early 2014, both trends would most likely have continued to reflect decreasing temperatures up through at least 2014 and perhaps for several years longer.

#### *2.1.4 Uncertainty*

Uncertainty in climate science has been referred to as a "monster," a "wicked problem," or "the 800 pound gorilla who is sitting in the next chair" [46]. Mathematically, estimating uncertainty is considered to be an ill-posed problem because different methods of estimating this quantity often produce different results.

In our work, the question of uncertainty in estimating the trends shown in **Figure 10** naturally arises. As stated by Moore et al. [47] regarding nonlinear trends, we expect the confidence interval of a nonlinear trend to be significantly smaller than it would be for a least-squares fit to the same data, since the data are

#### **Figure 9.**

*The trends obtained from SSA (blue) and EEMD (red) of the data from inside the bay are shown in (a). The trends obtained from the data outside the bay using the same color convention are shown in (b). (c) shows the trends inside (red) and outside (blue) the bay together based on the results of EEMD.*

not forced to fit any specified basis function. However, they acknowledge that it is not necessarily a simple problem to estimate uncertainty in nonlinear cases. In our case, since we have two independent estimates of the trends, the spread between them does provide at least a rudimentary estimate of the uncertainty if we assume that the true trend lies between the two estimated trends. Of course, we could also use a weighted average to reflect our greater confidence in the results from EEMD, but how to determine such weights is anything but clear. To proceed with equal weighting, one could go even further and assume that the two curves in each case correspond to ±1 standard deviation about the mean of a Gaussian distribution and, from that point, calculate the 95% confidence intervals. This approach is used in small sample theory in statistics when no other information is available (personal communication, Prof. David S. Crosby). This approach has the distinct advantage of using both estimates of the trend to estimate a confidence interval that would be associated with the expected value of the two trends obtained by calculating their mean.

We have proceeded to calculate confidence intervals for the trends inside and outside the bay. The results are shown in **Figure 10a** and **b**. There are several ways these intervals can be estimated. In this case, the differences between the trends and the mean values between them serve as a proxy for the standard deviations. We can calculate a global standard deviation for each record and use that estimate to obtain the 95% confidence intervals following the usual assumptions and tables given in any standard text on statistics. However, this approach yields confidence intervals that are constant over the entire record. A better approach in our view is to consider the proxy values locally and to calculate the 95% confidence intervals separately for each

**61**

**Figure 10.**

*this case, for the waters outside the bay.*

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

model would be better-suited to address this question in detail.

time point. This has the distinct advantage of emphasizing the increased uncertainty that arises at the end of each record at the price of indicating no uncertainty where the records intersect, a feature that is unrealistic. We also obtain the expected result that the uncertainty increases or decreases where the differences between the two records increase or decrease. Finally, we return to the question raised in Section 1.3 concerning the differences between the point observations acquired inside the bay, and the area-averaged observations acquired offshore. Although the uncertainties we have obtained address this question to some degree, simulations from an ocean

The modal patterns we have already observed are inherently smooth because they contain only the highest modes from the various EEMD decompositions. To examine the underlying processes that contribute to these patterns in more detail, we have stratified the data from inside and outside the bay by season where we define the summer season as the mean of June, July, and August and the winter season as the mean of November, December, and January. We have smoothed the

In **Figure 11a**, inside the bay (red) during summer, the patterns are strongly positive and are almost identical for the first two decades indicating that the process or

*The upper panel (a) shows the SSA- and EEMD-derived trends inside Monterey Bay together with one measure of uncertainty. The green dashed line shows the estimated true trend based on the mean of the SSA and EEMD trends. The green dotted lines show the 95% confidence intervals about the mean. In the lower panel (b), the original trends, the estimated mean value, and the 95% confidence intervals are again shown but, in* 

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

**2.2 Ocean processes and time scales**

results slightly using LOWESS for added clarity.

*2.2.1 Seasonal variability*

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

time point. This has the distinct advantage of emphasizing the increased uncertainty that arises at the end of each record at the price of indicating no uncertainty where the records intersect, a feature that is unrealistic. We also obtain the expected result that the uncertainty increases or decreases where the differences between the two records increase or decrease. Finally, we return to the question raised in Section 1.3 concerning the differences between the point observations acquired inside the bay, and the area-averaged observations acquired offshore. Although the uncertainties we have obtained address this question to some degree, simulations from an ocean model would be better-suited to address this question in detail.

### **2.2 Ocean processes and time scales**

#### *2.2.1 Seasonal variability*

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

not forced to fit any specified basis function. However, they acknowledge that it is not necessarily a simple problem to estimate uncertainty in nonlinear cases. In our case, since we have two independent estimates of the trends, the spread between them does provide at least a rudimentary estimate of the uncertainty if we assume that the true trend lies between the two estimated trends. Of course, we could also use a weighted average to reflect our greater confidence in the results from EEMD, but how to determine such weights is anything but clear. To proceed with equal weighting, one could go even further and assume that the two curves in each case correspond to ±1 standard deviation about the mean of a Gaussian distribution and, from that point, calculate the 95% confidence intervals. This approach is used in small sample theory in statistics when no other information is available (personal communication, Prof. David S. Crosby). This approach has the distinct advantage of using both estimates of the trend to estimate a confidence interval that would be associated with the expected value of the two trends obtained by

*The trends obtained from SSA (blue) and EEMD (red) of the data from inside the bay are shown in (a). The trends obtained from the data outside the bay using the same color convention are shown in (b). (c) shows the* 

*trends inside (red) and outside (blue) the bay together based on the results of EEMD.*

We have proceeded to calculate confidence intervals for the trends inside and outside the bay. The results are shown in **Figure 10a** and **b**. There are several ways these intervals can be estimated. In this case, the differences between the trends and the mean values between them serve as a proxy for the standard deviations. We can calculate a global standard deviation for each record and use that estimate to obtain the 95% confidence intervals following the usual assumptions and tables given in any standard text on statistics. However, this approach yields confidence intervals that are constant over the entire record. A better approach in our view is to consider the proxy values locally and to calculate the 95% confidence intervals separately for each

**60**

calculating their mean.

**Figure 9.**

The modal patterns we have already observed are inherently smooth because they contain only the highest modes from the various EEMD decompositions. To examine the underlying processes that contribute to these patterns in more detail, we have stratified the data from inside and outside the bay by season where we define the summer season as the mean of June, July, and August and the winter season as the mean of November, December, and January. We have smoothed the results slightly using LOWESS for added clarity.

In **Figure 11a**, inside the bay (red) during summer, the patterns are strongly positive and are almost identical for the first two decades indicating that the process or

#### **Figure 10.**

*The upper panel (a) shows the SSA- and EEMD-derived trends inside Monterey Bay together with one measure of uncertainty. The green dashed line shows the estimated true trend based on the mean of the SSA and EEMD trends. The green dotted lines show the 95% confidence intervals about the mean. In the lower panel (b), the original trends, the estimated mean value, and the 95% confidence intervals are again shown but, in this case, for the waters outside the bay.*

#### **Figure 11.**

*In both panels, the data have been stratified by season where summer corresponds to the average of May, June, and July and winter corresponds to the average of November, December, and January. In the upper panel (a), the data for summer inside (red) and outside (blue) the bay are shown. In the lower panel (b), the data for winter inside (red) and outside (blue) the bay are shown. LOWESS smoothing has been applied in each case with the degree of smoothing (alpha) set equal to 0.35 (see text for details). Finally, the points have not been connected to emphasize the fact that they represent seasonal values, and so the curves that are plotted are not continuous functions.*

processes responsible for contributing to the rapid warming included both domains. The rates of increase in temperature during this period approach 5°C/100 years! After 1940, these trend-like patterns moderate and gradually diverge, and by 2014 they have diverged to a point where bay waters are almost 1.5°C warmer than waters outside the bay. These patterns convey a more detailed picture of the variability that could not be observed in the original trends.

Continuing, inside the bay, although SSTs increased by over 1°C by 1940, between 1940 and the early 1970s, they actually decreased. The increase now occurs mainly between the early 1970s up to ~2000, after which they decrease until 2014. Outside the bay, SSTs actually decrease, albeit with significant variations along the way, until the end of the record (December 2013). Although the overriding pattern is downward, after the early 1970s, SSTs increase slightly until the late 1980s, after which they decrease rapidly, consistent with our previous observations.

Four points of note are as follows: (1) the patterns of rapidly increasing SST during the first two decades inside and outside the bay were almost identical, indicating that the warming process was a truly dominant phenomenon during that period; (2) SSTs actually decrease from 1940 to the early 1970s inside the bay; (3) cooling, for the most part, occurred outside the bay starting ~1940 (vs. ~1960) and continued until the end of the record; and (4) surface temperatures inside the bay were significantly higher than outside the bay during the summer, especially over the last 20 years or so.

**63**

**Figure 12.**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

first major maximum in the PDO index in or about 1940 (**Figure 12**).

increasing SSTs along the coast which is what we have observed.

*2.2.2 Interannual and interdecadal variabilities*

amplitude of the PDO during this period.

In **Figure 11b**, during the winter, the patterns inside (red) and outside (blue) the bay are, to a high degree, similar, even with the higher resolution. However, although the patterns are similar, the temperatures outside the bay reflect SSTs that are roughly 0.2–1°C higher during winter. In more detail, maxima in temperature occur at both locations circa 1940 and during the early 1960s. These maxima cooccur with El Nino warming episodes with the addition of the co-occurrence of the

What is common in all cases is the very rapid rates of warming during the first 20 years of each record and the slightly less rapid rates of cooling during the last 20 years or so. During each period the influence of the PDO is virtually unmistakable. Also, during the early years of the past century, coastal upwelling intensified due to an intensification of the subtropical high pressure system which overlies the central North Pacific during summer [20]. However, by the early 1920s, this intensification had apparently abated leading to weaker winds along the coast and reduced coastal upwelling. Weaker coastal upwelling is, of course, consistent with

As we have stated, based on the long-term trends inside and outside Monterey Bay, temperatures increased rapidly from 1920 through at least 1940 inside the bay. Outside the bay, temperatures continued to increase more gradually up to approximately 1960. A major El Nino occurred in 1940–1941 (**Figure 6**), and the Pacific decadal oscillation index (http://research.jsiao.washington.edu/pdo/PDO) had one of its two primary maxima during the past century circa 1940 (**Figure 12**) and, together, almost certainly contributed to these trends during this period. From the late 1940s to the late 1960s, trends inside the bay show little change, but by 1970 temperatures increase significantly up to the early 1990s, consistent with the massive El Nino episode of 1982–1983 plus a second maximum in the PDO circa 1990 (**Figure 12**). Inside the bay, by the mid-1990s, SSTs finally started to decrease, and they decreased steadily until 2014, again consistent with the strongly decreasing

*A smoothed version of the Pacific decadal oscillation index (°C) is shown (dashed curve—black) together with a smoothed version of the SST record from inside the bay (dashed curve—green). Superimposed on these plots* 

*are three regime shifts that occurred during the past century (vertical red lines).*

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

In **Figure 11b**, during the winter, the patterns inside (red) and outside (blue) the bay are, to a high degree, similar, even with the higher resolution. However, although the patterns are similar, the temperatures outside the bay reflect SSTs that are roughly 0.2–1°C higher during winter. In more detail, maxima in temperature occur at both locations circa 1940 and during the early 1960s. These maxima cooccur with El Nino warming episodes with the addition of the co-occurrence of the first major maximum in the PDO index in or about 1940 (**Figure 12**).

What is common in all cases is the very rapid rates of warming during the first 20 years of each record and the slightly less rapid rates of cooling during the last 20 years or so. During each period the influence of the PDO is virtually unmistakable. Also, during the early years of the past century, coastal upwelling intensified due to an intensification of the subtropical high pressure system which overlies the central North Pacific during summer [20]. However, by the early 1920s, this intensification had apparently abated leading to weaker winds along the coast and reduced coastal upwelling. Weaker coastal upwelling is, of course, consistent with increasing SSTs along the coast which is what we have observed.

#### *2.2.2 Interannual and interdecadal variabilities*

As we have stated, based on the long-term trends inside and outside Monterey Bay, temperatures increased rapidly from 1920 through at least 1940 inside the bay. Outside the bay, temperatures continued to increase more gradually up to approximately 1960. A major El Nino occurred in 1940–1941 (**Figure 6**), and the Pacific decadal oscillation index (http://research.jsiao.washington.edu/pdo/PDO) had one of its two primary maxima during the past century circa 1940 (**Figure 12**) and, together, almost certainly contributed to these trends during this period. From the late 1940s to the late 1960s, trends inside the bay show little change, but by 1970 temperatures increase significantly up to the early 1990s, consistent with the massive El Nino episode of 1982–1983 plus a second maximum in the PDO circa 1990 (**Figure 12**). Inside the bay, by the mid-1990s, SSTs finally started to decrease, and they decreased steadily until 2014, again consistent with the strongly decreasing amplitude of the PDO during this period.

#### **Figure 12.**

*A smoothed version of the Pacific decadal oscillation index (°C) is shown (dashed curve—black) together with a smoothed version of the SST record from inside the bay (dashed curve—green). Superimposed on these plots are three regime shifts that occurred during the past century (vertical red lines).*

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

*In both panels, the data have been stratified by season where summer corresponds to the average of May, June, and July and winter corresponds to the average of November, December, and January. In the upper panel (a), the data for summer inside (red) and outside (blue) the bay are shown. In the lower panel (b), the data for winter inside (red) and outside (blue) the bay are shown. LOWESS smoothing has been applied in each case with the degree of smoothing (alpha) set equal to 0.35 (see text for details). Finally, the points have not been connected to emphasize the fact that they represent seasonal values, and so the curves that are plotted are not* 

processes responsible for contributing to the rapid warming included both domains. The rates of increase in temperature during this period approach 5°C/100 years! After 1940, these trend-like patterns moderate and gradually diverge, and by 2014 they have diverged to a point where bay waters are almost 1.5°C warmer than waters outside the bay. These patterns convey a more detailed picture of the variability that

Continuing, inside the bay, although SSTs increased by over 1°C by 1940, between 1940 and the early 1970s, they actually decreased. The increase now occurs mainly between the early 1970s up to ~2000, after which they decrease until 2014. Outside the bay, SSTs actually decrease, albeit with significant variations along the way, until the end of the record (December 2013). Although the overriding pattern is downward, after the early 1970s, SSTs increase slightly until the late 1980s, after

Four points of note are as follows: (1) the patterns of rapidly increasing SST during the first two decades inside and outside the bay were almost identical, indicating that the warming process was a truly dominant phenomenon during that period; (2) SSTs actually decrease from 1940 to the early 1970s inside the bay; (3) cooling, for the most part, occurred outside the bay starting ~1940 (vs. ~1960) and continued until the end of the record; and (4) surface temperatures inside the bay were significantly higher than outside the bay during the summer, especially over the last

which they decrease rapidly, consistent with our previous observations.

**62**

20 years or so.

**Figure 11.**

*continuous functions.*

could not be observed in the original trends.

After 1960, trends outside the bay show a pattern that is in opposition to the trend inside the bay with SSTs decreasing steadily up to the early 1990s, consistent with the cooling trends observed by Garcia-Reyes and Largier [9]. By the mid-1990s, the rate of cooling increased significantly through 2013 and is likely due to the added influence of the PDO, a period where the amplitude of the PDO index likewise decreased rapidly.

#### *2.2.3 The PDO and the impact of regime shifts*

Two processes that have received considerable attention are El Nino warming events and the PDO because of their expected importance in contributing to the warming (cooling) process. The time scales of El Nino warming events off central California vary, but they last for at least 6 months and can persist for periods of up to 2 years. The El Nino episodes in 1940–1941, 1958–1959, and 1982–1983 are examples of events that lasted for almost 2 years. Although these events are transient, they may leave an imprint that is imbedded in memory of the ocean for decades [48].

The time scales of the long-term oscillations that characterize the PDO are 20–30 years [49]. However, the changes in physical properties associated with the change in phase of the PDO are far shorter. These changes, i.e., regime shifts, have time scales that are of the order of 6 months [50]. Since 1920, three major regime shifts have occurred: 1925–1926, 1945–1946, and 1976–1977. These events have each been associated with a phase change in the PDO.

**Figure 12** shows a smoothed version of the PDO index together with a smoothed version of the SST data from inside the bay. We note that the events in 1925–1926 and 1976–1977 occur when the slope of the PDO index is positive, leading to changes in temperature along the California coast that were positive, whereas the event in 1945–1946 occurred when the slope of the PDO was negative [50]. In this case, the event led to a decrease in long-term temperature along the coast of California. It is important to note that although the signs of these changes along the coast of California were as indicated above, in other parts of the North Pacific, basin changes of opposite sign in SST were observed with respect to 1976–1977 event [51, 52].

We now take a closer look at these events. Because they are buried in the day-today natural variability of the data, they are difficult to detect and localize. Breaker [50] used cumulative sums to identify and localize regime shifts that have occurred since the early 1920s. Regime shifts produce a characteristic pattern in the cumulative sum that allows us to estimate when the event occurred, its duration, and its midpoint, *to*.

Once a regime shift has occurred, are the changes that occur short-lived or are they sustained over longer periods? To address this question, we employed a method called the expanding mean [51]. We start at the midpoint of these events and calculate the mean values proceeding forward and backward in time and then compare the results after we have proceeded in each direction for several years or longer.

Near *to*, the mean values vary widely because only a few observations enter into the calculation, but after 2–3 years, the plots generally start converging to mean values that become relatively stable. The influence of El Nino episodes was avoided wherever possible. These plots were generated for each event.

The expanding mean plot for the 1925–1926 regime shift showed that stable mean differences first appeared at 1.5–2 years out from *to*, with values in the range of +0.3°C estimated between years 2 and 3.5 in the forward direction. The differences remain consistently positive out to year 6, but a major El Nino event in 1930 precluded a more extensive comparison. The expanding mean plots for the 1945–1946 regime shift revealed that a decrease in SST did occur along the central California coast. Here, the differences are greater after 2–3 years and were

**65**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

consistently negative out to 10 years. The El Nino of 1940–1941 notwithstanding, we estimate a mean decrease of at least −0.4°C. For the 1976–1977 event, from 3 to 6 years forward from *to*, the differences were consistently positive with values that ranged from about +0.1°C to almost +0.5°C. Overall, after the first ~2.5 years, positive differences between the two curves were sustained out to almost 10 years, with

These results suggest that small but sustained changes in the mean value of temperature occurred following the 1925–1926, 1945–1946, and 1976–1977 regime shifts. These changes appear to have been sustained for periods of up to a decade and perhaps for the entire period between regime shifts. As a result, these events, although brief in nature, may well contribute to the long-term trends that we have

The long-term trends obtained in the foregoing analyses and presented earlier were obtained using singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD). The results tend to be similar within each domain although significant differences arose. Based on our experience in this study and others, we favor the results obtained using EEMD for the reasons we discuss in Section 4.3.2. However, SSA provided valuable guidance in helping us decide on a

Monthly averaged SSTs from January 1920 through December 2013 for two adjacent areas, the central California coast and Monterey Bay, have been examined for long-term trends based on the results from EEMD. These trends show that from 1920 to 1940, temperatures increased rapidly in both domains. After 1940, the trends inside and outside the bay are basically different. Inside the bay the trends indicate that temperatures tended to increase from about 1950 through 1990, while outside the bay, they decreased continuously from about 1960 through 2013. Temperatures inside the bay also decreased after 1990 until the arrival of a major thermal anomaly in 2014. Of particular note is the period during the 1970s and 1980s where trends inside the bay indicate that temperatures increased at rates higher than at any other time prior to 1940. In early 2014, a major temperature anomaly referred to as the "Blob" arrived along the central California coast that lasted for almost 2 years [1], and made it virtually impossible to continue the analysis beyond that point since our primary goal was to compare long-term trends inside

Based on our nonseasonal results from EEMD, although SSTs inside the bay have decreased rapidly since the early 1990s, overall, if we consider the entire record, they have increased from about 12.6°C to almost 13.0°C over the 94 years from 1920 to 2014. Outside the bay SSTs may not have changed much over the entire 94-year period. They reached a maximum of slightly over 13.4°C circa 1960 with values of about 12.4°C in 1920 and a value of about 12.3°C at the end of 2013. For comparison, although we do not show the results from linear analyses that were performed, they indicate that the linear trend inside the bay is positive and statistically significant, whereas the trend outside the bay is negative and is not statistically significant.

To take a closer look at the data, a higher resolution analysis based on smoothing was conducted. The data were decomposed into two seasons, summer (MJJ) and winter (NDJ). Based on these decompositions (**Figure 11**), significant warming inside the bay since the 1970s has occurred only during the summer. Thus, there is most likely a connection to coastal upwelling, albeit indirect. Outside the bay, SSTs decreased continuously during this period. According to Garcia-Reyes and Largier [9], SSTs along the central California coast have decreased since the early 1980s

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

suitable definition for the long-term trend.

Monterey Bay and outside the bay off the open coast.

sought to identify.

**2.3 Discussion**

increases, on average, that were of the order of +0.2°C.

#### *Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

consistently negative out to 10 years. The El Nino of 1940–1941 notwithstanding, we estimate a mean decrease of at least −0.4°C. For the 1976–1977 event, from 3 to 6 years forward from *to*, the differences were consistently positive with values that ranged from about +0.1°C to almost +0.5°C. Overall, after the first ~2.5 years, positive differences between the two curves were sustained out to almost 10 years, with increases, on average, that were of the order of +0.2°C.

These results suggest that small but sustained changes in the mean value of temperature occurred following the 1925–1926, 1945–1946, and 1976–1977 regime shifts. These changes appear to have been sustained for periods of up to a decade and perhaps for the entire period between regime shifts. As a result, these events, although brief in nature, may well contribute to the long-term trends that we have sought to identify.

### **2.3 Discussion**

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

*2.2.3 The PDO and the impact of regime shifts*

been associated with a phase change in the PDO.

decades [48].

midpoint, *to*.

After 1960, trends outside the bay show a pattern that is in opposition to the trend inside the bay with SSTs decreasing steadily up to the early 1990s, consistent with the cooling trends observed by Garcia-Reyes and Largier [9]. By the mid-1990s, the rate of cooling increased significantly through 2013 and is likely due to the added influence of the PDO, a period where the amplitude of the PDO index likewise decreased rapidly.

Two processes that have received considerable attention are El Nino warming events and the PDO because of their expected importance in contributing to the warming (cooling) process. The time scales of El Nino warming events off central California vary, but they last for at least 6 months and can persist for periods of up to 2 years. The El Nino episodes in 1940–1941, 1958–1959, and 1982–1983 are examples of events that lasted for almost 2 years. Although these events are transient, they may leave an imprint that is imbedded in memory of the ocean for

The time scales of the long-term oscillations that characterize the PDO are 20–30 years [49]. However, the changes in physical properties associated with the change in phase of the PDO are far shorter. These changes, i.e., regime shifts, have time scales that are of the order of 6 months [50]. Since 1920, three major regime shifts have occurred: 1925–1926, 1945–1946, and 1976–1977. These events have each

**Figure 12** shows a smoothed version of the PDO index together with a smoothed version of the SST data from inside the bay. We note that the events in 1925–1926 and 1976–1977 occur when the slope of the PDO index is positive, leading to changes in temperature along the California coast that were positive, whereas the event in 1945–1946 occurred when the slope of the PDO was negative [50]. In this case, the event led to a decrease in long-term temperature along the coast of California. It is important to note that although the signs of these changes along the coast of California were as indicated above, in other parts of the North Pacific, basin changes of opposite sign in SST were observed with respect to 1976–1977 event [51, 52].

We now take a closer look at these events. Because they are buried in the day-today natural variability of the data, they are difficult to detect and localize. Breaker [50] used cumulative sums to identify and localize regime shifts that have occurred since the early 1920s. Regime shifts produce a characteristic pattern in the cumulative sum that allows us to estimate when the event occurred, its duration, and its

Once a regime shift has occurred, are the changes that occur short-lived or are they sustained over longer periods? To address this question, we employed a method called the expanding mean [51]. We start at the midpoint of these events and calculate the mean values proceeding forward and backward in time and then compare the results after we have proceeded in each direction for several years or longer.

Near *to*, the mean values vary widely because only a few observations enter into the calculation, but after 2–3 years, the plots generally start converging to mean values that become relatively stable. The influence of El Nino episodes was avoided

The expanding mean plot for the 1925–1926 regime shift showed that stable mean differences first appeared at 1.5–2 years out from *to*, with values in the range of +0.3°C estimated between years 2 and 3.5 in the forward direction. The differences remain consistently positive out to year 6, but a major El Nino event in 1930 precluded a more extensive comparison. The expanding mean plots for the 1945–1946 regime shift revealed that a decrease in SST did occur along the central California coast. Here, the differences are greater after 2–3 years and were

wherever possible. These plots were generated for each event.

**64**

The long-term trends obtained in the foregoing analyses and presented earlier were obtained using singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD). The results tend to be similar within each domain although significant differences arose. Based on our experience in this study and others, we favor the results obtained using EEMD for the reasons we discuss in Section 4.3.2. However, SSA provided valuable guidance in helping us decide on a suitable definition for the long-term trend.

Monthly averaged SSTs from January 1920 through December 2013 for two adjacent areas, the central California coast and Monterey Bay, have been examined for long-term trends based on the results from EEMD. These trends show that from 1920 to 1940, temperatures increased rapidly in both domains. After 1940, the trends inside and outside the bay are basically different. Inside the bay the trends indicate that temperatures tended to increase from about 1950 through 1990, while outside the bay, they decreased continuously from about 1960 through 2013. Temperatures inside the bay also decreased after 1990 until the arrival of a major thermal anomaly in 2014. Of particular note is the period during the 1970s and 1980s where trends inside the bay indicate that temperatures increased at rates higher than at any other time prior to 1940. In early 2014, a major temperature anomaly referred to as the "Blob" arrived along the central California coast that lasted for almost 2 years [1], and made it virtually impossible to continue the analysis beyond that point since our primary goal was to compare long-term trends inside Monterey Bay and outside the bay off the open coast.

Based on our nonseasonal results from EEMD, although SSTs inside the bay have decreased rapidly since the early 1990s, overall, if we consider the entire record, they have increased from about 12.6°C to almost 13.0°C over the 94 years from 1920 to 2014. Outside the bay SSTs may not have changed much over the entire 94-year period. They reached a maximum of slightly over 13.4°C circa 1960 with values of about 12.4°C in 1920 and a value of about 12.3°C at the end of 2013. For comparison, although we do not show the results from linear analyses that were performed, they indicate that the linear trend inside the bay is positive and statistically significant, whereas the trend outside the bay is negative and is not statistically significant.

To take a closer look at the data, a higher resolution analysis based on smoothing was conducted. The data were decomposed into two seasons, summer (MJJ) and winter (NDJ). Based on these decompositions (**Figure 11**), significant warming inside the bay since the 1970s has occurred only during the summer. Thus, there is most likely a connection to coastal upwelling, albeit indirect. Outside the bay, SSTs decreased continuously during this period. According to Garcia-Reyes and Largier [9], SSTs along the central California coast have decreased since the early 1980s

due to increased coastal upwelling which is directly related to increased upwellingfavorable winds. Our results are consistent with theirs where the data overlap, and so it is likely that the same processes have been at work back to at least the 1960s, and perhaps a decade or two earlier, off central California.

According to Bakun [10], the increase in coastal upwelling over the past several decades is related to increased heating in inland California and the surrounding area that has intensified the thermal low pressure system in the Southwestern United States. This intensified low pressure system together with the subtropical high pressure system off the coast of California has increased the pressure gradient between the two regions. This increase in the onshore-offshore pressure gradient in turn produces stronger winds along the California coast and thus more intense coastal upwelling. The increase in inland heating may be due to sustained climate warming or simply to long-term climate variability.

Why increased warming occurred inside the bay until ~1990, while cooling occurred continuously off the coast of central California since about 1960, is a curious fact if we assume that the behavior of bay waters is directly related to what happens further offshore. One possibility is that as coastal upwelling increased off the coast, more intense upwelling took place in the upwelling center off Point Ano Nuevo, located approximately 40 km north of Monterey Bay (**Figure 1**), whose waters are advected down the coast toward the bay. This flow is most likely a manifestation of the coastal jet which typically flows in close proximity to the coast [53]. This flow bifurcates when it reaches the bay and part flows into the bay and part flows offshore [15, 16]. If the down-coast flow increased as upwelling increased in the Ano Nuevo upwelling center, then the process of bifurcation or the location where it occurred could have been altered. Such a change might well have resulted in less upwelled water entering the bay and more going elsewhere. As a result, with less cold, upwelled water entering the bay, SSTs would have increased. In this case, the waters inside and outside the bay may still co-vary, but this co-variation could take the form of an inverse relationship rather than a direct one.

Continuing, why then did temperatures inside the bay start decreasing in the early 1990s, becoming more in line with what had been taking place outside the bay since the early 1960s? It is possible that what occurred during the 1960s inside the bay may have resulted in a basically unstable flow regime that could not be sustained indefinitely and so it eventually returned to its original state. Other possibilities exist, but this question presents an ideal opportunity to employ a hydrodynamic model of Monterey Bay and the surrounding area to address it.

To summarize the seasonal analyses, regardless of the season, there were very high rates of warming during the first 20 years of each record and only slightly less rapid rates of cooling after ~1990. The very high rates of increasing temperature are in agreement with Field et al. [20] who found that SSTs off southern California rose rapidly by the mid-1920s due to secular warming, consistent with the rapidly increasing SSTs we have observed off central California and Monterey Bay during the same period.

Before we leave the seasonal results, we point out that most of what has been said and will be said regarding the various analyses refers primarily to those results obtained from the EEMD modal decompositions, i.e., the long-term trends, and not from the seasonal analyses although these results have added additional insight at the shorter time scales.

In comparing the physical properties and flow along the central coast and Monterey Bay between November and February, i.e., during winter, waters inside and outside the bay are dominated by poleward flow associated with the Davidson Current [13]. As a result we expect that these waters tend to co-vary in unison and that the physical properties are similar. The results shown in **Figure 11b** are

**67**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

have contributed indirectly to higher SSTs inside Monterey Bay.

associated with them occurs on shorter time scales.

where similar embayments are found.

consistent with this view. However, during summer the situation is very different (**Figure 11a**), and so it is not surprising that differences in the trends occur for the

A number of processes contribute to the long-term trends in SST that we have observed. Interpreting these trends in terms of the oceanic processes that contribute to them is not necessarily straightforward. However, from the modal variances obtained from our EEMD decompositions, we can identify a number of these processes and, to some degree, estimate their relative importance. We start with coastal upwelling. It is one of the dominant processes along the California coast. The first mode from each EEMD decomposition contains variability with periods that range from less than 2 months to slightly greater than 6 months. In each case it accounts for approximately 15% of the total variance. According to our results and those of Garcia-Reyes and Largier [9], coastal upwelling has continued to intensify over at least the past several decades, in agreement with Bakun [10]. Offshore, SSTs have generally decreased since as early as 1940 based on our seasonal analyses. Thus, it is clear that coastal upwelling is a major contributor to lower SSTs offshore and may

A number of upwelling-related processes also fall within a similar range of time scales as coastal upwelling. These include offshore Ekman transport, Ekman pumping, the spring transition, and squirts and jets. Although not entirely separate from coastal upwelling, the growth and decay of cyclonic and anticyclonic eddies and the evolution of ocean fronts are included in this range. In the last case, with regard to eddies, although their life cycles often exceed 6 months, much of the variability

Although the annual cycle is the major contributor to the variances in SST inside and outside the bay, they have little or no apparent impact on the long-term trends. Conversely, El Nino warming episodes and the Pacific decadal oscillation are clearly important. It is difficult to estimate the variances associated with the El Nino and the PDO separately because they occupy at least four adjacent modes in our EEMD decompositions (**Figure 6**). According to the sequences, the El Nino episodes gradually coalesce into the PDO as we progress from mode 5 to mode 8 in each case. A similar coalescence takes place inside the bay for the same modes. Together, these modes account for only ~10% of the total variance, but the processes they represent make the greatest contributions to the long-term trends because they have the longest time scales. Finally, consistent with our observations, it is now well recognized that the PDO owes its existence in large measure to the ENSO phenomenon [54–56]. Although the variance of the PDO is relatively small in each case, its influence on the long-term behavior of the data is highly significant. Of particular note, our decompositions revealed that the variance of the PDO, although relatively small compared to the other modes, is far greater inside the bay than it is outside the bay. As a result, we would expect its impact on the long-term trends to be greater inside the bay. That this is the case is clearly shown in **Figure 9** where the maximum value of the long-term trend inside the bay occurs in the early 1990s and thus coincides with one of the two major maxima in the PDO index (**Figure 12**). Why the amplitude of the PDO is greater inside the bay may be due to its containment within a limited domain where its energy tends to accumulate more rapidly than it can dissipate. If we are correct, then this relationship may hold in other coastal regions

Unlike El Nino warming events that are intermittent in nature, the influence of the PDO, although less apparent, may be both continuous and transient. Transient influences may occur when there are phase changes in the PDO. These phase changes often correspond to regime shifts. The time scale of these events is on the order of 6 months. Our results suggest that small but sustained changes in

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

reasons we have just described.

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

consistent with this view. However, during summer the situation is very different (**Figure 11a**), and so it is not surprising that differences in the trends occur for the reasons we have just described.

A number of processes contribute to the long-term trends in SST that we have observed. Interpreting these trends in terms of the oceanic processes that contribute to them is not necessarily straightforward. However, from the modal variances obtained from our EEMD decompositions, we can identify a number of these processes and, to some degree, estimate their relative importance. We start with coastal upwelling. It is one of the dominant processes along the California coast. The first mode from each EEMD decomposition contains variability with periods that range from less than 2 months to slightly greater than 6 months. In each case it accounts for approximately 15% of the total variance. According to our results and those of Garcia-Reyes and Largier [9], coastal upwelling has continued to intensify over at least the past several decades, in agreement with Bakun [10]. Offshore, SSTs have generally decreased since as early as 1940 based on our seasonal analyses. Thus, it is clear that coastal upwelling is a major contributor to lower SSTs offshore and may have contributed indirectly to higher SSTs inside Monterey Bay.

A number of upwelling-related processes also fall within a similar range of time scales as coastal upwelling. These include offshore Ekman transport, Ekman pumping, the spring transition, and squirts and jets. Although not entirely separate from coastal upwelling, the growth and decay of cyclonic and anticyclonic eddies and the evolution of ocean fronts are included in this range. In the last case, with regard to eddies, although their life cycles often exceed 6 months, much of the variability associated with them occurs on shorter time scales.

Although the annual cycle is the major contributor to the variances in SST inside and outside the bay, they have little or no apparent impact on the long-term trends. Conversely, El Nino warming episodes and the Pacific decadal oscillation are clearly important. It is difficult to estimate the variances associated with the El Nino and the PDO separately because they occupy at least four adjacent modes in our EEMD decompositions (**Figure 6**). According to the sequences, the El Nino episodes gradually coalesce into the PDO as we progress from mode 5 to mode 8 in each case. A similar coalescence takes place inside the bay for the same modes. Together, these modes account for only ~10% of the total variance, but the processes they represent make the greatest contributions to the long-term trends because they have the longest time scales. Finally, consistent with our observations, it is now well recognized that the PDO owes its existence in large measure to the ENSO phenomenon [54–56].

Although the variance of the PDO is relatively small in each case, its influence on the long-term behavior of the data is highly significant. Of particular note, our decompositions revealed that the variance of the PDO, although relatively small compared to the other modes, is far greater inside the bay than it is outside the bay. As a result, we would expect its impact on the long-term trends to be greater inside the bay. That this is the case is clearly shown in **Figure 9** where the maximum value of the long-term trend inside the bay occurs in the early 1990s and thus coincides with one of the two major maxima in the PDO index (**Figure 12**). Why the amplitude of the PDO is greater inside the bay may be due to its containment within a limited domain where its energy tends to accumulate more rapidly than it can dissipate. If we are correct, then this relationship may hold in other coastal regions where similar embayments are found.

Unlike El Nino warming events that are intermittent in nature, the influence of the PDO, although less apparent, may be both continuous and transient. Transient influences may occur when there are phase changes in the PDO. These phase changes often correspond to regime shifts. The time scale of these events is on the order of 6 months. Our results suggest that small but sustained changes in

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

and perhaps a decade or two earlier, off central California.

take the form of an inverse relationship rather than a direct one.

model of Monterey Bay and the surrounding area to address it.

or simply to long-term climate variability.

due to increased coastal upwelling which is directly related to increased upwellingfavorable winds. Our results are consistent with theirs where the data overlap, and so it is likely that the same processes have been at work back to at least the 1960s,

According to Bakun [10], the increase in coastal upwelling over the past several decades is related to increased heating in inland California and the surrounding area that has intensified the thermal low pressure system in the Southwestern United States. This intensified low pressure system together with the subtropical high pressure system off the coast of California has increased the pressure gradient between the two regions. This increase in the onshore-offshore pressure gradient in turn produces stronger winds along the California coast and thus more intense coastal upwelling. The increase in inland heating may be due to sustained climate warming

Why increased warming occurred inside the bay until ~1990, while cooling occurred continuously off the coast of central California since about 1960, is a curious fact if we assume that the behavior of bay waters is directly related to what happens further offshore. One possibility is that as coastal upwelling increased off the coast, more intense upwelling took place in the upwelling center off Point Ano Nuevo, located approximately 40 km north of Monterey Bay (**Figure 1**), whose waters are advected down the coast toward the bay. This flow is most likely a manifestation of the coastal jet which typically flows in close proximity to the coast [53]. This flow bifurcates when it reaches the bay and part flows into the bay and part flows offshore [15, 16]. If the down-coast flow increased as upwelling increased in the Ano Nuevo upwelling center, then the process of bifurcation or the location where it occurred could have been altered. Such a change might well have resulted in less upwelled water entering the bay and more going elsewhere. As a result, with less cold, upwelled water entering the bay, SSTs would have increased. In this case, the waters inside and outside the bay may still co-vary, but this co-variation could

Continuing, why then did temperatures inside the bay start decreasing in the early 1990s, becoming more in line with what had been taking place outside the bay since the early 1960s? It is possible that what occurred during the 1960s inside the bay may have resulted in a basically unstable flow regime that could not be sustained indefinitely and so it eventually returned to its original state. Other possibilities exist, but this question presents an ideal opportunity to employ a hydrodynamic

To summarize the seasonal analyses, regardless of the season, there were very high rates of warming during the first 20 years of each record and only slightly less rapid rates of cooling after ~1990. The very high rates of increasing temperature are in agreement with Field et al. [20] who found that SSTs off southern California rose rapidly by the mid-1920s due to secular warming, consistent with the rapidly increasing SSTs we have observed off central California and Monterey Bay during

Before we leave the seasonal results, we point out that most of what has been said and will be said regarding the various analyses refers primarily to those results obtained from the EEMD modal decompositions, i.e., the long-term trends, and not from the seasonal analyses although these results have added additional insight at

In comparing the physical properties and flow along the central coast and Monterey Bay between November and February, i.e., during winter, waters inside and outside the bay are dominated by poleward flow associated with the Davidson Current [13]. As a result we expect that these waters tend to co-vary in unison and that the physical properties are similar. The results shown in **Figure 11b** are

**66**

the same period.

the shorter time scales.

the mean value of temperature occurred following the 1925–1926, 1945–1946, and 1976–1977 regime shifts. These changes appear to be sustained over periods of up to a decade or longer. Also, regarding the 1976–1977 event, in two related studies, one off the coast of Hawai'i [48] in the central Pacific and the second off the coast of South Korea [49] in the western Pacific, it was found that SSTs decreased rather than increased as they had off central and southern California during the 1976–1977 event [47]. We conclude by stating that the influence of the PDO may be twofold where its total impact is composed of continuous and transient contributions.

Finally, the long-term trend in SST inside the bay departs significantly from the trend further offshore over the 94-year period from 1920 to 2014. However, the differences in these trends were far greater during the 30-year period between 1960 and 1990 which imply that significant changes in the local circulation in and around the bay must have occurred during that period. We make an important distinction, however, between the differences in the trends we have observed and the extent to which they reflect independent behavior. It is likely that during the 1970s and 1980s, for example, where the trends differ significantly, these waters still co-varied but the relationship between them was not direct but indirect as we have said before. Thus, upwelling off the coast could be increasing at roughly the same time that upwelled waters entering Monterey Bay are decreasing.

## **3. Conclusions**

Although the results of Garcia-Reyes and Largier [9] indicate that coastal upwelling along the coast of California has intensified since the early 1980s, our results suggest that this cooling process has been at work since at least the early 1960s and possibly earlier.

Nonlinear methods were used throughout this study to estimate the long-term trends. In addition to fitting long-term variability in the data more realistically, nonlinear trends will, in most cases, have smaller confidence intervals than those associated with the corresponding linear trends. Also, of importance, the nonlinear trends often provide more insight into the processes that contribute to them.

The major thermal anomaly that occurred off the coast of central California in early 2014 made it virtually impossible to conduct a meaningful analysis of the long-term trends because of its magnitude and duration. Thus, this chapter serves as a period piece that spans a recent 94-year period during which we were able to observe and hopefully better understand the relationship between the waters of Monterey Bay and the waters further offshore along the central California coast.

We often find a sensitive interplay between resolving the trend and trying to interpret it. As the resolution is increased, new features often emerge that make it difficult to decide what part of the long-term variability contributes to the trend and what part does not. This problem was encountered in 2.1.2 of the present study.

Our results show that the amplitude of the Pacific decadal oscillation (PDO) is approximately twice as large inside Monterey Bay as it is outside the bay. This relationship most likely derives from the fact that Monterey Bay is partially enclosed. As a result, energy associated with the PDO that enters the bay may accumulate more rapidly than it can be dissipated, leading to higher amplitudes within the interior, overall. If we are correct, then we might expect to find a similar relationship in other coastal regions where embayments exist.

We conclude the warming that occurred during the first two decades of our data sets, i.e., from 1920 to 1940, was highly unusual. During this period SSTs increased at rates approaching 5°C/100 years. We know that SSTs off southern California also

**69**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

the contribution that El Ninos make to the formation of the PDO [54–56].

same period, global SSTs likewise increased significantly [57].

these terms can be reconciled, other possibilities exist.

cal PDO record that dates back to 1650.

would be referred to as a transfer function.

**Acknowledgements**

characteristics.

experienced a rapid increase in SST by the mid-1920s [20], and that during this

Using spectral decompositions of the data, we were able to illustrate over a sequence of modes the process of El Nino episodes evolving or coalescing into the Pacific decadal oscillation, consistent with recent theoretical results that emphasize

Although it would appear that the waters inside and outside Monterey Bay often act independently, this may not be the case. We do concur that these waters, to a large extent, co-vary. The point is that apparent independence and co-variation are not mutually exclusive terms. Although we have shown at least one way in which

In this study we examined three regime shifts that occurred in 1925–1926, in 1945–1946, and in 1976–1977 and found that small changes in the mean temperature, of order 0.5°C or less, accompanied these events based on the data from Monterey Bay. These regime shifts coincided in each case with a phase change in the PDO. Although these changes were relatively small, they were often sustained for periods of up to a decade or longer, and this tendency may be one of their defining

The physical behavior of regime shifts is not completely understood. However, it is becoming apparent, based on limited observations, that some regions within the Pacific basin exhibit an increase in SST, while at others, a decrease is observed during the same event, in this case, during the 1976–1977 regime shift. The spatial distribution of these changes in sign around the North Pacific also raises the possibility that what we may be observing, if we connect the dots, is a standing wave pattern. Such a standing wave would most likely have a wavelength governed by the dimensions of the basin, motion governed by the phase of the Pacific decadal oscillation, and a period of approximately 20–30 years, based on the intervals between the events we have examined. Periods in this range are in close agreement with [58] who estimated the mean interval between events to be 23 years based on the histori-

Finally, why is the work presented here of value? One reason is that by obtaining a better understanding of the relationship between bay waters and those further offshore, we are better able to predict the behavior of one system when we only have information regarding the other. In the signal processing world, such a relationship

Steve Worley and Zaihua Ji from NCAR are thanked for providing the SST data from ICOADS. Dr. Mark Denny from the Hopkins Marine Station is thanked for providing SST data adjacent to the Hopkins Marine Station for the purpose of evaluating the Hopkins SST data. David Crosby, Professor Emeritus of Statistics at American University, is thanked for suggesting the method of estimating the uncertainty associated with the trends obtained using SSA and EEMD. Finally, one anonymous reviewer is thanked for providing a number of helpful comments and suggestions for

improvement that have been incorporated in the final version of the text.

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

#### *Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

experienced a rapid increase in SST by the mid-1920s [20], and that during this same period, global SSTs likewise increased significantly [57].

Using spectral decompositions of the data, we were able to illustrate over a sequence of modes the process of El Nino episodes evolving or coalescing into the Pacific decadal oscillation, consistent with recent theoretical results that emphasize the contribution that El Ninos make to the formation of the PDO [54–56].

Although it would appear that the waters inside and outside Monterey Bay often act independently, this may not be the case. We do concur that these waters, to a large extent, co-vary. The point is that apparent independence and co-variation are not mutually exclusive terms. Although we have shown at least one way in which these terms can be reconciled, other possibilities exist.

In this study we examined three regime shifts that occurred in 1925–1926, in 1945–1946, and in 1976–1977 and found that small changes in the mean temperature, of order 0.5°C or less, accompanied these events based on the data from Monterey Bay. These regime shifts coincided in each case with a phase change in the PDO. Although these changes were relatively small, they were often sustained for periods of up to a decade or longer, and this tendency may be one of their defining characteristics.

The physical behavior of regime shifts is not completely understood. However, it is becoming apparent, based on limited observations, that some regions within the Pacific basin exhibit an increase in SST, while at others, a decrease is observed during the same event, in this case, during the 1976–1977 regime shift. The spatial distribution of these changes in sign around the North Pacific also raises the possibility that what we may be observing, if we connect the dots, is a standing wave pattern. Such a standing wave would most likely have a wavelength governed by the dimensions of the basin, motion governed by the phase of the Pacific decadal oscillation, and a period of approximately 20–30 years, based on the intervals between the events we have examined. Periods in this range are in close agreement with [58] who estimated the mean interval between events to be 23 years based on the historical PDO record that dates back to 1650.

Finally, why is the work presented here of value? One reason is that by obtaining a better understanding of the relationship between bay waters and those further offshore, we are better able to predict the behavior of one system when we only have information regarding the other. In the signal processing world, such a relationship would be referred to as a transfer function.

### **Acknowledgements**

Steve Worley and Zaihua Ji from NCAR are thanked for providing the SST data from ICOADS. Dr. Mark Denny from the Hopkins Marine Station is thanked for providing SST data adjacent to the Hopkins Marine Station for the purpose of evaluating the Hopkins SST data. David Crosby, Professor Emeritus of Statistics at American University, is thanked for suggesting the method of estimating the uncertainty associated with the trends obtained using SSA and EEMD. Finally, one anonymous reviewer is thanked for providing a number of helpful comments and suggestions for improvement that have been incorporated in the final version of the text.

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

that upwelled waters entering Monterey Bay are decreasing.

**3. Conclusions**

1960s and possibly earlier.

the mean value of temperature occurred following the 1925–1926, 1945–1946, and 1976–1977 regime shifts. These changes appear to be sustained over periods of up to a decade or longer. Also, regarding the 1976–1977 event, in two related studies, one off the coast of Hawai'i [48] in the central Pacific and the second off the coast of South Korea [49] in the western Pacific, it was found that SSTs decreased rather than increased as they had off central and southern California during the 1976–1977 event [47]. We conclude by stating that the influence of the PDO may be twofold where its total impact is composed of continuous and transient contributions.

Finally, the long-term trend in SST inside the bay departs significantly from the trend further offshore over the 94-year period from 1920 to 2014. However, the differences in these trends were far greater during the 30-year period between 1960 and 1990 which imply that significant changes in the local circulation in and around the bay must have occurred during that period. We make an important distinction, however, between the differences in the trends we have observed and the extent to which they reflect independent behavior. It is likely that during the 1970s and 1980s, for example, where the trends differ significantly, these waters still co-varied but the relationship between them was not direct but indirect as we have said before. Thus, upwelling off the coast could be increasing at roughly the same time

Although the results of Garcia-Reyes and Largier [9] indicate that coastal upwelling along the coast of California has intensified since the early 1980s, our results suggest that this cooling process has been at work since at least the early

trends often provide more insight into the processes that contribute to them. The major thermal anomaly that occurred off the coast of central California in early 2014 made it virtually impossible to conduct a meaningful analysis of the long-term trends because of its magnitude and duration. Thus, this chapter serves as a period piece that spans a recent 94-year period during which we were able to observe and hopefully better understand the relationship between the waters of Monterey Bay and the waters further offshore along the central California coast. We often find a sensitive interplay between resolving the trend and trying to interpret it. As the resolution is increased, new features often emerge that make it difficult to decide what part of the long-term variability contributes to the trend and what part

does not. This problem was encountered in 2.1.2 of the present study.

other coastal regions where embayments exist.

Nonlinear methods were used throughout this study to estimate the long-term trends. In addition to fitting long-term variability in the data more realistically, nonlinear trends will, in most cases, have smaller confidence intervals than those associated with the corresponding linear trends. Also, of importance, the nonlinear

Our results show that the amplitude of the Pacific decadal oscillation (PDO) is approximately twice as large inside Monterey Bay as it is outside the bay. This relationship most likely derives from the fact that Monterey Bay is partially enclosed. As a result, energy associated with the PDO that enters the bay may accumulate more rapidly than it can be dissipated, leading to higher amplitudes within the interior, overall. If we are correct, then we might expect to find a similar relationship in

We conclude the warming that occurred during the first two decades of our data sets, i.e., from 1920 to 1940, was highly unusual. During this period SSTs increased at rates approaching 5°C/100 years. We know that SSTs off southern California also

**68**

## **Author details**

Laurence C. Breaker1,2

1 Moss Landing Marine Laboratories, Moss Landing, CA, United States of America

2 University of Delaware, Newark, DE, United States of America

\*Address all correspondence to: laurence.breaker@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**71**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California…*

[8] Mendelssohn R, Schwing FB. Common and uncommon trends in SST and wind stress in the California and Peru-Chile current systems. Progress in Oceanography. 2002;**53**:141-162. Available from: https://www.infona.pl/resource/ bwmeta1.element.elsevier-027a36a5 dd40-39a3-a8ab-9e2456f06956/tab/

[9] Garcia-Reyes M, Largier J.

10.1029/2009JC005576

10.1029/2003GL017647

[12] Mooers CNK, Flagg CN, Boicourt WC. Prograde and

Observations of increased wind-driven coastal upwelling off central California. Journal of Geophysical Research. 2010, 2010;**115**:C04011, 8 pages. DOI:

[10] Bakun A. Global climate change and intensification of coastal ocean upwelling. Science. 1990;**247**:198-201. DOI: 10.1126/science.247.4939.198

[11] Snyder MA, Sloan LC, Diffenbaugh NS, Bell JL. Future climate change and upwelling in the California

Current. Geophysical Research Letters. 2003;**30**:1823, CLM, 8 pages. DOI:

retrograde fronts. In: Oceanic Fronts in Coastal Processes. Springer-Verlag; 1978. pp. 43-58. DOI: 10.1007/978-3-642-66987-3\_6

[13] Breaker LC, Broenkow WW. The circulation of Monterey Bay and related processes. Oceanography and Marine Biology. Annual Review. 1994,

1994;**32**:1-64. ISSN 0078-3218

[14] Lasker R. Food chains and fisheries: An assessment after 20 years. In: Towards a Theory on Biological-Physical Interactions in the World Ocean. Kluwer; 1988. pp. 173-182. Available from: https://link.springer.com/ chapter/10.1007/978-94-009-3023-0\_9

citations

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

[1] Bond NA, Cronin MF, Freeland H, Mantua N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophysical Research Letters. 2015;**42**:3414-3420. DOI:

[2] Lipphardt BL, Small D, Kirwan AD Jr, Wiggins S, Ide K, Grosch CE, et al. Synoptic Lagrangian maps: Application to surface transport in Monterey Bay. Journal of Marine Research. 2006;**64**:221- 247. Available from: http://www. journalofmarineresearch.org

[3] Skogsberg T. Hydrography of Monterey Bay, California: Thermal conditions, 1929-1933. Transactions of the American Philosophical Society. 1936;**29**:152. Available from: https:// www.amphilsoc.org/publications

[4] Skogsberg T, Phelps A. Hydrography

[5] Woodson CB et al. Local diurnal upwelling driven by sea breezes in northern Monterey Bay. Continental Shelf Research. 2007;**27**:2289-2302. DOI: 10.1016/j.csr.2007.05.014

[6] Graham NE, Largier JL. Upwelling shadows as nearshore retention sites: The example of northern Monterey Bay. Continental Shelf Research. 1997;**17**:509-532. DOI:

of Monterey Bay, California: Thermal conditions, part II, 1934- 1937. Transactions of the American Philosophical Society. 1946;**90**:350-386. Available from: https://www.amphilsoc.

org/publications

10.1029/2009JC005623

[7] Garcia-Reyes M, Largier J. Seasonality of coastal upwelling off central and northern California: New insights including temporal and spatial variability. Journal of Geophysical Research. 2012;**117**:C12013, 15 pages.

DOI: 10.1029/2011JC007629

10.1002/2015GL063306

**References**

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

## **References**

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 Moss Landing Marine Laboratories, Moss Landing, CA, United States of America

2 University of Delaware, Newark, DE, United States of America

\*Address all correspondence to: laurence.breaker@gmail.com

**70**

**Author details**

Laurence C. Breaker1,2

provided the original work is properly cited.

[1] Bond NA, Cronin MF, Freeland H, Mantua N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophysical Research Letters. 2015;**42**:3414-3420. DOI: 10.1002/2015GL063306

[2] Lipphardt BL, Small D, Kirwan AD Jr, Wiggins S, Ide K, Grosch CE, et al. Synoptic Lagrangian maps: Application to surface transport in Monterey Bay. Journal of Marine Research. 2006;**64**:221- 247. Available from: http://www. journalofmarineresearch.org

[3] Skogsberg T. Hydrography of Monterey Bay, California: Thermal conditions, 1929-1933. Transactions of the American Philosophical Society. 1936;**29**:152. Available from: https:// www.amphilsoc.org/publications

[4] Skogsberg T, Phelps A. Hydrography of Monterey Bay, California: Thermal conditions, part II, 1934- 1937. Transactions of the American Philosophical Society. 1946;**90**:350-386. Available from: https://www.amphilsoc. org/publications

[5] Woodson CB et al. Local diurnal upwelling driven by sea breezes in northern Monterey Bay. Continental Shelf Research. 2007;**27**:2289-2302. DOI: 10.1016/j.csr.2007.05.014

[6] Graham NE, Largier JL. Upwelling shadows as nearshore retention sites: The example of northern Monterey Bay. Continental Shelf Research. 1997;**17**:509-532. DOI: 10.1029/2009JC005623

[7] Garcia-Reyes M, Largier J. Seasonality of coastal upwelling off central and northern California: New insights including temporal and spatial variability. Journal of Geophysical Research. 2012;**117**:C12013, 15 pages. DOI: 10.1029/2011JC007629

[8] Mendelssohn R, Schwing FB. Common and uncommon trends in SST and wind stress in the California and Peru-Chile current systems. Progress in Oceanography. 2002;**53**:141-162. Available from: https://www.infona.pl/resource/ bwmeta1.element.elsevier-027a36a5 dd40-39a3-a8ab-9e2456f06956/tab/ citations

[9] Garcia-Reyes M, Largier J. Observations of increased wind-driven coastal upwelling off central California. Journal of Geophysical Research. 2010, 2010;**115**:C04011, 8 pages. DOI: 10.1029/2009JC005576

[10] Bakun A. Global climate change and intensification of coastal ocean upwelling. Science. 1990;**247**:198-201. DOI: 10.1126/science.247.4939.198

[11] Snyder MA, Sloan LC, Diffenbaugh NS, Bell JL. Future climate change and upwelling in the California Current. Geophysical Research Letters. 2003;**30**:1823, CLM, 8 pages. DOI: 10.1029/2003GL017647

[12] Mooers CNK, Flagg CN, Boicourt WC. Prograde and retrograde fronts. In: Oceanic Fronts in Coastal Processes. Springer-Verlag; 1978. pp. 43-58. DOI: 10.1007/978-3-642-66987-3\_6

[13] Breaker LC, Broenkow WW. The circulation of Monterey Bay and related processes. Oceanography and Marine Biology. Annual Review. 1994, 1994;**32**:1-64. ISSN 0078-3218

[14] Lasker R. Food chains and fisheries: An assessment after 20 years. In: Towards a Theory on Biological-Physical Interactions in the World Ocean. Kluwer; 1988. pp. 173-182. Available from: https://link.springer.com/ chapter/10.1007/978-94-009-3023-0\_9 [15] Rosenfeld LK, Schwing FB, Garfield N, Tracy DE. Bifurcated flow from an upwelling center: A cold water source for Monterey Bay. Continental Shelf Research. 1994;**14**:931-964. DOI: 10.1016/0278-4343(94)90058-2

[16] Tracy DE. Source of cold water in Monterey Bay observed by AVHRR satellite imagery [thesis]. Monterey, California: Naval Postgraduate School; 1990. p. 126

[17] Barry JP, Baxter CH, Sagarin RD, Gilman SE. Climate-related, long term faunal changes in a California rocky intertidal community. Science. 1995;**267**:672-675. Available from: https://www.ncbi.nlm.nih.gov/ pubmed/17745845

[18] Sagarin RD, Barry JP, Gilman SE, Baxter CH. Climate-related change in an intertidal community over short and long time scales. Ecological Monographs. 1999;**69**:465-490. Available from: http://www.bioone.org/ doi/pdf/10.3398/042.007.0120

[19] Breaker LC. What's happening in Monterey Bay on seasonal to interdecadal time scales? Continental Shelf Research. 2005;**25**:1159-1193. DOI: 10.1016/j.csr.2005.01.003

[20] Field DB, Baumgartner TR, Charles C, Ferriera-Bartrina V, Ohman MD. Planktonic foraminifera of the California Current reflect twentieth century warming. Science. 2006;**311**:63- 66. DOI: 10.1126/science.1116220

[21] Breaker LC, Broenkow WW, Denny MW. Reconstructing an 83-Year Time Series of Daily Sea Surface Temperature at Pacific Grove, California. Scripps Institution of Oceanography Library. Paper 7; 2006. Available from: http:// repositories.cdlib.org/sio/lib/7

[22] Cleveland WS. Robust, locally weighted regression and smoothing scatterplots. Journal of the

American Statistical Association. 1979;**74**:829-836. Available from: http://links.jstor.org/sici?sici=0162- 1459%28197912%2974%3A368%3C829 %3ARLWRAS%3E2.0.CO%3B2-L

[23] Fuller WA. Introduction to Statistical Time Series. Wiley Series on Probability and Statistics; 1996. pp. 475-476. Available from: https:// www.amazon.com/Introduction-Statistical-Time-Wayne-Fuller/ dp/0471552399

[24] Hamilton JD. Time Series Analysis. Princeton: Princeton University Press; 1994, First chapter. Available from: https://press.princeton.edu/links.html

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[26] Breaker LC, Loor HR, Carroll D. Trends in sea surface temperature off the coast of Ecuador and the major processes that contribute to them. Journal of Marine Systems. 2016;**164**:151-164. DOI: 10.1016/j. jmarsys.2016.09.002

[27] Esterby SR. Review of methods for the detection and estimation of trends with emphasis on water quality applications. Hydrological Processes. 1996;**10**:127-149. DOI: 10.1002/(SICI)1099- 1085(199602)10:2%3C127::AID-HYP354%3E3.0.CO;2-8

[28] Wu Z, Huang NE, Long SR, Peng C-K. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proceedings of the National Academy of Sciences. 2007;**104**:14889-14894. DOI: 10.1073/ pnas.0701020104

[29] Alexandrov T, Bianconcini S, Dagum EB, Maas P, McElroy T. A

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[38] Huang NE. Introduction to Hilbert-Huang transform and some recent developments. In: The Hilbert-Huang Transform in Engineering. Taylor & Francis; 2005. pp. 1-24. ISBN-13:

[39] Huang NE. Introduction to the Hilbert-Huang transform and its related mathematical problems. In: The Hilbert-Huang Transform and its Applications. World Scientific; 2005. pp. 1-26. ISBN

[40] Huang NE, Wu Z. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics. 2008;**46**:1-23.

[41] Vautard R, Yiou P, Ghil M. Singular

DOI: 10.1029/2007RG000228

spectrum analysis: A toolkit for short, noisy, chaotic signals. Physica D. 1992;**58**:95-126. DOI: 10.1016/0167-2789(92)90103-T

[42] Flandrin P, Goncalves P, Rilling G. EMD equivalent filter banks from interpretation to applications. In: The Hilbert-Huang Transform and its Applications. World Scientific; 2005. pp. 57-74. Available from: http://perso. ens-lyon.fr/patrick.flandrin/HHT05.pdf

[43] Checkley DM, Barth JA. Patterns and processes in the California Current System. Progress in Oceanography. 2009;**83**:49-64. DOI: 10.1016/j.

[44] Mantua N, Hare S. The Pacific decadal oscillation. Journal of Oceanography. 2002;**58**:35-44. DOI:

pocean.2009.07.028

10.1023/A:1015820616384

[45] Mann ME. On smoothing potentially non-stationary time series. Geophysical Research Letters. 2004;**31**:L07214, 4 pages. DOI: 10.1029/2004GL019569

[46] Curry JA, Webster PJ. Climate science and the uncertainty monster.

978-0-8493-3422-1

981-256-376-8

*DOI: http://dx.doi.org/10.5772/intechopen.85882*

review of some modern approaches to the problem of trend extraction. Econometric Reviews. 2012;**31**:593-624. Available from: http://scholar.google. de/citations?user=NUjVCEwAAAAJ&

[30] Alexandrov T. A method of trend extraction using singular spectrum analysis. Revstat—Statistical Journal. 2009;**7**:1-22. Available from: https://

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[32] Golyandina N, Nekrutkin V, Zhigljavsky A. Analysis of Time Series Structure: SSA and Related Techniques. Chapman & Hall/CRC; 2001. pp. 44-53.

[33] Ghil M et al. Advanced spectral methods for climatic time series. Reviews of Geophysics. 2002;**40**:1-41.

DOI: 10.1029/2000RG000092

10.100/978-3-642-34913-3

[34] Golyandina N, Zhigljavsky A. Singular Spectrum Analysis for Time Series. Springer; 2013. pp. 37-39. DOI:

[35] Golyandina N, Korobeynikov A, Zhigljavsky A. Singular Spectrum Analysis with R. Springer, eBook; 2018. DOI: 10.1007/978-3-662-57380-8

[36] Wu Z, Huang NE. Ensemble

[37] Huang NE et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, Series A. 1998;**454**:903-995.

DOI: 10.1098/rspa.1998.0193

empirical mode decomposition: A noiseassisted data analysis method. Advances in Adaptive Data Analysis. 2009;**1**:1-41. DOI: 10.1142/S1793536909000047

ISBN 1-58488- 194-1

arxiv.org/abs/0804.3367

hl=de

*Long-Term Changes in Sea Surface Temperature Off the Coast of Central California… DOI: http://dx.doi.org/10.5772/intechopen.85882*

review of some modern approaches to the problem of trend extraction. Econometric Reviews. 2012;**31**:593-624. Available from: http://scholar.google. de/citations?user=NUjVCEwAAAAJ& hl=de

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

American Statistical Association. 1979;**74**:829-836. Available from: http://links.jstor.org/sici?sici=0162- 1459%28197912%2974%3A368%3C829 %3ARLWRAS%3E2.0.CO%3B2-L

[23] Fuller WA. Introduction to Statistical Time Series. Wiley Series on Probability and Statistics; 1996. pp. 475-476. Available from: https:// www.amazon.com/Introduction-Statistical-Time-Wayne-Fuller/

[24] Hamilton JD. Time Series Analysis. Princeton: Princeton University Press; 1994, First chapter. Available from: https://press.princeton.edu/links.html

[25] Jevrejeva S, Grinsted A, Moore JC, Holgate S. Nonlinear trends and multiyear cycles in sea level records. Journal of Geophysical Research. 2006;**3**:C09012. DOI:

[26] Breaker LC, Loor HR, Carroll D. Trends in sea surface temperature off the coast of Ecuador and the major processes that contribute to them. Journal of Marine Systems. 2016;**164**:151-164. DOI: 10.1016/j.

[27] Esterby SR. Review of methods for the detection and estimation of trends with emphasis on water quality applications. Hydrological Processes. 1996;**10**:127-149. DOI: 10.1002/(SICI)1099- 1085(199602)10:2%3C127::AID-

10.1029/2005JC003229

jmarsys.2016.09.002

HYP354%3E3.0.CO;2-8

pnas.0701020104

[28] Wu Z, Huang NE, Long SR, Peng C-K. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proceedings of the National Academy of Sciences. 2007;**104**:14889-14894. DOI: 10.1073/

[29] Alexandrov T, Bianconcini S, Dagum EB, Maas P, McElroy T. A

dp/0471552399

[15] Rosenfeld LK, Schwing FB, Garfield N, Tracy DE. Bifurcated flow from an upwelling center: A cold water source for Monterey Bay. Continental Shelf Research. 1994;**14**:931-964. DOI: 10.1016/0278-4343(94)90058-2

[16] Tracy DE. Source of cold water in Monterey Bay observed by AVHRR satellite imagery [thesis]. Monterey, California: Naval Postgraduate School;

[17] Barry JP, Baxter CH, Sagarin RD, Gilman SE. Climate-related, long term faunal changes in a California rocky intertidal community. Science. 1995;**267**:672-675. Available from: https://www.ncbi.nlm.nih.gov/

[18] Sagarin RD, Barry JP, Gilman SE, Baxter CH. Climate-related change in an intertidal community over short and long time scales. Ecological Monographs. 1999;**69**:465-490.

Available from: http://www.bioone.org/

doi/pdf/10.3398/042.007.0120

10.1016/j.csr.2005.01.003

[20] Field DB, Baumgartner TR, Charles C, Ferriera-Bartrina V, Ohman MD. Planktonic foraminifera of the California Current reflect twentieth century warming. Science. 2006;**311**:63- 66. DOI: 10.1126/science.1116220

[21] Breaker LC, Broenkow WW, Denny MW. Reconstructing an 83-Year Time Series of Daily Sea Surface Temperature at Pacific Grove, California. Scripps Institution of Oceanography Library. Paper 7; 2006. Available from: http:// repositories.cdlib.org/sio/lib/7

[22] Cleveland WS. Robust, locally weighted regression and smoothing

scatterplots. Journal of the

[19] Breaker LC. What's happening in Monterey Bay on seasonal to interdecadal time scales? Continental Shelf Research. 2005;**25**:1159-1193. DOI:

1990. p. 126

pubmed/17745845

**72**

[30] Alexandrov T. A method of trend extraction using singular spectrum analysis. Revstat—Statistical Journal. 2009;**7**:1-22. Available from: https:// arxiv.org/abs/0804.3367

[31] Elsner JB, Tsonis AA. Singular Spectrum Analysis: A New Tool in Time Series Analysis. Plenum Press; 1999. pp. 51-65. DOI: 10.1007/978-3-642-34913-3

[32] Golyandina N, Nekrutkin V, Zhigljavsky A. Analysis of Time Series Structure: SSA and Related Techniques. Chapman & Hall/CRC; 2001. pp. 44-53. ISBN 1-58488- 194-1

[33] Ghil M et al. Advanced spectral methods for climatic time series. Reviews of Geophysics. 2002;**40**:1-41. DOI: 10.1029/2000RG000092

[34] Golyandina N, Zhigljavsky A. Singular Spectrum Analysis for Time Series. Springer; 2013. pp. 37-39. DOI: 10.100/978-3-642-34913-3

[35] Golyandina N, Korobeynikov A, Zhigljavsky A. Singular Spectrum Analysis with R. Springer, eBook; 2018. DOI: 10.1007/978-3-662-57380-8

[36] Wu Z, Huang NE. Ensemble empirical mode decomposition: A noiseassisted data analysis method. Advances in Adaptive Data Analysis. 2009;**1**:1-41. DOI: 10.1142/S1793536909000047

[37] Huang NE et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, Series A. 1998;**454**:903-995. DOI: 10.1098/rspa.1998.0193

[38] Huang NE. Introduction to Hilbert-Huang transform and some recent developments. In: The Hilbert-Huang Transform in Engineering. Taylor & Francis; 2005. pp. 1-24. ISBN-13: 978-0-8493-3422-1

[39] Huang NE. Introduction to the Hilbert-Huang transform and its related mathematical problems. In: The Hilbert-Huang Transform and its Applications. World Scientific; 2005. pp. 1-26. ISBN 981-256-376-8

[40] Huang NE, Wu Z. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics. 2008;**46**:1-23. DOI: 10.1029/2007RG000228

[41] Vautard R, Yiou P, Ghil M. Singular spectrum analysis: A toolkit for short, noisy, chaotic signals. Physica D. 1992;**58**:95-126. DOI: 10.1016/0167-2789(92)90103-T

[42] Flandrin P, Goncalves P, Rilling G. EMD equivalent filter banks from interpretation to applications. In: The Hilbert-Huang Transform and its Applications. World Scientific; 2005. pp. 57-74. Available from: http://perso. ens-lyon.fr/patrick.flandrin/HHT05.pdf

[43] Checkley DM, Barth JA. Patterns and processes in the California Current System. Progress in Oceanography. 2009;**83**:49-64. DOI: 10.1016/j. pocean.2009.07.028

[44] Mantua N, Hare S. The Pacific decadal oscillation. Journal of Oceanography. 2002;**58**:35-44. DOI: 10.1023/A:1015820616384

[45] Mann ME. On smoothing potentially non-stationary time series. Geophysical Research Letters. 2004;**31**:L07214, 4 pages. DOI: 10.1029/2004GL019569

[46] Curry JA, Webster PJ. Climate science and the uncertainty monster. Bulletin of the American Meteorological Society. 2011;**3139**(1):1667-1682. DOI: 10.1175/2011BAMS3139.1

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Chapter 4

Abstract

Krishna Kishore Osuri

heavy rainfall of >18 cm day<sup>1</sup>

sea surface temperature, wind speed

1. Introduction

75

Significance of Mesoscale Warm

Core Eddy on Marine and Coastal

Environment of the Bay of Bengal

Bay of Bengal (BoB) is an affluent region for the mesoscale (eddies) and synoptic scale (cyclones) systems. It occurs primarily through the seasonal variations, dynamical instabilities and equatorial wind forcing mechanisms. The individual or cumulative effect of these changes is vulnerable to the coastal and marine ecosystems. For example, tropical cyclone (TC) AILA experienced a warm core eddy (WCE) before the landfall, and consequently it intensified into a severe cyclonic storm (CS) and remained as a CS up to 15 h after the landfall. Its severity produces a

bution to the TC is witnessed during and after the landfall. Inappropriately, high resolution in-situ observations are not available to identify such important processes on different time and spatial scales. Therefore, the present chapter analyses the northern BoB eddy induced signals using both in-situ and satellite (advanced microwave scanning radiometer—AMSR-2) derived products. Two in-situ locations (BD08 and BD09) are employed for this study purpose. The eddy responses at noeddy, during and after eddy, have been analyzed. Besides, WCE imprints on the overlying atmosphere are also observed. The relationship between sea surface tem-

Marine environment usually influences or is influenced by the mesoscale (eddies) and synoptic scale (cyclones) processes. The horizontal scale of a typical mesoscale eddy is varying in between 100 and 200 km with a lifetime of 10–100 days [1–3]. The preserved shape carries/responsible for the mass or heat transport, turbulent current patterns, thermodynamic properties, potential fishing zones and upwelling processes over a long distance [3, 4]. Oceanic mesoscale eddies are of two types such as warm and cold core eddies. The significance of the warm (cold) core eddy is that it contains a higher (lower) temperature with elevated (lower) sea level in the center. The primary factors responsible for the genesis of the eddies were force exerted by the wind stress curl, meander separation, baroclinic instability and gradient wind balance [5]. Mesoscale eddies play an important role in

perature and wind speed over the BoB region is assessed.

Keywords: warm core eddy, tropical cyclone, in-situ, AMSR-2,

, thus leads to the coastal flooding. The eddy contri-

Nanda Kishore Reddy Busireddy, Kumar Ankur and

[54] Newman M, Gilbert GP, Alexander MA. ENSO-forced variability of the Pacific decadal oscillation. Journal of Climate. 2003;**16**:3853-3857. Available from: http://citeseerx.ist.psu.edu/ viewdoc/download?doi=10.1.1.477.8989 &rep=rep1&type=pdf

[55] Schneider N, Cornuelle BD. The forcing of the Pacific decadal oscillation. Journal of Climate. 2005;**18**:4355-4373. DOI: 10.1175/JCLI3527.1

[56] Shakun JD, Shaman J. Tropical origins of North and South Pacific decadal variability. Geophysical Research Letters. 2009;**36**:L19711, 5 pages. DOI: 10.1029/2009GL040313

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## Chapter 4

*Coastal and Marine Environments - Physical Processes and Numerical Modelling*

[53] Hickey BM. Then California Current System—Hypotheses and facts. Progress in Oceanography. 1979;**8**:191-279. DOI:

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and-trends/

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**74**

## Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment of the Bay of Bengal

Nanda Kishore Reddy Busireddy, Kumar Ankur and Krishna Kishore Osuri

## Abstract

Bay of Bengal (BoB) is an affluent region for the mesoscale (eddies) and synoptic scale (cyclones) systems. It occurs primarily through the seasonal variations, dynamical instabilities and equatorial wind forcing mechanisms. The individual or cumulative effect of these changes is vulnerable to the coastal and marine ecosystems. For example, tropical cyclone (TC) AILA experienced a warm core eddy (WCE) before the landfall, and consequently it intensified into a severe cyclonic storm (CS) and remained as a CS up to 15 h after the landfall. Its severity produces a heavy rainfall of >18 cm day<sup>1</sup> , thus leads to the coastal flooding. The eddy contribution to the TC is witnessed during and after the landfall. Inappropriately, high resolution in-situ observations are not available to identify such important processes on different time and spatial scales. Therefore, the present chapter analyses the northern BoB eddy induced signals using both in-situ and satellite (advanced microwave scanning radiometer—AMSR-2) derived products. Two in-situ locations (BD08 and BD09) are employed for this study purpose. The eddy responses at noeddy, during and after eddy, have been analyzed. Besides, WCE imprints on the overlying atmosphere are also observed. The relationship between sea surface temperature and wind speed over the BoB region is assessed.

Keywords: warm core eddy, tropical cyclone, in-situ, AMSR-2, sea surface temperature, wind speed

## 1. Introduction

Marine environment usually influences or is influenced by the mesoscale (eddies) and synoptic scale (cyclones) processes. The horizontal scale of a typical mesoscale eddy is varying in between 100 and 200 km with a lifetime of 10–100 days [1–3]. The preserved shape carries/responsible for the mass or heat transport, turbulent current patterns, thermodynamic properties, potential fishing zones and upwelling processes over a long distance [3, 4]. Oceanic mesoscale eddies are of two types such as warm and cold core eddies. The significance of the warm (cold) core eddy is that it contains a higher (lower) temperature with elevated (lower) sea level in the center. The primary factors responsible for the genesis of the eddies were force exerted by the wind stress curl, meander separation, baroclinic instability and gradient wind balance [5]. Mesoscale eddies play an important role in governing the atmospheric vortices such as tropical cyclones (TCs) and ultimately affect its intensity. For example, cold core eddy promotes TC weakening while, warm core eddy (WCE) amplifies the TC intensity. It is known fact that the coastal and marine environments are sensitive to such oceanic vortices.

major implication, when the WCE forms in the close proximity of the coastal region and a storm passes over on it. It poses a major socioeconomic impact on the coastal communities, agriculture and infrastructure, thus ultimately lead to the catastrophic damage. A best example related to this situation is TC Aila (2009) over the BoB. According to Regional Specialized Meteorological Center report (2010) and Sadhuram et al. [10], TC Aila suddenly gets intensified by 43% within a day before the landfall and caused for the 175 human deaths. These kind of similar events have been noticed around the global oceans. For example, Hurricane Opal (1995) in the North Atlantic Ocean and Super Typhoon Maemi (2003) in the western North Pacific under-went a WCE, and shows a sudden rapid intensification within a 24–36 h-time window [11, 12]. The principle behavior of these WCEs is that it acts like an insulating material against the ocean waters negative feedback and boost the TC intensity process [13]. While, it is true for cold core eddies thus provides a negative feedback to the storm and finally it gets weakens. However, there are other positive impacts associated with these cold core eddies that indirectly help the marine processes. It actually brings the subsurface chlorophyll maxima to the surface area and turn it into higher biological productivity in that region [14]. The detection of these eddies using the sea surface temperature (SST) gradient patterns

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment…

DOI: http://dx.doi.org/10.5772/intechopen.86243

is a difficult task because the attained surface variations are less. The better approach to deal with these mesoscale eddies are using the altimeter derived sea level anomalies (SLA) in which the high and the low SLA signal gives an indication

atmosphere coupled models and satellite products over the region.

SBE37

Vane + flux gate compass/ Lambrecht

In-situ SST Thermistor/Seabird Micro-CAT

Specifications of SST (°C) and wind speed (ms<sup>1</sup>

In-situ wind speed

AMSR-2 wind speed

Table 1.

77

Over the decade, understanding of these mesoscale eddy variability in the spatial and temporal manner has got a steady improvement. Bruce et al. [15] reported the mesoscale eddy features using the TOPEX altimeter data. Frenger et al. [16] analyzed the impact of these eddies on the atmospheric parameters such as winds, clouds and rainfall. Dandapat and Chakraborty [17] analysed the three dimensional structure of the mesoscale eddies using both the altimetry and Argo floats for the period 1993–2014. Busireddy et al. [18] shows the thermohaline structures associated with the WCE and its response on the overlying atmosphere. Despite these facts, the eddy-resolving models are still lacking to capture the realistic features of the ocean tractable in the BoB region. In summary, mesoscale eddies and its feedback to the atmospheric vortices suggest the urgency of high resolution ocean-

The present chapter is evolved by considering the availability of the existent high-resolution in-situ (buoy) and remote sensing (satellite derived) products and addresses the eddy induced signals in the ocean and atmospheric parameters. The overall discussion is followed primarily by choosing the (1) WCE during the normal conditions and in the presence of a cyclone environment, (2) sea surface temperature (SST) and wind speed data products verification of advanced microwave

Parameter Sensor type Resolution Accuracy Range

AMSR-2 SST Microwave radiometer 0.25° 0.25° 0.8°C (released) 2 to 35°C

0.0001°C 0.002°C 5 to 35°C

0.1 ms<sup>1</sup> 2% 0 to

1.5 ms<sup>1</sup> (released)

) parameters of both in-situ and AMSR-2 sensors.

35 ms<sup>1</sup>

0 to 30 ms<sup>1</sup>

of warm and cold core eddies [5].

The Bay of Bengal (BoB) is an eddy rich region and is located in the northeastern part of the Indian Ocean (Figure 1a). The location of the BoB in the global and regional prospective is shown in the Figure 1a. It is dominated by the several factors such as freshwater discharge, seasonal occurrence of storms, monsoonal reversal winds and upwelling plumes [6, 7]. All these factors contribute to the formation of eddies throughout the year in this bay. The persistent eddy environmental characteristics determine the weather of the ocean and its overlying atmosphere. For example, ocean heat content is considered to be an important parameter in the ocean perspective that can modulate the atmospheric vortices [8, 9]. In case of WCE, the preserved heat content provides the sufficient positive energy to the storm in terms of large enthalpy fluxes and thus helps in the intensification process. Similarly, the process is opposite in case of cold core eddy. Moreover, it also has a

#### Figure 1.

Sea level anomaly of (a) global oceans, on 14 June 2014. The blue rectangle represents the zoomed version of (b) Bay of Bengal showing the warm core eddy. (c and d) The statistical analysis of sea surface temperature (°C) between AMSR-2 satellite and BD08 and BD09 observations during the eddy period (05 May–25 July 2014). Gray and blue color circles in (b) shows the BD08 and BD09 moorings.

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment… DOI: http://dx.doi.org/10.5772/intechopen.86243

major implication, when the WCE forms in the close proximity of the coastal region and a storm passes over on it. It poses a major socioeconomic impact on the coastal communities, agriculture and infrastructure, thus ultimately lead to the catastrophic damage. A best example related to this situation is TC Aila (2009) over the BoB. According to Regional Specialized Meteorological Center report (2010) and Sadhuram et al. [10], TC Aila suddenly gets intensified by 43% within a day before the landfall and caused for the 175 human deaths. These kind of similar events have been noticed around the global oceans. For example, Hurricane Opal (1995) in the North Atlantic Ocean and Super Typhoon Maemi (2003) in the western North Pacific under-went a WCE, and shows a sudden rapid intensification within a 24–36 h-time window [11, 12]. The principle behavior of these WCEs is that it acts like an insulating material against the ocean waters negative feedback and boost the TC intensity process [13]. While, it is true for cold core eddies thus provides a negative feedback to the storm and finally it gets weakens. However, there are other positive impacts associated with these cold core eddies that indirectly help the marine processes. It actually brings the subsurface chlorophyll maxima to the surface area and turn it into higher biological productivity in that region [14]. The detection of these eddies using the sea surface temperature (SST) gradient patterns is a difficult task because the attained surface variations are less. The better approach to deal with these mesoscale eddies are using the altimeter derived sea level anomalies (SLA) in which the high and the low SLA signal gives an indication of warm and cold core eddies [5].

Over the decade, understanding of these mesoscale eddy variability in the spatial and temporal manner has got a steady improvement. Bruce et al. [15] reported the mesoscale eddy features using the TOPEX altimeter data. Frenger et al. [16] analyzed the impact of these eddies on the atmospheric parameters such as winds, clouds and rainfall. Dandapat and Chakraborty [17] analysed the three dimensional structure of the mesoscale eddies using both the altimetry and Argo floats for the period 1993–2014. Busireddy et al. [18] shows the thermohaline structures associated with the WCE and its response on the overlying atmosphere. Despite these facts, the eddy-resolving models are still lacking to capture the realistic features of the ocean tractable in the BoB region. In summary, mesoscale eddies and its feedback to the atmospheric vortices suggest the urgency of high resolution oceanatmosphere coupled models and satellite products over the region.

The present chapter is evolved by considering the availability of the existent high-resolution in-situ (buoy) and remote sensing (satellite derived) products and addresses the eddy induced signals in the ocean and atmospheric parameters. The overall discussion is followed primarily by choosing the (1) WCE during the normal conditions and in the presence of a cyclone environment, (2) sea surface temperature (SST) and wind speed data products verification of advanced microwave


Table 1.

Specifications of SST (°C) and wind speed (ms<sup>1</sup> ) parameters of both in-situ and AMSR-2 sensors.

governing the atmospheric vortices such as tropical cyclones (TCs) and ultimately affect its intensity. For example, cold core eddy promotes TC weakening while, warm core eddy (WCE) amplifies the TC intensity. It is known fact that the coastal

part of the Indian Ocean (Figure 1a). The location of the BoB in the global and regional prospective is shown in the Figure 1a. It is dominated by the several factors such as freshwater discharge, seasonal occurrence of storms, monsoonal reversal winds and upwelling plumes [6, 7]. All these factors contribute to the formation of eddies throughout the year in this bay. The persistent eddy environmental characteristics determine the weather of the ocean and its overlying atmosphere. For example, ocean heat content is considered to be an important parameter in the ocean perspective that can modulate the atmospheric vortices [8, 9]. In case of WCE, the preserved heat content provides the sufficient positive energy to the storm in terms of large enthalpy fluxes and thus helps in the intensification process. Similarly, the process is opposite in case of cold core eddy. Moreover, it also has a

Sea level anomaly of (a) global oceans, on 14 June 2014. The blue rectangle represents the zoomed version of (b) Bay of Bengal showing the warm core eddy. (c and d) The statistical analysis of sea surface temperature (°C) between AMSR-2 satellite and BD08 and BD09 observations during the eddy period (05 May–25 July

2014). Gray and blue color circles in (b) shows the BD08 and BD09 moorings.

The Bay of Bengal (BoB) is an eddy rich region and is located in the northeastern

and marine environments are sensitive to such oceanic vortices.

Coastal and Marine Environments - Physical Processes and Numerical Modelling

Figure 1.

76

scanning radiometer (AMSR-2) over the BoB region and (3) SST-wind speed relationships during the WCE, near coastal region and open oceans using satellite and in-situ measurements. This study signifies the inevitability of high resolution products in the coastal and marine environments over the BoB. The resolution and accuracy of the satellite and in-situ products is demonstrated in Table 1. The buoys (BD08 and BD09) collects both the surface met and ocean parameters with depth. The thermohaline features are nearly similar at both the locations before and after the warm core eddy period. However, significant differences are seen during warm core eddy period owing to its spatial variation. The vertical thermohaline structures exhibited ridge and trough structures throughout the eddy life. It is worthwhile to note that the structures are prominent during developing stage of warm core eddy, however, they are nearly uniform in the weakening phase of warm eddy. More details on the eddy induced signals before, during and after the warm core eddy can be obtained from Busireddy et al. [18].

region. The buoys provide the incessant time series measurements of ocean temperature and salinity profiles. Therefore, the estimated Rossby radius (L = NH/f) at 18°N latitude using the buoy observations is 39 km and is consistent with Chen et al.

to the Space Application Centre, Indian Space Research Organization (ISRO), India (https://www.mosdac.gov.in/oceanic-eddies-detection), the radius of the present eddy derived from AVISO data is 170 km. The fact to be noted that the eddy radius (170 km) is much bigger than the Rossby radius (39 km) means that there

The statistical analysis of SST between the in-situ and AMSR-2 is carried out at two moored buoy locations in the northern BoB (BD08 at 89°E/18°N and BD09 at 89°E/17.5°N) to quantify the uncertainty of the satellite derived SST in the presence of WCE (Figure 1c and d). The AMSR-2 data description and its validation information are made available online at http://www.remss.com/missions/amsr/. Considering the surface ocean temperatures, the buoys observed an unusual increase of 3–4°C during the WCE period. In order to capture these kinds of similar variations using the satellite, primarily, the capability of the AMSR-2 SST is verified with respect to the BD08 and BD09 during the eddy period. The statistical analyses show a root mean square error (RMSE) of 0.5–0.6°C at both the buoy locations. The correlation and bias are varying in between 0.7–0.8 and 0.07–0.13°C, respectively (Figure 1c and d). As stated in Reddy et al. [23], the AMSR-2 mean SST error over the BoB is limited to 0.3°C. In this situation, the observed RMSE (0.5–0.6°C) in the WCE period is doubled as that of mean RMSE (0.3°C) over the BoB. It infers that AMSR-2 SST shows a large error in the presence of mesoscale eddy. However,

), H is the scale height or

1

). According

[22]. Here, N is the Brunt-Vaisala frequency (0.0179 s<sup>1</sup>

DOI: http://dx.doi.org/10.5772/intechopen.86243

equivalent depth (100 m), and f is Coriolis frequency (4.505 <sup>10</sup><sup>5</sup> <sup>s</sup>

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment…

are some other effects contributing to this large-scale eddy formation.

these error values are within the satellite prescribed release accuracy range (i.e., 0.8°C). The results provide that AMSR-2 is over estimating for the higher

In addition to the ocean responses, the WCE provides it signatures on the surface meteorological variables. The noticeable variations on the surface-air temperature (SAT) and sea-surface temperature (SST) anomalies, air-sea specific humidity values and enthalpy fluxes during the WCE period is observed [18]. During the eddy progression, i.e., from genesis to peak stage, both the SAT and SST anomalies show similar changes and exhibits its peak magnitude of 3°C at the peak stage. In contrast, significant differences have been noticed during the peak-todissipation stage. The mean differences between the SAT and SST anomalies are 0.01 and 0.6°C, respectively. These differences indirectly cause for the changes in the surface turbulent fluxes, which is the main source for weather systems (i.e., cyclones). It is observed that the strong air-sea temperature differences in the WCE lead to the sharp increase in the latent heat flux and sensible heat flux of 504 and

, respectively [18]. The comparison of these values with the climatology

revealed that the eddy induced fluxes are higher by 274% and 370% [7, 24]. Table 2

corresponding AVISO SLA values during the eddy evolution. In order to understand clearly, the WCE period is broadly classified into various phases such as no-eddy, genesis, intensifying phase, peak stage, phase and dissipation phase, respectively [18]. The obtained results indicated that for all the parameters, values are increasing beginning from the no-eddy, genesis and reaches to its higher values at the peak stage. Thereafter, it reaches to its normal values during the dissipation stage. For example, the estimated ocean heat content at peak stage shows an increment of 258%, when compared to the no-eddy conditions [18]. Similarly, the other parameters such as wind speed, SST and SLA also exhibits a similar increment of 51, 3.6

illustrates the estimated values of buoy (BD08) air-sea parameters and its

and 98%, respectively when compared to that of no-eddy conditions.

SST values.

142 W m<sup>2</sup>

79

## 2. North Bay of Bengal

The Bay of Bengal is surrounded by India (in the west and northwest side), Bangladesh (in the north), Myanmar and the Andaman/Nicobar Islands of India (in the east) and Sri Lanka (in the south) (Figure 1a). A number of major rivers belonging to the India, Bangladesh and Myanmar is flowing into the northern BoB. In the tropics, Northern BoB has a unique feature among the world as it is dominated by the low surface salinity due to seasonal reversal of the monsoon winds, freshwater discharge from the major rivers and the seasonal occurrence of cyclones [19]. During the summer monsoon season (June-September), northern BoB is conducive for the several meteorological systems that brings rainfall to the central and Northern parts of India. Alike, the massive river outflow, which is coupled with rainfall eventually drain into the bay region. These large supplies of fresh water and rainfall resulting in the strong near surface layer stratification led to the formation of the barrier layer. Likewise, during winter season (November-December), the region experiences a strong surface layer temperature inversion due to the sustained salinity stratification of the summer monsoon. Moreover, the region is active for the mesoscale eddies that are formed due to the active coastal upwelling Kelvin wave during the spring intermonsoon period (March-May) [18, 20].

## 2.1 Warm core eddy

A warm core eddy has been identified using the spatial maps of sea level anomalies (SLA) obtained from the Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) over the northern BoB (Figure 1b). It is noted that the size of the eddy is around 5° 5°. Chelton et al. [21] criteria are applied for the eddy identification purpose and realized that it is centered at around 89°E and 18°N with a life period of 3 months (05 May 2014–25 July 2014) in the northern BoB. The eddy was surrounded by the several alternative warm and cold core rings (Figure 1b). The further details regarding the genesis and its propagation characteristics are demonstrated in Busireddy et al. [18]. In general, the eddy size is often associated with the Rossby radius of deformation which is increased from higher to lower latitudes. It is termed as the length scale at which rotational effects become equally important as the buoyancy or gravitational effects in a flow field. From Figure 3b of Chen et al. [22], the Rossby radius of deformation values are 60 km at 20°N and 130 km at 9°N. Incidentally, two of the moored buoys (BD08 and BD09), deployed earlier by National Institute of Ocean Technology, are located within the eddy

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment… DOI: http://dx.doi.org/10.5772/intechopen.86243

region. The buoys provide the incessant time series measurements of ocean temperature and salinity profiles. Therefore, the estimated Rossby radius (L = NH/f) at 18°N latitude using the buoy observations is 39 km and is consistent with Chen et al. [22]. Here, N is the Brunt-Vaisala frequency (0.0179 s<sup>1</sup> ), H is the scale height or equivalent depth (100 m), and f is Coriolis frequency (4.505 <sup>10</sup><sup>5</sup> <sup>s</sup> 1 ). According to the Space Application Centre, Indian Space Research Organization (ISRO), India (https://www.mosdac.gov.in/oceanic-eddies-detection), the radius of the present eddy derived from AVISO data is 170 km. The fact to be noted that the eddy radius (170 km) is much bigger than the Rossby radius (39 km) means that there are some other effects contributing to this large-scale eddy formation.

The statistical analysis of SST between the in-situ and AMSR-2 is carried out at two moored buoy locations in the northern BoB (BD08 at 89°E/18°N and BD09 at 89°E/17.5°N) to quantify the uncertainty of the satellite derived SST in the presence of WCE (Figure 1c and d). The AMSR-2 data description and its validation information are made available online at http://www.remss.com/missions/amsr/. Considering the surface ocean temperatures, the buoys observed an unusual increase of 3–4°C during the WCE period. In order to capture these kinds of similar variations using the satellite, primarily, the capability of the AMSR-2 SST is verified with respect to the BD08 and BD09 during the eddy period. The statistical analyses show a root mean square error (RMSE) of 0.5–0.6°C at both the buoy locations. The correlation and bias are varying in between 0.7–0.8 and 0.07–0.13°C, respectively (Figure 1c and d). As stated in Reddy et al. [23], the AMSR-2 mean SST error over the BoB is limited to 0.3°C. In this situation, the observed RMSE (0.5–0.6°C) in the WCE period is doubled as that of mean RMSE (0.3°C) over the BoB. It infers that AMSR-2 SST shows a large error in the presence of mesoscale eddy. However, these error values are within the satellite prescribed release accuracy range (i.e., 0.8°C). The results provide that AMSR-2 is over estimating for the higher SST values.

In addition to the ocean responses, the WCE provides it signatures on the surface meteorological variables. The noticeable variations on the surface-air temperature (SAT) and sea-surface temperature (SST) anomalies, air-sea specific humidity values and enthalpy fluxes during the WCE period is observed [18]. During the eddy progression, i.e., from genesis to peak stage, both the SAT and SST anomalies show similar changes and exhibits its peak magnitude of 3°C at the peak stage. In contrast, significant differences have been noticed during the peak-todissipation stage. The mean differences between the SAT and SST anomalies are 0.01 and 0.6°C, respectively. These differences indirectly cause for the changes in the surface turbulent fluxes, which is the main source for weather systems (i.e., cyclones). It is observed that the strong air-sea temperature differences in the WCE lead to the sharp increase in the latent heat flux and sensible heat flux of 504 and 142 W m<sup>2</sup> , respectively [18]. The comparison of these values with the climatology revealed that the eddy induced fluxes are higher by 274% and 370% [7, 24]. Table 2 illustrates the estimated values of buoy (BD08) air-sea parameters and its corresponding AVISO SLA values during the eddy evolution. In order to understand clearly, the WCE period is broadly classified into various phases such as no-eddy, genesis, intensifying phase, peak stage, phase and dissipation phase, respectively [18]. The obtained results indicated that for all the parameters, values are increasing beginning from the no-eddy, genesis and reaches to its higher values at the peak stage. Thereafter, it reaches to its normal values during the dissipation stage. For example, the estimated ocean heat content at peak stage shows an increment of 258%, when compared to the no-eddy conditions [18]. Similarly, the other parameters such as wind speed, SST and SLA also exhibits a similar increment of 51, 3.6 and 98%, respectively when compared to that of no-eddy conditions.

scanning radiometer (AMSR-2) over the BoB region and (3) SST-wind speed relationships during the WCE, near coastal region and open oceans using satellite and in-situ measurements. This study signifies the inevitability of high resolution products in the coastal and marine environments over the BoB. The resolution and accuracy of the satellite and in-situ products is demonstrated in Table 1. The buoys (BD08 and BD09) collects both the surface met and ocean parameters with depth. The thermohaline features are nearly similar at both the locations before and after the warm core eddy period. However, significant differences are seen during warm core eddy period owing to its spatial variation. The vertical thermohaline structures exhibited ridge and trough structures throughout the eddy life. It is worthwhile to note that the structures are prominent during developing stage of warm core eddy, however, they are nearly uniform in the weakening phase of warm eddy. More details on the eddy induced signals before, during and after the warm core eddy can

Coastal and Marine Environments - Physical Processes and Numerical Modelling

The Bay of Bengal is surrounded by India (in the west and northwest side), Bangladesh (in the north), Myanmar and the Andaman/Nicobar Islands of India (in the east) and Sri Lanka (in the south) (Figure 1a). A number of major rivers belonging to the India, Bangladesh and Myanmar is flowing into the northern BoB. In the tropics, Northern BoB has a unique feature among the world as it is dominated by the low surface salinity due to seasonal reversal of the monsoon winds, freshwater discharge from the major rivers and the seasonal occurrence of cyclones [19]. During the summer monsoon season (June-September), northern BoB is conducive for the several meteorological systems that brings rainfall to the central and Northern parts of India. Alike, the massive river outflow, which is coupled with rainfall eventually drain into the bay region. These large supplies of fresh water and rainfall resulting in the strong near surface layer stratification led to the formation of the barrier layer. Likewise, during winter season (November-December), the region experiences a strong surface layer temperature inversion due to the sustained salinity stratification of the summer monsoon. Moreover, the region is active for the mesoscale eddies that are formed due to the active coastal upwelling Kelvin wave

A warm core eddy has been identified using the spatial maps of sea level anomalies (SLA) obtained from the Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) over the northern BoB (Figure 1b). It is noted that the size of the eddy is around 5° 5°. Chelton et al. [21] criteria are applied for the eddy identification purpose and realized that it is centered at around 89°E and 18°N with a life period of 3 months (05 May 2014–25 July 2014) in the northern BoB. The eddy was surrounded by the several alternative warm and cold core rings (Figure 1b). The further details regarding the genesis and its propagation characteristics are demonstrated in Busireddy et al. [18]. In general, the eddy size is often associated with the Rossby radius of deformation which is increased from higher to lower latitudes. It is termed as the length scale at which rotational effects become equally important as the buoyancy or gravitational effects in a flow field. From Figure 3b of Chen et al. [22], the Rossby radius of deformation values are 60 km at 20°N and 130 km at 9°N. Incidentally, two of the moored buoys (BD08 and BD09), deployed earlier by National Institute of Ocean Technology, are located within the eddy

during the spring intermonsoon period (March-May) [18, 20].

be obtained from Busireddy et al. [18].

2. North Bay of Bengal

2.1 Warm core eddy

78

## 2.2 Role of warm core eddy in the intensification of AILA (May 2009) cyclone over BoB

The greatest contributors to the coastal flooding and its associated damage are mainly due to the landfalling cyclones. It is generally formed over the warm waters (>26.5°C) and gains the remarkable amount of energy from the ocean [25]. The typical size of the TCs are 200–2000 km with a time period of 1–2 weeks [26]. Aila is a severe cyclonic storm that formed over the northern BoB and made landfall near the Sagar Island on 25 May 2009. The strong winds and storm surge cause for the 175 human deaths (http://www.rsmcnewdelhi.imd.gov.in). The prominence of cyclone is that it intensified from cyclone to severe cyclonic storm within a few hours before the landfall. The later studies revealed that one of the reasons for sudden intensification is due to the presence of WCE in near at the coastal region of the West Bengal. Figure 2 shows the AILA cyclone track and its corresponding time series analysis of SLA, cyclone intensity and wind intensity (kt) values. The SLA depicts the occurrence of WCE close proximity to the West Bengal coast. During the Aila cyclone movement, it encountered a WCE on 24 May 2009 as a cyclonic storm and get intensified into a severe cyclonic storm within a short time span. It is observed that WCE maintains an SST of 31°C during the time and favors the storm intensity increase by 43%. The high ocean heat content and deep isothermal warm

layer within the eddy inhibit the storm induced cooling and further help in the intensification process [10]. From Figure 2b, the time series analysis of SLA and wind intensity shows a usual value ranging from 7.5 to 9 cm and 25–32 knots before it arrives into the eddy region. Once the TC encountered the eddy (24 May 2009), SLA shows a sudden increment of 9–20 cm with the corresponding increase in wind intensity from 32 to 47 knots. Sadhuram et al. [10] explained that high SST and large enthalpy fluxes beneath the eddy provides a positive feedback and support to the TC intensification process. Along with this, cyclone intensity also shows a positive trend throughout the TC life period. Moreover, the cyclone made landfall on 25 May 2018 so the SLA is absent and wind intensity values decreases due to the land

(a) Maximum sustained wind swath in the life span of the TC AILA (23–26 May 2009) and (b) 24-h

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment…

DOI: http://dx.doi.org/10.5772/intechopen.86243

accumulated rainfall during landfall day valid at 03 UTC 26 May 2009.

Figure 3 shows the spatial distribution of wind speed and rainfall associated with the Aila cyclone during the landfall. The wind speed was obtained from FiNaL (FNL) analyses of the National Center for Environmental Prediction (NCEP) and the rainfall (3-hourly) from the Tropical Rainfall Measuring Mission. The structural wind swath analyses show a peak wind intensity of >45 knots that persists over the Bangladesh even after the landfall. As mentioned earlier, the presence of WCE during the AILA passage supports its intensification into a severe cyclone, while the abundant soil moisture from the land surface conditions (Delta region of the River Ganga) helps to sustain/maintain the intensity for next 15 h. The analysis from the spatial distribution of 24-h accumulated rainfall during the landfall exhibits a high

dred kilometers away from the coast. The down-pouring of heavy rain in some places around the Sundarban area lead to the coastal inundation and flooding. According to the India Meteorological Department, New Delhi, a storm surge of 3 m is experienced in the western regions of Bangladesh and it is 2–3 m over the

As we know that SST is a vital parameter in the coastal and marine environment that can regulate both the physical and biological processes. The SST changes are directly associated with the magnitude of the wind speed. Higher (lower) the magnitude lower (higher) the SST values. Here, the performance of the AMSR-2 SST and wind speed is verified at two in-situ locations (BD12—94°E, 10.3°N and BD14—88°E, 7°N) over the BoB for the period 2013–2014. Out of these two buoys,

Sundarban area region (http://www.rsmcnewdelhi.imd.gov.in).

3. SST and wind speed analysis over the BoB

. Moreover, these rainfall are elongated to a few hun-

interaction.

81

Figure 3.

rainfall of <sup>15</sup>–18 cm day<sup>1</sup>


Table 2.

Quantification measures of BD08 atmosphere-ocean parameters during no-eddy to dissipation stages.

#### Figure 2.

AILA tropical cyclone passing through the warm core eddy in (a) and its corresponding time series analysis of sea level anomaly (SLA), cyclone intensity (CI) and wind intensity (kt) values along the track shown in (b). Shaded background in (a) and vertical dashed line in (b) represents the SLA and warm core eddy crossing day respectively.

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment… DOI: http://dx.doi.org/10.5772/intechopen.86243

Figure 3.

2.2 Role of warm core eddy in the intensification of AILA (May 2009)

Coastal and Marine Environments - Physical Processes and Numerical Modelling

The greatest contributors to the coastal flooding and its associated damage are mainly due to the landfalling cyclones. It is generally formed over the warm waters (>26.5°C) and gains the remarkable amount of energy from the ocean [25]. The typical size of the TCs are 200–2000 km with a time period of 1–2 weeks [26]. Aila is a severe cyclonic storm that formed over the northern BoB and made landfall near the Sagar Island on 25 May 2009. The strong winds and storm surge cause for the 175 human deaths (http://www.rsmcnewdelhi.imd.gov.in). The prominence of cyclone is that it intensified from cyclone to severe cyclonic storm within a few hours before the landfall. The later studies revealed that one of the reasons for sudden intensification is due to the presence of WCE in near at the coastal region of the West Bengal. Figure 2 shows the AILA cyclone track and its corresponding time series analysis of SLA, cyclone intensity and wind intensity (kt) values. The SLA depicts the occurrence of WCE close proximity to the West Bengal coast. During the Aila cyclone movement, it encountered a WCE on 24 May 2009 as a cyclonic storm and get intensified into a severe cyclonic storm within a short time span. It is observed that WCE maintains an SST of 31°C during the time and favors the storm intensity increase by 43%. The high ocean heat content and deep isothermal warm

) Wind speed (ms<sup>1</sup>

Before/no-eddy 40 4.9 30.0 25.4 Genesis 69 1.4 30.1 26.1 Intensifying phase 109 4.7 30.4 38.3 Peak stage 139 7.4 31.1 50.5 Weakening phase 100 7.2 29.7 35.8 Dissipation stage 7 5.0 28.6 13.7

Quantification measures of BD08 atmosphere-ocean parameters during no-eddy to dissipation stages.

) SST (°C) SLA (cm)

AILA tropical cyclone passing through the warm core eddy in (a) and its corresponding time series analysis of sea level anomaly (SLA), cyclone intensity (CI) and wind intensity (kt) values along the track shown in (b). Shaded background in (a) and vertical dashed line in (b) represents the SLA and warm core eddy crossing day

cyclone over BoB

Phases/parameters OHC (kJ cm<sup>2</sup>

Figure 2.

Table 2.

respectively.

80

(a) Maximum sustained wind swath in the life span of the TC AILA (23–26 May 2009) and (b) 24-h accumulated rainfall during landfall day valid at 03 UTC 26 May 2009.

layer within the eddy inhibit the storm induced cooling and further help in the intensification process [10]. From Figure 2b, the time series analysis of SLA and wind intensity shows a usual value ranging from 7.5 to 9 cm and 25–32 knots before it arrives into the eddy region. Once the TC encountered the eddy (24 May 2009), SLA shows a sudden increment of 9–20 cm with the corresponding increase in wind intensity from 32 to 47 knots. Sadhuram et al. [10] explained that high SST and large enthalpy fluxes beneath the eddy provides a positive feedback and support to the TC intensification process. Along with this, cyclone intensity also shows a positive trend throughout the TC life period. Moreover, the cyclone made landfall on 25 May 2018 so the SLA is absent and wind intensity values decreases due to the land interaction.

Figure 3 shows the spatial distribution of wind speed and rainfall associated with the Aila cyclone during the landfall. The wind speed was obtained from FiNaL (FNL) analyses of the National Center for Environmental Prediction (NCEP) and the rainfall (3-hourly) from the Tropical Rainfall Measuring Mission. The structural wind swath analyses show a peak wind intensity of >45 knots that persists over the Bangladesh even after the landfall. As mentioned earlier, the presence of WCE during the AILA passage supports its intensification into a severe cyclone, while the abundant soil moisture from the land surface conditions (Delta region of the River Ganga) helps to sustain/maintain the intensity for next 15 h. The analysis from the spatial distribution of 24-h accumulated rainfall during the landfall exhibits a high rainfall of <sup>15</sup>–18 cm day<sup>1</sup> . Moreover, these rainfall are elongated to a few hundred kilometers away from the coast. The down-pouring of heavy rain in some places around the Sundarban area lead to the coastal inundation and flooding. According to the India Meteorological Department, New Delhi, a storm surge of 3 m is experienced in the western regions of Bangladesh and it is 2–3 m over the Sundarban area region (http://www.rsmcnewdelhi.imd.gov.in).

## 3. SST and wind speed analysis over the BoB

As we know that SST is a vital parameter in the coastal and marine environment that can regulate both the physical and biological processes. The SST changes are directly associated with the magnitude of the wind speed. Higher (lower) the magnitude lower (higher) the SST values. Here, the performance of the AMSR-2 SST and wind speed is verified at two in-situ locations (BD12—94°E, 10.3°N and BD14—88°E, 7°N) over the BoB for the period 2013–2014. Out of these two buoys,

RMSE ¼

DOI: http://dx.doi.org/10.5772/intechopen.86243

correlation rð Þ¼ <sup>1</sup>

3.2 SST—wind speed relationship over the BoB

Figure 5.

83

Relationship between SST (°C) and wind speed (m s�<sup>1</sup>

that warm core eddy period considers only the BD08 observations.

1 <sup>n</sup> <sup>∑</sup><sup>n</sup>

n � 1

observations, respectively. x and y indicates the temporal mean of satellite and in-situ measurements and whereas sx and sy shows the standard deviation of it.

where n, xi, and xobs represents the total number of samples, satellite and in-situ

This section briefly explains the SST dependency on the surface wind speed over the BoB region (Figure 5). The analysis comprises of northern to southern BoB at different locations and scenarios such as eddy environment (BD08), near a coastal region (BD12) and open ocean areas (BD10 and BD14). Note that the SST-wind relationship is evaluated at BD08 location only during the WCE period. Because, the

r

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment…

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

<sup>i</sup>¼1ð Þ ð Þ xi � xobs <sup>=</sup>xobs

2

<sup>∑</sup>ð Þ xi � <sup>x</sup>=sx yi � <sup>y</sup> � �=sy<sup>Þ</sup> (3)

) of AMSR-2 and in-situ measurements during

(a) warm core eddy period (b and c) at BD10 and BD14—open ocean and (d) BD12—coastal region. Note

Bias ¼ xi � xobs (2)

(1)

Figure 4.

Scatter plot analysis of AMSR-2 SST at (a) BD12—coastal region and (c) BD14—open ocean. (b) and (d) are same as (a) and (c) but for the wind speed during the period 2013–2014.

BD12 is situated near at the coastal region, i.e., east of the Andaman Island (100–150 km) and the BD14 is placed in the open ocean, i.e., southern BoB region. Figure 4 shows the statistical analyses of AMSR-2 SST and wind speed with respect to the in-situ observations. The formulations used in the present study are described in the Section 3.1. The SST statistics revealed that the BD12 and BD14 show an RMSE (Eq. (1)) error of 0.4 and 0.3°C (Figure 4a and c). Considering its correlation (r) and bias, BD12 (BD14) shows 0.95 (0.94) and 0.2°C (0.02°C), respectively (Eqs. (2) and (3)). It means that expected AMSR-2 accuracy is less towards the coastal region as compared to the open ocean. The similar statistics are conducted for the satellite derived wind speed. The results showed that an RMSE of 1.1 and 1.3 m s<sup>1</sup> , respectively, with a bias and correlation of 0.9 m s<sup>1</sup> and 0.4 (0.02) at BD12 and BD14 locations (Figure 5b and d). However, all these obtained SST and wind speed errors are within the prescribed accuracy mentioned by the AMSR-2 (0.8°C and 1.5 m s<sup>1</sup> ).

#### 3.1 Statistical formulation used

A common set of mathematical and statistical techniques has been used to assess a performance of the AMSR-2 satellite measurements over the BoB region. It could further helpful to quantify the uncertainty and as well as for the rational use of the AMSR-2 information over the current region. The study employs the most widely used metrics such as bias, linear correlation and root mean square error, respectively. The formulas related to the error statistics are as follows.

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment… DOI: http://dx.doi.org/10.5772/intechopen.86243

$$RMSE = \sqrt{\frac{1}{n} \sum\_{i=1}^{n} \left( (\mathbf{x}\_i - \mathbf{x}\_{obs}) / \mathbf{x}\_{obs} \right)^2} \tag{1}$$

$$\text{Bias} = \mathfrak{x}\_i - \mathfrak{x}\_{obs} \tag{2}$$

$$\text{correlation } (r) = \frac{1}{n-1} \sum (\mathbf{x}\_i - \overline{\mathbf{x}}/\mathbf{s}\_\mathbf{x}) \left( \mathbf{y}\_i - \overline{\mathbf{y}} \right) / s\_\mathbf{y} \tag{3}$$

where n, xi, and xobs represents the total number of samples, satellite and in-situ observations, respectively. x and y indicates the temporal mean of satellite and in-situ measurements and whereas sx and sy shows the standard deviation of it.

#### 3.2 SST—wind speed relationship over the BoB

This section briefly explains the SST dependency on the surface wind speed over the BoB region (Figure 5). The analysis comprises of northern to southern BoB at different locations and scenarios such as eddy environment (BD08), near a coastal region (BD12) and open ocean areas (BD10 and BD14). Note that the SST-wind relationship is evaluated at BD08 location only during the WCE period. Because, the

#### Figure 5.

Relationship between SST (°C) and wind speed (m s�<sup>1</sup> ) of AMSR-2 and in-situ measurements during (a) warm core eddy period (b and c) at BD10 and BD14—open ocean and (d) BD12—coastal region. Note that warm core eddy period considers only the BD08 observations.

BD12 is situated near at the coastal region, i.e., east of the Andaman Island

Coastal and Marine Environments - Physical Processes and Numerical Modelling

are same as (a) and (c) but for the wind speed during the period 2013–2014.

1.3 m s<sup>1</sup>

82

Figure 4.

(0.8°C and 1.5 m s<sup>1</sup>

3.1 Statistical formulation used

).

tively. The formulas related to the error statistics are as follows.

(100–150 km) and the BD14 is placed in the open ocean, i.e., southern BoB region. Figure 4 shows the statistical analyses of AMSR-2 SST and wind speed with respect to the in-situ observations. The formulations used in the present study are described in the Section 3.1. The SST statistics revealed that the BD12 and BD14 show an RMSE (Eq. (1)) error of 0.4 and 0.3°C (Figure 4a and c). Considering its correlation (r) and bias, BD12 (BD14) shows 0.95 (0.94) and 0.2°C (0.02°C), respectively (Eqs. (2) and (3)). It means that expected AMSR-2 accuracy is less towards the coastal region as compared to the open ocean. The similar statistics are conducted for the satellite derived wind speed. The results showed that an RMSE of 1.1 and

Scatter plot analysis of AMSR-2 SST at (a) BD12—coastal region and (c) BD14—open ocean. (b) and (d)

, respectively, with a bias and correlation of 0.9 m s<sup>1</sup> and 0.4 (0.02) at

BD12 and BD14 locations (Figure 5b and d). However, all these obtained SST and wind speed errors are within the prescribed accuracy mentioned by the AMSR-2

A common set of mathematical and statistical techniques has been used to assess a performance of the AMSR-2 satellite measurements over the BoB region. It could further helpful to quantify the uncertainty and as well as for the rational use of the AMSR-2 information over the current region. The study employs the most widely used metrics such as bias, linear correlation and root mean square error, respecestimated variations are minimal at both the BD08 and BD09 so the analysis related to the BD09 is excluded here. Figure 5a shows the SST-wind speed relation during the WCE period. The relationship shows a negative trend inferring that SST decreases with an increase in wind speed for both AMSR-2 and in-situ observations. The observed slope of the fitted line is high for the AMSR-2 with an SST difference of 3.4°C and is limited to 2°C in case of BD08. However, the long time period (2013–2014) analyses at the same location shows a contradictory relation indicating SST increases with an increase in wind speed [23]. Figure 5b shows the SST-wind speed relation over the central BoB region, i.e., BD10. Here, the relation displays a positive linear trend, addressing SST increases with an increase in wind speed [23]. Moreover, the deviation of SST and wind speed values of the fitted line is higher (lower) for the low (high) wind conditions. The data spread could be due to the fact that this region is active for the seasonal storms and monsoons. Considering the same relationship over the BD12 (eastern BoB) and BD14 (southern BoB), the SST decreases with an increase in wind speed (Figure 5c and d). It means that higher (lower) SST values are corresponding to the lower (higher) wind speed values. The present analysis also revealed that obtained AMSR-2 fitted line slope is relatively larger than the buoys fitted line. It could be due to the positive bias of SST and negative bias of wind speed over the BoB. In summary, satellite is underestimating the SST and wind speed variations because the ocean characteristics changes with the season and latitude over the BoB.

opposite relation (i.e., positive relation) indicating SST increases with an increase in the wind speed. The overall results directed that AMSR-2 is able to replicate the buoy induced signals with positive bias in the SST and negative bias in the wind

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment…

The authors gratefully acknowledge the financial support of SERB (ECR/2016/ 001637), and the SERB—Purdue Overseas Visiting Doctoral Fellowship (OVDF) scheme (SB/S9/Z-03/2017), Govt. of India. The authors owe thanks to the Department of Space (DOS), Govt. of India, for providing the warm core eddy features.

The authors declare that they do not have any competing interests to publish this

AVISO archiving, validation, and interpretation of satellite oceanographic

Nanda Kishore Reddy Busireddy1,2, Kumar Ankur1 and Krishna Kishore Osuri<sup>1</sup>

1 Department of Earth and Atmospheric Sciences, National Institute of Technology

2 Department of Earth, Atmospheric, and Planetary Sciences, Purdue University,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*

AMSR-2 advanced microwave scanning radiometer-2

\*Address all correspondence to: osurikishore@gmail.com

provided the original work is properly cited.

speed over the BoB.

DOI: http://dx.doi.org/10.5772/intechopen.86243

Acknowledgements

Conflict of interest

book chapter.

List of acronyms

BoB Bay of Bengal

TC tropical cyclone WCE warm core eddy

Author details

Rourkela, Odisha, India

West Lafayette, IN, USA

85

RMSE root mean square error SAT surface air temperature SLA sea level anomalies SST sea surface temperature

## 4. Conclusions

This chapter addresses the mesoscale warm core eddies and high-resolution products prominence in the Bay of Bengal. In-situ observations are used to analyze and quantify the errors of the AMSR-2 during the WCE over the northern BoB. The eddy has a life period of 81 days with a 5° 5° size. The statistical analysis of AMSR-2 SST with in-situ observations during the eddy period show an RMSE of 0.5–0.6°C. Interestingly, the obtained SST error is double that of the mean error calculated over the BoB region. In addition, the eddy impact on the overlying atmosphere is analyzed with showing an increase in the enthalpy fluxes and surface atmospheric parameter values. The similar event has been undertaken in the presence of AILA cyclone and the eddy role in the intensification process is observed. Briefly, Aila encounters the WCE on 24 May 2009 and after spending a few hours, it gets intensified into a severe cyclonic storm. The time series analysis of satellite and met-ocean parameters depicted that SLA and wind intensity shows its peak value of 20 cm and >45 knots after it passes through the WCE. Due to this sudden intensification, the coastal region receives a heavy rainfall (>18 mm day<sup>1</sup> ) and strong winds (>45 knots) that led to the coastal inundation and flooding problems.

The AMSR-2 SST and wind speed parameters are evaluated qualitatively and quantitatively with respect to the buoy observations over the BoB. The statistical analysis of SST at BD12 and BD14 locations show an RMSE of 0.4 and 0.3°C and whereas wind speed shows an RMSE of 1.1 and 1.3 m s<sup>1</sup> , respectively. Along with this, the AMSR-2 SST and wind speed relationship is assessed in different aspects such as WCE, open and near coastal regions of the BoB. These analyses cover the whole BoB region and the obtained SST and wind speed interactions helpful to understand the air-sea interaction processes. The relationship exhibits a both linear positive and negative slopes over the BoB. It infers that wind speed and SST shows contrasting features within the BoB region. For example, BD08, BD12 and BD14 displays the negative relation inferring SST decreases with an increase in wind speed in both AMSR-2 and buoy observations. However, BD10 location shows the

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment… DOI: http://dx.doi.org/10.5772/intechopen.86243

opposite relation (i.e., positive relation) indicating SST increases with an increase in the wind speed. The overall results directed that AMSR-2 is able to replicate the buoy induced signals with positive bias in the SST and negative bias in the wind speed over the BoB.

## Acknowledgements

estimated variations are minimal at both the BD08 and BD09 so the analysis related to the BD09 is excluded here. Figure 5a shows the SST-wind speed relation during the WCE period. The relationship shows a negative trend inferring that SST

Coastal and Marine Environments - Physical Processes and Numerical Modelling

decreases with an increase in wind speed for both AMSR-2 and in-situ observations. The observed slope of the fitted line is high for the AMSR-2 with an SST difference of 3.4°C and is limited to 2°C in case of BD08. However, the long time period (2013–2014) analyses at the same location shows a contradictory relation indicating SST increases with an increase in wind speed [23]. Figure 5b shows the SST-wind speed relation over the central BoB region, i.e., BD10. Here, the relation displays a positive linear trend, addressing SST increases with an increase in wind speed [23]. Moreover, the deviation of SST and wind speed values of the fitted line is higher (lower) for the low (high) wind conditions. The data spread could be due to the fact that this region is active for the seasonal storms and monsoons. Considering the same relationship over the BD12 (eastern BoB) and BD14 (southern BoB), the SST decreases with an increase in wind speed (Figure 5c and d). It means that higher (lower) SST values are corresponding to the lower (higher) wind speed values. The present analysis also revealed that obtained AMSR-2 fitted line slope is relatively larger than the buoys fitted line. It could be due to the positive bias of SST and negative bias of wind speed over the BoB. In summary, satellite is underestimating the SST and wind speed variations because the ocean characteristics changes with

This chapter addresses the mesoscale warm core eddies and high-resolution products prominence in the Bay of Bengal. In-situ observations are used to analyze and quantify the errors of the AMSR-2 during the WCE over the northern BoB. The eddy has a life period of 81 days with a 5° 5° size. The statistical analysis of AMSR-2 SST with in-situ observations during the eddy period show an RMSE of 0.5–0.6°C. Interestingly, the obtained SST error is double that of the mean error calculated over the BoB region. In addition, the eddy impact on the overlying atmosphere is analyzed with showing an increase in the enthalpy fluxes and surface atmospheric parameter values. The similar event has been undertaken in the presence of AILA cyclone and the eddy role in the intensification process is observed. Briefly, Aila encounters the WCE on 24 May 2009 and after spending a few hours, it gets intensified into a severe cyclonic storm. The time series analysis of satellite and met-ocean parameters depicted that SLA and wind intensity shows its peak value of 20 cm and >45 knots after it passes through the WCE. Due to this sudden intensifi-

) and strong

, respectively. Along with

cation, the coastal region receives a heavy rainfall (>18 mm day<sup>1</sup>

whereas wind speed shows an RMSE of 1.1 and 1.3 m s<sup>1</sup>

winds (>45 knots) that led to the coastal inundation and flooding problems.

The AMSR-2 SST and wind speed parameters are evaluated qualitatively and quantitatively with respect to the buoy observations over the BoB. The statistical analysis of SST at BD12 and BD14 locations show an RMSE of 0.4 and 0.3°C and

this, the AMSR-2 SST and wind speed relationship is assessed in different aspects such as WCE, open and near coastal regions of the BoB. These analyses cover the whole BoB region and the obtained SST and wind speed interactions helpful to understand the air-sea interaction processes. The relationship exhibits a both linear positive and negative slopes over the BoB. It infers that wind speed and SST shows contrasting features within the BoB region. For example, BD08, BD12 and BD14 displays the negative relation inferring SST decreases with an increase in wind speed in both AMSR-2 and buoy observations. However, BD10 location shows the

the season and latitude over the BoB.

4. Conclusions

84

The authors gratefully acknowledge the financial support of SERB (ECR/2016/ 001637), and the SERB—Purdue Overseas Visiting Doctoral Fellowship (OVDF) scheme (SB/S9/Z-03/2017), Govt. of India. The authors owe thanks to the Department of Space (DOS), Govt. of India, for providing the warm core eddy features.

## Conflict of interest

The authors declare that they do not have any competing interests to publish this book chapter.

## List of acronyms


## Author details

Nanda Kishore Reddy Busireddy1,2, Kumar Ankur1 and Krishna Kishore Osuri<sup>1</sup> \*

1 Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, Odisha, India

2 Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA

\*Address all correspondence to: osurikishore@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment…

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[26] Niyogi D, Subramanian S, Osuri KK. The role of land surface processes on tropical cyclones: Introduction to Land

Science. 2018;127(1):14

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Section 3

Coastal Processes and

HF Communications

in Coastal and Marine

Environments

## Section 3
