**Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)**

Marcela Hebe González

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55494

## **1. Introduction**

Argentina is located in the southeastern continental extreme of South America. The Andes Mountain Range extends 7240km all along the western region of the country with a mean height of 3660m above mean sea level (a.m.s.l.). Two topographical regions can be distin‐ guished in the country: north of 40°S, the range is high and dense preventing the humidity access from the Pacific Ocean. As a result, atmospheric flow is dominated by the South Atlantic High and winds prevail from the northeast. An intermittent low pressure system, whose likely origin is a combination of thermal and dynamical effects, is located between 20º and 30ºS, in a dry and relatively high area east of the Andes. This system is observed throughout the year, but it is deeper in summer than in winter. When this low is present, northerly flow is favored at low levels over the subtropical region. Therefore, the water vapor entering at low levels comes either from the tropical continent or from the Atlantic Ocean. In the first case, the easterly low-level flow at low latitudes is channeled towards the south between the Bolivian Plateau and the Brazilian Planalto, advecting warm and humid air to southern Brazil, Paraguay, Uruguay and subtropical Argentina. South of 40ºS, the mean flow is from the west during all months and there are intermittent interruptions of polar fronts from the southwest associated with the displacement of Rossby waves over the Pacific ocean [1]. Storms occur frequently and erosion is one of the main features of the region.

The Argentinean Patagonia region is located between 30° and 55°S, where the wind circulation is predominantly westerly all year round, being more intense during winter. As a consequence of the interaction between wind patterns and orography, maximum precipitation (approxi‐ mately 1600 mm/year in northern Patagonia) occurs in the vicinity of the mountains; decreas‐ ing eastward over Patagonian region especially in winter [2, 3] to a low of 300-500 mm/year. Consequently, eastern Patagonia is arid to semi-arid with hydrographic network that begins

© 2013 González; licensee InTech. This is an open access article 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. © 2013 González; licensee InTech. This is a paper 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.

generally in the Andes Mountains and flows towards the Atlantic Ocean. Important rivers include the Negro, the Neuquen and the Limay river basins. The most favorable conditions for precipitation in the Patagonian Mountains and its eastern slopes are stationary fronts especially when the cold side of the anticyclone brings moist winds from the Atlantic. The result is stratiform clouds and extended rainfall. In few cases, rainfall occurs due to blocking high pressure systems located in Patagonia or the adjacent Atlantic resulting in extended periods with cloudiness and precipitation.

between rainfall and various circulation patterns. The model showed that sea surface temper‐ ature (SST) in the tropical Indian Ocean, the wave train over the Pacific Ocean and the ENSO phase observed in the previous month collectively explain 66,5% of the variability of winter precipitation. A similar model was built for forecasting spring rainfall, showing that the dynamical systems are the main factors that contribute to generate spring precipitation

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

135

Despite such studies, much remains that is unknown or poorly understood about the inter‐ annual rainfall variability in Patagonia. For example, the extent to which SSTs influence the annual rainfall remains unclear. The objective of this study therefore is to better understand the connection between the dynamics of the large scale atmospheric forcing, primarily atmospheric circulation patterns and sea surface temperature (SST) conditions and rainfall in

Slow variations in the earth's boundary conditions (i.e. sea surface temperature) can influence global atmospheric circulation and thus, precipitation. A warming or cooling of some region of the oceans can act as a remote forcing generating teleconnections. Indeed, the most relevant SST pattern in the Pacific Ocean is the El Niño-Southern Oscillation (ENSO). The SST anomalies in tropical Pacific generate a Rossby wave pattern which propagates meridionally towards the middle-latitudes from the tropical source [12, 13, and 14]. This pattern, called the "Pacific South American Pattern", is described by Mo [1]. Some authors [15, 16], have studied the relation between above normal rainfall and "El Niño" events in northeastern Argentina. The authors found that Southern Brazil presents the strongest average signal in El Niño events. They showed that the general behavior toward opposite signals in the precipitation and circulation anomalies over Southern South America during almost the same periods of the El Niño and

La Niña events indicates a large degree of linearity in the response to these events.

Related to SST anomalies but located in the Indian Ocean, is the "Indian Ocean Dipole" (IOD) [17]. A positive IOD period is characterized by cooler than normal water in the tropical eastern Indian Ocean and warmer than normal water in the tropical western Indian Ocean and it has been associated with decreased rainfall in central and southern Australia. For South America, Chan [18] showed that IOD excites a dipolar pattern in rainfall anomalies between subtropical La Plata basin and central Brazil where rainfall is reduced (enhanced) over the latter (former) during austral spring. It is also associated with a Rossby wave pattern extending from the subtropical south Indian Ocean to the subtropical South Atlantic. Liu [19] found evidence for the teleconnection detected between IOD and rainfall in Southern South America using the theory of planetary waves [20] and showed that the energy propagation path of planetary waves is approximately along the path of Rossby wave train, a possible dynamic explanation

The relative intensity of the sub-tropical high and sub-polar low belts determines the intensity of westerlies over the Pacific Ocean and this factor highly influences rainfall regime in Patagonia. Another teleconnection that influences Patagonian rainfall is the Antarctic Oscil‐ lation, an annular-like pattern called "Southern Annular Mode" (SAM) [21]. The positive phase of the SAM is defined by negative pressure anomalies at high latitudes (higher than 65ºS) combined with wave-like pattern in the middle-latitudes. This feature increases zonal winds,

accounting for 30% of spring rainfall variance.

Patagonian region.

for such teleconnection pattern.

Neither the spatial nor temporal variability of rainfall in Patagonia have been widely studied, probably due to the limited amount of data that exists in this sparsely populated area with few measuring stations. Using linear regression adjustment to annual rainfall for the period 1950-1999, Castañeda and González [4] detected positive trends with 95% significance level on the order of about 2,5 mm/year in the northern and 1,23 mm/year in southern Patagonia, while precipitation decreased in the western and central zones. The authors used an alternative nonlinear methodology to detect a number of statistically significant breakpoints in order to identify homogeneous periods of stationary rainfall. This method is based on adjusting the data with consecutive linear segments, between periods with significant trends. Their results show that northeastern Patagonia experienced an abrupt change in the mean annual rainfall in the mid-to late 1960s. In the northwest, however, this change occurred later, in the mid-to late 1970s. Barros and Mattio [5] and Barros and Rodriguez Sero [6] analyzed long-term changes in the precipitation over the northern plateau of the Patagonia, especially during the 1940's. They found positive annual rainfall anomalies in northern and central Patagonia (Chubut province) especially in the 1940s and near the Andes Mountains in central Patagonia during the period 1920-1965. However, they detected negative annual rainfall anomalies in southern Patagonia (South of Santa Cruz province) in the 1950s. Minetti et al [7] performed an analysis of nonlinear trends in precipitation over Argentina and Chile using polynomial functions and spectral estimations. They showed that increasing quasi-linear trends were encountered all over Patagonia in the last century with no sign of stabilization in the average value while west of the Andes, in Chile, decreasing trends were observed. Other authors have studied sub-regions within Patagonia. For example, Russian et al [8] studied the relationship between interannual rainfall variability in Northern Patagonia, and tropospheric circulation. The authors detected statistical significant (α=0,05) connections with El Niño/Southern Oscillation (ENSO) and Southern Annular Mode (SAM).

In contrast, the Comahue region, in northwestern Patagonia has been extensively studied because of the economic significance of the hydroelectric power stations operating in the region. Gonzalez and Vera [9] and Gonzalez et al [10] analyzed the connection between Comahue annual rainfall amounts and circulation patterns. The analysis for the Limay River Basin showed that the most important source of predictability came from the interannual variability of surface temperature in the tropical Indian Ocean and the ENSO phase while in the Neuquen River Basin geopotential heights at low levels in the Pacific Ocean, associated with the Rossby wave train that extend along the South Pacific, resulted in the best indicator of rainfall variability. Gonzalez and Cariaga [11] have developed a scheme prediction for winter and spring rainfall in the Comahue region in order to investigate the association between rainfall and various circulation patterns. The model showed that sea surface temper‐ ature (SST) in the tropical Indian Ocean, the wave train over the Pacific Ocean and the ENSO phase observed in the previous month collectively explain 66,5% of the variability of winter precipitation. A similar model was built for forecasting spring rainfall, showing that the dynamical systems are the main factors that contribute to generate spring precipitation accounting for 30% of spring rainfall variance.

generally in the Andes Mountains and flows towards the Atlantic Ocean. Important rivers include the Negro, the Neuquen and the Limay river basins. The most favorable conditions for precipitation in the Patagonian Mountains and its eastern slopes are stationary fronts especially when the cold side of the anticyclone brings moist winds from the Atlantic. The result is stratiform clouds and extended rainfall. In few cases, rainfall occurs due to blocking high pressure systems located in Patagonia or the adjacent Atlantic resulting in extended

Neither the spatial nor temporal variability of rainfall in Patagonia have been widely studied, probably due to the limited amount of data that exists in this sparsely populated area with few measuring stations. Using linear regression adjustment to annual rainfall for the period 1950-1999, Castañeda and González [4] detected positive trends with 95% significance level on the order of about 2,5 mm/year in the northern and 1,23 mm/year in southern Patagonia, while precipitation decreased in the western and central zones. The authors used an alternative nonlinear methodology to detect a number of statistically significant breakpoints in order to identify homogeneous periods of stationary rainfall. This method is based on adjusting the data with consecutive linear segments, between periods with significant trends. Their results show that northeastern Patagonia experienced an abrupt change in the mean annual rainfall in the mid-to late 1960s. In the northwest, however, this change occurred later, in the mid-to late 1970s. Barros and Mattio [5] and Barros and Rodriguez Sero [6] analyzed long-term changes in the precipitation over the northern plateau of the Patagonia, especially during the 1940's. They found positive annual rainfall anomalies in northern and central Patagonia (Chubut province) especially in the 1940s and near the Andes Mountains in central Patagonia during the period 1920-1965. However, they detected negative annual rainfall anomalies in southern Patagonia (South of Santa Cruz province) in the 1950s. Minetti et al [7] performed an analysis of nonlinear trends in precipitation over Argentina and Chile using polynomial functions and spectral estimations. They showed that increasing quasi-linear trends were encountered all over Patagonia in the last century with no sign of stabilization in the average value while west of the Andes, in Chile, decreasing trends were observed. Other authors have studied sub-regions within Patagonia. For example, Russian et al [8] studied the relationship between interannual rainfall variability in Northern Patagonia, and tropospheric circulation. The authors detected statistical significant (α=0,05) connections with El Niño/Southern

In contrast, the Comahue region, in northwestern Patagonia has been extensively studied because of the economic significance of the hydroelectric power stations operating in the region. Gonzalez and Vera [9] and Gonzalez et al [10] analyzed the connection between Comahue annual rainfall amounts and circulation patterns. The analysis for the Limay River Basin showed that the most important source of predictability came from the interannual variability of surface temperature in the tropical Indian Ocean and the ENSO phase while in the Neuquen River Basin geopotential heights at low levels in the Pacific Ocean, associated with the Rossby wave train that extend along the South Pacific, resulted in the best indicator of rainfall variability. Gonzalez and Cariaga [11] have developed a scheme prediction for winter and spring rainfall in the Comahue region in order to investigate the association

periods with cloudiness and precipitation.

134 Climate Variability - Regional and Thematic Patterns

Oscillation (ENSO) and Southern Annular Mode (SAM).

Despite such studies, much remains that is unknown or poorly understood about the inter‐ annual rainfall variability in Patagonia. For example, the extent to which SSTs influence the annual rainfall remains unclear. The objective of this study therefore is to better understand the connection between the dynamics of the large scale atmospheric forcing, primarily atmospheric circulation patterns and sea surface temperature (SST) conditions and rainfall in Patagonian region.

Slow variations in the earth's boundary conditions (i.e. sea surface temperature) can influence global atmospheric circulation and thus, precipitation. A warming or cooling of some region of the oceans can act as a remote forcing generating teleconnections. Indeed, the most relevant SST pattern in the Pacific Ocean is the El Niño-Southern Oscillation (ENSO). The SST anomalies in tropical Pacific generate a Rossby wave pattern which propagates meridionally towards the middle-latitudes from the tropical source [12, 13, and 14]. This pattern, called the "Pacific South American Pattern", is described by Mo [1]. Some authors [15, 16], have studied the relation between above normal rainfall and "El Niño" events in northeastern Argentina. The authors found that Southern Brazil presents the strongest average signal in El Niño events. They showed that the general behavior toward opposite signals in the precipitation and circulation anomalies over Southern South America during almost the same periods of the El Niño and La Niña events indicates a large degree of linearity in the response to these events.

Related to SST anomalies but located in the Indian Ocean, is the "Indian Ocean Dipole" (IOD) [17]. A positive IOD period is characterized by cooler than normal water in the tropical eastern Indian Ocean and warmer than normal water in the tropical western Indian Ocean and it has been associated with decreased rainfall in central and southern Australia. For South America, Chan [18] showed that IOD excites a dipolar pattern in rainfall anomalies between subtropical La Plata basin and central Brazil where rainfall is reduced (enhanced) over the latter (former) during austral spring. It is also associated with a Rossby wave pattern extending from the subtropical south Indian Ocean to the subtropical South Atlantic. Liu [19] found evidence for the teleconnection detected between IOD and rainfall in Southern South America using the theory of planetary waves [20] and showed that the energy propagation path of planetary waves is approximately along the path of Rossby wave train, a possible dynamic explanation for such teleconnection pattern.

The relative intensity of the sub-tropical high and sub-polar low belts determines the intensity of westerlies over the Pacific Ocean and this factor highly influences rainfall regime in Patagonia. Another teleconnection that influences Patagonian rainfall is the Antarctic Oscil‐ lation, an annular-like pattern called "Southern Annular Mode" (SAM) [21]. The positive phase of the SAM is defined by negative pressure anomalies at high latitudes (higher than 65ºS) combined with wave-like pattern in the middle-latitudes. This feature increases zonal winds,

decreases heat exchange between poles and mid-latitudes and so modifies storm tracks. Previous papers have shown SAM's influence on rainfall variability in some regions of South America. For example, Silvestri and Vera [22] found significant (α=0,05) relation between SAM and rainfall amount in southeastern South America particularly during November and December. The authors found that the AAO influence was particularly strong during winter and late spring although of opposite sign and AAO positive (negative) phases were associated with the intensification of an upper-level anticyclonic (cyclonic) anomaly, weakened (en‐ hanced) moisture convergence and decreased (increased) precipitation over southern South America. Reboita [23] detected a decrease of frontal activity when SAM is in a positive phase. These findings are consistent with the results of other investigators. For example, Zheng and Frederiksen [24] showed that the SAM affects summer rainfall variability in the New Zealand sector. They found New Zealand was drier especially in the South Island, when the 500-hPa height pattern had an anomalous high centered well south of New Zealand, with positive anomalies extending over much of the country. Reason and Rouault [25] showed that wetter (drier) winters in western South Africa occur during the negative (positive) SAM phase. The combined effect of the pressure patterns over the oceans and the SST anomalies in close proximity to the continent is another precipitation forcing. When SST is high, evaporation over the ocean is enhanced and moist onshore winds became intensified. Then, advection of moist air from the Atlantic Ocean intensifies and the air above the continent is more likely to generate precipitation. Moreover, significant SST anomalies over the oceans can modify the trajectories of pressure systems from the west.

considered. Seasonal rainfall is calculated as the accumulated precipitation in summer (December-January-February, DJF), autumn (March-April-May, MAM), winter (June-July-

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

137

The following paragraphs describe the indexes used to analyze the relationship between

El Niño-Southern Oscillation effect is evaluated using the mean SST in the EN3.4 region (ENSO) and they are obtained from the Climate Prediction Center (CPC, http:// www.cpc.ncep.noaa.gov/products/ ) from National Oceanic and Atmospheric Administration

The IOD is commonly measured by an index defined as the difference between SST in the western (50°E to 70°E and 10°S to 10°N) and eastern (90°E to 110°E and 10°S to 0°S) equatorial Indian Ocean. The index is called the Dipole Mode Index (DMI) [17]. Data are obtained from SST DMI dataset derived from HadlSST dataset (http://www.jamstec.go.jp/frcgc/research/d1/

The SAM pattern is represented quantitatively by an index called Antarctic Oscillation (AAO), defined as the difference in the normalized monthly zonal mean sea level pressure between

To analyze the influence of the surrounding Atlantic and Pacific Ocean SST, five indices are defined (figure 2) as: mean SST in (38ºS to 47ºS and 68ºW to 60ºW) (S1), mean SST in (47ºS to 55ºS and 68ºW to 60ºW) (S2), both in Atlantic Ocean; mean SST in (35ºS to 43ºS and 71ºW to 78ºW) (S3), mean SST in (43ºS to 55ºS and 71ºW to 78ºW) (S4) in the Pacific Ocean coast. Finally, mean SST in (55ºS to 70ºS and 78ºW to 60ºW) (S5) in ocean between Argentina and Antarctic

Additionally, the 500 and 1000 Hpa geopotential height data are used to define the following

GH1: mean 500 Hpa geopotential height field in (30º to 40ºS and 120ºW to 80ºW) in the South

Pacific Ocean, representative of the intensity of sub-tropical high.

40ºS and 65ºS [21]. This index is obtained from http://ljp.lasg.ac.cn/dct/page/65572

August, JJA) and spring (September-October-November, SON).

seasonal rainfall and the different forcings described above.

**Figure 1.** Stations used in the study

iod/DATA/dmi\_HadISST.txt)

(NOAA).

is defined.

indices (see also figure 3):

The objective of this chapter is to better understand the possible global circulation patterns that influence seasonal precipitation in the Patagonian region of Argentina. A second objective is to determine the relationships between regional patterns, like the surrounding oceans SST or wind circulation and seasonal rainfall. This chapter is organized as follows: Section 2 describes the dataset and the methodology; Section 3 presents the results and section 4 discusses the mayor major findings and conclusions.

## **2. Data and methodology**

Monthly rainfall data at 19 stations in Patagonia for the period 1981-2010 are used in this study. The data are derived from different sources including the National Meteorological Service (SMN) of Argentina, the Secretariat of Hydrology of Argentina (SRH) and Territorial Authority of the Limay, Neuquen and Negro rivers basins (AIC). The area of study is located between 37°S and 55°S and between 72ºW and 63ºW (Figure 1). All the selected stations have less than 20% of missing monthly rainfall data and their quality has been carefully proved. Some techniques were applied with that purpose: we discriminated cases with no precipitation in one month from missing data, no stations have records affected by changes of location and instrumentation. Rainfall greater that percentile 95 was controlled in order to detect outliers. In a consistency check, the monthly time series of observed rainfall at each station was compared with a nearby station, using double mass curve analysis, to see if it was physically or climatologically consistent. Suspicious observations according to inconsistencies were not considered. Seasonal rainfall is calculated as the accumulated precipitation in summer (December-January-February, DJF), autumn (March-April-May, MAM), winter (June-July-August, JJA) and spring (September-October-November, SON).

**Figure 1.** Stations used in the study

decreases heat exchange between poles and mid-latitudes and so modifies storm tracks. Previous papers have shown SAM's influence on rainfall variability in some regions of South America. For example, Silvestri and Vera [22] found significant (α=0,05) relation between SAM and rainfall amount in southeastern South America particularly during November and December. The authors found that the AAO influence was particularly strong during winter and late spring although of opposite sign and AAO positive (negative) phases were associated with the intensification of an upper-level anticyclonic (cyclonic) anomaly, weakened (en‐ hanced) moisture convergence and decreased (increased) precipitation over southern South America. Reboita [23] detected a decrease of frontal activity when SAM is in a positive phase. These findings are consistent with the results of other investigators. For example, Zheng and Frederiksen [24] showed that the SAM affects summer rainfall variability in the New Zealand sector. They found New Zealand was drier especially in the South Island, when the 500-hPa height pattern had an anomalous high centered well south of New Zealand, with positive anomalies extending over much of the country. Reason and Rouault [25] showed that wetter (drier) winters in western South Africa occur during the negative (positive) SAM phase. The combined effect of the pressure patterns over the oceans and the SST anomalies in close proximity to the continent is another precipitation forcing. When SST is high, evaporation over the ocean is enhanced and moist onshore winds became intensified. Then, advection of moist air from the Atlantic Ocean intensifies and the air above the continent is more likely to generate precipitation. Moreover, significant SST anomalies over the oceans can modify the trajectories

The objective of this chapter is to better understand the possible global circulation patterns that influence seasonal precipitation in the Patagonian region of Argentina. A second objective is to determine the relationships between regional patterns, like the surrounding oceans SST or wind circulation and seasonal rainfall. This chapter is organized as follows: Section 2 describes the dataset and the methodology; Section 3 presents the results and section 4

Monthly rainfall data at 19 stations in Patagonia for the period 1981-2010 are used in this study. The data are derived from different sources including the National Meteorological Service (SMN) of Argentina, the Secretariat of Hydrology of Argentina (SRH) and Territorial Authority of the Limay, Neuquen and Negro rivers basins (AIC). The area of study is located between 37°S and 55°S and between 72ºW and 63ºW (Figure 1). All the selected stations have less than 20% of missing monthly rainfall data and their quality has been carefully proved. Some techniques were applied with that purpose: we discriminated cases with no precipitation in one month from missing data, no stations have records affected by changes of location and instrumentation. Rainfall greater that percentile 95 was controlled in order to detect outliers. In a consistency check, the monthly time series of observed rainfall at each station was compared with a nearby station, using double mass curve analysis, to see if it was physically or climatologically consistent. Suspicious observations according to inconsistencies were not

of pressure systems from the west.

136 Climate Variability - Regional and Thematic Patterns

**2. Data and methodology**

discusses the mayor major findings and conclusions.

The following paragraphs describe the indexes used to analyze the relationship between seasonal rainfall and the different forcings described above.

El Niño-Southern Oscillation effect is evaluated using the mean SST in the EN3.4 region (ENSO) and they are obtained from the Climate Prediction Center (CPC, http:// www.cpc.ncep.noaa.gov/products/ ) from National Oceanic and Atmospheric Administration (NOAA).

The IOD is commonly measured by an index defined as the difference between SST in the western (50°E to 70°E and 10°S to 10°N) and eastern (90°E to 110°E and 10°S to 0°S) equatorial Indian Ocean. The index is called the Dipole Mode Index (DMI) [17]. Data are obtained from SST DMI dataset derived from HadlSST dataset (http://www.jamstec.go.jp/frcgc/research/d1/ iod/DATA/dmi\_HadISST.txt)

The SAM pattern is represented quantitatively by an index called Antarctic Oscillation (AAO), defined as the difference in the normalized monthly zonal mean sea level pressure between 40ºS and 65ºS [21]. This index is obtained from http://ljp.lasg.ac.cn/dct/page/65572

To analyze the influence of the surrounding Atlantic and Pacific Ocean SST, five indices are defined (figure 2) as: mean SST in (38ºS to 47ºS and 68ºW to 60ºW) (S1), mean SST in (47ºS to 55ºS and 68ºW to 60ºW) (S2), both in Atlantic Ocean; mean SST in (35ºS to 43ºS and 71ºW to 78ºW) (S3), mean SST in (43ºS to 55ºS and 71ºW to 78ºW) (S4) in the Pacific Ocean coast. Finally, mean SST in (55ºS to 70ºS and 78ºW to 60ºW) (S5) in ocean between Argentina and Antarctic is defined.

Additionally, the 500 and 1000 Hpa geopotential height data are used to define the following indices (see also figure 3):

GH1: mean 500 Hpa geopotential height field in (30º to 40ºS and 120ºW to 80ºW) in the South Pacific Ocean, representative of the intensity of sub-tropical high.

The leading patterns of rainfall variability on seasonal time scales were obtained through a Principal Component Analysis (PCA), in the T-mode [26]. Therefore, the principal scores (PC) describe the spatial patterns while the loadings provide the temporal evolution of the patterns. The significant PCs have been detected using the Kaiser criterion [26] in which the eigenvalue must be greater than 1 in order to be considered. The resulting patterns were compared with


GH5

GH4

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

GH3

http://dx.doi.org/10.5772/55494

139

GH1

GH2

The following figures show the correlation fields between seasonal rainfall and all the defined indices. Figures 4 and 5 show simultaneous correlations between JJA rainfall and defined indices. No significant correlations between rainfall and SST are observed. The only exception is a negative significant correlation between JJA rainfall and S5 (figure 4e) is present in a small area in eastern Patagonia, suggesting that cold waters south of the continent could displace systems trajectories towards the north over the continental region. Winter rainfall is increased by the positive phase of ENSO in the eastern of Patagonia (figure 4f). This result agrees with other authors who have investigated the impacts of "El Niño" in South America [14, 15, 16]. Figure 6 shows the temporal pattern of JJA rainfall at station 87860 (located in eastern Pata‐ gonia, see figure 1) and ENSO (figure 6a) and S5 (Figure 6b). Figures 5a and 5b show that JJA rainfall is enhanced especially in central and eastern Patagonia, when sub-tropical high and sub-polar lows are weakened (lower sub-tropical high and higher sub-polar low). So, the western flow weakens too and heat exchange between high and middle latitudes increases. This fact facilitates the meridional displacement towards the north of the precipitation systems associated with Rossby waves from the Pacific Ocean [1, 12, 13]. This result agrees with that of Aravena and Luckman [27]. Winter rainfall also increases in the western part of the study region with negative anomalies of geopotential heights over Patagonia (figure 5d) and

correlation maps detailed above.


**Figure 3.** Definition of 500 Hpa Geopotential Height indexes.

**3. Results**

**Figure 2.** Definition of SST indexes.

GH2: mean 500 Hpa geopotential height field in (60º to 70ºS and 120ºW to 80ºW) in the South Pacific Ocean, representative of the intensity of sub-polar low belt

GH3: mean 500 Hpa geopotential height field in (36º to 55ºS and 65ºW to 55ºW) in the sur‐ rounding South Atlantic Ocean

GH4: mean 500 Hpa geopotential height field in (36º to 55ºS and 72ºW to 65ºW) over Patagonian region

GH5: mean 500 Hpa geopotential height field in (36º to 55ºS and 72ºW to 82ºW) in the sur‐ rounding South Pacific Ocean

The same indices are defined using 1000 Hpa geopotential heights and the same results are obtained, so they will not be detailed in this paper. SST and geopotential height data are from NCEP reanalysis.

Simultaneous correlations between seasonal (DJF, MAM, JJA and SON) rainfall in the 19 stations and indices defined below (ENSO, AAO, DMI, S1, S2, S3, S4, S5, GH1, GH2, GH3, GH4 and GH5) are calculated. Correlations greater than 0,37 (0,31) are significant at 95% (90%) confidence level. The results are used to describe the relationship between seasonal rainfall and circulation forcing.

**Figure 3.** Definition of 500 Hpa Geopotential Height indexes.

The leading patterns of rainfall variability on seasonal time scales were obtained through a Principal Component Analysis (PCA), in the T-mode [26]. Therefore, the principal scores (PC) describe the spatial patterns while the loadings provide the temporal evolution of the patterns. The significant PCs have been detected using the Kaiser criterion [26] in which the eigenvalue must be greater than 1 in order to be considered. The resulting patterns were compared with correlation maps detailed above.

#### **3. Results**

GH2: mean 500 Hpa geopotential height field in (60º to 70ºS and 120ºW to 80ºW) in the South


S 5

S 1

S 2

GH3: mean 500 Hpa geopotential height field in (36º to 55ºS and 65ºW to 55ºW) in the sur‐

GH4: mean 500 Hpa geopotential height field in (36º to 55ºS and 72ºW to 65ºW) over Patagonian

GH5: mean 500 Hpa geopotential height field in (36º to 55ºS and 72ºW to 82ºW) in the sur‐

The same indices are defined using 1000 Hpa geopotential heights and the same results are obtained, so they will not be detailed in this paper. SST and geopotential height data are from

Simultaneous correlations between seasonal (DJF, MAM, JJA and SON) rainfall in the 19 stations and indices defined below (ENSO, AAO, DMI, S1, S2, S3, S4, S5, GH1, GH2, GH3, GH4 and GH5) are calculated. Correlations greater than 0,37 (0,31) are significant at 95% (90%) confidence level. The results are used to describe the relationship between seasonal rainfall

Pacific Ocean, representative of the intensity of sub-polar low belt

rounding South Atlantic Ocean

**Figure 2.** Definition of SST indexes.








S 3

S 4


138 Climate Variability - Regional and Thematic Patterns

rounding South Pacific Ocean

NCEP reanalysis.

and circulation forcing.

region

The following figures show the correlation fields between seasonal rainfall and all the defined indices. Figures 4 and 5 show simultaneous correlations between JJA rainfall and defined indices. No significant correlations between rainfall and SST are observed. The only exception is a negative significant correlation between JJA rainfall and S5 (figure 4e) is present in a small area in eastern Patagonia, suggesting that cold waters south of the continent could displace systems trajectories towards the north over the continental region. Winter rainfall is increased by the positive phase of ENSO in the eastern of Patagonia (figure 4f). This result agrees with other authors who have investigated the impacts of "El Niño" in South America [14, 15, 16]. Figure 6 shows the temporal pattern of JJA rainfall at station 87860 (located in eastern Pata‐ gonia, see figure 1) and ENSO (figure 6a) and S5 (Figure 6b). Figures 5a and 5b show that JJA rainfall is enhanced especially in central and eastern Patagonia, when sub-tropical high and sub-polar lows are weakened (lower sub-tropical high and higher sub-polar low). So, the western flow weakens too and heat exchange between high and middle latitudes increases. This fact facilitates the meridional displacement towards the north of the precipitation systems associated with Rossby waves from the Pacific Ocean [1, 12, 13]. This result agrees with that of Aravena and Luckman [27]. Winter rainfall also increases in the western part of the study region with negative anomalies of geopotential heights over Patagonia (figure 5d) and surrounding Pacific Ocean (figure 5e), showing the influence of low systems displacing over the continent from the Pacific Ocean. Figure 6c shows the temporal behavior of JJA rainfall in Angostura in northwestern Patagonia (see figure 1) and GH5. Meanwhile, positive anomalies of geopotential heights over Atlantic Ocean coast (figure 5c) tend to increase rainfall in northeastern Patagonia, as indicated by statistically significant (α=0.05) correlation centered on (39ºS; 64ºW), probably because of the increase of moist flow from the Atlantic when an anticyclonic system is present in that ocean.

**Figure 5.** Idem Figure 4 for GH1 (a), GH2 (b), GH (c), GH4 (d), GH5 (e) and AAO (f).

Figures 7 and 8 show simultaneous correlations between SON rainfall and defined indices. The result show that western Patagonia spring rainfall tends to be increased by cold waters in the Atlantic Ocean coast (figure 7 a and 7b) and in the south of the continent (figure 7e). Cold waters over this part of the ocean probably causes systems to displace towards the northeast over the continent as a result of the continental warming which provides the energy necessary for its development. Another factor that influences spring rainfall in northwestern Patagonia is a positive phase of IOD (figure 7g), acting as a source of Rossby waves in the Indian ocean and a warm phase of ENSO (figure 7f). SON rainfall is enhanced by weakened sub-tropical highs (figure 8a) and sub-polar lows (figure 8b) in western and southern Patagonia. This fact implies that rainfall in that area is favored by negative phase of AAO (figure 8f). Besides, more

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

141

**Figure 4.** Correlation between JJA rainfall and S1 (a), S2 (b), S3 (c), S4 (d), S5 (e), ENSO (f) and IOD (g) as defined in the text. Areas with positive (negative) significant correlations are red (blue). Correlations greater than 0,31 (0,37) are sig‐ nificant at 90 (95)% confidence level. Stations were plotted.

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina) http://dx.doi.org/10.5772/55494 141

**Figure 5.** Idem Figure 4 for GH1 (a), GH2 (b), GH (c), GH4 (d), GH5 (e) and AAO (f).

surrounding Pacific Ocean (figure 5e), showing the influence of low systems displacing over the continent from the Pacific Ocean. Figure 6c shows the temporal behavior of JJA rainfall in Angostura in northwestern Patagonia (see figure 1) and GH5. Meanwhile, positive anomalies of geopotential heights over Atlantic Ocean coast (figure 5c) tend to increase rainfall in northeastern Patagonia, as indicated by statistically significant (α=0.05) correlation centered on (39ºS; 64ºW), probably because of the increase of moist flow from the Atlantic when an

(b) S2 JJA



> -55 -53 -51 -49 -47 -45 -43 -41 -39 -37 -35

**Figure 4.** Correlation between JJA rainfall and S1 (a), S2 (b), S3 (c), S4 (d), S5 (e), ENSO (f) and IOD (g) as defined in the text. Areas with positive (negative) significant correlations are red (blue). Correlations greater than 0,31 (0,37) are sig‐



(e) S5 JJA


(g) IOD JJA


(c) S3 JJA


(d) S4 JJA


(f) ENSO JJA


anticyclonic system is present in that ocean.

140 Climate Variability - Regional and Thematic Patterns

(a) S1 JJA




nificant at 90 (95)% confidence level. Stations were plotted.


> Figures 7 and 8 show simultaneous correlations between SON rainfall and defined indices. The result show that western Patagonia spring rainfall tends to be increased by cold waters in the Atlantic Ocean coast (figure 7 a and 7b) and in the south of the continent (figure 7e). Cold waters over this part of the ocean probably causes systems to displace towards the northeast over the continent as a result of the continental warming which provides the energy necessary for its development. Another factor that influences spring rainfall in northwestern Patagonia is a positive phase of IOD (figure 7g), acting as a source of Rossby waves in the Indian ocean and a warm phase of ENSO (figure 7f). SON rainfall is enhanced by weakened sub-tropical highs (figure 8a) and sub-polar lows (figure 8b) in western and southern Patagonia. This fact implies that rainfall in that area is favored by negative phase of AAO (figure 8f). Besides, more

Running Title

1

3

5

11






(c) S3 SON

http://dx.doi.org/10.5772/55494

143

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)


(d) S4 SON


(f) ENSO SON





**Figure 7.** Idem figure 4 for SON.


(b) S2 SON

**Figure 6.** Temporal patterns of (a) JJA rainfall in 87860 station and ENSO, (b) JJA rainfall in 87860 station and S5 and (c) JJA rainfall in Angostura station and GH5.

rainfall is observed when cyclonic anomalies are present over Atlantic (figure 8c) and Pacific (figure 8e) coasts and over the continent (figure 8d). Figure 9 shows temporal patterns of SON rainfall in western Patagonia (87803 station, see figure 1) and ENSO (figure 9a), S5 (figure 9b) and GH5 (figure 9c).

**Figure 7.** Idem figure 4 for SON.

11

rainfall is observed when cyclonic anomalies are present over Atlantic (figure 8c) and Pacific (figure 8e) coasts and over the continent (figure 8d). Figure 9 shows temporal patterns of SON rainfall in western Patagonia (87803 station, see figure 1) and ENSO (figure 9a), S5 (figure 9b)

**Figure 6.** Temporal patterns of (a) JJA rainfall in 87860 station and ENSO, (b) JJA rainfall in 87860 station and S5 and

1980 1985 1990 1995 2000 2005 2010

Angostura JJA GH5 JJA

1980 1985 1990 1995 2000 2005 2010

1980 1985 1990 1995 2000 2005 2010

87860 JJA S5 JJA

87860 JJA ENSO JJA

25.5 26.0 26.5 27.0 27.5 28.0 28.5 29.0

> ‐9 ‐8 ‐7 ‐6 ‐5 ‐4 ‐3 ‐2 ‐1 0

**JJA GH5 (m)**

**JJA S5 (ºC)**

**JJA ENSO (ºC)**

and GH5 (figure 9c).

Running Title

2 (a)

142 Climate Variability - Regional and Thematic Patterns

4 (b)

**JJA rainfall (mm)**

(c) JJA rainfall in Angostura station and GH5.

**JJA rainfall (mm)**

**JJA rainfall (mm)**

6 (c)

1

3

5

14 Book Title

1980 1985 1990 1995 2000 2005 2010

1980 1985 1990 1995 2000 2005 2010

1980 1985 1990 1995 2000 2005 2010

Figures 10 and 11 show simultaneous correlations between MAM rainfall and defined indices. No significant correlation is detected between rainfall and sea surface temperature in the Atlantic Ocean (figures 10a and 10b). Warm waters in the Pacific coast (figures 10c and 10d) and a positive phase of ENSO (figure 10f) are related to greater than normal autumn rainfall

87803 SON SON GH5

87803 SON SON S5

87803 SON SON ENSO

24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 28.5 29.0 29.5

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

‐3.5 ‐3.0 ‐2.5 ‐2.0 ‐1.5 ‐1.0 ‐0.5 0.0

**SON GH5 (m)**

**SON S5 (ºC)**

**SON ENSO (ºC)**

http://dx.doi.org/10.5772/55494

145

2 (a)

4 (b)

**Figure 9.** Temporal patterns of SON rainfall in 87803 station and ENSO (a), S5 (b) and GH5 (c).

**SON 87803 (mm)**

**SON 87803 (mm)**

**SON 87803 (mm)**

6 (c)

1

3

5

7 8

**Figure 8.** Idem figure 5 for SON.

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina) http://dx.doi.org/10.5772/55494 145

14 Book Title


(c) GH3 SON



**Figure 8.** Idem figure 5 for SON.


(e) GH5 SON



(d) GH4 SON




1

3

5

7 8


144 Climate Variability - Regional and Thematic Patterns

(a) GH1 SON

**Figure 9.** Temporal patterns of SON rainfall in 87803 station and ENSO (a), S5 (b) and GH5 (c).

Figures 10 and 11 show simultaneous correlations between MAM rainfall and defined indices. No significant correlation is detected between rainfall and sea surface temperature in the Atlantic Ocean (figures 10a and 10b). Warm waters in the Pacific coast (figures 10c and 10d) and a positive phase of ENSO (figure 10f) are related to greater than normal autumn rainfall all over Patagonia. Figure 12a shows the time evolution of MAM rainfall in central Patagonia (87774 station, see figure 1) and S5. As in the case of winter and spring rainfall, weakened subtropical highs (figure 11a) and sub-polar lows (figure 11b) and therefore negative phase of AAO (figure 11f) are associated with enhanced rainfall in most of Patagonia. Besides, autumn rainfall in the northwest (Comahue region) is enhanced by cyclonic anomalies in the Atlantic (figure 11c), Pacific (figure 11e) and continental (figure 11d) sectors. Figure 12b shows the temporal pattern of MAM rainfall in northwestern Patagonia (Angostura station, see figure 1) and GH5.



**Figure 11.** Idem figure 5 for MAM.

(a) GH1 MAM

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

147

**Figure 10.** Idem figure 4 for MAM.

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina) http://dx.doi.org/10.5772/55494 147

**Figure 11.** Idem figure 5 for MAM.

all over Patagonia. Figure 12a shows the time evolution of MAM rainfall in central Patagonia (87774 station, see figure 1) and S5. As in the case of winter and spring rainfall, weakened subtropical highs (figure 11a) and sub-polar lows (figure 11b) and therefore negative phase of AAO (figure 11f) are associated with enhanced rainfall in most of Patagonia. Besides, autumn rainfall in the northwest (Comahue region) is enhanced by cyclonic anomalies in the Atlantic (figure 11c), Pacific (figure 11e) and continental (figure 11d) sectors. Figure 12b shows the temporal pattern of MAM rainfall in northwestern Patagonia (Angostura station, see figure




(e) S5 MAM


(g) IOD MAM



(c) S3 MAM


(d) S4 MAM


(b) S2 MAM

1) and GH5.



**Figure 10.** Idem figure 4 for MAM.


146 Climate Variability - Regional and Thematic Patterns

Running Title

1

3

5 6

17



> -55 -53 -51 -49 -47 -45 -43 -41 -39 -37 -35

**Figure 13.** Idem figure 4 for DJF.



> -55 -53 -51 -49 -47 -45 -43 -41 -39 -37 -35



(e) S5 DJF


(g) IOD DJF


(c) S3 DJF

http://dx.doi.org/10.5772/55494

149

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)


(d) S4 DJF


(f) ENSO DJF


(b) S2 DJF


Figures 13 and 14 show simultaneous correlations between DJF rainfall and defined indices. Summer rainfall is very scarce all over Patagonia. No influence is observed between summer precipitation and SST (figure 13). Only cyclonic anomalies over the Atlantic (figure 14c) and Pacific (figure 14e) surrounding oceans and over the continent (figure 14d) are related to enhanced rainfall in the west of Patagonia. Figure 15 shows the time evolution of DJF rainfall in western Patagonia (87803 station, see figure 1) and GH5.

**Figure 13.** Idem figure 4 for DJF.

17

12.0 12.5 13.0 13.5 14.0 14.5 15.0

**MAM GH5 (m)**

**MAM S3 (ºC)**

Running Title

2 (a)

148 Climate Variability - Regional and Thematic Patterns

4 (b)

in western Patagonia (87803 station, see figure 1) and GH5.

**MAM**

**Angostura (mm)**

**MAM 87774 (mm)**

1980 1990 2000 2010

1980 1985 1990 1995 2000 2005 2010

**Figure 12.** Temporal patterns of (a) MAM rainfall in 87774 station and S3 and (b) MAM rainfall in Angostura station

Figures 13 and 14 show simultaneous correlations between DJF rainfall and defined indices. Summer rainfall is very scarce all over Patagonia. No influence is observed between summer precipitation and SST (figure 13). Only cyclonic anomalies over the Atlantic (figure 14c) and Pacific (figure 14e) surrounding oceans and over the continent (figure 14d) are related to enhanced rainfall in the west of Patagonia. Figure 15 shows the time evolution of DJF rainfall

MAM Angostura MAM GH5

MAM 87774 MAM S3

1

3

5 6

and GH5.

Number of loading JJA SON MAM DJF

Total variance explained by significant patterns.

**Table 1.** Variance explained by significant loadings (%)

 43,9 39,3 43,6 22,7 19,9 15,6 13,2 18,4 13,1 10,5 11,1 14,1 6,3 7,3 6,3 9,2 5 5,8 6,1 8,1 4,9 4,2 5,3 3,7 3,7 4,8 8 4,1 9 3,5

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

151

88,2 87,1 88,2 90,1

INDICES Ei 1 Ei 2 Ei 3 Ei 4 EN34 0,135 0,559 0,158 0,086 AAO 0,173 -0,174 -0,014 0,275 IOD 0,420 0,358 0,035 0,247 S1 -0,009 0,197 0,407 0,144 S2 0,109 -0,171 0,317 0,220 S3 -0,274 0,149 0,179 -0,195 S4 0,046 -0,242 0,158 0,034 S5 -0,005 -0,435 -0,119 0,036 GH1 -0,323 -0,435 0,091 -0,163 GH2 0,079 0,499 -0,033 -0,153 GH3 -0,308 0,046 -0,321 -0,201 GH4 -0,403 0,033 -0,351 -0,332 GH5 -0,520 0,009 -0,315 -0,428

**Table 2.** Correlation between indices defined in the text with the first four temporal patterns (eigenvectors 1, 2, 3 and

4) derived from PCA in JJA. Correlations significant at α=0.05 are shaded.

**Figure 14.** Idem figure 5 for DJF.

In an attempt to reproduce the patterns identified by the indices tested, a PCA analysis was applied to accumulated seasonal rainfall anomalies. The first four spatial patterns of rainfall anomaly are detailed in figures 16 (JJA), 17 (SON), 18 (MAM) and 19 (DJF). Table 1 details the variance explained by each significant pattern. Tables 2, 3, 4 and 5 detailed the correlations between each eigenvector and indices defined in the text for JJA, SON, MAM and DJF, respectively.

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina) http://dx.doi.org/10.5772/55494 151


**Table 1.** Variance explained by significant loadings (%)



(d) GH4 DJF



In an attempt to reproduce the patterns identified by the indices tested, a PCA analysis was applied to accumulated seasonal rainfall anomalies. The first four spatial patterns of rainfall anomaly are detailed in figures 16 (JJA), 17 (SON), 18 (MAM) and 19 (DJF). Table 1 details the variance explained by each significant pattern. Tables 2, 3, 4 and 5 detailed the correlations between each eigenvector and indices defined in the text for JJA, SON, MAM and DJF,


(f) AAO DJF




150 Climate Variability - Regional and Thematic Patterns

**Figure 14.** Idem figure 5 for DJF.

respectively.

(a) GH1 DJF


**Table 2.** Correlation between indices defined in the text with the first four temporal patterns (eigenvectors 1, 2, 3 and 4) derived from PCA in JJA. Correlations significant at α=0.05 are shaded.


INDICES Ei 1 Ei 2 Ei 3 Ei 4 EN34 0,097 0,280 0,055 0,087 AAO 0,105 0,198 0,224 -0,179 IOD 0,033 0,138 0,330 0,142 S1 0,044 0,002 0,036 -0,237 S2 -0,292 0,098 -0,218 0,144 S3 0,007 0,022 -0,055 0,075 S4 -0,266 0,070 -0,082 0,281 S5 -0,498 -0,068 0,272 0,288 GH1 -0,015 -0,093 -0,018 0,016 GH2 -0,209 0,264 0,021 0,253 GH3 -0,030 -0,013 -0,264 -0,245 GH4 -0,017 -0,113 -0,184 -0,174 GH5 0,017 -0,146 -0,127 -0,114

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

153

GH1 -0,015 -0,093 -0,018 0,016 GH2 -0,209 0,264 0,021 0,253 GH3 -0,030 -0,013 -0,264 -0,245 GH4 -0,017 -0,113 -0,184 -0,174 GH5 0,017 -0,146 -0,127 -0,114

**DJF GH5 (m)**

**Table 5.** Idem Table 2 for DJF

Table 5. Idem Table 2 for DJF

**Figure 15.** Temporal patterns of DJM rainfall in 87803 station and GH5.

**DJF 87803 (mm)**

Figure 15.Temporal patterns of DJM rainfall in 87803 station and GH5.

1980 1985 1990 1995 2000 2005 2010

DJF 87803 DJF GH5

**Table 3.** Idem Table 2 for SON


**Table 4.** Idem Table 2 for MAM

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina) http://dx.doi.org/10.5772/55494 153

GH1 -0,015 -0,093 -0,018 0,016 GH2 -0,209 0,264 0,021 0,253 GH3 -0,030 -0,013 -0,264 -0,245 GH4 -0,017 -0,113 -0,184 -0,174 GH5 0,017 -0,146 -0,127 -0,114


**Table 5.** Idem Table 2 for DJF

INDICES Ei 1 Ei 2 Ei 3 Ei 4 EN34 -0,615 0,238 0,250 -0,271 AAO 0,509 0,055 -0,086 0,046 IOD -0,432 0,421 0,144 -0,169 S1 0,287 0,135 -0,182 -0,298 S2 0,287 -0,074 -0,337 -0,172 S3 -0,002 -0,103 0,012 -0,364 S4 -0,062 0,000 -0,343 -0,369 S5 0,558 -0,160 -0,332 0,218 GH1 0,559 -0,276 0,064 0,197 GH2 -0,628 0,181 0,084 -0,120 GH3 0,646 0,028 -0,317 -0,121 GH4 0,644 0,011 -0,265 -0,232 GH5 0,665 -0,104 -0,125 -0,282

INDICES Ei 1 Ei 2 Ei 3 Ei 4 EN34 0,038 -0,074 -0,205 -0,383 AAO 0,438 0,110 -0,071 0,334 IOD 0,026 0,020 -0,095 -0,092 S1 -0,217 0,026 -0,272 -0,253 S2 0,244 0,138 -0,168 0,056 S3 -0,123 0,217 -0,261 -0,238 S4 0,212 0,212 -0,103 -0,104 S5 0,181 0,278 -0,044 0,352 GH1 0,208 0,087 0,249 0,403 GH2 -0,144 -0,189 -0,157 -0,399 GH3 0,511 -0,042 -0,149 0,096 GH4 0,596 0,084 -0,105 0,095 GH5 0,679 0,106 -0,009 0,117

**Table 3.** Idem Table 2 for SON

152 Climate Variability - Regional and Thematic Patterns

**Table 4.** Idem Table 2 for MAM

**Figure 15.** Temporal patterns of DJM rainfall in 87803 station and GH5.

Figure 15.Temporal patterns of DJM rainfall in 87803 station and GH5.

Table 5. Idem Table 2 for DJF

**Figure 16.** First four spatial patterns of JJA rainfall anomaly derived from CPA analysis during 1981-2010 period.





**Figure 17.** Idem Figure 16 for SON










SON 3cp


SON 1cp
















SON 4cp


SON 2cp

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

155

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina) http://dx.doi.org/10.5772/55494 155

**Figure 17.** Idem Figure 16 for SON



**Figure 16.** First four spatial patterns of JJA rainfall anomaly derived from CPA analysis during 1981-2010 period.












154 Climate Variability - Regional and Thematic Patterns

JJA 3cp


JJA 1cp
















JJA 4cp


JJA 2cp



Some similarities with the results previously described are detected when the correlations between the eigenvectors and indices are analyzed. In relation to the observed correlations in JJA (Table 2) one can see that the correlation between S5 and the second eigenvector (-0,435) reflects the fact that precipitation increases in eastern Patagonia (Figure 16, JJA 2cp) when S5 is low. Similarly, precipitation increases in this region during the warm phase of ENSO (correlation between ENSO and the second eigenvector 0,559), and when sub-tropical highs (correlation between GH1 and the second eigenvector -0,435) and sub-polar lows (correlation


between GH2 and the second eigenvector 0,499) are weaken.


**Figure 19.** Idem Figure 16 for DJF










DJF 3cp


DJF cp1


















DJF 4cp


DJF 2cp

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

157

**Figure 18.** Idem Figure 16 for MAM

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina) http://dx.doi.org/10.5772/55494 157

**Figure 19.** Idem Figure 16 for DJF





**Figure 18.** Idem Figure 16 for MAM










156 Climate Variability - Regional and Thematic Patterns

MAM 3cp


MAM 1cp
















MAM 4cp

MAM 2cp


Some similarities with the results previously described are detected when the correlations between the eigenvectors and indices are analyzed. In relation to the observed correlations in JJA (Table 2) one can see that the correlation between S5 and the second eigenvector (-0,435) reflects the fact that precipitation increases in eastern Patagonia (Figure 16, JJA 2cp) when S5 is low. Similarly, precipitation increases in this region during the warm phase of ENSO (correlation between ENSO and the second eigenvector 0,559), and when sub-tropical highs (correlation between GH1 and the second eigenvector -0,435) and sub-polar lows (correlation between GH2 and the second eigenvector 0,499) are weaken.

Table 3 shows that in SON, the correlation of the third eigenvector with S2 (-0,337) and S5 (-0,332) reflects the fact that in western Patagonia (Figure 17, 3cp) precipitation increases when S5 and S2 are low. The correlation between IOD and the second eigenvector (0,421) and the first eigenvector (-0,432) indicates that in western Patagonia (Figure 17, 1cp) and in north‐ western Patagonia (Figure 17, 2cp) precipitation decreases during negative phase IOD. Also the correlation between AAO and the first eigenvector (0,509) also indicates enhanced precipitation in western Patagonia during negative phase of AAO. Sub-tropical highs (corre‐ lation between GH1 and the first eigenvector 0,559) and the sub-polar lows (correlation between GH2 and the first eigenvector -0,628) weaken and during the positive phase of ENSO (correlation between first eigenvector and ENSO -0,615), indicate an increase in precipitation in western Patagonia (figure 17, 1CP).

heights and SST patterns. The principal method of analysis was the correlation and the PCA

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

159

The results show that the factors which affect precipitation most highly depend on the season and the region. Cyclonic anomalies over the surrounding Atlantic and Pacific Oceans and over the continent enhance precipitation all over Patagonia and in all seasons, although in winter the Atlantic influence is less. The weakening of sub-tropical hights and sub-polar lows over the Pacific Ocean is associated with above normal precipitation throughout Patagonia in

Some influences of the SST patterns are present. Warm phase of ENSO enhances rainfall in eastern Patagonia in autumn and winter and in western and southern Patagonia in spring. Warm water in the Pacific coasts tends to increase rainfall in central and eastern Patagonia in autumn while cold Atlantic coast enhances precipitation in west and south Patagonia in spring. The IOD signal is present only in autumn in the western and southern parts of the study region.

Future analysis will investigate the relative contribution of each forcing to seasonal rainfall

Rainfall data were provided by the Territorial Authority of the Limay, Neuquen and Negro rivers basins (AIC), the National Meteorological Service of Argentina (SMN) and the Secre‐ tariat of Hydrology of Argentina (SRH). This research was supported by UBACyT 2010-2012 CC02, UBACyT 2011-2014 01/Y128 and CONICET PIP 112-200801-00195, PICT 2010-2110.

Department of Atmospheric and Oceanic Science - FCEN-University of Buenos Aires, Re‐ search Center of Ocean and Atmosphere – CONICET/UBA; UMI-IFAECI/CNRS, CIMA - 2º piso, Pabellón II, Ciudad Universitaria, Ciudad Autónoma de Buenos Aires, Argentina

[1] Mo, K. C. Relationships between low frequency variability in the Southern Hemi‐ sphere and sea surface temperature anomalies. J. Climate (2000). , 13-3599.

transition seasons (MAM and SON) in the eastern part in winter.

but that objective is beyond the scope of this paper.

Address all correspondence to: gonzalez@cima.fcen.uba.ar

applied to seasonal rainfall.

**Acknowledgements**

**Author details**

**References**

Marcela Hebe González

Table 4 shows that the correlation between fourth eigenvector with AAO in MAM is 0.334 and this indicates that in central-eastern Patagonia (Figure 18, 4CP) enhanced precipitation occurs during the negative phase of AAO. Furthermore, when sub-tropical highs (correlation between GH1 and the fourth eigenvector = 0.403) and the sub-polar lows (correlation between GH2 and the fourth eigenvector = -0.399) weakens, enhanced precipitation occurs in central Patagonia (Figure 18, 4CP).

No significant correlation was found in DJF probably because rainfall is scarce in the Patagonia Plateau and the greatest precipitation occurs in winter, near the Andes Mountains.

The Patagonia region in Argentina is of particular interest and is the source of water for hydropower generation for the national grid, the local subsistence economies, agriculture (fruit production) and mining. This complexity is expressed in social and environmental exploitation processes, inequitable distribution of access-control of natural resources, in population displacement and urban growth. Recent climate studies indicate potential future increase in water stress in Patagonia region in Argentina, which will affect the ecological productivity and ecosystem services [28]. Consequently, it is of particular interest to identify current trends and anticipate future scenarios that might lead to environmental and social conflicts. From a climatic point of view, many authors have studied this issue in the South American region [29,30] and found significant changes in precipitation since the beginning of the century. But there are indications of changes in rainfall trends during the past 30 years in some regions [31]. As low frequency variabilities are fed by individual events that occur every year, there is an urgent need for the study of the interannual variability as well as the potential for predicting seasonal rainfall based on teleconnection indices.

## **4. Conclusions**

This findings reported in this chapter are a preliminary attempts to identify the main circula‐ tion patterns related to precipitation variability in the Patagonian region of Argentina. The chapter analyzed the relative significance of the various teleconnections responsible for precipitation variability. The teleconnections analyzed include ENSO, AAO, IOD, geopotential heights and SST patterns. The principal method of analysis was the correlation and the PCA applied to seasonal rainfall.

The results show that the factors which affect precipitation most highly depend on the season and the region. Cyclonic anomalies over the surrounding Atlantic and Pacific Oceans and over the continent enhance precipitation all over Patagonia and in all seasons, although in winter the Atlantic influence is less. The weakening of sub-tropical hights and sub-polar lows over the Pacific Ocean is associated with above normal precipitation throughout Patagonia in transition seasons (MAM and SON) in the eastern part in winter.

Some influences of the SST patterns are present. Warm phase of ENSO enhances rainfall in eastern Patagonia in autumn and winter and in western and southern Patagonia in spring. Warm water in the Pacific coasts tends to increase rainfall in central and eastern Patagonia in autumn while cold Atlantic coast enhances precipitation in west and south Patagonia in spring. The IOD signal is present only in autumn in the western and southern parts of the study region.

Future analysis will investigate the relative contribution of each forcing to seasonal rainfall but that objective is beyond the scope of this paper.

## **Acknowledgements**

Table 3 shows that in SON, the correlation of the third eigenvector with S2 (-0,337) and S5 (-0,332) reflects the fact that in western Patagonia (Figure 17, 3cp) precipitation increases when S5 and S2 are low. The correlation between IOD and the second eigenvector (0,421) and the first eigenvector (-0,432) indicates that in western Patagonia (Figure 17, 1cp) and in north‐ western Patagonia (Figure 17, 2cp) precipitation decreases during negative phase IOD. Also the correlation between AAO and the first eigenvector (0,509) also indicates enhanced precipitation in western Patagonia during negative phase of AAO. Sub-tropical highs (corre‐ lation between GH1 and the first eigenvector 0,559) and the sub-polar lows (correlation between GH2 and the first eigenvector -0,628) weaken and during the positive phase of ENSO (correlation between first eigenvector and ENSO -0,615), indicate an increase in precipitation

Table 4 shows that the correlation between fourth eigenvector with AAO in MAM is 0.334 and this indicates that in central-eastern Patagonia (Figure 18, 4CP) enhanced precipitation occurs during the negative phase of AAO. Furthermore, when sub-tropical highs (correlation between GH1 and the fourth eigenvector = 0.403) and the sub-polar lows (correlation between GH2 and the fourth eigenvector = -0.399) weakens, enhanced precipitation occurs in central Patagonia

No significant correlation was found in DJF probably because rainfall is scarce in the Patagonia

The Patagonia region in Argentina is of particular interest and is the source of water for hydropower generation for the national grid, the local subsistence economies, agriculture (fruit production) and mining. This complexity is expressed in social and environmental exploitation processes, inequitable distribution of access-control of natural resources, in population displacement and urban growth. Recent climate studies indicate potential future increase in water stress in Patagonia region in Argentina, which will affect the ecological productivity and ecosystem services [28]. Consequently, it is of particular interest to identify current trends and anticipate future scenarios that might lead to environmental and social conflicts. From a climatic point of view, many authors have studied this issue in the South American region [29,30] and found significant changes in precipitation since the beginning of the century. But there are indications of changes in rainfall trends during the past 30 years in some regions [31]. As low frequency variabilities are fed by individual events that occur every year, there is an urgent need for the study of the interannual variability as well as the potential for predicting

This findings reported in this chapter are a preliminary attempts to identify the main circula‐ tion patterns related to precipitation variability in the Patagonian region of Argentina. The chapter analyzed the relative significance of the various teleconnections responsible for precipitation variability. The teleconnections analyzed include ENSO, AAO, IOD, geopotential

Plateau and the greatest precipitation occurs in winter, near the Andes Mountains.

in western Patagonia (figure 17, 1CP).

158 Climate Variability - Regional and Thematic Patterns

seasonal rainfall based on teleconnection indices.

(Figure 18, 4CP).

**4. Conclusions**

Rainfall data were provided by the Territorial Authority of the Limay, Neuquen and Negro rivers basins (AIC), the National Meteorological Service of Argentina (SMN) and the Secre‐ tariat of Hydrology of Argentina (SRH). This research was supported by UBACyT 2010-2012 CC02, UBACyT 2011-2014 01/Y128 and CONICET PIP 112-200801-00195, PICT 2010-2110.

## **Author details**

Marcela Hebe González

Address all correspondence to: gonzalez@cima.fcen.uba.ar

Department of Atmospheric and Oceanic Science - FCEN-University of Buenos Aires, Re‐ search Center of Ocean and Atmosphere – CONICET/UBA; UMI-IFAECI/CNRS, CIMA - 2º piso, Pabellón II, Ciudad Universitaria, Ciudad Autónoma de Buenos Aires, Argentina

## **References**

[1] Mo, K. C. Relationships between low frequency variability in the Southern Hemi‐ sphere and sea surface temperature anomalies. J. Climate (2000). , 13-3599.

[2] Prohaska, F. J. Climates of Central and South America". In: World Survey of Clima‐ tology. Amsterdam: Elsevier Cientific Publishing Company; (1976). , 57-69.

[17] Saji, N. H, Goswami, B. N, Vinayachandran, P. N, & Yamagata, T. A dipole mode in

Some Indicators of Interannual Rainfall Variability in Patagonia (Argentina)

http://dx.doi.org/10.5772/55494

161

[18] Chan, S, Behera, S. K, & Yamagata, T. Indian Ocean Dipole influence on South American rainfall. Geophysical Research Letter (2008). L14S12 DOPI: 10.1029/2008GL034204.

[19] Liu, N, Chen, H, & Lu, L. Teleconnection of IOD Signal in the Upper Troposphere over

[20] Hoskins, B. J, & Karoly, D. J. The steady linear response of a spherical atmosphere to

[21] Thompson, D. W, & Wallace, J. M. Annular modes in the extratropical circulation. Part

[22] Silvestri, G, & Vera, C. Antarctic Oscillation signal on precipitation anomalies over

[23] Reboita, M. S, & Ambrizzi, T. and Da Rocha, R. Relationship between the Southern Annular Mode and Southers Hemisphere atmospheric systems. Revista Brasilera de

[24] Zheng, X, & Frederiksen, C. A study of predictable patterns for seasonal forecasting

[25] Reason, C, & Rouault, M. Links between the Antartic Oscillation and winter rainfall

[27] Aravena, J. C, & Luckman, B. H. Spatio-temporal patterns in Southern South Ameri‐

[28] Paruelo, J. Valoración de servicios ecosistémicos y planificación del uso del territorio: ¿es necesario hablar de dinero?. In: Laterra P, Jobbagy EG y Paruelo JM (ed.) Expan‐ sión e intensificación agrícola en Argentina: Valoración de bienes y servicios ecosisté‐

[29] Barros, V, Doyle, M, & Camilloni, I. Precipitation trends in southeastern South Amer‐ ica: relationship with ENSO phases and the low-level circulation. Theoretical and

[30] Liebmann, B, Vera, C. S, Carvalho, L, Camilloni, I, Hoerling, M, Allured, D, Barros, V, Báez, J, & Bidegain, M. An Observed Trend in Central South American Precipita‐

[31] González, M. H, Dominguez, D, & Nuñez, M. N. Long term and interannual rainfall variability in Argentinean Chaco plain region. In: Martin O and Roberts T (ed.) Rain‐ fall: Behavior, Forecasting and Distribution. New York: Nova Science Publishers Inc;

over western South Africa. Geophys Res Lett. (2005). DOI:GL022419.

[26] Green, P. Analysing Multivariate Data. New York: Dryden Press; (1978).

micos para el ordenamiento territorial. Buenos Aires: INTA; (2012).

Southern High Latitudes. Journal of Oceanography (2007). , 63, 155-157.

thermal and orographic forcing. J. Atmos. Sci.(1981). , 38-1179.

I: Month-to-month variability. J. Climate (2000). , 13, 1000-1016.

southeastern South America. Geophys Res Lett. (2003). , 30-21.

of New Zealand rainfall. J. Climate (2006). , 19-3320.

ca. Int. J. Climatol. (2008). DOI:joc.1761.

tion. J. Climate (2004). , 17(22), 4357-4367.

Meteorologia (2009). , 24-1.

Appl. Climatology (2008).

(2012). , 1-21.

the tropical Indian Ocean. Nature 401; , 360-363.


[17] Saji, N. H, Goswami, B. N, Vinayachandran, P. N, & Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 401; , 360-363.

[2] Prohaska, F. J. Climates of Central and South America". In: World Survey of Clima‐ tology. Amsterdam: Elsevier Cientific Publishing Company; (1976). , 57-69.

[3] Russian, G, Agosta, E, & Compagnucci, R. H. Variabilidad interanual e interdecádica

[4] Castañeda, M. E, & González, M. H. Some aspects related to precipitation variability in the Patagonia region in Southern South America. Atmósfera (2008). , 21(3), 303-317.

[5] Barros, V. R, & Mattio, H. F. Tendencias y fluctuaciones en las precipitaciones de la

[6] Barros, V R. and Rodriguez Sero JA. Estudio de las fluctuaciones y tendencias de la precipitación en el Chubut utilizando funciones ortogonales empíricas. GEOACTA

[7] Minetti, J. L, Vargas, W. M, Poblete, A. G, Acuña, L. R, & Casagrande, G. Non-linear trends and low frequency oscillations in annual precipitation over Argentina and Chile,

[8] Russian, G. F, Agosta, E. A, & Compagnucci, R. H. Circulación troposférica de gran escala asociada a la precipitacion en Patagonia norte. In Proceedings of CONGREMET

[9] González, M. H, & Vera, C. S. On the interannual winter rainfall variability in South‐

[10] González, M. H, Skansi, M. M, & Losano, F. A statistical study of seasonal winter rainfall prediction in the Comahue region (Argentine). ATMOSFERA (2010). , 23(3),

[11] González, M. H, & Cariaga, M. L. Estimating winter and spring rainfall in the Coma‐ hue region (Argentine) using statistical techniques. Advances in Environmental Re‐

[12] Kidson, J. Principal modes of southern hemisphere low frequency variability ob‐

[13] Nogues PaegleJ and Mo, KC. Linkages between Summer Rainfall Variability over South America and Sea Surface Temperature Anomalies. J. Climate (2002). , 15,

[14] Ropelewski, C, & Halpert, M. Global and Regional scale precipitation patterns associ‐

[15] Grimm, A, Barros, V, & Doyle, M. Climate variability in Southern South America as‐

[16] Vera, C, Silvestri, G, Barros, V, & Carril, A. Differences in El Niño response in South‐

sociated with El Niño and La Niña events. J. Climate (2002). , 13-35.

ern Andes. International Journal of Climatology (2010). , 30-643.

tained from NCEP-NCAR reanalyses. J. Climate (1999). , 1-1177.

ated with El Niño. Mon Wea Rev (1987). , 110-1606.

ern Hemisphere. J. Climate (2004). , 17-9.

de la precipitación en la Patagonia norte. Geoacta (2010). , 35-27.

región patagónica. Meteorológica (1978). VIII-IX , 237-248.

(1979). , 10(1), 1979-204.

160 Climate Variability - Regional and Thematic Patterns

277-294.

1389-1407.

Atmósfera (2003). , 1931-1999.

search (2011). , 11, 103-118.

XI, 28May-1June (2012). Mendoza, Argentina.


**Section 4**

**Section IV**

**Section 4**

**Section IV**

**Chapter 7**

**High-Resolution Surface Observations for**

Climate monitoring is a foundational component of understanding climate variability across the globe and regionally. In the United States and other countries, surface measurements of temperature and precipitation have been taken routinely by human observers (longest records) and by automated stations overseen by the federal government. Budget plateaus or reductions have resulted in the elimination of many observing stations as well as delays in adopting new measurement technologies or techniques. The future of climate monitoring may look bleak at a time when knowledge of our climate record is critical for water resource management, military and emergency preparedness, infrastructure planning, agricultural production,

Fortunately, innovation has accelerated in the academic and private sectors, leading to new climate monitoring infrastructure that is revitalizing how the nation might address these growing challenges. The establishment of regional "mesonets" –– surface observing networks that measure atmospheric and soil variables at least hourly at spatial resolutions of 10-50 km –– has not only been successful in providing meteorologists with vital, real-time information for weather forecasting but has helped to document previously unknown or seemingly rare regional climate phenomena. These networks also provide high-resolution data for increased understanding of climate variability in both space and time. Data from high-quality, real-time mesonets now are used extensively by researchers, educators, and practitioners. It appears that these networks will become a vital component of our long-term monitoring capacity in

This chapter will discuss the current status of monitoring the climate of the United States using surface observing stations, the evolution of regional mesonets, examples of new knowledge generated about regional climate variability as a result of these mesonets, and what the future

> © 2013 McPherson; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2013 McPherson; licensee InTech. This is a paper 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.

distribution, and reproduction in any medium, provided the original work is properly cited.

Additional information is available at the end of the chapter

**Climate Monitoring**

Renee A. McPherson

**1. Introduction**

the United States.

http://dx.doi.org/10.5772/56044

tourism, and science education.
