**2. Data and methodology**

Earlier version of these flux products have been described elsewhere (Chiu et al., 2008). The product versions described here represent the most updated versions as of the writing of this report.

#### **2.1 HOAPS**

Detail descriptions of the latest version of HOAPS, (version 3, or HOAPS-3) are given in Andersson et al. (2010). Bulk variables are derived from SSM/I data except for the SST which is derived from the Advanced Very High Resolution Radiometer (AVHRR) Oceans Pathfinder SST product. A neural network algorithm is used to derive *U*. The *Q*a is obtained using the linear relationship of Bentamy et al. (2003). The *Q*s is computed from the AVHRR SST using the Magnus formula (Murray, 1967) with a constant salinity correction factor of 0.98. The near-surface air temperature (*T*a) is estimated from the SST using the assumptions of 80% constant relative humidity and a constant surface-air temperature difference of 1 K. Latent and sensible heat fluxes are calculated using the Coupled Ocean–Atmosphere Response Experiment (COARE) 2.6a bulk algorithm (Fairall et al., 1996, 2003).

The HOAPS-3 data sets cover the time period from July 1987 to December 2005. HOAPS-G pentad and monthly data sets with 0.5-degree resolution and HOAPS-C twice daily data set with 1-degree resolution are available at the website (http://www.hoaps.zmaw.de).

#### **2.2 J-OFURO**

The updated version of J-OFURO, (version 2, J-OFURO2) is described in Tomita et al. (2010). Bulk variables *U*, *Q*a and SST (*Q*s) are determined by multi-satellite and multiple satellite sensors. *U* is obtained from a combination of microwave radiometers (SSM/I, AMSR-E and TMI) and scatterometers (ERS-1, ERS-2 and QuikSCAT). *Q*a is derived from SSM/I measurements. SST is taken from the Merged satellite and in-situ data Global Daily SST (MGDSST, Sakurai et al. 2005) analysis provided by Japanese Meteorological Agency (JMA). *T*a is obtained from NCEP/DOE reanalysis. COARE 3.0 bulk algorithm (Fairall et al., 2003) is used to estimate LHF and SHF. The J-OFURO2 covers the time period from January 1988 to December 2006. Daily and monthly means with 1-degree resolution are available at the website (http://dtsv.scc.u-tokai.ac.jp/j-ofuro).

#### **2.3 GSSTF**

262 Remote Sensing – Applications

(ENSO), respectively. An updated version of SSM/I version 6 (V6) data released by Remote Sensing Systems (RSS) in 2006 [as used by Wentz et al. (2007), see http://www.ssmi.com] that calibrates all SSM/I sensors is available in 2008. Shie et al. (2009) have reprocessed and forward processed GSSTF2 to version 2b (GSSTF2b, Shie et al. 2010; Shie 2010) using the SSM/I V6 data (including total precipitable water, brightness temperature, and wind speed retrieval), covering the period July 1987–December 2008. We provide an assessment of these

The data and methodology are described in Section 2. Section 3 presents the trends of these products, compares GSSTF2 and GSSTF2b for the pre 2000 periods, assesses the post 2000 performance, and examines the GSSTF2b Set1 and Set2 differences. Summary and

Earlier version of these flux products have been described elsewhere (Chiu et al., 2008). The product versions described here represent the most updated versions as of the writing of

Detail descriptions of the latest version of HOAPS, (version 3, or HOAPS-3) are given in Andersson et al. (2010). Bulk variables are derived from SSM/I data except for the SST which is derived from the Advanced Very High Resolution Radiometer (AVHRR) Oceans Pathfinder SST product. A neural network algorithm is used to derive *U*. The *Q*a is obtained using the linear relationship of Bentamy et al. (2003). The *Q*s is computed from the AVHRR SST using the Magnus formula (Murray, 1967) with a constant salinity correction factor of 0.98. The near-surface air temperature (*T*a) is estimated from the SST using the assumptions of 80% constant relative humidity and a constant surface-air temperature difference of 1 K. Latent and sensible heat fluxes are calculated using the Coupled Ocean–Atmosphere

The HOAPS-3 data sets cover the time period from July 1987 to December 2005. HOAPS-G pentad and monthly data sets with 0.5-degree resolution and HOAPS-C twice daily data set

The updated version of J-OFURO, (version 2, J-OFURO2) is described in Tomita et al. (2010). Bulk variables *U*, *Q*a and SST (*Q*s) are determined by multi-satellite and multiple satellite sensors. *U* is obtained from a combination of microwave radiometers (SSM/I, AMSR-E and TMI) and scatterometers (ERS-1, ERS-2 and QuikSCAT). *Q*a is derived from SSM/I measurements. SST is taken from the Merged satellite and in-situ data Global Daily SST (MGDSST, Sakurai et al. 2005) analysis provided by Japanese Meteorological Agency (JMA). *T*a is obtained from NCEP/DOE reanalysis. COARE 3.0 bulk algorithm (Fairall et al., 2003) is used to estimate LHF and SHF. The J-OFURO2 covers the time period from January 1988 to December 2006. Daily and monthly means with 1-degree resolution are available at the

Response Experiment (COARE) 2.6a bulk algorithm (Fairall et al., 1996, 2003).

with 1-degree resolution are available at the website (http://www.hoaps.zmaw.de).

data products and examine their "trends" and variability.

discussion are presented in Section 4.

**2. Data and methodology** 

this report.

**2.1 HOAPS** 

**2.2 J-OFURO** 

website (http://dtsv.scc.u-tokai.ac.jp/j-ofuro).

The GSSTF2 product has daily and monthly fields with a 1°x1° resolution for July 1987– December 2000 (Chou et al., 2001), based on the method of Chou et al. (1997) with some improvements (Chou et al., 2003). The temporal and spatial resolutions of GSSTF2b are the same as those of GSSTF2, except that GSSTF2b product covers a longer period (July 1987– December 2008).

GSSTF2b dataset is processed using improved input datasets, namely the recently released NCEP SST analysis, and a uniform (across satellites) surface wind and microwave brightness temperature (TB) V6 dataset from the SSM/I produced by RSS. Table 1 summarizes characteristics of input data and parameters for HOAPS3, J-OFURO2, GSSTF2 and GSSTF2b, in that order. As we focus on LHF, only detailed descriptions and discussions on input parameters of LHF are presented.

A major improvement in the input parameters of GSSTF2b is the use of the newly released SSM/I V6 product (see discussions in http://www.ssmi.com). The SSM/I V6 product removes the spurious wind speed trends found in the Wentz/RSS SSM/I V4 wind speed retrievals. To be consistent, the SSM/I V4 total precipitable water (*W*) and bottom-layer precipitable water (*WB*) used in GSSTF2 are replaced by the corresponding SSM/I V6 products in the production of GSSTF2b. Moreover, the weekly 1° spatial resolution Optimum Interpolation (OI) SST version 1 (V1) dataset (Reynolds & Smith, 1994) used in GSSTF2 is replaced by the improved OI SST version 2 (V2) dataset. The OI SST V2 has a lower satellite bias, a new sea ice algorithm, and an improved OI analysis (Reynolds et al., 2002) resulting in a modest reduction of the satellite bias and global residual biases of roughly −0.03°C. The major improvement in the V2 analysis shows up at high latitudes where local differences between the old and new analysis can exceed 1 °C due to the application of a new sea ice algorithm. There are two GSSTF2b sets, Set1 and Set2 (Shie et al., 2010; Shie 2010). Set1 is developed using all available DMSP SSM/I sensor data. In a preliminary analysis, it was noted that there are large trends associated with LHF which are mostly attributed to the DMSP F13 and F15 satellites. Set2 was produced by excluding satellite retrievals that are judged to caused relatively large artificial trends in LHF (mostly post 2000) from Set1. Consequently Set2 is identical to Set1 before 1997 and shows a smaller trend than Set1 for the whole period, while Set1 has better spatial coverage (less missing data). Hilburn & Shie (2011) further found a drift in the Earth incidence angle (EIA) associated with the SSM/I sensors on the DMSP satellites that introduces artificial trends in the SSM/I TB data. These artificial trends introduce large changes in the boundary water (*WB*), which affects the *Q*a, and thus the LHF retrievals. An improved version, GSSTF2c, incorporating the corrected SSM/I brightness temperature, has been produced as of this writing (Shie et al., 2011). The retrieved *WB*, *Q*a and LHF have genuinely improved, particularly in the trends post 2000 (Shie & Hilburn, 2011). An extensive study involving the GSSTF2c will be presented in a separate paper.

In this chapter, "trend" is used to indicate results from linear regression analysis and/or Empirical Mode Decomposition (EMD) for the period of study. Linear regression of the time series with time is used to detect linear trends and the significance can be estimated from the slope of the regression. EMD is based on local characteristic time scales of the data and is therefore applicable for analyzing nonlinear and non-stationary processes (Huang et al., 1998). It decomposes the time series into a finite and often small number of intrinsic mode

Oceanic Evaporation: Trends and Variability 265

the variance explained by the EOF is plotted against its EOF number. The variance

number of time samples. Linear regression between the logarithms of *λ*n vs *n* is computed.

Figure 1 shows the area weighted global (60°N–60°S) average LHF of HOAPS3, J-OFURO2, GSSTF2, and Set1 and Set2 of GSSTF2b. The merged OAFLUX product is included for comparison. Visual inspection of the data products does not show large missing gaps in the spatial distribution of the products. All satellite products-HOAPS3, J-OFURO2 and all GSSTF datasets show increases while there is no obvious trend in OAFLUX. All products have similar global means in the early period 1988–1991. The means of GSSTF2b (both Set1 and Set2) are generally lower than that of GSSTF2 which is the highest among all products. All products show a dip in 1991 and an increase afterwards. The dip is clearly evident in HOAPS3. GSSTF2b Set1 and Set2 are identical up to 1998 after which they diverge, but tend

Fig. 1. Time series of global (60°N–60°S) oceanic average latent heat flux derived from

The zonal annual averages of LHF are depicted in Figure 2. The general features of the zonal means are quite similar among these products: they showed maxima in the subtropics, minima at the poles and relative minima at the equator. The subtropical maxima in the southern hemisphere are slighter higher than those in the northern hemisphere for the same product. While GSSTF2 is the highest among these estimates, the

HOAPS3, J-OFURO2, GSSTF2, GSSTF2b Set1 and Set2 and OAFLUX.

where *λ*n is the *n*th eigenvalue and *N* is the

1 / *N n i i* 

explained by the *n*th EOF is given by

**3.1 Global and zonal average** 

to come close again after 2006.

**3. Results** 

EOFs above the regression line are judged to be significant.


Table 1. Characteristics of input data and parameters for HOAPS3, J-OFURO2, GSSTF2 and GSSTF2b.

functions (IMFs) of increasing time scales. The existence of a trend is dependent on the length of the dataset. If the last IMF (one with longest time scale) is monotonically increasing or decreasing, a trend is indicated. The EMD is a more stringent test for significance of "trends."

Non-seasonal variability is examined using Empirical Orthogonal Function (EOF) analysis. Non-seasonal data are obtained by subtracting the monthly climatology of the study period from the monthly data. EOF analysis decomposes a spatio-temporal dataset into a series of orthogonal spatial EOF patterns and the associated time series (also called principal components). The test proposed by North et al. (1982) is used to judge the EOFs to see if they are significant and distinct. To examine the significance of each EOF, the logarithm of the variance explained by the EOF is plotted against its EOF number. The variance explained by the *n*th EOF is given by 1 / *N n i i* where *λ*n is the *n*th eigenvalue and *N* is the

number of time samples. Linear regression between the logarithms of *λ*n vs *n* is computed. EOFs above the regression line are judged to be significant.

#### **3. Results**

264 Remote Sensing – Applications

Datasets HOAPS3 J-OFURO2 GSSTF2 GSSTF2b

Fairall et al.

SSM/I, AMSR-E, TMI, ERS-1, ERS-2 and QuikSCAT

*W*/*WB* N/A N/A Wentz V4 (1997) Wentz V6 (2007)

MGDSST Sakurai et al. (2005)

Schlussel et al. (1995)

NCEP/DOE Reanalysis (V2)

> Daily and monthly

Jan. 1988 – Dec. 2006

Table 1. Characteristics of input data and parameters for HOAPS3, J-OFURO2, GSSTF2 and

functions (IMFs) of increasing time scales. The existence of a trend is dependent on the length of the dataset. If the last IMF (one with longest time scale) is monotonically increasing or decreasing, a trend is indicated. The EMD is a more stringent test for

Non-seasonal variability is examined using Empirical Orthogonal Function (EOF) analysis. Non-seasonal data are obtained by subtracting the monthly climatology of the study period from the monthly data. EOF analysis decomposes a spatio-temporal dataset into a series of orthogonal spatial EOF patterns and the associated time series (also called principal components). The test proposed by North et al. (1982) is used to judge the EOFs to see if they are significant and distinct. To examine the significance of each EOF, the logarithm of

Spatial resolution 1°x1°, 0.5°x0.5° 1°x1° 1°x1° 1°x1° Spatial coverage Global Oceans Global Oceans Global Oceans Global Oceans

*U* (vector) N/A N/A Atlas et al. (1996)

(2003) Chou (1993) Chou (1993)

NCEP/NCAR Reanalysis (V1) Reynolds & Smith (1994)

Chou et al. (1995, 1997)

NCEP/NCAR Reanalysis (V1)

> Daily and monthly

Jul. 1987 – Dec. 2000

Wentz V4 (1997) Wentz V6 (2007)

CCMP Level-2.5 (SSM/I, TMI, and AMSR-E)

NCEP/DOE Reanalysis (V2) Reynolds et al. (2002)

Chou et al. (1995, 1997)

NCEP/DOE Reanalysis (V2)

> Daily and monthly

Jul. 1987 – Dec. 2008

*C*E (transfer coefficient)

*U* (speed)

*Q*<sup>a</sup>

*T*a

Temporal resolution

Temporal coverage

significance of "trends."

GSSTF2b.

Fairall et al. (1996, 2003)

SSM/I V6 TB and neural network algorithm

Bentamy et al. (2003)

Estimated from SST with assumptions of 80% humidity and 1 K surfaceair temperature difference

> Twice daily, pentad and monthly

Jul. 1987 – Dec. 2005

SST AVHRR

#### **3.1 Global and zonal average**

Figure 1 shows the area weighted global (60°N–60°S) average LHF of HOAPS3, J-OFURO2, GSSTF2, and Set1 and Set2 of GSSTF2b. The merged OAFLUX product is included for comparison. Visual inspection of the data products does not show large missing gaps in the spatial distribution of the products. All satellite products-HOAPS3, J-OFURO2 and all GSSTF datasets show increases while there is no obvious trend in OAFLUX. All products have similar global means in the early period 1988–1991. The means of GSSTF2b (both Set1 and Set2) are generally lower than that of GSSTF2 which is the highest among all products. All products show a dip in 1991 and an increase afterwards. The dip is clearly evident in HOAPS3. GSSTF2b Set1 and Set2 are identical up to 1998 after which they diverge, but tend to come close again after 2006.

Fig. 1. Time series of global (60°N–60°S) oceanic average latent heat flux derived from HOAPS3, J-OFURO2, GSSTF2, GSSTF2b Set1 and Set2 and OAFLUX.

The zonal annual averages of LHF are depicted in Figure 2. The general features of the zonal means are quite similar among these products: they showed maxima in the subtropics, minima at the poles and relative minima at the equator. The subtropical maxima in the southern hemisphere are slighter higher than those in the northern hemisphere for the same product. While GSSTF2 is the highest among these estimates, the

Oceanic Evaporation: Trends and Variability 267

GSSTF2b

1988–2000 10.44 9.88 5.98 7.75 6.51 3.71 1988–2005 N/A 11.69 6.34 7.35 7.62 2.41 1988–2008 N/A 10.45 7.08 N/A N/A 1.50 Table 2. Linear trends of LHF products (in W m–2 decade–1) for the different periods. All

Fig. 3. Linear trends of LHF (W m−2 decade−1) over the period 1988−2005. Contours give the

We performed EMD analyses for both GSSTF2 and GSSTF2b for the periods 1988–2000 and 1988−2008, respectively. Figure 4 shows the last IMFs from EMD analysis of GSSTF2b global average (60°N–60°S) LHF for the period 1988–2008, along with those of HOAPS3, J-OFURO2 and OAFLUX. EMD analysis of GSSTF2 global average LHF for the period 1988–2000 (Chiu et al., 2008; Xing, 2006) showed an increase starting around 1990 before the Mt Pinatubo eruption event in 1991. The last IMF of GSSTF2b Set2 and HOAPS3 show a dip around 1990 while J-OFURO2 and GSSTF2b Set1 show monotonic increases. From EMD analysis, a "trend" cannot be ascertained for the whole period for GSSTF2b Set2 and

Set2 HOAPS3 J-OFURO2 OAFLUX

Period GSSTF2 GSSTF2b

values are significant at 99% level.

trends above 95% confidence level.

HOAPS3.

**3.3 EMD analysis of global average LHF** 

Set1

Fig. 2. Zonal annual mean of LHF for oceanic evaporation computed from HOAPS3, J-OFURO2, GSSTF2, GSSTF2b Set1 and Set2 and OAFLUX.

GSSTF2b Set2 is slightly lower than HOAPS3 but higher than J-OFURO2 and OAFLUX at their maxima. Poleward of 30°, the GSSTF zonal means are generally higher than the other products.

#### **3.2 Trend analysis**

Linear regression analyses of the time series with time were performed on the global mean time series. The significance of the slopes of the regression (trend) is tested using a t-test. The degree of freedom for the significance test takes into account the serial correlation of the time series (Angell, 1981; Chiu & Newell, 1983). GSSTF2 shows the largest trend. It is followed by GSSTF2b Set1 while the GSSTF2b Set2 trend is comparable to HOAPS3 and J-OFURO2 for the period of overlap (1988–2005). OAFLUX exhibits the smallest trend. Table 2 summarizes our results.

To map out the geographic differences, Figure 3 compares the spatial distribution of linear trends of GSSTF2b Set1 and Set2, HOAPS3, J-OFURO2 and OAFLUX. The linear trends are calculated for the common time period 1988–2005. While the magnitudes of the trends are different, the locations of maximum change are similar among HOAPS3, J-OFURO2 and GSSTF2b Set1 and Set2 - all show increasing trends in the storm tracks in the north Atlantic and north Pacific, the oceanic dry zones off the Inter-tropical Convergence Zone (ITCZ) in the western south Pacific and in latitude bands between 30−40°S off the coast of Australia and in the Indian Ocean. OAFLUX shows increasing trends in the storm tracks in both the North Atlantic and North Pacific, and in the eastern coastal regions off South America and Australia. There are large areas showing a decrease, notably in the south Indian Ocean, tropical eastern North Pacific and in North Atlantic.

Fig. 2. Zonal annual mean of LHF for oceanic evaporation computed from HOAPS3, J-

GSSTF2b Set2 is slightly lower than HOAPS3 but higher than J-OFURO2 and OAFLUX at their maxima. Poleward of 30°, the GSSTF zonal means are generally higher than the other

Linear regression analyses of the time series with time were performed on the global mean time series. The significance of the slopes of the regression (trend) is tested using a t-test. The degree of freedom for the significance test takes into account the serial correlation of the time series (Angell, 1981; Chiu & Newell, 1983). GSSTF2 shows the largest trend. It is followed by GSSTF2b Set1 while the GSSTF2b Set2 trend is comparable to HOAPS3 and J-OFURO2 for the period of overlap (1988–2005). OAFLUX exhibits the smallest trend. Table 2

To map out the geographic differences, Figure 3 compares the spatial distribution of linear trends of GSSTF2b Set1 and Set2, HOAPS3, J-OFURO2 and OAFLUX. The linear trends are calculated for the common time period 1988–2005. While the magnitudes of the trends are different, the locations of maximum change are similar among HOAPS3, J-OFURO2 and GSSTF2b Set1 and Set2 - all show increasing trends in the storm tracks in the north Atlantic and north Pacific, the oceanic dry zones off the Inter-tropical Convergence Zone (ITCZ) in the western south Pacific and in latitude bands between 30−40°S off the coast of Australia and in the Indian Ocean. OAFLUX shows increasing trends in the storm tracks in both the North Atlantic and North Pacific, and in the eastern coastal regions off South America and Australia. There are large areas showing a decrease, notably in the south Indian Ocean,

OFURO2, GSSTF2, GSSTF2b Set1 and Set2 and OAFLUX.

tropical eastern North Pacific and in North Atlantic.

products.

**3.2 Trend analysis** 

summarizes our results.


Table 2. Linear trends of LHF products (in W m–2 decade–1) for the different periods. All values are significant at 99% level.

Fig. 3. Linear trends of LHF (W m−2 decade−1) over the period 1988−2005. Contours give the trends above 95% confidence level.

#### **3.3 EMD analysis of global average LHF**

We performed EMD analyses for both GSSTF2 and GSSTF2b for the periods 1988–2000 and 1988−2008, respectively. Figure 4 shows the last IMFs from EMD analysis of GSSTF2b global average (60°N–60°S) LHF for the period 1988–2008, along with those of HOAPS3, J-OFURO2 and OAFLUX. EMD analysis of GSSTF2 global average LHF for the period 1988–2000 (Chiu et al., 2008; Xing, 2006) showed an increase starting around 1990 before the Mt Pinatubo eruption event in 1991. The last IMF of GSSTF2b Set2 and HOAPS3 show a dip around 1990 while J-OFURO2 and GSSTF2b Set1 show monotonic increases. From EMD analysis, a "trend" cannot be ascertained for the whole period for GSSTF2b Set2 and HOAPS3.

Oceanic Evaporation: Trends and Variability 269

Figure 7 shows the spatial distribution of trends in *U*, *DQ* and *Q*a in Set 1 and Set2. It can be seen that the trend pattern in *U* is almost identical in both Set1 and Set2. Major difference is found in the *DQ* field, the magnitudes of the trends in Set1 are larger than that in Set2. In the equatorial eastern Pacific, in portion of the South China Sea, and the Bay of Bengal, Set2 actually shows a decreasing but non-significant trend. For the *Q*a trend, Set1 shows large areas of decreases. While the patterns are similar for Set2, the magnitudes are much reduced, and in some areas, such as the eastern North Pacific and the bulk of the North

LHF is a product of the surface wind (*U*) and the humidity difference (*DQ*=*Q*s–*Q*a) if we assume the variation in *C*E is small (Equation 1). Judging from the change pattern of *U* and

> *LHF U DQ LHF U DQ*

*DQ Q Q s a*

where *δx* represent the change in the quantity *x*, *δx/x* represent fractional changes in *x*, and the over-bar *x* represents global average of *x*. For GSSTF2 (1988–2000), the terms in equation (2) is approximately 17%, 6%, and 11%, in that order (Xing, 2006). Most of the

Figure 8 shows the time series of global average *U*, *DQ* and *Q*a for GSSTF2b Set1 and Set2, respectively. It clearly indicates the divergence of Set1 and Set2 in late 1997. The large difference in LHF between Set1 and Set2 is mostly attributed to *DQ*, which is due to the higher *Q*a in Set2. The difference in *U* between the datasets is small. There is a large decrease

 

(2)

(3)

 

*DQ* they are essentially decoupled. Equation (1) can be integrated globally to get

increase in *DQ* was attributed to increase in *Qs* and decrease in *Qa* .

in *Q*a at the end of 2008.

Fig. 6. Linear trends of zonal mean GSSTF2b Set1 and Set2 LHF.

Atlantic, the decreasing trends actually reverse to increasing trends.

Fig. 4. The last IMFs (W m–2) from EMD analysis of global (60°N–60°S) average LHF for GSSTF2b Set1 (black), GSSTF2b Set2 (red), and OAFLUX (blue) for the period 1988–2008, HOAPS3 (purple)for the period 1988–2005 and J-OFURO2 (green) for the period 1988–2006.

#### **3.4 GSSTF2b Set1 and Set2**

In this subsection, we analyze the difference between GSSTF2b Set1 and Set2. The linear trends of GSSTF2b LHF for the entire period 1988–2008 are depicted in Figure 5. The magnitudes of the trends are reduced when compared to GSSTF2 (see Chiu et al., 2008) and Set1 shows larger trends than Set2, as anticipated.

Trends of the zonal averages are shown in Figure 6. Both Set1 and Set2 show similar trend patterns while the magnitude in Set2 is much reduced. Maximum increasing trends are located in the subtropics, with maxima at 30°S and 35°N, poleward of the latitude of maximum zonal evaporation, while the trend at the thermal equator (~5°N) shows a minimum.

Fig. 5. Linear trends of GSSTF2b Set1 and Set2 LHF (1988–2008). Unit in W m−2 decade−1. Contours give the trends above 95% confidence level.

Fig. 4. The last IMFs (W m–2) from EMD analysis of global (60°N–60°S) average LHF for GSSTF2b Set1 (black), GSSTF2b Set2 (red), and OAFLUX (blue) for the period 1988–2008, HOAPS3 (purple)for the period 1988–2005 and J-OFURO2 (green) for the period 1988–2006.

In this subsection, we analyze the difference between GSSTF2b Set1 and Set2. The linear trends of GSSTF2b LHF for the entire period 1988–2008 are depicted in Figure 5. The magnitudes of the trends are reduced when compared to GSSTF2 (see Chiu et al., 2008) and

Trends of the zonal averages are shown in Figure 6. Both Set1 and Set2 show similar trend patterns while the magnitude in Set2 is much reduced. Maximum increasing trends are located in the subtropics, with maxima at 30°S and 35°N, poleward of the latitude of maximum zonal

Fig. 5. Linear trends of GSSTF2b Set1 and Set2 LHF (1988–2008). Unit in W m−2 decade−1.

evaporation, while the trend at the thermal equator (~5°N) shows a minimum.

**3.4 GSSTF2b Set1 and Set2** 

Set1 shows larger trends than Set2, as anticipated.

Contours give the trends above 95% confidence level.

Fig. 6. Linear trends of zonal mean GSSTF2b Set1 and Set2 LHF.

Figure 7 shows the spatial distribution of trends in *U*, *DQ* and *Q*a in Set 1 and Set2. It can be seen that the trend pattern in *U* is almost identical in both Set1 and Set2. Major difference is found in the *DQ* field, the magnitudes of the trends in Set1 are larger than that in Set2. In the equatorial eastern Pacific, in portion of the South China Sea, and the Bay of Bengal, Set2 actually shows a decreasing but non-significant trend. For the *Q*a trend, Set1 shows large areas of decreases. While the patterns are similar for Set2, the magnitudes are much reduced, and in some areas, such as the eastern North Pacific and the bulk of the North Atlantic, the decreasing trends actually reverse to increasing trends.

LHF is a product of the surface wind (*U*) and the humidity difference (*DQ*=*Q*s–*Q*a) if we assume the variation in *C*E is small (Equation 1). Judging from the change pattern of *U* and *DQ* they are essentially decoupled. Equation (1) can be integrated globally to get

$$\frac{\delta \overline{LHF}}{\overline{LHF}} \approx \frac{\delta \overline{U}}{\overline{U}} + \frac{\delta \overline{DQ}}{\overline{DQ}}\tag{2}$$

$$
\delta \overline{\delta D \mathbb{Q}} = \delta \overline{Q\_s} - \delta \overline{Q\_a} \tag{3}
$$

where *δx* represent the change in the quantity *x*, *δx/x* represent fractional changes in *x*, and the over-bar *x* represents global average of *x*. For GSSTF2 (1988–2000), the terms in equation (2) is approximately 17%, 6%, and 11%, in that order (Xing, 2006). Most of the increase in *DQ* was attributed to increase in *Qs* and decrease in *Qa* .

Figure 8 shows the time series of global average *U*, *DQ* and *Q*a for GSSTF2b Set1 and Set2, respectively. It clearly indicates the divergence of Set1 and Set2 in late 1997. The large difference in LHF between Set1 and Set2 is mostly attributed to *DQ*, which is due to the higher *Q*a in Set2. The difference in *U* between the datasets is small. There is a large decrease in *Q*a at the end of 2008.

Oceanic Evaporation: Trends and Variability 271

Fig. 8. Time series of global average surface humidity difference (*DQ*), surface air humidity

Empirical Orthogonal Function (EOF) analyses are performed on LHF of both GSSTF2b Set1 and Set2 for the period 1988–2008. Monthly means for the entire period are first removed from the data to form the non-seasonal dataset. The first, second and third EOF of Set1

Fig. 9. Spatial patterns of the first (top) and second (bottom) EOF of GSSTF2b LHF for the period 1988–2008. The variances explained are 8.6% (11.5%) and 4.3% (4.3%) for EOF1 and

(*Q*a) and wind speed (*U*) for GSSTF2b Set1 and Set2.

**3.5 EOF analyses and teleconnections** 

EOF2 for Set2 (Set1), respectively.

Fig. 7. Linear trends of surface wind speed (*U*, upper panel), surface humidity difference (*DQ*=*Q*s–*Q*a, middle panel), and surface air humidity (*Q*a, lower panel) for GSSTF2b Set1 (left column) and Set2 (right column). Units are in m s−1 decade−1 for *U* and in g kg–1 decade– 1 for *DQ* and *Q*a. Contours give the trends above 95% confidence level.

Table 3 shows the changes in LHF in Set1 and Set2 of GSSTF2b (1988−2008) and the associated changes in *U*, *DQ* and the changes in *Q*s and *Q*a. The change in *DQ* contribute most to the change in LHF for both Set1 and Set2, while the changes in *DQ* is due both to an increase in *Q*s and decrease in *Q*a. The difference in the change of LHF between Set1 (23.1%) and Set2 (15.5%) is mostly attributed to *DQ* (20.0% vs. 12.3%) and changes in *Q*a (–0.51 g kg–1 vs. –0.24 g kg–1). It is clear that the impact of DMSP F13 is the introduction of a much lower *Q*a, thus affecting *DQ* and ultimately LHF.


Table 3. Summary of changes in LHF, *U* and *DQ* and *Q*s and *Q*a for GSSTF2b Set1 and Set2.

Fig. 7. Linear trends of surface wind speed (*U*, upper panel), surface humidity difference (*DQ*=*Q*s–*Q*a, middle panel), and surface air humidity (*Q*a, lower panel) for GSSTF2b Set1 (left column) and Set2 (right column). Units are in m s−1 decade−1 for *U* and in g kg–1 decade–

Table 3 shows the changes in LHF in Set1 and Set2 of GSSTF2b (1988−2008) and the associated changes in *U*, *DQ* and the changes in *Q*s and *Q*a. The change in *DQ* contribute most to the change in LHF for both Set1 and Set2, while the changes in *DQ* is due both to an increase in *Q*s and decrease in *Q*a. The difference in the change of LHF between Set1 (23.1%) and Set2 (15.5%) is mostly attributed to *DQ* (20.0% vs. 12.3%) and changes in *Q*a (–0.51 g kg–1 vs. –0.24 g kg–1). It is clear that the impact of DMSP F13 is the introduction of a much lower

*DQ*

Set1 23.1% 3.1% 20.0% 0.73 0.22 –0.51 Set2 15.5% 3.1% 12.3% 0.46 0.22 –0.24 Table 3. Summary of changes in LHF, *U* and *DQ* and *Q*s and *Q*a for GSSTF2b Set1 and Set2.

*DQ* 

*DQ* (g kg–1)

*Qs* (g kg–1)

*Qa* (g kg–1)

1 for *DQ* and *Q*a. Contours give the trends above 95% confidence level.

*Q*a, thus affecting *DQ* and ultimately LHF.

*LHF U DQ LHF U DQ*

*<sup>U</sup>*

*U* 

 

Fig. 8. Time series of global average surface humidity difference (*DQ*), surface air humidity (*Q*a) and wind speed (*U*) for GSSTF2b Set1 and Set2.

#### **3.5 EOF analyses and teleconnections**

Empirical Orthogonal Function (EOF) analyses are performed on LHF of both GSSTF2b Set1 and Set2 for the period 1988–2008. Monthly means for the entire period are first removed from the data to form the non-seasonal dataset. The first, second and third EOF of Set1

Fig. 9. Spatial patterns of the first (top) and second (bottom) EOF of GSSTF2b LHF for the period 1988–2008. The variances explained are 8.6% (11.5%) and 4.3% (4.3%) for EOF1 and EOF2 for Set2 (Set1), respectively.

Oceanic Evaporation: Trends and Variability 273

weights of the same sign everywhere (Chiu et al., 2008). However, in GSSTF2b, there are regions of opposite sign in the South China Sea area in both Set1 and Set2, with higher weights in Set2 in the centers of highs. The EOF2 patterns for Set1 and Set2 are almost identical. Similarity is also noted for the EOF2 pattern derived from GSSTF2 (Chiu et al.,

The time series of EOF1 also show a slight increasing trend, suggesting an increase of LHF over most of the global ocean. The associated time series of EOF2 have a significant correlation of 0.73 (0.72 for Set1) with SOI, reaffirming that EOF2 - ENSO events association. The third EOF pattern (not shown) is characterized by a negative - positive - negative (– + –) zonal changes centering around 10−20°N, 20−30°N and north of 40°N in the north Pacific and a (+ – +) centering at 20°N, 30°N and 50°N in the North Atlantic. The pattern correlations for EOF1, EOF2 and EOF3 between Set1 and Set2 are 0.08, 0.49, and 0.39, and the corresponding temporal correlation for the associated time series are 0.92, 0.97, and 0.83,

EOF3 is reminiscent of the North Atlantic Oscillation (NAO) and the North Pacific Oscillation (NPO) patterns (Walker & Bliss, 1932; Wallace & Gultzer, 1981, Hurrell et al., 2003). These teleconnection patterns are further discussed in terms of an atmospheric annular mode, or Arctic Oscillation (AO), showing the opposition between subtropical highs and the polar lows (Wallace, 2000; Deser, 2000; Aubaum et al., 2001). We compute the correlations between an AO index with the time series of EOF3 and found no significant correlation with Set2, however a correlation of 0.32 is found for EOF3 Set1, significant at 95%

Four satellite based sea surface latent heat flux (LHF) products, HOSAPS3, J-OFURO2, GSSTF2, and GSSTF2b (Set1 and Set2) and a merged analysis OAFLUX are compared. Linear trend analysis of all satellite based products show large increasing trend, with GSSTF2 the largest, followed by GSSTF2b Set1, J-OFURO2, HOAPS3, and GSSTF2b Set2. OAFLUX exhibits the lowest linear increasing trend. Most of the satellite products used SSM/I as input. Small drifts in the SSM/I brightness temperature (TB) associated with changes in Earth incidence angle (EIA) was noted in most of the SSM/I data (Hilburn & Shie, 2011; Shie & Hilburn, 2011). Because of the sensitivity of the boundary layer water (*WB*) to the TB, these small drifts can introduce artificial trends in bulk quantities such as *Q*a. A second data set, GSSTF2b Set2, which excludes satellite retrievals that were judged to introduce these biases, was introduced. The most-excluded satellite data are SSM/I onboard the DMSP F13 and F15 satellites. The new set, GSSTF2b Set2, was found to have a much reduced increasing trend, the magnitude of which is comparable to HOAPS3 and J-OFURO2 for the period of overlap. To account for the drift in the EIA, a new version of GSSTF, GSSTF2c, that takes account of the correction in EIA, has been completed as of this writing, and will be officially released to the public via NASA/GES DISC by the end of October 2011

Empirical Mode Decomposition (EMD) analyses, which are designed for examining nonstationary non-homogeneous time series, are performed on the global LHF. The last IMF of GSSTF2b Set1 shows a monotonic increase indicating the existence of a trend in this period.

(Shie et al., 2011). These trend issues will be revisited after its release.

2008).

in that order.

level.

**4. Summary and discussion** 

explains 11.5%, 4.3%, and 3.4% of the total variance, and the corresponding variance explained for Set2 are 8.6%, 4.3% and 3.4%. Figure 9 shows the spatial patterns of the first (EOF1) and second (EOF2) EOF for Set1 and Set2, respectively. Their associated time series, accompanied by a rescaled Southern Oscillation Index (SOI), are presented in Figure 10. The general patterns of EOF1 of Set1 and Set2 are very similar, with large weights in subtropical Indian Ocean, the dry zone in the eastern tropical South Pacific and South Atlantic. They also bear striking resemblance to the first EOF pattern computed from GSSTF2 which shows

Fig. 10. Time series associated with EOF of non-seasonal GSSTF2b Set2 LHF for the period 1988–2008 and the SOI for the same period. The SOI is divided by 10 and the time series of the first EOF is shifted by –0.4 units for clarity. The lower panel shows the same for Set1.

explains 11.5%, 4.3%, and 3.4% of the total variance, and the corresponding variance explained for Set2 are 8.6%, 4.3% and 3.4%. Figure 9 shows the spatial patterns of the first (EOF1) and second (EOF2) EOF for Set1 and Set2, respectively. Their associated time series, accompanied by a rescaled Southern Oscillation Index (SOI), are presented in Figure 10. The general patterns of EOF1 of Set1 and Set2 are very similar, with large weights in subtropical Indian Ocean, the dry zone in the eastern tropical South Pacific and South Atlantic. They also bear striking resemblance to the first EOF pattern computed from GSSTF2 which shows

Fig. 10. Time series associated with EOF of non-seasonal GSSTF2b Set2 LHF for the period 1988–2008 and the SOI for the same period. The SOI is divided by 10 and the time series of the first EOF is shifted by –0.4 units for clarity. The lower panel shows the same for Set1.

weights of the same sign everywhere (Chiu et al., 2008). However, in GSSTF2b, there are regions of opposite sign in the South China Sea area in both Set1 and Set2, with higher weights in Set2 in the centers of highs. The EOF2 patterns for Set1 and Set2 are almost identical. Similarity is also noted for the EOF2 pattern derived from GSSTF2 (Chiu et al., 2008).

The time series of EOF1 also show a slight increasing trend, suggesting an increase of LHF over most of the global ocean. The associated time series of EOF2 have a significant correlation of 0.73 (0.72 for Set1) with SOI, reaffirming that EOF2 - ENSO events association.

The third EOF pattern (not shown) is characterized by a negative - positive - negative (– + –) zonal changes centering around 10−20°N, 20−30°N and north of 40°N in the north Pacific and a (+ – +) centering at 20°N, 30°N and 50°N in the North Atlantic. The pattern correlations for EOF1, EOF2 and EOF3 between Set1 and Set2 are 0.08, 0.49, and 0.39, and the corresponding temporal correlation for the associated time series are 0.92, 0.97, and 0.83, in that order.

EOF3 is reminiscent of the North Atlantic Oscillation (NAO) and the North Pacific Oscillation (NPO) patterns (Walker & Bliss, 1932; Wallace & Gultzer, 1981, Hurrell et al., 2003). These teleconnection patterns are further discussed in terms of an atmospheric annular mode, or Arctic Oscillation (AO), showing the opposition between subtropical highs and the polar lows (Wallace, 2000; Deser, 2000; Aubaum et al., 2001). We compute the correlations between an AO index with the time series of EOF3 and found no significant correlation with Set2, however a correlation of 0.32 is found for EOF3 Set1, significant at 95% level.
