**4. Summary and discussion**

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

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.

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The corresponding IMF of the other data products do not show monotonic increases, hence trends cannot be ascertained.

To examine the attribution of the increase in GSSTF2b Set1 and Set2, the linear trends in both the surface wind (*U*) and surface humidity difference (*DQ*) are computed. There is no significant difference between the trend patterns in the wind field. However, large difference in the *DQ* trend is noted. The *DQ* trend difference is attributed to a reduction in the negative trend in surface air humidity (*Q*a) in Set2.

A major difference between GSSTF2 and GSSTF2b is the use of RSS SSM/I V4 for GSSTF2 and RSS SSM/I V6 for GSSTF2b. The changes in LHF, *U*, and *DQ* are 16%, 6%, and 11% for GSSTF2 (Xing, 2006). The corresponding changes are 23%, 3%, and 20% for GSSTF2b Set1 and 16%, 3%, and 12% for Set2 (Table 3). The use of RSS SSM/I V6 products reduces the wind trend from 6% to 3% but increases the *DQ* trend from 11% to 20% for Set1. The exclusion of F13 and F15 data reduces the LHF trend to 16%, mostly due to a reduction in the *DQ* trend to 12% (from 20%) with *U* changes remain at 3%.

Interannual variability is examined using EOF analyses. The first three significant nonseasonal EOF patterns are similar, and they explaining 10.5%, 4.3% and 3.4% for Set1 and 8.6%, 4.3% and 3.4% for Set2, respectively. The first EOF pattern of GSSTF2 for 1998–2000, with opposite changes between the equatorial eastern Pacific and the subtropics in the Pacific and Indian ocean, may be indicative of an enhance Hadley circulation (Chiu & Xing, 2004; Chiu et al., 2008). Observations also indicate large decadal variability in the Hadley Circulation (Wielicki et al., 2002; Cess & Udelhofen, 2003; Chen et al., 2002; Mitas and Clements, 2005).

This seesaw pattern is much reduced in the EOF1 pattern in both GSSTF2b Set1 and Set2 of 1998–2008, which may indicate a reduction, change of phase, or mixing of the signal with the trend in GSSTF2b. The contribution to the total variance is smaller for Set2, which excluded DMSP datasets that contains large long-term trends introduced by drifts in the Earth incidence angle in the SSM/I sensors. The difference in the fraction of variance explained in Set1 and Set2 is attributed to the artificial trend in F13 and F15 and the EIA drift effect.

Examination of the trends of the zonal means show that the latitude of maximum increase, situated in the subtropics, is found poleward of the LHF maximum in the tropic. This pattern is consistent with the expansion of the Hadley Circulation associated with global warming as predicted in climate models (Lu et al., 2007).

The EOF2 patterns of Set1 and Set2 are almost identical, both contributed to 4.3% of the variance of the dataset. The association with the El Nino/Southern Oscillation phenomena is corroborated by a high correlation between their time series and an index of the Southern Oscillation (SOI). The patterns for EOF3 and their associated time series are also similar, indicating that both GSSTF2b Set1 and Set2 are useful for examining interannual variability.

#### **5. Acknowledgment**

This study is supported by the MEaSUREs Program of NASA Science Mission Directorate-Earth Science Division. The authors are especially grateful to their program manager M. Maiden and program scientist J. Entin for their valuable supports of this research.

#### **6. References**

274 Remote Sensing – Applications

The corresponding IMF of the other data products do not show monotonic increases, hence

To examine the attribution of the increase in GSSTF2b Set1 and Set2, the linear trends in both the surface wind (*U*) and surface humidity difference (*DQ*) are computed. There is no significant difference between the trend patterns in the wind field. However, large difference in the *DQ* trend is noted. The *DQ* trend difference is attributed to a reduction in

A major difference between GSSTF2 and GSSTF2b is the use of RSS SSM/I V4 for GSSTF2 and RSS SSM/I V6 for GSSTF2b. The changes in LHF, *U*, and *DQ* are 16%, 6%, and 11% for GSSTF2 (Xing, 2006). The corresponding changes are 23%, 3%, and 20% for GSSTF2b Set1 and 16%, 3%, and 12% for Set2 (Table 3). The use of RSS SSM/I V6 products reduces the wind trend from 6% to 3% but increases the *DQ* trend from 11% to 20% for Set1. The exclusion of F13 and F15 data reduces the LHF trend to 16%, mostly due to a reduction in

Interannual variability is examined using EOF analyses. The first three significant nonseasonal EOF patterns are similar, and they explaining 10.5%, 4.3% and 3.4% for Set1 and 8.6%, 4.3% and 3.4% for Set2, respectively. The first EOF pattern of GSSTF2 for 1998–2000, with opposite changes between the equatorial eastern Pacific and the subtropics in the Pacific and Indian ocean, may be indicative of an enhance Hadley circulation (Chiu & Xing, 2004; Chiu et al., 2008). Observations also indicate large decadal variability in the Hadley Circulation (Wielicki et al., 2002; Cess & Udelhofen, 2003; Chen et al., 2002; Mitas and

This seesaw pattern is much reduced in the EOF1 pattern in both GSSTF2b Set1 and Set2 of 1998–2008, which may indicate a reduction, change of phase, or mixing of the signal with the trend in GSSTF2b. The contribution to the total variance is smaller for Set2, which excluded DMSP datasets that contains large long-term trends introduced by drifts in the Earth incidence angle in the SSM/I sensors. The difference in the fraction of variance explained in Set1 and Set2 is attributed to the artificial trend in F13 and F15 and the EIA

Examination of the trends of the zonal means show that the latitude of maximum increase, situated in the subtropics, is found poleward of the LHF maximum in the tropic. This pattern is consistent with the expansion of the Hadley Circulation associated with global

The EOF2 patterns of Set1 and Set2 are almost identical, both contributed to 4.3% of the variance of the dataset. The association with the El Nino/Southern Oscillation phenomena is corroborated by a high correlation between their time series and an index of the Southern Oscillation (SOI). The patterns for EOF3 and their associated time series are also similar, indicating that both GSSTF2b Set1 and Set2 are useful for examining interannual variability.

This study is supported by the MEaSUREs Program of NASA Science Mission Directorate-Earth Science Division. The authors are especially grateful to their program manager M.

Maiden and program scientist J. Entin for their valuable supports of this research.

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**5. Acknowledgment** 

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<http://disc.sci.gsfc.nasa.gov/daac-

bin/DataHoldingsMEASURES.pl?PROGRAM\_List=ChungLinShie>

**11** 

*Japan* 

Hiroaki Kuze

**Multi-Wavelength and Multi-Direction Remote** 

**Sensing of Atmospheric Aerosols and Clouds** 

Aerosols are liquid and solid particles floating in the atmosphere. Aerosol particles are originated from both natural and anthropogenic origins (Seinfeld & Pandis, 1998). In regard to the radiation balance of the Earth's atmosphere, aerosols reflect solar radiation back to space (direct effect), thus reducing the influence of greenhouse gases, though some type of aerosol causes opposite effects due to absorption of radiation. At the same time, aerosol particles work as nuclei for cloud condensation (indirect effect). Knowledge on these radiative effects of aerosol and cloud, however, is still insufficient so that uncertainties remain in the prediction of future global warming trends (IPCC, 2007). In this respect, intensive efforts are needed to evaluate the optical/physical properties of aerosols and

In order to obtain better understanding of these particulate matters, what is obviously needed is the monitoring technique that enables the retrieval of their optical properties. In this chapter, we propose multi-wavelength and multi-directional remote sensing of atmospheric aerosols and clouds. The proposed method consists of the application of ground-based radiation measurement, lidar measurement, differential optical absorption spectroscopy (DOAS), and satellite observations using natural as well as artificial light sources. Such combinatory approach makes it possible to measure various aspects of radiation transfer through the atmosphere, especially the influence of tropospheric aerosols and clouds. Also, the data provided from the ground-based solar irradiance/sky radiance measurement and DOAS are valuable for precisely characterizing the optical property of aerosol particles near the ground level, including the information from both particulate scattering and gaseous absorption. Such ground data are also indispensable for the atmospheric correction of satellite remote sensing data in and around the visible range of the radiation spectrum. The multi-wavelength and multi-directional observation schemes

The method of differential optical absorption spectroscopy (DOAS) provides a useful tool for monitoring atmospheric pollutants through the measurement of optical extinction (i.e., the sum of absorption and scattering) over a light path length of a few kilometres (Yoshii et al., 2003, Lee et al., 2009; Si et al., 2005; Kuriyama et al., 2011). The DOAS method in the

clouds by means of both ground- and satellite-based remote sensing observations.

treated in the present chapter are summarized in Table 1.

**2. Differential optical absorption spectroscopy (DOAS)** 

**1. Introduction** 

*Centre for Environmental Remote Sensing (CEReS), Chiba University* 

