**Oceanic Evaporation: Trends and Variability**

Long S. Chiu1, Si Gao1 and Chung-Lin Shie2

*1Department of Atmospheric, Oceanic and Earth Sciences, George Mason University 2UMBC/JCET, NASA/GSFC USA* 

#### **1. Introduction**

260 Remote Sensing – Applications

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The global water and energy cycles are strongly coupled as two essential components of earth system. They play important roles in altering the Earth's climate.

Oceanic evaporation, or sea surface latent heat flux (LHF) divided by latent heat of vaporization (*L*v), is a key component of global water and energy cycle. In a bulk aerodynamic formulation, it is determined by the transfer coefficient of evaporation, *C*E, and bulk parameters such as surface wind speed (*U*), surface saturated and near-surface air specific humidity (*Q*s and *Q*a),

$$
\Delta HF = \rho L\_v C\_E L I (Q\_s - Q\_a) \tag{1}
$$

where sea surface saturated humidity is determined by sea surface temperature (SST) and salinity, and *ρ* is density of moist air. The transfer coefficient is dependent on the stability of the atmosphere and the sea state (Liu et al., 1979; Zeng et al., 1988). Historically, marine surface observations have provided the basis for estimating these oceanic turbulent fluxes (e.g. Bunker, 1976; Cayan, 1992; da Silva et al., 1994; Esbensen & Kushnir, 1981; Hastenrath, 1980; Hsiung, 1985; Isemer & Hasse, 1985, 1987; Josey et al., 1998; Oberhuber, 1988; Renfrew et al., 2002; Weare et al., 1981). The advent of remote sensing techniques offers means to retrieve a number of surface bulk variables. Microwave radiation interacts directly with water molecules and hence is effective in providing water vapor information. The sea surface emissivity is affected by the sea state and foam conditions, which is related to surface wind. For instance, global microwave measurement of the Special Sensor Microwave Imager (SSM/I) on board a series of Defense Meteorological Satellite Program (DMSP) satellites has been used to retrieve near-surface air humidity and winds over the ocean.

At present there are several remote sensing products of global ocean surface latent heat flux. They include the NASA/Goddard Satellite-based Surface Turbulent Flux (GSSTF) dataset version 1 (Chou et al., 1997) and version 2 (GSSTF2, Chou et al., 2003), the Japanese Ocean Flux utilizing Remote Sensing Observations (J-OFURO) dataset (Kubota et al., 2002) and the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) dataset (Grassl et al., 2000). Chiu et al. (2008) examined "trends" and variations in these global oceanic evaporation products for the period 1988–2000. They found a long-term increase in global average LHF that started around 1990 in GSSTF2. They argued that the dominant patterns may be related to an enhancement of Hadley circulation and El Niño-Southern Oscillation

Oceanic Evaporation: Trends and Variability 263

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–

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

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

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

**2.3 GSSTF** 

December 2008).

on input parameters of LHF are presented.

GSSTF2c will be presented in a separate paper.

(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 data products and examine their "trends" and variability.

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 discussion are presented in Section 4.
