A SATELLITE-BASED GLOBAL OCEAN SURFACE TURBULENT FLUXES DATASET AND THE IMPACT OF THE ASSOCIATED SSM/I BRIGHTNESS TEMPERATURE

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1 A SATELLITE-BASED GLOBAL OCEAN SURFACE TURBULENT FLUXES DATASET AND THE IMPACT OF THE ASSOCIATED SSM/I BRIGHTNESS TEMPERATURE Chung-Lin Shie, kyle hilburn UMBC/JCET, Code 613.1, NASA/GSFC, Greenbelt, Maryland, USA Remote Sensing Systems, Santa Rosa, California, USA Abstract Accurate ocean surface turbulent flux measurements are crucial to understanding the global water and energy cycle changes. Remote sensing is a valuable tool for global monitoring of these flux measurements. The GSSTF (Goddard Satellite-based Surface Turbulent Fluxes) algorithm was thus developed and applied to remote sensing research and applications. In a recent project funded by the NASA/Making Earth System data records for Use in Research Environments (MEaSUREs) Program to revive processing of the GSSTF dataset derived from improved input remote sensing data and model reanalysis, the daily global (1 o x1 o ) GSSTF2b (Version-2b) dataset (July 1987-December 2008) was produced, as well as officially distributed by NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) in October, 2010 (Shie et al. 2010; Shie 2010a; Shie et al. 2009). The GSSTF2b dataset was produced using upgraded and improved input datasets such as the Special Sensor Microwave Imager (SSM/I) Version-6 (V6) product (i.e., brightness temperature [Tb], total precipitable water [W], and wind speed [U]) and the NCEP/DOE Reanalysis-2 (R2) product (i.e., sea skin temperature [SKT], 2-meter air temperature [Tair], and sea level pressure [SLP]). The GSSTF2b was found to generally agree better with available ship measurements obtained from several field experiments in 1999 than the preceding product GSSTF2 in all the three flux components latent heat flux [LHF], sensible heat flux [SHF], and wind stress [WST] (Shie 2010a,b). GSSTF2b was also favored, particularly for LHF and SHF in an intercomparison study accessing eleven products of ocean surface turbulent fluxes (Brunke et al. 2011). However, the assessment was based primarily on the available cruise observations prior to 2001 that was before a temporal trend of the globally averaged LHF starting to appear in GSSTF2b. Shie (2010a,b) suggested that the LHF trend was related to trends originally found in the globally averaged SSM/I Tb s, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b bottom-layer (the lowest atmospheric 500 meter layer) precipitable water [WB], then the surface specific humidity [Qa], and subsequently LHF. The SSM/I Tb s trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb s by genuinely removing the artifactitious trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb s has been completed very recently. GSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF -- the temporal trends of WB and LHF are genuinely reduced after the proper adjustments/treatments in the SSM/I Tb s. In closing, we believe that the insightful Rice Cooker Theory by Shie (2010a,b), i.e., To produce a good and trustworthy output product (delicious cooked rice ) depends not only on a well-functioned model/algorithm ( rice cooker ), but also on a genuine and reliable input data ( raw rice ) with good quality should help us better comprehend the positive impact of the corrected Tb on the subsequently improved LHF of GSSTF2c. INTRODUCTION The Earth s climate is characterized by numerous processes that couple the atmosphere, ocean and land systems. The global water cycle s provision of water to terrestrial storage, reservoirs, and rivers counts on the global excess of evaporation to precipitation over the oceans. This ocean evaporation excess would vary and ultimately lead to variations in the amount of freshwater that is transported and

2 precipitated over continental regions. The air-sea fluxes of momentum, radiation and freshwater (precipitation evaporation) play a very essential role in a wide variety of atmospheric and oceanic phenomena. Remote sensing is an important tool for global monitoring of the accurate ocean surface turbulent flux measurements that are key to understanding the global water and energy cycle changes. The GSSTF algorithm has been developed and applied to remote sensing research and applications since mid 1990s (Chou et al. 1995; Chou et al. 1997; Chou et al. 2003). In a project recently funded by the NASA/Making Earth System data records for Use in Research Environments (MEaSUREs) Program aiming to revive processing of the GSSTF dataset derived from improved input remote sensing data and model reanalysis, GSSTF2b was thus produced and distributed to the scientific community in October Like the preceding product GSSTF2 (July 1987-December 2000), yet with a longer temporal coverage (July 1987-December 2008), GSSTF2b has so far bridged a 8-year gap and restarted the provision of a useful long-term turbulent surface flux dataset to the scientific community for studies of energy and water cycle studies at various spatial (global or regional) and temporal (long- or short-term) scales. The GSSTF2b dataset was produced using upgraded and improved input datasets such as the SSM/I V6 product (i.e., Tb, W, U) and the NCEP/DOE R2 product (i.e., SKT, Tair, and SLP). The input datasets previously applied for producing GSSTF2 were the SSM/I Version-4 (V4) product and the NCEP Reanalysis-1 (R1) product. Thanks to the basically improved quality of the aforementioned input parameters, GSSTF2b was found to generally agree better with available ship measurements obtained from several field experiments in 1999 than GSSTF2 did in all three flux components, LHF, SHF, WST (Shie 2010a,b). GSSTF2b was also found favorable, particularly for LHF and SHF, in an intercomparison study that accessed eleven products of ocean surface turbulent fluxes, in which GSSTF2 and GSSTF2b were also included (Brunke et al. 2011). Shie (2010a,b) equated such a genuine improvement in the GSSTF product (i.e., from GSSTF2 to GSTF2b) to an insightful Rice Cooker Theory : To produce a good and trustworthy output product (delicious cooked rice ) depends not only on a well-functioned model/algorithm ( rice cooker ), but also on a genuine and reliable input data ( raw rice ) with good quality. The Rice Cooker Theory should further help us better comprehend the impact of the brightness temperature on the subsequently retrieved surfaces latent heat flux from the very recently upgraded version, i.e., GSSTF2c, that is addressed as follows. A temporal trend appeared in the globally averaged LHF of GSSTF2b, particularly post year Shie (2010a,b) attributed the LHF trend to the trends originally found in the globally averaged SSM/I Tb s, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b WB, then Qa, and subsequently LHF. The SSM/I Tb s trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb s by genuinely removing the artifactitious trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb s has been completed very recently. GSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF -- the temporal trends of WB and LHF are genuinely reduced after the proper adjustments/treatments in the SSM/I Tb s. The GSSTF Model that includes the WB-Tb relation (Schulz et al. 1993) and the bulk flux algorithm (Chou et al., 2003) used for producing both GSSTF2b and GSSTF2c is described in the following section. The genuine impact of Tb on WB, Qa, and LHF, respectively is studied based primarily on a comparison of GSSTF2b and GSSTF2c that is presented in section of Results and Discussions followed by an acknowledgement in the last section. THE GSSTF MODEL The instantaneous/daily surface specific humidity Qa is retrieved from the instantaneous/daily total precipitable water W and bottom-layer (the lowest atmospheric 500 meter layer) precipitable water WB using an Empirical Orthogonal Function (EOF) algorithm (not shown here), while Qa depends highly on WB yet weakly on W (details can be found in Chou et al.1995 and Chou et al. 1997). The daily WB is derived from the SSM/I Tb based on the WB-Tb relation by Schulz et al. (1993): WB = b 0 + b 1 Tb(19v) + b 2 Tb(19h) + b 3 Tb(22v) + b 4 Tb(37v) (1)

3 where WB in gcm -2, Tb in deg K, and b 0, b 1, b 2, b 3, and b 4 are , , , and , respectively. The quality of Tb s directly affects the performance of WB that has been one major focal point addressed throughout this paper. The bulk flux model used for producing the GSSTF2b and GSSTF2c fluxes is essentially the same as that for GSSTF2 (Chou et al., 2003). Similar to GSSTF2, GSSTF2b requires the same methodology and same kinds of input data such as the SSM/I surface/10-m wind speeds, total precipitable water, bottom-layer precipitable water, and the NCEP-NCAR reanalysis of sea surface skin temperature, 2-m air temperature, and sea level pressure. The air-sea turbulent fluxes, i.e., wind stress (τ), sensible heat flux (SHF), and latent heat flux (LHF) can be given in the following bulk aerodynamic formula: τ = ρ C D (U Us) 2, SHF = ρ Cp C H (U Us) (θs θa), LHF = ρ Lυ C E (U Us) (Qs Qa), (2a) (2b) (2c) where ρ is air density, Cp the isobaric specific heat, Lυ the latent heat of vaporization, C D, C H, C E the three respective bulk transfer coefficients, and Us is the negligibly small ocean surface current (about 0.55 of frictional velocity). The input parameters are the wind speed (U), the sea surface temperature (θs), the air potential temperature (θa), the specific humidity (Qa) at the reference height, and the saturation specific humidity (Qs) at the sea surface temperature. For a given θs (or SKT) and wind, temperature, and humidity at the measurement or reference heights within the atmospheric surface layer, the scaling parameters are solved through the roughness lengths and dimensionless gradients of wind, temperature, and humidity. The dimensionless gradients of wind, potential temperature, and humidity are functions of the stability parameter z/l, where z is the measurement height, and L the Monin Obukhov length, which depends on the scaling parameters or fluxes (detailed description can be found in Chou et al. 2003). Accordingly, the transfer coefficients, which reflect the efficiency of the vertical transportation of momentum, heat, and moisture flux, are a non-linear function of the vertical gradient in wind speed, temperature and water vapor near the surface and, therefore, are affected by the stability of the surface air. RESULTS AND DISCUSSIONS As aforementioned, GSSTF2c (Shie et al. 2011) using the corrected Tb s (Hilburn and Shie 2011a,b) has been completed very recently. A brief description of the GSSTF2c dataset and the general features of the three major fluxes, i.e., latent heat flux, sensible flux, and wind stress are presented in the first subsection. The impact of the brightness temperature on the retrieved GSSTF parameters such as bottom-layer precipitable water, surface specific humidity and latent heat flux by comparing the GSSTF2c and GSSTF2b is presented in the second subsection. The GSSTF2c dataset: The GSSTF2c datasets (July 1987-December 2008) consist of daily, monthly, and monthly and yearly climatology 1 latitude x 1 longitude gridded global (90 N to 90 S) ocean surface latent heat flux, sensible heat flux, zonal and meridional wind stresses, surface (10-m) air specific humidity, lowest 500- m precipitable water, 10-m wind speed, and sea-air humidity difference from July 1987 to December A general feature of the three major fluxes can be genuinely demonstrated via a global climatology. Figure 1 shows the GSSTF2c yearly global climatology (over ) of WST, LHF, and SHF, respectively. The maximum wind speed and stress are located in the trade wind belts and extratropical storm track regions, while the minimum wind speed and stress are located in the weak wind areas of the intertropical convergence zone, South Pacific convergence zone, and tropical Indian Ocean, as well as the subtropical highs (Fig. 1a). The maximum LHF is located in the trade wind belts and in the western boundary current regions of Kuroshio (the Pacific Ocean) and the Gulf Stream (the Atlantic Ocean) due to a hybrid coupling of high winds and large air-sea humidity difference, i.e., Qs-

4 Qa (Fig. 1b). The minimum LHF occurs in the eastern equatorial Pacific and Atlantic Oceans due to the upwelling-induced cold sea surface temperature (SST) associated with weak winds, and in the high latitudes due to the poleward decreased SST. The SHF is generally very small, as compared to LHF, due to a commonly small difference between SST and Tair, except for slightly larger fluxes in the northwestern regions of the North Pacific and North Atlantic Oceans arising from cold air outbreaks, e.g., during the winter season for Northern Hemisphere, which is not particularly shown here (Fig. 1c). (a) (b) (c) Figure 1: The GSSTF2c yearly climatology of (a) wind stress, (b) latent heat flux, (c) sensible heat flux averaged over Arrows indicate wind stress direction.

5 The impact of Tb on WB, Qa and LHF: The GSSTF2c (July 1987-Dec 2008) studied here is a complete, upgraded version following the GSSTF2b by applying the corrected SSM/I V6 Tb s with proper treatments for the SSM/I EIA variations for each individual satellite. In a previous study performed earlier this year involving the partially produced GSSTF2c ( ) at the time, Shie and Hilburn (2011) showed a preliminary, yet promising result that the temporal trends of the globally averaged WB, Qa and LHF were genuinely reduced among the individual satellites accessed (e.g., F11, F13, and F14) due to the reduced Tb trends after the corrections of EIA. As the entire GSSTF2c (i.e., the combined dataset based on all the individual satellite retrieves) has now been accessed, the Tb impact on the retrieved GSSTF parameters are extensively examined and presented here. Figure 2 shows that the time series of the two domain averaged (i.e., quasi-global and tropical Pacific in the upper and lower panel, respectively) parameters, i.e., WB (Fig. 2a), Qa (Fig. 2b), and LHF (Fig. 2c) are significantly affected by Tb for both GSSTF2b (including Set1 & Set2) and GSSTF2c during Note that Set2 was the second dataset of GSSTF2b that certain individual satellite retrievals causing relatively higher trends in LHF (mostly post 1998) were artificially removed from the original/first set of GSSTF2b, i.e., Set1. Consequently, Set2 possesses smaller trends than Set1 post 1998 (Set2 is basically identical to Set1 prior to 1997), yet at the expense of generating more missing data (Shie 2010a). In Fig. 2a, the WB (retrieved directly from Tb based on Eq. 1) of GSSTF2c (in green) shows a much smaller decreasing trend than GSSTF2b-Set1 (in red) post 2000, while GSSTF2c also shows a smaller decreasing trend than both GSSTF2b-Set1 & Set2 (in blue) during (as Set1 and Set2 are identical) for both regions of quasi-global and tropical Pacific. The GSSTF2c also shows a promising trend improvement in WB compared to GSSTF2b-Set2 during 2008 since the individual retrievals involving F13 and F15 were effectively improved. For the quasi-global region (0-360 o E, 60 o S-60 o N), WB has negative trends of -5.4% and -2.36% during 21 years (from Jan 1988 to Dec 2008) for GSSTF2b-Set1 & Set2, respectively, based on a linear regression estimate. The negative trend significantly reduces to -0.91% for GSSTF2c. For the tropical Pacific region ( o E, 30 o S-30 o N), WB has the trends of -4.16%, %, and -0.96% for GSSTF2b-Set1, Set2 and GSSTF2c, respectively. Correspondingly, the Qa (highly dependent of WB) shown in Fig. 2b has demonstrated a pattern very close to that of WB shown in Fig. 2a. For the quasi-global region (0-360 o E, 60 o S-60 o N), Qa has negative trends of -4.14% and % during 21 years for GSSTF2b-Set1 & Set2, respectively. The negative trend significantly reduces to -0.65% for GSSTF2c. For the tropical Pacific region ( o E, 30 o S-30 o N), Qa has the trends of -3.44%, -1.73%, and -0.75% for GSSTF2b-Set1, Set2 and GSSTF2c, respectively. Qa has the decreasing trends that are comparable to those of the WB, but slightly smaller in magnitude, since Qa is highly dependent of WB, but also partially dependent of W (total precipitable water) that generally has no or negligible trends. The LHF depends on a combined effect of wind speed (U) and Qs-Qa that may lead to a more complicated trend pattern than Qa or WB, yet LHF may be reasonably assumed as inversely depending on Qa or WB. In Fig 2c, GSSTF2c (in green) shows a much smaller increasing trend than GSSTF2b- Set1 (in red) post 2000, while GSSTF2c also shows a smaller increasing trend than both GSSTF2b- Set1 & Set2 (in blue) during for both regions of quasi-global and tropical Pacific. The GSSTF2c also shows a promising trend improvement in LHF compared to GSSTF2b-Set2 during For the quasi-global region (0-360 o E, 60 o S-60 o N), LHF has positive trends of 23.15% and 15.6% during 21 years (from Jan 1988 to Dec 2008) for GSSTF2b-Set1 & Set2, respectively, based on a linear regression estimate. The trend significantly reduces to 10.96% for GSSTF2c. For the tropical Pacific region ( o E, 30 o S-30 o N), LHF has the trends of 20.02%, 14.01%, and 10.00% for GSSTF2b- Set1, Set2 and GSSTF2c, respectively. The magnitude of the increasing trend of LHF is considerably larger than that of the decreasing trend of WB or Qa since LHF, as mentioned earlier, involves a nonlinear relationship between U and Qs-Qa, or more accurately, among U, Qa, and Qs. Potentially, any possible increasing trend of U, SST (Qs), or combined could all play a critical role by multiplying the existing Qa (due to WB) trend (even of a small magnitude) into a larger trend in LHF. We have successfully demonstrated the genuinely reduced trends of WB, Qa (both negative) and LHF (positive) in GSSTF2c, which has been produced using the properly corrected SSM/I Tb s. However, whether the still existing trends found in the GSSTF2c parameters are realistic or still partially artifacts? It may require us to further perform more follow-up studies before we may be ably getting closer to answer that question involving a very complicated and sensitive trend issue.

6 (a) (b) (c) Figure 2: Time series of the globally averaged interpolated (a) WB (g cm -2 ), (b) Qa (g kg -1 ) and (c) LHF (W m -2 ) for GSSTF2b-Set1 (in red), GSSTF2b-Set2 (in blue), and GSSTF2c (in green), respectively, from Jan 1988 to Dec 2008.

7 ACKNOWLEDGEMENT The first author would like to dedicate this paper to his mentor: the late research scientist S.-H. Chou (aka Sue). Without her genuine intelligence, intuition, great vision, and perseverance, even the lately revived GSSTF2b and GSSTF2c products would have not been possible. Thanks also go to L. Chiu, R. Adler, I. Lin, E. Nelkin, and J. Ardizzone for their crucial contributions, particularly in the production of GSSTF2b. The first author also owes a special thanks to A. Savtchenko for his precious help in converting the GSSTF2b and GSSTF2c datasets from the original binary format into the HDF-EOS5 format before their official distributions via NASA GES DISC. This study is supported by the MEaSUREs Program of NASA Science Mission Directorate-Earth Science Division. The first author is especially grateful to its program manger M. Maiden and program scientist J. Entin for their valuable supports of this research. REFERENCES Brunke, A. M., Z. Wang, X. Zeng, M. Bosilovich, and C.-L. Shie, (2011) An assessment of the uncertainties in ocean surface turbulent fluxes in 11 reanalysis, satellite-derived, and combined global data sets. J. Climate, (in press) Chou, S.-H., R. M. Atlas, C-L. Shie and J. Ardizzone, (1995) Estimates of Surface Humidity and Latent Heat Fluxes over Oceans from SSM/I Data. Monthly Weather Review, 123, pp Chou, S.-H., C-L. Shie, R. M. Atlas, and J. Ardizzone, (1997) Air-sea Fluxes Retrieved from Special Sensor Microwave Imager Data. Journal of Geophysical Research, 102, No. C6, pp Chou, S.-H., E. Nelkin, J. Ardizzone, R. Atlas, and C.-L. Shie, (2003) Surface turbulent heat and momentum fluxes over global oceans based on the Goddard satellite retrieval, version 2 (GSSTF2). J. Climate, 16, pp Hilburn, K. A., and C-L. Shie, (2011a) The impact of incidence angle variations on climate trends from simple water vapor retrieval algorithms. The 2011 EUMETSAT Meteorological Satellite Conference, Oslo, Norway, 5-9 September Hilburn, K. A., and C.-L. Shie, (2011b) Decadal trends and variability in Special Sensor Microwave Imager (SSM/I) brightness temperatures and Earth incidence angle. Report number , Remote Sensing Systems, 53 pp Schultz, J., P. Schluessel, and H. Grassl, (1993) Water vapor in the atmospheric boundary layer over oceans from SSM/I measurements. Int. J. Remote Sensing, 14, pp Shie, C.-L., L. S. Chiu, R. Adler, P. Xie, I-I Lin, F.-C. Wang, E. Nelkin, R. Chokngamwong, W. S. Olson, and D. A. Chu, (2009) A note on reviving the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) dataset. Advances in Atmospheric Sciences, 26(6), pp Shie, C.-L., L. S. Chiu, R. Adler, I-I Lin, E. Nelkin, and J. Ardizzone, (2010) The Goddard Satellite- Based Surface Turbulent Fluxes Dataset --- Version 2b (GSSTF2b). Distributed via NASA GES DISC in October GSSTF2b of HDF-EOS5 format can be downloaded from ftp://aurapar1u.ecs.nasa.gov/data/s4pa/gsstf/ or Shie, C.-L., (2010a) Science background for the reprocessing and Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2b) Data Set for Global Water and Energy Cycle Research. Science Document for the Distributed GSSTF2b via Goddard Earth Sciences (GES) Data and Information Services Center (DISC), 18 pp Available online: Shie, C.-L., (2010b) A recently revived dataset of satellite-based global air-sea surface turbulent fluxes

8 (GSSTF2b) features and applications. Paper presented at 17th AMS Conference on Satellite Meteorology and Oceanography, Annapolis, MD Available online: Shie, C.-L., and K. Hilburn, (2011) A newly revived satellite-based global air-sea surface turbulent fluxes dataset and its dependence on the SSM/I brightness temperature. The 2011 IEEE IGARSS, Vancouver, Canada, July. Extended Abstract, 4 pp, Proceedings (published in CD) Shie, C.-L., K. A. Hilburn, L. S. Chiu, R. Adler, I-I Lin, E. Nelkin, and J. Ardizzone, (2011) The Goddard Satellite-Based Surface Turbulent Fluxes Dataset --- Version 2c (GSSTF2c). GSSTF2c of HDF-EOS5 format scheduled for official distribution via NASA GES DISC by October 2011