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Franklin R. Robertson, Michael G. Bosilovich, Junye Chen, and Timothy L. Miller

prominent at tropical latitudes and also over the southern oceans. Also apparent is the effect of the SSM/I sensors, whose data begin in late 1987 and tend to decrease the drying from other observations in the subtropics but dry the high latitudes, particularly in the SH. The increasing amplitude of the apparent SSM/I-induced changes over the Southern Ocean likely results from the increasing number of those sensors deployed with time during the 1990s. Presumably, the SSM/I and AMSU-A effects in the NH

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Michael G. Bosilovich, Franklin R. Robertson, and Junye Chen

weak bias. The ECMWF and JRA reanalyses have generally weaker cloud effect everywhere (with the exception of some increased cloud effect in ERA40 tropical oceans). In this global evaluation, high-latitude clouds and radiative effects are not apparent. Cullather and Bosilovich (2011a , b ) examine the high-latitude regional water and energy budgets more closely. Fig . 1. Annual differences (1990 − 2001) of MERRA and other reanalyses longwave cloud effect from that of the surface radiation budget

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Kyle F. Itterly and Patrick C. Taylor

large regional variability in the diurnal cycle representation in MERRA and ERA-Interim. The largest NRMSE OLR errors are found in oceanic convective regions, a feature common to both reanalyses. NRMSE OLR values range between 40% and 400% over convective oceans, with the largest values occurring over the ITCZ, the western Pacific, and central Indian Ocean. The magnitude and structure of these maximum errors is similar in ERA-Interim and MERRA. The absolute RMSE OLR (not shown), however, is

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Sun Wong, Eric J. Fetzer, Brian H. Kahn, Baijun Tian, Bjorn H. Lambrigtsen, and Hengchun Ye

1. Introduction Precipitation has a direct impact on society. Changes of precipitation on global and regional scales have long been studied and linked to the changing climate (e.g., Allen and Ingram 2002 ; Dai et al. 1997 ; Sun et al. 2007 ; Trenberth et al. 2003 ). It is important to understand how the global hydrological cycle is changing with increasing atmospheric greenhouse gas loading ( Schneider et al. 2010 ; Stephens and Ellis 2008 ; Trenberth et al. 2003 ) and its potential

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Yonghong Yi, John S. Kimball, Lucas A. Jones, Rolf H. Reichle, and Kyle C. McDonald

surface temperature and soil moisture patterns and is relatively insensitive to solar illumination, clouds, and atmospheric aerosol effects. The AMSR-E sensor onboard the polar orbiting NASA EOS Aqua satellite has 1:30 a.m./p.m. (descending/ascending orbit) equatorial crossings and has been providing global, multifrequency microwave radiometric brightness temperature ( T b ) measurements every 1–3 days since June of 2002. The AMSR-E sensor measures H and V polarization T b at six frequencies

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Franklin R. Robertson and Jason B. Roberts

distributed quantities, indicating that the model physics had no mean bias. Since the previous budget discussion shows that this is not the case, here we explore some significant regional details. Figure 12 shows the vertically integrated increments of enthalpy and water vapor, dhdt-ana and dq v dt-ana, respectively. Implicit in the water and heat balance are also the effects of u - and υ -component momentum forcing. Also shown in Fig. 12 is the divergence of the u - and υ -wind component

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J. Brent Roberts, Franklin R. Robertson, Carol A. Clayson, and Michael G. Bosilovich

, can be found in standard texts ( Stull 1988 ; Kraus and Businger 1994 ). Models vary in the application of surface parameterizations through the choice of several parameters such as roughness lengths, inclusion of ocean-wave effects, and the use of stability functions. Improvements in modeled fluxes can be made through improved estimation of the near-surface variables and/or the improved parameterization of these parameters that effectively control the transfer rates of momentum, heat, and

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David H. Bromwich, Julien P. Nicolas, and Andrew J. Monaghan

well with 157 ± 9 mm yr −1 obtained by Monaghan et al. (2006a) for 1985–2001 with a regional climate model forced at the boundaries by NCEP-2. The observation-based dataset from A06 yields a somewhat lower value (143 ± 4 mm yr −1 ) as a result of lower coastal accumulation. Close to the 155–160 mm yr −1 range are the results from van de Berg et al. (2006) , 166 mm yr −1 for 1980–2002, based on a regional climate model simulation forced by ERA-40, and from Krinner et al. (2007) , 151 mm yr

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Behnjamin J. Zib, Xiquan Dong, Baike Xi, and Aaron Kennedy

the annual average, CFs in MERRA agree best (+2.7% over BAR, −4.9% over NYA) with observations over the two Arctic sites, while those reanalyzed from R2 agree the least (−35.8% over BAR, −17.6% over NYA). The best agreement in CF throughout the reanalyses occurs during the warm season (May–October) with the exception of R2. b. SW-down Seasonal CF biases can have considerable effects on the radiation fields in each of the reanalyses. Surface radiation fluxes play a vital role in the Arctic climate

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Benjamin A. Schenkel and Robert E. Hart

, intensity, and the life cycle of intensity. The importance of properly representing TCs may have implications that extend to accurately depicting their larger-scale environment within reanalyses. While the aggregate impact of TCs upon the coupled climate has remained unquantified, localized effects of TC passage include SST anomalies lasting up to two months after TC passage ( Schenkel and Hart 2010 ) and moisture and temperature anomalies that exist for as long as several weeks ( Hart et al. 2007

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