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

the formulation of the budgets is discussed by Rienecker et al. (2007) and Suarez et al. (2011) . 3. Water and energy budgets a. Global mean climatology TFK09 collect the global energy budget data from various sources, observational and reanalyses, and close it with consistency arguments from dataset intercomparisons, to determine estimates for principal energy flux components and balance. However, each term exhibits large variations among the different observing systems and reanalyses, so any

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

the whole globe (90°S–90°N). c. Closure of the MERRA water vapor budget A general circulation model balances its water vapor budget such that its Σ should equal to its predicted P − E . Since data reanalysis assimilates observations, the model will achieve balance among Σ, P − E , and the q tendency from the analysis. This can be observed by the discrepancy between MERRA–MERRA Σ and MERRA P − E by comparing Figs. 3c,f with Figs. 5b,d , respectively. The difference between MERRA P

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

highlighted the relationship between the MERRA trends and the initial availability of Advanced Microwave Sounding Unit-A (AMSU-A) data. The goal of this paper is to provide a more detailed characterization of these time-dependent biases and explore the efficacy of a statistical correction strategy to reduce these artificial trends in water and energy fluxes. After reviewing the data used in the study ( section 2 ), we begin by analyzing the temporal variability of the water vapor and heat budget

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Franklin R. Robertson and Jason B. Roberts

scales, Lin and Mapes (2004) found a systematic reduction in net TOA-absorbed energy over the western Pacific associated with the passage of enhanced deep convection. Spencer et al. (2007) showed this intraseasonal variability to be a robust property of heat balance fluctuations for the tropical ocean domain as a whole. 5. Energy budgets In this section the fluxes and state variables are used to construct water and energy budget variations averaged over the tropical oceans (20°N–S). The

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Michele M. Rienecker, Max J. Suarez, Ronald Gelaro, Ricardo Todling, Julio Bacmeister, Emily Liu, Michael G. Bosilovich, Siegfried D. Schubert, Lawrence Takacs, Gi-Kong Kim, Stephen Bloom, Junye Chen, Douglas Collins, Austin Conaty, Arlindo da Silva, Wei Gu, Joanna Joiner, Randal D. Koster, Robert Lucchesi, Andrea Molod, Tommy Owens, Steven Pawson, Philip Pegion, Christopher R. Redder, Rolf Reichle, Franklin R. Robertson, Albert G. Ruddick, Meta Sienkiewicz, and Jack Woollen

some insights into the system’s performance. Other papers in the series analyze various aspects of the scientific quality of MERRA. For example, Bosilovich et al. (2011) evaluate MERRA from an energy and water budget perspective; Robertson et al. (2011) analyze the effects of the changing observing system on MERRA’s energy and water fluxes; Schubert et al. (2011) highlight the usefulness of MERRA for characterizing the nature and forcing of short-term climate extremes, such as heat waves and

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Brian E. Mapes and Julio T. Bacmeister

lacks almost completely (see Figs. 3g, 4g, 6g, and 7g of Kim et al. 2009 ). That same model (slightly updated but still lacking an MJO) underpins the new Modern-Era Reanalysis for Research and Applications (MERRA) ( Rienecker et al. 2008 , 2011 ). A novelty of the MERRA over earlier reanalyses is that its datasets include comprehensive sets of budget terms for the model state variables (e.g., wind, temperature, moisture, and ozone). These Eulerian budgets balance exactly, by construction: a term

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

radiation fluxes as compared with surface observations. However, while clouds do play a crucial role in the behavior of atmospheric radiation transport, other parameters, including cloud and aerosol properties, sea ice properties, land and ocean surface properties, and columnar loading of gases such as water vapor and ozone, can also have significant impacts on the surface radiation budget. Since clouds are known to vary in their effects on radiation transfer as a function of height, it would be useful

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Kyle F. Itterly and Patrick C. Taylor

timing and intensity unrealistically force the surface water and energy budget, leading to errors in surface runoff and evaporation ( Del Genio and Wu 2010 ). Decker et al. (2012) found significant errors in the diurnal cycle of surface turbulent fluxes in reanalysis models as well. Slingo et al. (2003) evaluated the diurnal cycle of the Hadley Centre Coupled Model, version 3 (HadCM3) GCM over the tropics and found the largest differences between the GCM and observations occur over the Maritime

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

1. Introduction Over the last decade, there has been increasing evidence of a positive contribution of the Antarctic Ice Sheet to global sea level rise ( Allison et al. 2009 ). The corresponding ice mass loss is mainly driven by enhanced ice discharge into the ocean from West Antarctica and the Antarctic Peninsula ( Rignot et al. 2008 ; Pritchard et al. 2009 ); see geographic names in Fig. 1 . There is, however, considerable uncertainty as to how the ice sheet’s surface mass balance (SMB) has

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

Montana (UM)] daily air temperature retrievals and daily observations from the global World Meteorological Organization (WMO) weather station network ( Fig. 1a ); the WMO observations were also used to evaluate the accuracy of VPD from GEOS-4 and MERRA. The Global Energy and Water Cycle Experiment (GEWEX) surface radiation budget (SRB) dataset and daily observations from a variety of in situ stations ( Fig. 1b ) were employed to evaluate solar radiation from GEOS-4 and MERRA. Surface soil moisture

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