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Natalie P. Thomas, Michael G. Bosilovich, Allison B. Marquardt Collow, Randal D. Koster, Siegfried D. Schubert, Amin Dezfuli, and Sarith P. Mahanama

Applications, version 2 (MERRA-2; Gelaro et al. 2017 ), is the primary tool used in this analysis. Hourly data from MERRA-2 are available at a spatial resolution of 0.625° longitude by 0.5° latitude starting in January 1980. MERRA-2 is a global atmospheric reanalysis with a variety of updates relative to the original MERRA ( Rienecker et al. 2011 ). Among these is the inclusion of an observation-driven precipitation field to force the land surface ( Reichle et al. 2017a ). An evaluation of the MERRA-2

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Rolf H. Reichle, Clara S. Draper, Q. Liu, Manuela Girotto, Sarith P. P. Mahanama, Randal D. Koster, and Gabrielle J. M. De Lannoy

Earth Observing System, version 5 (GEOS-5), AGCM. Another new element in MERRA-2 is its use of observations-based precipitation data products to drive the land surface water budget (and aerosol wet deposition). Precipitation is the dominant driver of land surface hydrologic conditions. In most reanalysis systems, including the original MERRA, the precipitation seen by the land surface is generated by the system’s AGCM following the assimilation of atmospheric observations. The model

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Rolf H. Reichle, Q. Liu, Randal D. Koster, Clara S. Draper, Sarith P. P. Mahanama, and Gary S. Partyka

1. Introduction Retrospective analysis (reanalysis) data products provide global, subdaily estimates of atmospheric and land surface conditions across several decades. Such products are based on the assimilation of a large amount of in situ and remote sensing observations into an atmospheric general circulation model (AGCM) and are among the most widely used datasets in Earth science. The recent Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al

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Franklin R. Robertson, Michael G. Bosilovich, and Jason B. Roberts

series a 3-month running smoother has been applied for display purposes. Table 1. Trend statistics (mm day −1 decade −1 ) for VMFC* over land for various reanalyses and P − ET for LSM members over the period 1979–2012. In parentheses are errors calculated using lag-1 statistics to account for serial autocorrelation. LSMs and other related diagnostic models constrained by observations of precipitation, near-surface atmospheric variables, and radiation offer an independent estimate of terrestrial

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Michael G. Bosilovich, Franklin R. Robertson, Lawrence Takacs, Andrea Molod, and David Mocko

-observation reanalyses has been documented where the global ocean to land moisture transport increases over time in satellite data reanalyses much more than in the other estimates ( Robertson et al. 2014 ). Robertson et al. (2014) estimated global moisture transport from ocean to land using three multiple sources: evaporation minus precipitation ( E − P ) over the global oceans, reanalysis moisture flux convergence over land, and observationally constrained land surface model precipitation minus evaporation ( P

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Clara S. Draper, Rolf H. Reichle, and Randal D. Koster

1. Introduction The NASA Global Modeling and Assimilation Office recently released the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2; Gelaro et al. 2017 ). This new global reanalysis product replaces and extends the original MERRA atmospheric reanalysis ( Rienecker et al. 2011 ), as well as the MERRA-Land reanalysis ( Reichle et al. 2011 ). In addition to several other major advances, MERRA-2 uses observed precipitation in place of model

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Laura M. Hinkelman

, but also at averages over the land and ocean. Other work by Wang and Dickinson (2013) concentrated on LW fluxes, while Dolinar et al. (2016) evaluated TOA cloud radiative effect, cloud amount, and precipitation using satellite, reanalysis, and surface observational data. Evaluations focused on specific regions have also been carried out. These include Cullather and Bosilovich (2012) , looking at the polar energy budgets; Zib et al. (2012) , comparing surface radiative fluxes and cloud

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Ronald Gelaro, Will McCarty, Max J. Suárez, Ricardo Todling, Andrea Molod, Lawrence Takacs, Cynthia A. Randles, Anton Darmenov, Michael G. Bosilovich, Rolf Reichle, Krzysztof Wargan, Lawrence Coy, Richard Cullather, Clara Draper, Santha Akella, Virginie Buchard, Austin Conaty, Arlindo M. da Silva, Wei Gu, Gi-Kong Kim, Randal Koster, Robert Lucchesi, Dagmar Merkova, Jon Eric Nielsen, Gary Partyka, Steven Pawson, William Putman, Michele Rienecker, Siegfried D. Schubert, Meta Sienkiewicz, and Bin Zhao

assimilated within a global data assimilation system. Other new developments in MERRA-2 relevant to IESA focus on aspects of the cryosphere and stratosphere, including the representation of ozone, and on the use of precipitation observations to force the land surface. At the same time, basic aspects of the MERRA-2 system, such as the variational analysis algorithm and observation handling, are largely unchanged since MERRA. Also unchanged is the preparation of most conventional data sources used

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Kevin Hodges, Alison Cobb, and Pier Luigi Vidale

coupled atmosphere–ocean–land surface–sea ice reanalysis. NCEP-CFSR, MERRA, and MERRA-2 all use different versions of the 3D variational data assimilation (3D-Var) scheme: the Grid-point Statistical Interpolation (GSI) scheme ( Shao et al. 2016 ). For MERRA and MERRA-2 the Incremental Analysis Update (IAU; Bloom et al. 1996 ; Rienecker et al. 2011 ) system is also used. The data period used for all the reanalyses is 1979–2012, except for MERRA-2, which starts in 1980. A key difference between the

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C. A. Randles, A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y. Shinozuka, and C. J. Flynn

retrievals over the oceans. After 2000, we assimilate bias-corrected AOD derived from MODIS level-2 radiances, first from the Terra spacecraft (10:30 local solar time equator crossing) and after 2002 also from the Aqua spacecraft (13:30 local solar time equator crossing). In both cases, we use the same radiances that are provided with operational MODIS retrievals ( Levy et al. 2007 ). Over land we use the radiances from the MODIS “Dark Target” land algorithm that are not available over bright

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