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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

moisture ( section 3b ), snow ( section 3c ), streamflow ( section 3d ), and interception loss fraction ( section 3e ) are evaluated against independent data. Finally, section 4 provides a summary of the findings and conclusions. 2. Data a. The MERRA-2 data product and system 1) Overview The MERRA-2 reanalysis is produced by the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO) using the GEOS-5.12.4 system ( Bosilovich et al. 2015 , 2016 ; Gelaro et

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

1. Introduction Moisture transport to land from the global oceans is a crucial process linking the global water and energy cycles and is also at the heart of societal concerns regarding terrestrial water availability, food security, exposure to extreme weather events, and climate change. Recent best estimates of the net atmospheric transport of water to land ( Rodell et al. 2015 ) put the climatological amount at 45.8 ± 6.7 × 10 3 km 3 yr −1 , or about 40% of precipitation falling over land

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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

et al. 2011 ; Wu et al. 2012 ; Teng et al. 2013 ; Kornhuber et al. 2019 ; Röthlisberger et al. 2019 ). Lehmann and Coumou (2015) reported a link between storm track activity and heat extremes. For daytime heat waves, land–atmosphere interactions are also highly relevant, as daytime heat leads to depletion of soil moisture and a subsequent reduction in evaporative cooling ( Fischer et al. 2007 ; Miralles et al. 2014 ). Thus, droughts and heat waves are often linked, although the strength of

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

-generated precipitation at the land surface during the atmospheric model integration. The use of observed precipitation in MERRA-2 was refined from the approach used for MERRA-Land ( Reichle et al. 2017b ), which was an offline (land only) replay of MERRA forced by atmospheric fields from MERRA but with the precipitation forcing corrected using gauge-based observations. The motivation for using observed precipitation in reanalyses is that precipitation is the main driver of soil moisture, which in turn controls the

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Allison B. Marquardt Collow and Mark A. Miller

the day and one at night. 3. Results a. Moisture, clouds, and aerosols Meteorological contrasts between the wet and dry seasons, as shown by Collow et al. (2016b) , can be linked to seasonal variations in the radiation budget at the TOA, at the surface, and within the atmospheric column. Figures 5 and 10 from Collow et al. (2016b) have been expanded upon to include a comparison to MERRA-2 and can be seen in Fig. 1 . A seasonal cycle is present in the observations of atmospheric moisture, with

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Allison B. Marquardt Collow, Michael G. Bosilovich, and Randal D. Koster

available at a spatial resolution of 0.625° longitude × 0.5° latitude, and daily averages ending at 1200 UTC were computed to match the temporal resolution of the precipitation observations. MERRA-2 features additional observing systems and improvements to the hydrological cycle not present in the original MERRA ( Rienecker et al. 2011 ), including the forcing of the land surface with observation-based precipitation fields and the implementation of a moisture constraint, preventing an imbalance in

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

substantial, and the effect of the observations on the interannual variability is apparent. This demonstrates the uncertainty of the reanalyses, even for globally averaged values. However, Trenberth et al. (2011) note that moisture divergence determined from water vapor transport is more robust compared to that determined from the water flux terms (evaporation and precipitation). ERA-Interim, with a smaller estimated increment, produces tropical atmospheric divergence in line with merged evaporation and

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

-generated precipitation, however, is corrected with precipitation observations before reaching the land surface. Observation-corrected precipitation was also used in the Climate Forecast System Reanalysis (CFSR; Saha et al. 2010 ; Meng et al. 2012 ) and in MERRA-Land ( Reichle et al. 2011 ; Reichle 2012 ). The latter is an offline, land-only reanalysis product. It provides significantly better land surface moisture storage dynamics than the original MERRA product because the MERRA-Land precipitation forcing was

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Richard I. Cullather and Sophie M. J. Nowicki

communicating melt energy to the ice sheet surface via increased downwelling longwave fluxes. Recently, Tedesco et al. (2016a) noted the role of Arctic high pressure systems in producing increased melt over the northern regions of the GrIS during the 2016 season. The large-scale atmospheric circulation may produce increased melt by providing cloudless conditions that enhance solar radiation, by the advection of warm air over the ice sheet ( Fettweis et al. 2011 ), by providing moisture and cloud cover

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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

variable for moisture used in recent versions of GSI and MERRA-2 differs from the one used in MERRA. Whereas MERRA used the so-called pseudorelative humidity ( Dee and da Silva 2003 ) defined by the water vapor mixing ratio scaled by its saturation value, MERRA-2 uses the normalized pseudorelative humidity ( Holm 2003 ) defined by the pseudorelative humidity scaled by its background error standard deviation. The latter has a near Gaussian error distribution, making it more suitable for the minimization

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