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Bin Guan, Duane E. Waliser, and F. Martin Ralph

individual AR transports horizontally roughly 5 × 10 8 kg s −1 of water (as vapor), which is comparable to 27 times the discharge of water (as liquid) by the Mississippi River into the Gulf of Mexico. However, because the study had “only” 21 cases, a question remained as to how representative these cases are of ARs more globally. The current study takes advantage of this unprecedented set of airborne observations by conducting an intercomparison of key AR characteristics calculated from the dropsonde

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Richard I. Cullather and Sophie M. J. Nowicki

also important to consider results in the context of the physical processes involved. This serves as an additional motivating factor in examining individual ice sheet melt events in more detail. Fig . 1. (a) Greenland surface elevation from Bamber et al. (2001) contoured every 100 m. Boundaries of major GrIS drainage basins of Zwally et al. (2012) are indicated and numbered. (b) Time series of daily melt area derived from passive microwave data ( Mote, 2007 ) corresponding to each basin for 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

. Nevertheless, despite some deficiencies, previous studies (e.g., Buchard et al. 2015 , 2016 ), the current study, and a companion evaluation paper ( Buchard et al. 2017 , hereinafter Part II ) demonstrate that the aerosol assimilation system does indeed show considerable skill in simulating numerous observable aerosol properties. The purpose of this study is to describe the MERRA-2 aerosol data assimilation system, provide initial validation of the analyzed AOD fields, and suggest applications of the

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V. Buchard, C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, and H. Yu

) in the model ( Buchard et al. 2015 ). MERRA-2 uses the same OC optical properties as this previous work. We are currently evaluating revised optical tables for biomass burning aerosol to account for “brown carbon” absorption in the UV (e.g., Hammer et al. 2016 ). Finally, compared with Buchard et al. (2015) , the MERRA-2 388-nm AAOD shows improved agreement with OMI, particularly in parts of Asia and over the Indo-Gangetic plain in India, regions impacted by complex mixtures of both dust and

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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

by the corrected estimates, although they can be indirectly modified through subsequent feedback with the land surface. h. Sea surface temperature and sea ice concentration The boundary conditions for SST and SIC in MERRA were based on the 1° resolution weekly (or monthly) product of Reynolds et al. (2002) . In MERRA-2, SST and SIC boundary conditions are instead based on currently available high-resolution (finer than 1° resolution) daily products. However, as there exists no continuous source

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

product ( Reichle and Liu 2014 ). This rescaling of the CMAP data simply merges two observationally based products and should not be confused with the correction algorithm that merges the observations with the model-based background ( section 2c ). GPCPv2.1 was the most current version when the preparation of MERRA-2 inputs began; it differs over land from GPCPv2.2 mainly in the version of the input gauge data from the Global Precipitation Climatology Centre. Rescaling the CMAP data with the GPCPv2

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Kevin Hodges, Alison Cobb, and Pier Luigi Vidale

beyond, which is useful for TC-related extratropical risk analysis and GCM assessment ( Haarsma et al. 2013 ). The use of reanalysis could also assist in the identification of subtropical and hybrid tropical storms ( Roth 2002 ; Guishard et al. 2009 ), which are also associated with severe weather, providing a more complete set of tropical storm data for use in GCM assessment than is perhaps currently present in best-track data; the inclusion of these types of storms in the best-track datasets is

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Allison B. Marquardt Collow and Mark A. Miller

in order to reduce temporal and spatial mismatches in the data between the two boundaries in their study area, a 3-h average was required. Previous studies have shown the radiation budget, and its controls in one tropical location may not be indicative of that in another. A lack of comprehensive knowledge of the tropical climate is a weakness in our understanding of the global energy budget, so more detailed studies, such as the current one, are desired. Collow et al. (2016a) , Miller et al

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

the reanalyses’ surface radiation fields for JJA against gridded observations from the Clouds and the Earth’s Radiant Energy System (CERES) and Energy Balanced and Filled (EBAF) dataset ( Kato et al. 2013 ). Finally, to test whether the changes in the surface energy budget from MERRA to MERRA-2 have affected the atmospheric boundary layer, we also evaluate the JJA monthly mean daily minimum and maximum against observations from the Climatic Research Unit (CRU) at the University of East Anglia

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

boundary forcing. 2. Data a. Reanalyses Presently there are several contemporary reanalysis datasets and different configurations of reanalyses that can provide context for the changes in the water cycle from MERRA to MERRA-2. ERA-Interim is the latest from ECMWF ( Dee et al. 2011 ), and it supersedes their previous reanalysis (ERA-40; all acronyms are listed in appendix B ). Likewise, The Japanese 55-year Reanalysis ( Ebita et al. 2011 ; Kobayashi et al. 2015 ) supersedes the previous JRA-25

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