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

global precipitation and surface evaporation ( Reichle and Liu 2014 ; Takacs et al. 2016 ). MERRA-2 also features numerous developments in the underlying model ( Molod et al. 2015 ), such as in the surface layer and boundary layer parameterizations and in the cumulus convection scheme. The data assimilation has been updated to the latest Gridpoint Statistical Interpolation analysis scheme version and includes global dry mass constraints that help minimize spurious temporal variability effects

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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

1. Introduction Retrospective analysis (reanalysis) data products are based on the assimilation of a vast number of in situ and remote sensing observations into an atmospheric general circulation model (AGCM) and provide global, subdaily estimates of atmospheric and land surface conditions across several decades. The recent Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2016, manuscript submitted to J. Climate ), provides data beginning in

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Young-Kwon Lim, Robin M. Kovach, Steven Pawson, and Guillaume Vernieres

. Geophys. Res. Lett. , 36 , L12705 , doi: 10.1029/2009GL038847 . 10.1029/2009GL038847 Bechtold , P. , M. Köhler , T. Jung , F. Doblas-Reyes , M. Leutbecher , M. J. Rodwell , F. Vitart , and G. Balsamo , 2008 : Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales . Quart. J. Roy. Meteor. Soc. , 134 , 1337 – 1351 , doi: 10.1002/qj.289 . 10.1002/qj.289 Bell , G. D. , M. Halpert , and M. L’Heureux , 2016 : ENSO and

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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

atmosphere, DIAL/HSRL retrieves aerosol profiles above and below the aircraft. Figure 3 compares the campaign median extinction and backscatter profiles from the various phases of the DISCOVER-AQ and SEAC 4 RS campaigns to MERRA-2 profiles collocated along the aircraft trajectories. In general, given the variability of the profiles encountered (shading) and the model resolution, MERRA-2 shows good agreement with the observed vertical profiles of extinction and backscatter over the continental United

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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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Krzysztof Wargan and Lawrence Coy

-2 and in the model. The 2009 SSW was chosen for this case study because the increase of the TIL’s strength associated with it was exceptionally strong compared to other recent SSW events. In a later part of the paper, we place the analysis in the broader context of other boreal winters in the past decade. Older data assimilation systems failed to represent the TIL correctly ( Birner et al. 2006 ) owing to excessive vertical smoothing. However, more recent reanalyses do reproduce the near

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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

is not completely eliminated due in part to the influence of other sensors on the analyzed AOD. During the EOS period, analysis statistics are also slightly better over ocean compared to land, due to a combination of observability (fewer observations over land versus ocean) and a prevalence of aerosol source regions over land (greater aerosol type variability) ( Gelaro et al. 2017 ; Randles et al. 2016 ). Generally, forecasted AOD departs only slightly from collocated assimilated observations

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

be introduced in section 2d ). CPCU precipitation (again, used directly in MERRA-Land and MERRA-2) and a dataset based on CRU data ( Jung et al. 2011 ) are used as predictive (regression) variables in the MTE. However, these meteorological data have little impact on the MTE monthly anomalies, which are instead driven by the vegetation variability as observed by the fraction of absorbed photosynthetically active radiation (fPAR; Jung et al. 2010 ). When 20% of the FLUXNET training data was

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