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Krzysztof Wargan and Lawrence Coy

systematic quantitative discrepancies between the TIL’s strength in these different datasets. The paper is organized as follows. Section 2 describes MERRA-2 and provides definitions of the tropopause, static stability, and TIL’s strength used in this study. The results of reanalysis diagnostics and model forecast experiments, including the 2009 SSW case study and a discussion of other recent major SSW events are presented in section 3 . Section 4 is devoted to discussion and conclusions. 2. Data and

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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

analyzed aerosol fields that are radiatively coupled to the atmosphere. To our knowledge, this is the first multidecadal reanalysis within which meteorological and aerosol observations are jointly assimilated into a global assimilation system, although other operational forecasting centers are actively developing similar capabilities (e.g., Benedetti et al. 2009 ; Sekiyama et al. 2010 ; Lynch et al. 2016 ). Previously, the GMAO had performed an offline aerosol reanalysis (the MERRA Aerosol

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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

; Lynch et al. 2016 ). Moreover, on regional scales, several studies have shown positive impacts of assimilation of satellite and/or ground-based aerosol observations on air quality forecasts ( Li et al. 2013 ; Chen et al. 2014 ; Schwartz et al. 2014 ; Saide et al. 2014 ; McHenry et al. 2015 ). In addition to assimilating bias-corrected Moderate Resolution Imaging Spectroradiometer (MODIS) AOD from both the Terra and Aqua satellites, MERRA-2 includes assimilation of bias-corrected AOD from the

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Kevin Hodges, Alison Cobb, and Pier Luigi Vidale

the last 100 yr ( Landsea 2007 ; Landsea et al. 2009 ), resulting in uncertainty in the interannual variability and trend detection. The use of reanalyses to detect TCs provides an opportunity to reduce this uncertainty ( Truchelut et al. 2013 ), by allowing the creation of a larger data sample that, when used in conjunction with the historic observational data, can help to provide more confidence in TC numbers than the observations alone. Reanalyses combine observations with a short forecast

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

; Kobayashi et al. 2015 ) that blend diverse measurements of wind, moisture, and temperature as well as other observations with first-guess estimates from model short-term forecasts. While reanalyses effectively reconcile observations with physically based dynamical models, there are a number of practical problems that result in moisture transport fields typically having substantial systematic time-dependent biases ( Trenberth et al. 2011 ; Robertson et al. 2011 ; Lorenz and Kunstmann 2012 ; Trenberth

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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

1. Introduction Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of meteorological observations, typically spanning an extended segment of the historical data record. The process relies on an underlying forecast model to combine disparate observations in a physically consistent manner, enabling production of gridded datasets for a broad range of variables, including ones that are sparsely or not directly observed. As such, and

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

Niño was forecasted for 2014/15 boreal winter because of the strong WWB and substantial buildup of warm water volume over the equatorial Pacific in early 2014 ( McGregor et al. 2016 ; McPhaden 2015 ). However, this El Niño did not develop as expected, primarily due to an unexpected occurrence of a large easterly wind burst that hindered further growth of El Niño in summer ( Min et al. 2015 ; Levine and McPhaden 2016 ). The 2014 forecast has thus been described as a “busted” forecast ( Larson and

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Lawrence Coy, Krzysztof Wargan, Andrea M. Molod, William R. McCarty, and Steven Pawson

circulation in a longer record (1992–99) of the Met Office assimilation for UARS , though the vertical component of the circulation was scaled by a factor of 1.4 to account for the underestimate of the QBO temperature signal in the assimilation system. Pawson and Fiorino (1998) examined the QBO in the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA). They found

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Lawrence Coy, Paul A. Newman, Steven Pawson, and Leslie R. Lait

analyses provide an opportunity for comparing their representation of the tropical zonal mean momentum budget during the QBO disruption. Here, we use a ±5° latitude average and examine the same momentum budget terms for MERRA-2 as presented in Osprey et al. (2016 , their Fig. 2b) for the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis. Four terms of the 40-hPa zonal mean momentum budget for November 2015 through April 2016 are plotted in Fig. 4 . They consist of the

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

seasonal and geographical variations in the two AR measures, first regionally, then globally. 2. Data and methodology a. Reanalyses and AR detection Global fields of specific humidity and vector winds are provided by two reanalysis products, namely, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011 ) and the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA

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