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–atmosphere–sea ice) model to derive the best estimate of the state of the atmosphere and land surface. The National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)’s Global Modeling and Assimilation Office (GMAO) are three major centers that have recently produced “second generation” reanalysis datasets. While they are the best approximation of the state of
–atmosphere–sea ice) model to derive the best estimate of the state of the atmosphere and land surface. The National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)’s Global Modeling and Assimilation Office (GMAO) are three major centers that have recently produced “second generation” reanalysis datasets. While they are the best approximation of the state of
are used for a variety of applications, including as a source for the development and verification of climate models, forcing data for numerous user models, examining forecast skill, estimation of renewable energy resources, investigation of extreme weather and climatic events, and health risk assessments. These datasets may also be an essential tool for performing studies in data-sparse regions such as the Arctic. Given its unique environmental characteristics and extreme surface conditions
are used for a variety of applications, including as a source for the development and verification of climate models, forcing data for numerous user models, examining forecast skill, estimation of renewable energy resources, investigation of extreme weather and climatic events, and health risk assessments. These datasets may also be an essential tool for performing studies in data-sparse regions such as the Arctic. Given its unique environmental characteristics and extreme surface conditions
are only affected by changes in the observing system ( Thorne and Vose 2010 ). Previous climate-scale studies of TCs utilizing reanalyses have included Hart et al. (2007) who used the 40-year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ) to quantify the environmental “memory” of TC passage. The ERA-40 was also used by Sriver and Huber (2006) to calculate TC power dissipation ( Emanuel 2005 ) to argue that increases in sea surface
are only affected by changes in the observing system ( Thorne and Vose 2010 ). Previous climate-scale studies of TCs utilizing reanalyses have included Hart et al. (2007) who used the 40-year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ) to quantify the environmental “memory” of TC passage. The ERA-40 was also used by Sriver and Huber (2006) to calculate TC power dissipation ( Emanuel 2005 ) to argue that increases in sea surface
a numerical weather prediction model anchored with a variety of meteorological observations. Importantly, these observations do not include precipitation rates. Three new global reanalyses have been produced since 2008: the European Centre for Medium-Range Weather Forecasts “Interim” reanalysis (ERA-Interim) ( Simmons et al. 2006 ; Uppala et al. 2008 ); the National Aeronautics and Space Administration Modern Era Retrospective-Analysis for Research and Applications (MERRA) ( Bosilovich et al
a numerical weather prediction model anchored with a variety of meteorological observations. Importantly, these observations do not include precipitation rates. Three new global reanalyses have been produced since 2008: the European Centre for Medium-Range Weather Forecasts “Interim” reanalysis (ERA-Interim) ( Simmons et al. 2006 ; Uppala et al. 2008 ); the National Aeronautics and Space Administration Modern Era Retrospective-Analysis for Research and Applications (MERRA) ( Bosilovich et al
1. Introduction Reanalyses combine model fields with observations distributed irregularly in space and time into a spatially complete gridded meteorological dataset, with an unchanging model and analysis system spanning the historical data record. The earlier generations of reanalyses from the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction (NOAA/NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the Japan Meteorological
1. Introduction Reanalyses combine model fields with observations distributed irregularly in space and time into a spatially complete gridded meteorological dataset, with an unchanging model and analysis system spanning the historical data record. The earlier generations of reanalyses from the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction (NOAA/NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the Japan Meteorological
Bourras et al. (2002) . Finally, sea surface turbulent fluxes can be derived from global model results that have been constrained by surface and rawinsonde observations and satellite measurements. Such products are called reanalyses and are produced by some of the major modeling centers, such as the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency
Bourras et al. (2002) . Finally, sea surface turbulent fluxes can be derived from global model results that have been constrained by surface and rawinsonde observations and satellite measurements. Such products are called reanalyses and are produced by some of the major modeling centers, such as the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency
1. Introduction There are many ways to learn from the confrontation of an atmosphere model with observations, in service of model improvement. The study of initial tendencies [or errors in one-time-step forecasts, Klinker and Sardeshmukh (1992) ] is appealing because the effect of a model process error is localized. However, initialization shock may dominate the results, making interpretation subtle (e.g., Judd et al. 2008 ). At the other extreme of time scale, the biases of unconstrained
1. Introduction There are many ways to learn from the confrontation of an atmosphere model with observations, in service of model improvement. The study of initial tendencies [or errors in one-time-step forecasts, Klinker and Sardeshmukh (1992) ] is appealing because the effect of a model process error is localized. However, initialization shock may dominate the results, making interpretation subtle (e.g., Judd et al. 2008 ). At the other extreme of time scale, the biases of unconstrained
. Data a. Reanalyses There exist several atmospheric reanalyses for the period of 1979 through current time. The Japanese 25-yr Reanalysis (JRA-25), released for use in March 2006 ( Onogi et al. 2005 , 2007 ); the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40; Uppala et al. 2005 ), which stops in August 2002; and the National Centers for Atmospheric Research–Department of Energy second reanalysis (NCEP–DOE R2; Kanamitsu et al. 2002 ) represent the second generation
. Data a. Reanalyses There exist several atmospheric reanalyses for the period of 1979 through current time. The Japanese 25-yr Reanalysis (JRA-25), released for use in March 2006 ( Onogi et al. 2005 , 2007 ); the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40; Uppala et al. 2005 ), which stops in August 2002; and the National Centers for Atmospheric Research–Department of Energy second reanalysis (NCEP–DOE R2; Kanamitsu et al. 2002 ) represent the second generation
study of the land surface water budget, including streamflow, droughts, soil moisture, and snow processes ( Dai and Trenberth 2002 ; Su and Lettenmaier 2009 ; Sheffield and Wood 2008 ; Burke et al. 2010 ; Brown et al. 2010 ), the estimation of the land carbon budget ( Zhao et al. 2006 ; Yi et al. 2011 ), and, possibly, the calibration and verification of seasonal climate forecasting systems ( Saha et al. 2006 ) and the generation of climate data records ( Thorne and Vose 2010 ; Dee et al. 2010
study of the land surface water budget, including streamflow, droughts, soil moisture, and snow processes ( Dai and Trenberth 2002 ; Su and Lettenmaier 2009 ; Sheffield and Wood 2008 ; Burke et al. 2010 ; Brown et al. 2010 ), the estimation of the land carbon budget ( Zhao et al. 2006 ; Yi et al. 2011 ), and, possibly, the calibration and verification of seasonal climate forecasting systems ( Saha et al. 2006 ) and the generation of climate data records ( Thorne and Vose 2010 ; Dee et al. 2010
1. Introduction A comprehensive representation of the Earth system is required to understand the physical processes that drive climate variability and change. Retrospective analysis—referred to as reanalysis—is one technique for generating such a dataset. Reanalysis data products are generated using data assimilation techniques to constrain a forecast model solution against in situ, satellite, and other observations (e.g., Bosilovich et al. 2011 ). Reanalysis provides continuous and consistent
1. Introduction A comprehensive representation of the Earth system is required to understand the physical processes that drive climate variability and change. Retrospective analysis—referred to as reanalysis—is one technique for generating such a dataset. Reanalysis data products are generated using data assimilation techniques to constrain a forecast model solution against in situ, satellite, and other observations (e.g., Bosilovich et al. 2011 ). Reanalysis provides continuous and consistent
