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1. Introduction Seasonal prediction, along with subseasonal prediction, has long been considered a gap in current forecasting capability ( Weisheimer and Palmer 2014 ; Vitart 2014 ). Skillful seasonal predictions can provide useful information for decision-makers across a variety of sectors, ranging from energy and agriculture to transportation and public health ( National Academies of Sciences, Engineering, and Medicine 2016 ). Improved seasonal prediction skill is thus of profound
1. Introduction Seasonal prediction, along with subseasonal prediction, has long been considered a gap in current forecasting capability ( Weisheimer and Palmer 2014 ; Vitart 2014 ). Skillful seasonal predictions can provide useful information for decision-makers across a variety of sectors, ranging from energy and agriculture to transportation and public health ( National Academies of Sciences, Engineering, and Medicine 2016 ). Improved seasonal prediction skill is thus of profound
-horizontal-resolution models ( Murakami et al. 2012b ; Knutson et al. 2013 ; Manganello et al. 2014 ; Bhatia et al. 2018 ; Bacmeister et al. 2018 ). Another common use of climate models is for TC dynamical forecasts on seasonal ( Vitart et al. 2001 ; Camargo and Barnston 2009 ; Manganello et al. 2016 ; Camp et al. 2019 ; G. Zhang et al. 2019 ; W. Zhang et al. 2019 ) and subseasonal time scales ( Lee et al. 2018 ; Camp et al. 2018 ; Gregory et al. 2019 ; Zhao et al. 2019 ). A recent review on this topic is
-horizontal-resolution models ( Murakami et al. 2012b ; Knutson et al. 2013 ; Manganello et al. 2014 ; Bhatia et al. 2018 ; Bacmeister et al. 2018 ). Another common use of climate models is for TC dynamical forecasts on seasonal ( Vitart et al. 2001 ; Camargo and Barnston 2009 ; Manganello et al. 2016 ; Camp et al. 2019 ; G. Zhang et al. 2019 ; W. Zhang et al. 2019 ) and subseasonal time scales ( Lee et al. 2018 ; Camp et al. 2018 ; Gregory et al. 2019 ; Zhao et al. 2019 ). A recent review on this topic is
Outcomes of NOAA MAPP Model Diagnostics Task Force activities to promote process-oriented diagnosis of models to accelerate development are described. Realistic climate and weather forecasting models grounded in sound physical principles are necessary to produce confidence in projections of future climate for the next century and predictions for days to seasons. However, global models continue to suffer from important and often common biases that impact their ability to provide reliable
Outcomes of NOAA MAPP Model Diagnostics Task Force activities to promote process-oriented diagnosis of models to accelerate development are described. Realistic climate and weather forecasting models grounded in sound physical principles are necessary to produce confidence in projections of future climate for the next century and predictions for days to seasons. However, global models continue to suffer from important and often common biases that impact their ability to provide reliable
particular interest is the impact of parameterizations on ETCs; here, we will focus on parameterized convection. In both the GFDL and the GISS GCMs, there is a single convection parameterization used globally, meaning the schemes in the models are usually designed with attention on the tropics. In contrast, the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) was developed originally for forecasting midlatitude weather. These different constraints on parameterized physics motivate
particular interest is the impact of parameterizations on ETCs; here, we will focus on parameterized convection. In both the GFDL and the GISS GCMs, there is a single convection parameterization used globally, meaning the schemes in the models are usually designed with attention on the tropics. In contrast, the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) was developed originally for forecasting midlatitude weather. These different constraints on parameterized physics motivate
1. Introduction Since the 1970s, it has been well known that global climate models (GCMs) are able to simulate vortices with characteristics similar to tropical cyclones (TCs; Manabe et al. 1970 ; Camargo and Wing 2016 ). As GCMs are also able to reproduce the relationship between TCs and El Niño–Southern Oscillation (ENSO), they have been used to develop dynamical TC seasonal forecasts ( Vitart and Stockdale 2001 ; Camargo and Barnston 2009 ). More recently, with the aid of rapid increases
1. Introduction Since the 1970s, it has been well known that global climate models (GCMs) are able to simulate vortices with characteristics similar to tropical cyclones (TCs; Manabe et al. 1970 ; Camargo and Wing 2016 ). As GCMs are also able to reproduce the relationship between TCs and El Niño–Southern Oscillation (ENSO), they have been used to develop dynamical TC seasonal forecasts ( Vitart and Stockdale 2001 ; Camargo and Barnston 2009 ). More recently, with the aid of rapid increases
1. Introduction The study of tropical cyclones (TCs) in climate models has long been difficult because of the conflict between the high resolution necessary to accurately simulate TCs and the need to perform long, global simulations. In recent years, however, enormous progress has been made in the ability of general circulation models (GCMs) to simulate TCs from subseasonal to seasonal and longer time scales ( Camargo and Wing 2016 ). Global forecast models have become a more reliable source of
1. Introduction The study of tropical cyclones (TCs) in climate models has long been difficult because of the conflict between the high resolution necessary to accurately simulate TCs and the need to perform long, global simulations. In recent years, however, enormous progress has been made in the ability of general circulation models (GCMs) to simulate TCs from subseasonal to seasonal and longer time scales ( Camargo and Wing 2016 ). Global forecast models have become a more reliable source of
Interpolation V2 dataset ( Reynolds et al. 2002 ) were used as the boundary conditions. All models were integrated for 20 years and archived from 1991 to 2010, with the exception of SPCAM3, which is only archived from 1986 to 2003 for a total of 18 years. The ECMWF AMIP historical run was run with the Integrated Forecast System (IFS; cycle 36r4) atmospheric circulation model. The forcing and boundary conditions are set according to the CMIP5 historical forcing with SST and SIC derived from the Hadley Centre
Interpolation V2 dataset ( Reynolds et al. 2002 ) were used as the boundary conditions. All models were integrated for 20 years and archived from 1991 to 2010, with the exception of SPCAM3, which is only archived from 1986 to 2003 for a total of 18 years. The ECMWF AMIP historical run was run with the Integrated Forecast System (IFS; cycle 36r4) atmospheric circulation model. The forcing and boundary conditions are set according to the CMIP5 historical forcing with SST and SIC derived from the Hadley Centre
the purpose of this paper is not to argue for using statistical, data-driven, or regression models in place of physically based or process-based models for operational forecasting of terrestrial hydrological systems. We do not want to do this because of the potential for nonstationarity—some type of mechanistic understanding of the system is necessary to predict under changing conditions ( Milly et al. 2008 ). That being said, we cannot ignore the fact that regression models regularly outperform
the purpose of this paper is not to argue for using statistical, data-driven, or regression models in place of physically based or process-based models for operational forecasting of terrestrial hydrological systems. We do not want to do this because of the potential for nonstationarity—some type of mechanistic understanding of the system is necessary to predict under changing conditions ( Milly et al. 2008 ). That being said, we cannot ignore the fact that regression models regularly outperform
al. 2008 ). Surface winds are obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011 ). Horizontal grid intervals are 1° × 1° for WOA13 and OAFlux, 2.5° × 2.5° for GPCP rain, and 1.5° × 1.5° for the ERA-Interim reanalysis. The time series we use for each dataset extend from 1979 to 2015 for GPCP rainfall and the ERA-Interim reanalysis, 1984 to 2009 for OAFlux surface heat flux, and 1985 to 2014 for OAFlux evaporative flux
al. 2008 ). Surface winds are obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011 ). Horizontal grid intervals are 1° × 1° for WOA13 and OAFlux, 2.5° × 2.5° for GPCP rain, and 1.5° × 1.5° for the ERA-Interim reanalysis. The time series we use for each dataset extend from 1979 to 2015 for GPCP rainfall and the ERA-Interim reanalysis, 1984 to 2009 for OAFlux surface heat flux, and 1985 to 2014 for OAFlux evaporative flux
forecast system . J. Hydrometeor. , 10 , 623 – 643 , https://doi.org/10.1175/2008JHM1068.1 . 10.1175/2008JHM1068.1 Best , M. J. , P. M. Cox , and D. Warrilow , 2005 : Determining the optimal soil temperature scheme for atmospheric modelling applications . Bound.-Layer Meteor. , 114 , 111 – 142 , https://doi.org/10.1007/s10546-004-5075-3 . 10.1007/s10546-004-5075-3 Best , M. J. , and Coauthors , 2011 : The Joint UK Land Environment Simulator (JULES), model description - Part 1
forecast system . J. Hydrometeor. , 10 , 623 – 643 , https://doi.org/10.1175/2008JHM1068.1 . 10.1175/2008JHM1068.1 Best , M. J. , P. M. Cox , and D. Warrilow , 2005 : Determining the optimal soil temperature scheme for atmospheric modelling applications . Bound.-Layer Meteor. , 114 , 111 – 142 , https://doi.org/10.1007/s10546-004-5075-3 . 10.1007/s10546-004-5075-3 Best , M. J. , and Coauthors , 2011 : The Joint UK Land Environment Simulator (JULES), model description - Part 1
