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larger scales. These feedbacks are a function of water table depth ( Kollet and Maxwell 2008a ; Maxwell and Kollet 2008a ; Maxwell et al. 2007 ) and seasonal atmospheric conditions ( Kollet and Maxwell 2008a ), and are expected to vary across different geographic and climatic regions. A small, but growing body of work has addressed the inclusion of groundwater and detailed representations of surface hydrology into atmospheric models. York et al. (2002) coupled a single-column atmospheric
larger scales. These feedbacks are a function of water table depth ( Kollet and Maxwell 2008a ; Maxwell and Kollet 2008a ; Maxwell et al. 2007 ) and seasonal atmospheric conditions ( Kollet and Maxwell 2008a ), and are expected to vary across different geographic and climatic regions. A small, but growing body of work has addressed the inclusion of groundwater and detailed representations of surface hydrology into atmospheric models. York et al. (2002) coupled a single-column atmospheric
high-resolution global wave climatology and future wave climate projections have been produced by a single dynamical model, not to mention a coupled atmosphere–wave model. Using an atmosphere–wave coupled model has additional advantages. 1) In the coupled model, the wind is passed to the wave model at every time step. When using a stand-alone wave model, saved winds, usually at a 3–6-h interval, are used to force the wave model, and thus the wind field needs to be interpolated onto finer time
high-resolution global wave climatology and future wave climate projections have been produced by a single dynamical model, not to mention a coupled atmosphere–wave model. Using an atmosphere–wave coupled model has additional advantages. 1) In the coupled model, the wind is passed to the wave model at every time step. When using a stand-alone wave model, saved winds, usually at a 3–6-h interval, are used to force the wave model, and thus the wind field needs to be interpolated onto finer time
recent studies suggest that coupling a 1D ocean model to a hurricane model may be sufficient for capturing the storm-induced sea surface temperature (SST) cooling in the region providing heat energy to the hurricane ( Emanuel et al. 2004 ; Lin et al. 2005 , 2008 ; Bender et al. 2007 ; Davis et al. 2008 ). If in fact a 1D model is sufficient, valuable computational resources can be saved as compared to coupled models that employ a fully three-dimensional (3D) ocean component. The purpose of this
recent studies suggest that coupling a 1D ocean model to a hurricane model may be sufficient for capturing the storm-induced sea surface temperature (SST) cooling in the region providing heat energy to the hurricane ( Emanuel et al. 2004 ; Lin et al. 2005 , 2008 ; Bender et al. 2007 ; Davis et al. 2008 ). If in fact a 1D model is sufficient, valuable computational resources can be saved as compared to coupled models that employ a fully three-dimensional (3D) ocean component. The purpose of this
1. Introduction Coupled data assimilation (CDA) is quickly growing in importance as operational prediction centers around the world transition to the use of fully coupled forecast models, with the intention of transitioning to more seamless prediction between time scales ranging from weather (days to weeks) to seasonal (weeks to months). The primary initialization strategy used by most prediction centers for coupled models is to initialize each model component (e.g., the atmosphere or
1. Introduction Coupled data assimilation (CDA) is quickly growing in importance as operational prediction centers around the world transition to the use of fully coupled forecast models, with the intention of transitioning to more seamless prediction between time scales ranging from weather (days to weeks) to seasonal (weeks to months). The primary initialization strategy used by most prediction centers for coupled models is to initialize each model component (e.g., the atmosphere or
1. Introduction Given the importance of the balance and coherence of different model components (or media) in coupled model initialization, it has been realized that for the purpose of climate estimation and model initialization, data assimilation should be performed within a coupled model framework (e.g., Chen et al. 1995 ; Zhang et al. 2007 ; Chen 2010 ). When the observed data in one or more media are assimilated into the model, information is exchanged among different media in the
1. Introduction Given the importance of the balance and coherence of different model components (or media) in coupled model initialization, it has been realized that for the purpose of climate estimation and model initialization, data assimilation should be performed within a coupled model framework (e.g., Chen et al. 1995 ; Zhang et al. 2007 ; Chen 2010 ). When the observed data in one or more media are assimilated into the model, information is exchanged among different media in the
1. Introduction The future spatiotemporal change in the global monsoon is one of the deepest concerns worldwide because the monsoon determines fundamental characteristics of the earth’s climate and its rain provides a major water resource to more than two-thirds of the world’s population. Coupled global climate models (CGCMs) have been used to predict future climate changes. However, because of our limited knowledge of the highly complex climate system, determining the accuracy of the CGCMs
1. Introduction The future spatiotemporal change in the global monsoon is one of the deepest concerns worldwide because the monsoon determines fundamental characteristics of the earth’s climate and its rain provides a major water resource to more than two-thirds of the world’s population. Coupled global climate models (CGCMs) have been used to predict future climate changes. However, because of our limited knowledge of the highly complex climate system, determining the accuracy of the CGCMs
orography ( Junge et al. 2005 ). The impact of atmospheric horizontal resolution at climate scales has received comparably less attention. Most investigations have been conducted with prescribed SST distribution for relatively short periods using observed SST ( Roeckner et al. 1996 ; Duffy et al. 2003 ; Stratton 1999 ; Pope and Stratton 2002 ) or as time-slice experiments with SST from lower-resolution coupled models ( May and Roeckner 2001 ; May 2003 , 2001 ). There is almost no documentation of
orography ( Junge et al. 2005 ). The impact of atmospheric horizontal resolution at climate scales has received comparably less attention. Most investigations have been conducted with prescribed SST distribution for relatively short periods using observed SST ( Roeckner et al. 1996 ; Duffy et al. 2003 ; Stratton 1999 ; Pope and Stratton 2002 ) or as time-slice experiments with SST from lower-resolution coupled models ( May and Roeckner 2001 ; May 2003 , 2001 ). There is almost no documentation of
.g., McPhaden 1999 ) and confirmed by some model and theoretical studies (e.g., Perigaud and Cassou 2000 ; Fedorov et al. 2003 ; Eisenman et al. 2005 ; Zavala-Garay et al. 2005 ). Multiscale ocean–atmosphere processes in other tropical oceans and the extratropics may also affect ENSO and seasonal climate variations in certain regions. This cannot be represented in the simple models. Sophisticated ocean–atmosphere coupled general circulation models (GCMs) should resolve both ENSO and its dynamically
.g., McPhaden 1999 ) and confirmed by some model and theoretical studies (e.g., Perigaud and Cassou 2000 ; Fedorov et al. 2003 ; Eisenman et al. 2005 ; Zavala-Garay et al. 2005 ). Multiscale ocean–atmosphere processes in other tropical oceans and the extratropics may also affect ENSO and seasonal climate variations in certain regions. This cannot be represented in the simple models. Sophisticated ocean–atmosphere coupled general circulation models (GCMs) should resolve both ENSO and its dynamically
1. Introduction In recent years there has been a growing interest in the scientific community in studying ocean–atmosphere coupled models ( Liu 1993 ; Frankignoul et al. 1997 ; Barsugli and Battisti 1998 ; Goodman and Marshall 1999 , hereinafter GM99 ; Ferreira et al. 2001 ; White et al. 1998 ; Neelin and Weng 1999 ; White 2000a ; Gallego and Cessi 2000 ; Cessi and Paparella 2001 ; Colin de Verdière and Blanc 2001 ; Kravtsov and Robertson 2002 , to mention a few). Different
1. Introduction In recent years there has been a growing interest in the scientific community in studying ocean–atmosphere coupled models ( Liu 1993 ; Frankignoul et al. 1997 ; Barsugli and Battisti 1998 ; Goodman and Marshall 1999 , hereinafter GM99 ; Ferreira et al. 2001 ; White et al. 1998 ; Neelin and Weng 1999 ; White 2000a ; Gallego and Cessi 2000 ; Cessi and Paparella 2001 ; Colin de Verdière and Blanc 2001 ; Kravtsov and Robertson 2002 , to mention a few). Different
overestimated over southern Africa in stand-alone AGCM simulations. Poor spatial representation of the boundary SSTs over the surrounding oceans might also lead to noticeable biases of precipitation forecasts ( Rouault et al. 2003b ). Encouraging is a study by Landman and Beraki (2012) that suggested ocean–atmosphere coupled general circulation models might have improved skill in forecasting seasonal precipitation over southern Africa, which could further be improved when a multimodel forecast approach is
overestimated over southern Africa in stand-alone AGCM simulations. Poor spatial representation of the boundary SSTs over the surrounding oceans might also lead to noticeable biases of precipitation forecasts ( Rouault et al. 2003b ). Encouraging is a study by Landman and Beraki (2012) that suggested ocean–atmosphere coupled general circulation models might have improved skill in forecasting seasonal precipitation over southern Africa, which could further be improved when a multimodel forecast approach is
