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1. Introduction In weather and climate models, various important processes occur on scales that are too fine to be resolved. These processes must therefore be represented by subgrid models or “parameterizations”; for an introduction and overview, see, for example, Mote and O’Neill (2000) , Randall (2000) , and Kalnay (2003) . A formal theoretical framework on which to build a subgrid model can be obtained by applying a spatial filter to the governing equations (e.g., Leonard 1975 ; Germano
1. Introduction In weather and climate models, various important processes occur on scales that are too fine to be resolved. These processes must therefore be represented by subgrid models or “parameterizations”; for an introduction and overview, see, for example, Mote and O’Neill (2000) , Randall (2000) , and Kalnay (2003) . A formal theoretical framework on which to build a subgrid model can be obtained by applying a spatial filter to the governing equations (e.g., Leonard 1975 ; Germano
1. Introduction The representation of cumulus convection, generally called cumulus parameterization, has almost always been at the core of efforts to numerically model the atmospheric phenomena because cumulus convection plays a central role in most of the interactions between physical processes in the atmosphere ( Arakawa 2004 ). This is because a cumulus parameterization scheme (CPS) should represent the impacts of convection in terms of environmental conditions, whereas a microphysics scheme
1. Introduction The representation of cumulus convection, generally called cumulus parameterization, has almost always been at the core of efforts to numerically model the atmospheric phenomena because cumulus convection plays a central role in most of the interactions between physical processes in the atmosphere ( Arakawa 2004 ). This is because a cumulus parameterization scheme (CPS) should represent the impacts of convection in terms of environmental conditions, whereas a microphysics scheme
et al. 2022 ). Furthermore, recent studies have found that the bulk-Richardson number (Ri b ), in which local gradients present in Ri are approximated as bulk gradients, is sometimes better than MOST for parameterizing near-surface temperature, moisture, wind gradients ( Lee and Buban 2020 ), as well as u ∗ , sensible heat flux ( H ), and latent heat flux ( E ) ( Lee et al. 2021 ). For this reason, in the present study, we extended the studies by Lee and Buban (2020) and Lee et al. (2021) by
et al. 2022 ). Furthermore, recent studies have found that the bulk-Richardson number (Ri b ), in which local gradients present in Ri are approximated as bulk gradients, is sometimes better than MOST for parameterizing near-surface temperature, moisture, wind gradients ( Lee and Buban 2020 ), as well as u ∗ , sensible heat flux ( H ), and latent heat flux ( E ) ( Lee et al. 2021 ). For this reason, in the present study, we extended the studies by Lee and Buban (2020) and Lee et al. (2021) by
distinction between cloud types; in nature, there is a continuum between these cloud types. Recent advances in the representation of subgrid-scale processes in climate models, for example, the Cloud Layers Unified by Binomials (CLUBB) parameterization ( Golaz et al. 2002 ; Larson and Golaz 2005 ; Larson et al. 2012 ; Larson and Griffin 2013 ; Griffin and Larson 2013 ) or the parameterizations of Cheng and Xu (2009) and Boutle et al. (2014) , use a unified framework that can represent clouds and
distinction between cloud types; in nature, there is a continuum between these cloud types. Recent advances in the representation of subgrid-scale processes in climate models, for example, the Cloud Layers Unified by Binomials (CLUBB) parameterization ( Golaz et al. 2002 ; Larson and Golaz 2005 ; Larson et al. 2012 ; Larson and Griffin 2013 ; Griffin and Larson 2013 ) or the parameterizations of Cheng and Xu (2009) and Boutle et al. (2014) , use a unified framework that can represent clouds and
. 2 Increasing horizontal resolution does not ameliorate this problem, as long as the existing cumulus parameterization schemes are employed and individual clouds are not resolved ( Dirmeyer et al. 2012 ). The poorly simulated PDC has adverse effects on the surface energy budget, the atmospheric branch of the hydrological cycle, and the cloud–radiation interactions. These defects have a negative impact on GCMs' performance in weather and climate forecasts and in data assimilation. Recently, the
. 2 Increasing horizontal resolution does not ameliorate this problem, as long as the existing cumulus parameterization schemes are employed and individual clouds are not resolved ( Dirmeyer et al. 2012 ). The poorly simulated PDC has adverse effects on the surface energy budget, the atmospheric branch of the hydrological cycle, and the cloud–radiation interactions. These defects have a negative impact on GCMs' performance in weather and climate forecasts and in data assimilation. Recently, the
scales, so the overturning effect of MLEs must be parameterized. Even “eddy resolving” GCMs with O (10 km) grids require parameterization of the still unresolved submesoscale. The buoyancy fluxes needed for the buoyancy budget in a GCM may be spectrally decomposed into three categories: fluxes by resolved large-scale and mesoscale phenomena ( ), 1 submesoscale fluxes ( ), and smaller-scale turbulent, solar, and diffusive fluxes ℱ. The double overline indicates horizontal averaging onto the grid
scales, so the overturning effect of MLEs must be parameterized. Even “eddy resolving” GCMs with O (10 km) grids require parameterization of the still unresolved submesoscale. The buoyancy fluxes needed for the buoyancy budget in a GCM may be spectrally decomposed into three categories: fluxes by resolved large-scale and mesoscale phenomena ( ), 1 submesoscale fluxes ( ), and smaller-scale turbulent, solar, and diffusive fluxes ℱ. The double overline indicates horizontal averaging onto the grid
1. Introduction Cumulus parameterization schemes are used in atmospheric models at horizontal resolutions of about 5 km or coarser (e.g., Mizuta et al. 2006 ; Kanada et al. 2008 ) so that the effect of subgrid-scale cumulus clouds can be taken into consideration. Simulations by models at horizontal resolutions from 1 to 3 km have some success without cumulus schemes (e.g., Lilly 1990 ; Posselt et al. 2008 ; Satoh et al. 2008 ; Eito et al. 2010 ). However, horizontal resolution on the
1. Introduction Cumulus parameterization schemes are used in atmospheric models at horizontal resolutions of about 5 km or coarser (e.g., Mizuta et al. 2006 ; Kanada et al. 2008 ) so that the effect of subgrid-scale cumulus clouds can be taken into consideration. Simulations by models at horizontal resolutions from 1 to 3 km have some success without cumulus schemes (e.g., Lilly 1990 ; Posselt et al. 2008 ; Satoh et al. 2008 ; Eito et al. 2010 ). However, horizontal resolution on the
1. Introduction One of the challenges to improve simulated precipitation lies in the elaboration of the cloud and precipitation processes, which are parameterized by a microphysics scheme (MPS) as well as a cumulus parameterization scheme (CPS) in a regional climate or mesoscale model. An MPS simulates the precipitation based on gridcell-mean (i.e., resolved) variables when the gridcell-mean relative humidity is greater than 100%. Meanwhile, a CPS simulates the precipitation depending on the
1. Introduction One of the challenges to improve simulated precipitation lies in the elaboration of the cloud and precipitation processes, which are parameterized by a microphysics scheme (MPS) as well as a cumulus parameterization scheme (CPS) in a regional climate or mesoscale model. An MPS simulates the precipitation based on gridcell-mean (i.e., resolved) variables when the gridcell-mean relative humidity is greater than 100%. Meanwhile, a CPS simulates the precipitation depending on the
longer decadal time scale, one must also consider how the frequency and geographical distribution of lightning will be affected in the changing climate to guide the policy makers in terms of urban development, transportation, and energy strategy. In numerical weather and climate prediction models, lightning is parameterized using various large-scale (i.e., grid-mean) atmospheric conditions. The goal of lightning parameterization is to predict the lightning flash density [ f (flashes per square
longer decadal time scale, one must also consider how the frequency and geographical distribution of lightning will be affected in the changing climate to guide the policy makers in terms of urban development, transportation, and energy strategy. In numerical weather and climate prediction models, lightning is parameterized using various large-scale (i.e., grid-mean) atmospheric conditions. The goal of lightning parameterization is to predict the lightning flash density [ f (flashes per square
1. Introduction Parameterizations of atmospheric boundary layer (ABL) turbulence in global weather and climate prediction models must skillfully handle many different turbulence regimes. Many ABL parameterizations have been developed using extensive measurements of boundary layers over land across the diurnal and seasonal cycle in diverse weather regimes. However, most of the earth’s surface is covered by ocean. Marine boundary layers play an important role not only for surface fluxes, but also
1. Introduction Parameterizations of atmospheric boundary layer (ABL) turbulence in global weather and climate prediction models must skillfully handle many different turbulence regimes. Many ABL parameterizations have been developed using extensive measurements of boundary layers over land across the diurnal and seasonal cycle in diverse weather regimes. However, most of the earth’s surface is covered by ocean. Marine boundary layers play an important role not only for surface fluxes, but also
