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1. Introduction A key issue in understanding tropical climate and its variability is our limited knowledge about the interactions between moist convection and the large-scale tropical circulation. General circulation models (GCMs) are a powerful tool for studying the large-scale circulation, but they suffer from a range of problems associated with the need to parameterize convection (e.g., Lin et al. 2006 ; Dai 2006 ; Lin et al. 2008 ; Randall et al. 2007 ). Cloud-resolving models (CRMs
1. Introduction A key issue in understanding tropical climate and its variability is our limited knowledge about the interactions between moist convection and the large-scale tropical circulation. General circulation models (GCMs) are a powerful tool for studying the large-scale circulation, but they suffer from a range of problems associated with the need to parameterize convection (e.g., Lin et al. 2006 ; Dai 2006 ; Lin et al. 2008 ; Randall et al. 2007 ). Cloud-resolving models (CRMs
-independent globally based results are consistent with the more limited regional observations reported by Shackford (1960) , Goodman et al. (1988 , 2005) , and others. Most cloud-resolving numerical models now have the capability of computing fields of mixing ratios of multiple species of hydrometeors, including several important ice-phase species. Thus, cloud-resolving models, such as the Weather Research and Forecasting (WRF; Skamarock et al. 2005 ) model, can now provide time- and space-dependent simulated
-independent globally based results are consistent with the more limited regional observations reported by Shackford (1960) , Goodman et al. (1988 , 2005) , and others. Most cloud-resolving numerical models now have the capability of computing fields of mixing ratios of multiple species of hydrometeors, including several important ice-phase species. Thus, cloud-resolving models, such as the Weather Research and Forecasting (WRF; Skamarock et al. 2005 ) model, can now provide time- and space-dependent simulated
1. Introduction One-dimensional (1D) models of radiative–convective equilibrium (RCE) have played an important role in our understanding of Earth’s equilibrium climate sensitivity ( Schlesinger 1986 ) from the early days of Manabe and Wetherald (1967) through to the recent work of Kluft et al. (2019) . Over the past two decades, it has become possible to replace those 1D models with 2D or 3D cloud-resolving models (CRMs), allowing for a more realistic treatment of clouds and convection
1. Introduction One-dimensional (1D) models of radiative–convective equilibrium (RCE) have played an important role in our understanding of Earth’s equilibrium climate sensitivity ( Schlesinger 1986 ) from the early days of Manabe and Wetherald (1967) through to the recent work of Kluft et al. (2019) . Over the past two decades, it has become possible to replace those 1D models with 2D or 3D cloud-resolving models (CRMs), allowing for a more realistic treatment of clouds and convection
on small spatial scales is not well specified from observations alone ( Stephens and Kummerow 2007 ). Cloud-resolving models (CRMs) are used to study the spatial and temporal variability of clouds and their environment ( Khairoutdinov and Randall 2003 ). The cloud fields produced by CRMs cannot be easily compared with observed cloud fields because the initial and boundary conditions are not available on cloud-resolving scales. To improve the modeled representation, observations should be
on small spatial scales is not well specified from observations alone ( Stephens and Kummerow 2007 ). Cloud-resolving models (CRMs) are used to study the spatial and temporal variability of clouds and their environment ( Khairoutdinov and Randall 2003 ). The cloud fields produced by CRMs cannot be easily compared with observed cloud fields because the initial and boundary conditions are not available on cloud-resolving scales. To improve the modeled representation, observations should be
spacing from 12 up to 4 km have been implemented in many centers, and it is likely that in the coming years, models with 1-km horizontal grid spacing will become increasingly common ( Lean et al. 2008 ). At a horizontal resolution of 50 km, some improvements can be made in the simulation of the diurnal cycle ( Ploshay and Lau 2010 ) in comparison with lower-resolution models. By increasing the horizontal resolution to 10 km, one can begin to resolve cloud systems and the interactions between them
spacing from 12 up to 4 km have been implemented in many centers, and it is likely that in the coming years, models with 1-km horizontal grid spacing will become increasingly common ( Lean et al. 2008 ). At a horizontal resolution of 50 km, some improvements can be made in the simulation of the diurnal cycle ( Ploshay and Lau 2010 ) in comparison with lower-resolution models. By increasing the horizontal resolution to 10 km, one can begin to resolve cloud systems and the interactions between them
measurements, a lightning network, and a balloon-borne sounding network. Additionally, five research aircraft were operated to measure cloud properties and the state of the atmosphere. The observational network was designed, in part, to facilitate thorough evaluation of cloud-scale model simulations of the TWP-ICE period, and this paper explores the performance of nested cloud-system-resolving simulations of two periods of active convection during TWP-ICE. The Maritime Continent and the northern parts
measurements, a lightning network, and a balloon-borne sounding network. Additionally, five research aircraft were operated to measure cloud properties and the state of the atmosphere. The observational network was designed, in part, to facilitate thorough evaluation of cloud-scale model simulations of the TWP-ICE period, and this paper explores the performance of nested cloud-system-resolving simulations of two periods of active convection during TWP-ICE. The Maritime Continent and the northern parts
1. Introduction Accurate cloud-resolving model (CRM) simulations of cloud cover and cloud water content for boundary layer clouds are difficult to achieve without vertical grid spacing well below 100 m, especially for inversion-topped stratocumulus. In part, this is because accurate simulation of these clouds requires accurately representing the entrainment of air into the cloud layer ( Stevens et al. 2003 ). Bretherton et al. (1999a) , for example, examined the output from 13 cloud-resolving
1. Introduction Accurate cloud-resolving model (CRM) simulations of cloud cover and cloud water content for boundary layer clouds are difficult to achieve without vertical grid spacing well below 100 m, especially for inversion-topped stratocumulus. In part, this is because accurate simulation of these clouds requires accurately representing the entrainment of air into the cloud layer ( Stevens et al. 2003 ). Bretherton et al. (1999a) , for example, examined the output from 13 cloud-resolving
dependence of surface albedo on SZA used by the National Centers for Environmental Prediction (NCEP) Global Forecast Systems (GFS) and those derived from satellite observations. Recently, Yang et al. (2008) showed the dependence of a snow-free surface albedo on SZA using the surface albedo data obtained during 1997–2005 from nine measurement stations whose surface types and locations are very different from each other. During the past decade, cloud-resolving models (CRMs) have been widely used to
dependence of surface albedo on SZA used by the National Centers for Environmental Prediction (NCEP) Global Forecast Systems (GFS) and those derived from satellite observations. Recently, Yang et al. (2008) showed the dependence of a snow-free surface albedo on SZA using the surface albedo data obtained during 1997–2005 from nine measurement stations whose surface types and locations are very different from each other. During the past decade, cloud-resolving models (CRMs) have been widely used to
precipitation extremes that occur with warming in simulations with a cloud-resolving model (CRM; sometimes referred to as a cloud system–resolving model). We compare the precipitation extremes in a control simulation and in a simulation with a higher sea surface temperature (SST) to address the following questions: How much do precipitation extremes increase with warming? Are there substantial changes in the magnitudes of the vertical velocities associated with these precipitation extremes? Can we derive a
precipitation extremes that occur with warming in simulations with a cloud-resolving model (CRM; sometimes referred to as a cloud system–resolving model). We compare the precipitation extremes in a control simulation and in a simulation with a higher sea surface temperature (SST) to address the following questions: How much do precipitation extremes increase with warming? Are there substantial changes in the magnitudes of the vertical velocities associated with these precipitation extremes? Can we derive a
controls on high-latitude lapse rates and surface inversion strength is needed. Recent work by Cronin and Tziperman (2015) found amplified surface warming and weaker surface inversions over high-latitude winter continents due to increasing insulation of the surface by optically thick liquid clouds in a single-column model. The goal of this study is to test the viability and strength of this mechanism—increased surface insulation by low clouds in a warmer world—in a model that resolves cloud dynamics
controls on high-latitude lapse rates and surface inversion strength is needed. Recent work by Cronin and Tziperman (2015) found amplified surface warming and weaker surface inversions over high-latitude winter continents due to increasing insulation of the surface by optically thick liquid clouds in a single-column model. The goal of this study is to test the viability and strength of this mechanism—increased surface insulation by low clouds in a warmer world—in a model that resolves cloud dynamics
