Abstract
The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.
Original language | English (US) |
---|---|
Article number | e2020MS002138 |
Journal | Journal of Advances in Modeling Earth Systems |
Volume | 12 |
Issue number | 9 |
DOIs | |
State | Published - Sep 1 2020 |
All Science Journal Classification (ASJC) codes
- Global and Planetary Change
- Environmental Chemistry
- Earth and Planetary Sciences(all)
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Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations. / Wing, Allison A.; Stauffer, Catherine L.; Becker, Tobias et al.
In: Journal of Advances in Modeling Earth Systems, Vol. 12, No. 9, e2020MS002138, 01.09.2020.Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations
AU - Wing, Allison A.
AU - Stauffer, Catherine L.
AU - Becker, Tobias
AU - Reed, Kevin A.
AU - Ahn, Min Seop
AU - Arnold, Nathan P.
AU - Bony, Sandrine
AU - Branson, Mark
AU - Bryan, George H.
AU - Chaboureau, Jean Pierre
AU - De Roode, Stephan R.
AU - Gayatri, Kulkarni
AU - Hohenegger, Cathy
AU - Hu, I. Kuan
AU - Jansson, Fredrik
AU - Jones, Todd R.
AU - Khairoutdinov, Marat
AU - Kim, Daehyun
AU - Martin, Zane K.
AU - Matsugishi, Shuhei
AU - Medeiros, Brian
AU - Miura, Hiroaki
AU - Moon, Yumin
AU - Müller, Sebastian K.
AU - Ohno, Tomoki
AU - Popp, Max
AU - Prabhakaran, Thara
AU - Randall, David
AU - Rios-Berrios, Rosimar
AU - Rochetin, Nicolas
AU - Roehrig, Romain
AU - Romps, David M.
AU - Ruppert, James H.
AU - Satoh, Masaki
AU - Silvers, Levi G.
AU - Singh, Martin S.
AU - Stevens, Bjorn
AU - Tomassini, Lorenzo
AU - van Heerwaarden, Chiel C.
AU - Wang, Shuguang
AU - Zhao, Ming
N1 - Funding Information: We acknowledge high‐performance computing support from Cheyenne at the NCAR‐Wyoming Supercomputing Center (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. A. A. W. and C. L. S. acknowledge support from NSF Grant 1830724, K. A. R. and L. G. S. acknowledge support from NSF Grant 1830729. S. d.R. thanks Laurens Wester for his contribution to setting up DALES for the RCE simulations. F. J. acknowledges support from the Netherlands eScience Center (NLeSC) under Grant No. 027.015.G03. DALES and MicroHH simulations were carried out on the Dutch national e‐infrastructure with the support of SURF Cooperative. For DALES, we also acknowledge the use of ECMWF's computing and archive facilities. CvH acknowledges funding from the Dutch Research Council (NWO), Project Number VI.Vidi.192.068. The NICAM simulation is conducted under the Future LAtency core‐based General‐purpose Supercomputer with HIgh Productivity (FLAGSHIP2020) project, which is promoted by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan, and contributed by the Integrated Research Program for Advancing Climate Model (TOUGOU) by MEXT. Computer resources for running Meso‐NH were allocated by GENCI through Project 90569. The contributions of T. R. J. are supported by the Natural Environment Research Council (NERC) under the joint NERC/Met Office ParaCon program's Circle‐A project, NE/N013735/1. The UKMO CRM simulations and data processing have been conducted using Monsoon2, a collaborative High Performance Computing facility funded by the Met Office and NERC, the NEXCS High Performance Computing facility funded by the Natural Environmental Research Council and delivered by the Met Office, and JASMIN, the UK collaborative data analysis facility. G. B., B. M., and R. R.‐B. are supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. B. M. acknowledges the Regional and Global Model Analysis component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy's Office of Biological and Environmental Research via National Science Foundation IA 1844590 and NSF Award 000057‐00414. Computational resources for running SCALE were the K computer provided by the RIKEN R‐CCS through the HPCI System Research project (Project ID:hp170323) and Oakbridge‐FX by Information Technology Center, The University of Tokyo. Z. M. and S. W. acknowledge support from NSF AGS‐1543932, Z. M. additionally acknowledges support from NASA Headquarters under the NASA Earth and Space Science Fellowship Program Grant 80NSSC18K1347. R. R. and T. B. acknowledge support from the European Union's Horizon 2020 research and innovation programme CONSTRAIN under Grant Agreement No 820829. M. S. acknowledges resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government, and is funded through Australian Research Council DECRA Award (DE190100866). M. K. was supported by NSF Grant AGS1418309 to Stony Brook University. We thank Zhihong Tan for useful comments on the manuscript. Funding Information: We acknowledge high-performance computing support from Cheyenne at the NCAR-Wyoming Supercomputing Center (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. A. A. W. and C. L. S. acknowledge support from NSF Grant 1830724, K. A. R. and L. G. S. acknowledge support from NSF Grant 1830729. S. d.R. thanks Laurens Wester for his contribution to setting up DALES for the RCE simulations. F. J. acknowledges support from the Netherlands eScience Center (NLeSC) under Grant No. 027.015.G03. DALES and MicroHH simulations were carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. For DALES, we also acknowledge the use of ECMWF's computing and archive facilities. CvH acknowledges funding from the Dutch Research Council (NWO), Project Number VI.Vidi.192.068. The NICAM simulation is conducted under the Future LAtency core-based General-purpose Supercomputer with HIgh Productivity (FLAGSHIP2020) project, which is promoted by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan, and contributed by the Integrated Research Program for Advancing Climate Model (TOUGOU) by MEXT. Computer resources for running Meso-NH were allocated by GENCI through Project 90569. The contributions of T. R. J. are supported by the Natural Environment Research Council (NERC) under the joint NERC/Met Office ParaCon program's Circle-A project, NE/N013735/1. The UKMO CRM simulations and data processing have been conducted using Monsoon2, a collaborative High Performance Computing facility funded by the Met Office and NERC, the NEXCS High Performance Computing facility funded by the Natural Environmental Research Council and delivered by the Met Office, and JASMIN, the UK collaborative data analysis facility. G. B., B. M., and R. R.-B. are supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. B. M. acknowledges the Regional and Global Model Analysis component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy's Office of Biological and Environmental Research via National Science Foundation IA 1844590 and NSF Award 000057-00414. Computational resources for running SCALE were the K computer provided by the RIKEN R-CCS through the HPCI System Research project (Project ID:hp170323) and Oakbridge-FX by Information Technology Center, The University of Tokyo. Z. M. and S. W. acknowledge support from NSF AGS-1543932, Z. M. additionally acknowledges support from NASA Headquarters under the NASA Earth and Space Science Fellowship Program Grant 80NSSC18K1347. R. R. and T. B. acknowledge support from the European Union's Horizon 2020 research and innovation programme CONSTRAIN under Grant Agreement No 820829. M. S. acknowledges resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government, and is funded through Australian Research Council DECRA Award (DE190100866). M. K. was supported by NSF Grant AGS1418309 to Stony Brook University. We thank Zhihong Tan for useful comments on the manuscript. Publisher Copyright: ©2020. The Authors.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.
AB - The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.
UR - http://www.scopus.com/inward/record.url?scp=85086879900&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086879900&partnerID=8YFLogxK
U2 - 10.1029/2020MS002138
DO - 10.1029/2020MS002138
M3 - Article
C2 - 33042391
AN - SCOPUS:85086879900
SN - 1942-2466
VL - 12
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
IS - 9
M1 - e2020MS002138
ER -