Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data

Abstract Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and s...

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Main Authors: Kumar Roy, Parthasarathi Mukhopadhyay, R. P. M. Krishna, B. Khouider, B. B. Goswami
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2021-08-01
Series:Earth and Space Science
Subjects:
Online Access:https://doi.org/10.1029/2020EA001455
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spelling doaj-ca704b08a9064b94a12a299d521d20442021-08-27T12:25:43ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842021-08-0188n/an/a10.1029/2020EA001455Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR DataKumar Roy0Parthasarathi Mukhopadhyay1R. P. M. Krishna2B. Khouider3B. B. Goswami4Indian Institute of Tropical Meteorology Ministry of Earth Sciences Pune IndiaIndian Institute of Tropical Meteorology Ministry of Earth Sciences Pune IndiaIndian Institute of Tropical Meteorology Ministry of Earth Sciences Pune IndiaDepartment of Mathematics and Statistics University of Victoria Victoria BC CanadaIrreversible Climate Change Research Center (IRCC) Yonsei University Seoul South KoreaAbstract Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 (SMCM‐CTRL) in lieu of the pre‐existing revised simplified Arakawa–Schubert (RSAS) cumulus scheme and has shown essential improvements in different large‐scale features of tropical convection. But the sensitivity of the SMCM parameterization from the observations is yet to be ascertained. Radar data during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign is used to tune the SMCM in the present manuscript. The DYNAMO radar observations have been used to calibrate the SMCM using a Bayesian inference procedure to generate key time scale parameters for the transition probabilities of the underlying Markov chains of the SMCM as implemented in CFS (hereafter SMCM‐DYNAMO). SMCM‐DYNAMO improves many aspects of the mean state climate compared to RSAS, and SMCM‐CTRL. Significant improvement is noted in the rainfall probability distribution function over the global tropics. The global distribution of different types of clouds, particularly low‐level clouds, is also improved. The convective and large‐scale rainfall simulations are investigated in detail.https://doi.org/10.1029/2020EA001455stochastic multi‐cloud modelDYNAMO RADAR data for constraining the SMCMDYNAMO constrained SMCM used in CFSv2Atmospheric mean state
collection DOAJ
language English
format Article
sources DOAJ
author Kumar Roy
Parthasarathi Mukhopadhyay
R. P. M. Krishna
B. Khouider
B. B. Goswami
spellingShingle Kumar Roy
Parthasarathi Mukhopadhyay
R. P. M. Krishna
B. Khouider
B. B. Goswami
Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data
Earth and Space Science
stochastic multi‐cloud model
DYNAMO RADAR data for constraining the SMCM
DYNAMO constrained SMCM used in CFSv2
Atmospheric mean state
author_facet Kumar Roy
Parthasarathi Mukhopadhyay
R. P. M. Krishna
B. Khouider
B. B. Goswami
author_sort Kumar Roy
title Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data
title_short Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data
title_full Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data
title_fullStr Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data
title_full_unstemmed Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data
title_sort evaluation of mean state in ncep climate forecast system (version 2) simulation using a stochastic multicloud model calibrated with dynamo radar data
publisher American Geophysical Union (AGU)
series Earth and Space Science
issn 2333-5084
publishDate 2021-08-01
description Abstract Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 (SMCM‐CTRL) in lieu of the pre‐existing revised simplified Arakawa–Schubert (RSAS) cumulus scheme and has shown essential improvements in different large‐scale features of tropical convection. But the sensitivity of the SMCM parameterization from the observations is yet to be ascertained. Radar data during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign is used to tune the SMCM in the present manuscript. The DYNAMO radar observations have been used to calibrate the SMCM using a Bayesian inference procedure to generate key time scale parameters for the transition probabilities of the underlying Markov chains of the SMCM as implemented in CFS (hereafter SMCM‐DYNAMO). SMCM‐DYNAMO improves many aspects of the mean state climate compared to RSAS, and SMCM‐CTRL. Significant improvement is noted in the rainfall probability distribution function over the global tropics. The global distribution of different types of clouds, particularly low‐level clouds, is also improved. The convective and large‐scale rainfall simulations are investigated in detail.
topic stochastic multi‐cloud model
DYNAMO RADAR data for constraining the SMCM
DYNAMO constrained SMCM used in CFSv2
Atmospheric mean state
url https://doi.org/10.1029/2020EA001455
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