Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models

Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and centra...

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Vydané v:Quarterly journal of the Royal Meteorological Society Ročník 150; číslo 759; s. 911 - 936
Hlavní autori: Tao, Cheng, Xie, Shaocheng, Ma, Hsi‐Yen, Bechtold, Peter, Cui, Zeyu, Vaillancourt, Paul A., Van Weverberg, Kwinten, Wang, Yi‐Chi, Wong, May, Yang, Jing, Zhang, Guang J., Choi, In‐Jin, Tang, Shuaiqi, Wei, Jiangfeng, Wu, Wen‐Ying, Zhang, Meng, Neelin, J. David, Zeng, Xubin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 01.01.2024
Wiley Subscription Services, Inc
Wiley Blackwell (John Wiley & Sons)
Predmet:
Air parcels
Boundary layers
Climate
Climate models
Clouds
Convection
Diurnal
Diurnal cycle
General circulation models
Intercomparison
Mesoscale systems
Physics
Precipitation
Rain
Rainfall intensity
Tropical environments
Variability
Weather forecasting
United States > US
ISSN:0035-9009, 1477-870X
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Abstract Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large‐scale environment in convective triggering processes helps resolve the phase‐locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light‐to‐moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low‐resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes. Key findings: (1) The model bias of too weak rainfall intensity of diurnal cycle of precipitation in climate simulations is improved in weather hindcasts over the central United States (2) Models with a revised convection trigger that allows air parcels to be lifted above the boundary layer show much better simulation of nocturnal rainfall which is often associated with the propagation of mesoscale systems. (3) Including convective memory in cumulus parameterizations helps to decrease the model bias of too frequent light‐to‐moderate rain but it also weakens the diurnal variability. The composite mean diurnal cycle of precipitation (mm·day−1) for (a) climate runs (June–August, 2011–2018) and (b) hindcast runs (June 1–July 15, 2015). Results from the five‐day hindcasts are averaged over Day 2 to Day 5 hindcast lead time. Domain‐mean precipitation from merged radar gauge products that is available from the ARM continuous forcing data set and the VARANAL for PECAN are used in (a) and (b) respectively.
AbstractList Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large‐scale environment in convective triggering processes helps resolve the phase‐locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light‐to‐moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low‐resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes. Key findings: (1) The model bias of too weak rainfall intensity of diurnal cycle of precipitation in climate simulations is improved in weather hindcasts over the central United States (2) Models with a revised convection trigger that allows air parcels to be lifted above the boundary layer show much better simulation of nocturnal rainfall which is often associated with the propagation of mesoscale systems. (3) Including convective memory in cumulus parameterizations helps to decrease the model bias of too frequent light‐to‐moderate rain but it also weakens the diurnal variability. The composite mean diurnal cycle of precipitation (mm·day−1) for (a) climate runs (June–August, 2011–2018) and (b) hindcast runs (June 1–July 15, 2015). Results from the five‐day hindcasts are averaged over Day 2 to Day 5 hindcast lead time. Domain‐mean precipitation from merged radar gauge products that is available from the ARM continuous forcing data set and the VARANAL for PECAN are used in (a) and (b) respectively.
Abstract Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large‐scale environment in convective triggering processes helps resolve the phase‐locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light‐to‐moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low‐resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes.
Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large‐scale environment in convective triggering processes helps resolve the phase‐locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light‐to‐moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low‐resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes.
Author Zhang, Guang J.
Van Weverberg, Kwinten
Tang, Shuaiqi
Bechtold, Peter
Ma, Hsi‐Yen
Choi, In‐Jin
Xie, Shaocheng
Wang, Yi‐Chi
Zeng, Xubin
Wong, May
Cui, Zeyu
Vaillancourt, Paul A.
Tao, Cheng
Wu, Wen‐Ying
Yang, Jing
Neelin, J. David
Zhang, Meng
Wei, Jiangfeng
Author_xml – sequence: 1
  givenname: Cheng
  orcidid: 0000-0001-8500-5019
  surname: Tao
  fullname: Tao, Cheng
  organization: Lawrence Livermore National Laboratory
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  givenname: Shaocheng
  orcidid: 0000-0001-8931-5145
  surname: Xie
  fullname: Xie, Shaocheng
  email: xie2@llnl.gov
  organization: Lawrence Livermore National Laboratory
– sequence: 3
  givenname: Hsi‐Yen
  surname: Ma
  fullname: Ma, Hsi‐Yen
  organization: Lawrence Livermore National Laboratory
– sequence: 4
  givenname: Peter
  orcidid: 0000-0002-1967-3382
  surname: Bechtold
  fullname: Bechtold, Peter
  organization: European Centre for Medium‐Range Weather Forecasts
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  givenname: Zeyu
  surname: Cui
  fullname: Cui, Zeyu
  organization: Tsinghua University
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  givenname: Paul A.
  surname: Vaillancourt
  fullname: Vaillancourt, Paul A.
  organization: Environment and Climate Change Canada
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  givenname: Kwinten
  surname: Van Weverberg
  fullname: Van Weverberg, Kwinten
  organization: Met Office
– sequence: 8
  givenname: Yi‐Chi
  surname: Wang
  fullname: Wang, Yi‐Chi
  organization: Academia Sinica
– sequence: 9
  givenname: May
  surname: Wong
  fullname: Wong, May
  organization: National Center for Atmospheric Research
– sequence: 10
  givenname: Jing
  surname: Yang
  fullname: Yang, Jing
  organization: Environment and Climate Change Canada
– sequence: 11
  givenname: Guang J.
  surname: Zhang
  fullname: Zhang, Guang J.
  organization: Scripps Institution of Oceanography
– sequence: 12
  givenname: In‐Jin
  surname: Choi
  fullname: Choi, In‐Jin
  organization: Korea Institute of Atmospheric Prediction Systems
– sequence: 13
  givenname: Shuaiqi
  orcidid: 0000-0002-8946-9205
  surname: Tang
  fullname: Tang, Shuaiqi
  organization: Pacific Northwest National Laboratory
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  givenname: Jiangfeng
  surname: Wei
  fullname: Wei, Jiangfeng
  organization: Nanjing University of Information Science and Technology
– sequence: 15
  givenname: Wen‐Ying
  surname: Wu
  fullname: Wu, Wen‐Ying
  organization: Lawrence Livermore National Laboratory
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  givenname: Meng
  surname: Zhang
  fullname: Zhang, Meng
  organization: Lawrence Livermore National Laboratory
– sequence: 17
  givenname: J. David
  surname: Neelin
  fullname: Neelin, J. David
  organization: University of California, Los Angeles
– sequence: 18
  givenname: Xubin
  surname: Zeng
  fullname: Zeng, Xubin
  organization: University of Arizona
BackLink https://www.osti.gov/biblio/2280463$$D View this record in Osti.gov
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1999; 12
2022; 35
2003; 4
2022; 37
2008; 21
1992; 49
2012; 25
2003; 84
2019a; 11
2019b; 147
2021a; 126
2012; 140
2014; 119
2004; 85
2006; 119
2019; 6
2019; 5
2005; 110
2023; 15
2015; 120
2020; 148
2008; 128
2006; 19
2020; 146
1991
2004; 109
2001; 129
2016; 16
2008b; 134
2015; 7
2003; 30
2022; 49
2023; 60
2001; 127
2021; 14
2021; 13
1990; 118
2021; 16
2009; 36
2008a; 134
2022
2021
2017; 17
2004; 17
2020
2000; 105
2017; 98
2019
2008; 136
2008; 134
2016; 29
2018; 10
2014; 71
2022; 148
2016; 9
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Snippet Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of...
Abstract Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of...
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StartPage 911
SubjectTerms Air parcels
Boundary layers
Climate
Climate models
Clouds
Convection
Diurnal
Diurnal cycle
General circulation models
Intercomparison
Mesoscale systems
Physics
Precipitation
Rain
Rainfall intensity
Tropical environments
Variability
Weather forecasting
Title Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fqj.4629
https://www.proquest.com/docview/2937821952
https://www.osti.gov/biblio/2280463
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