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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| Veröffentlicht in: | Quarterly journal of the Royal Meteorological Society Jg. 150; H. 759; S. 911 - 936 |
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Chichester, UK
John Wiley & Sons, Ltd
01.01.2024
Wiley Subscription Services, Inc Wiley Blackwell (John Wiley & Sons) |
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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. |
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| 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 – sequence: 2 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 – sequence: 5 givenname: Zeyu surname: Cui fullname: Cui, Zeyu organization: Tsinghua University – sequence: 6 givenname: Paul A. surname: Vaillancourt fullname: Vaillancourt, Paul A. organization: Environment and Climate Change Canada – sequence: 7 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 – sequence: 14 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 – sequence: 16 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 |
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| CitedBy_id | crossref_primary_10_1029_2024JD043020 crossref_primary_10_1002_qj_4781 crossref_primary_10_1029_2024MS004630 crossref_primary_10_5194_acp_24_7041_2024 crossref_primary_10_1029_2023GL108090 crossref_primary_10_1029_2024JD040828 crossref_primary_10_1029_2024MS004315 crossref_primary_10_1029_2024JD043102 crossref_primary_10_1360_SSTe_2024_0241 crossref_primary_10_1029_2024GL109945 crossref_primary_10_1029_2024MS004569 |
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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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| 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 |
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