Taiwan Earth System Model Version 1: Description and Evaluation of Mean State

The Taiwan Earth System Model (TaiESM) version 1 is developed based on Community Earth System Model version 1.2.2 of National Center for Atmospheric Research. Several innovated physical and chemical parameterizations, including trigger functions for deep convection, cloud macrophysics, aerosol, and...

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Main Authors: Lee, Wei-Liang, Wang, Yi-Chi, Shiu, Chein-Jung, Tsai, I-chun, Tu, Chia-Ying, Lan, Yung-Yao, Chen, Jen-Ping, Pan, Hau-Lu, Hsu, Huang-Hsiung
Format: Text
Language:English
Published: 2020
Subjects:
Online Access:https://doi.org/10.5194/gmd-2019-377
https://gmd.copernicus.org/preprints/gmd-2019-377/
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spelling ftcopernicus:oai:publications.copernicus.org:gmdd82776 2023-05-15T18:18:00+02:00 Taiwan Earth System Model Version 1: Description and Evaluation of Mean State Lee, Wei-Liang Wang, Yi-Chi Shiu, Chein-Jung Tsai, I-chun Tu, Chia-Ying Lan, Yung-Yao Chen, Jen-Ping Pan, Hau-Lu Hsu, Huang-Hsiung 2020-01-31 application/pdf https://doi.org/10.5194/gmd-2019-377 https://gmd.copernicus.org/preprints/gmd-2019-377/ eng eng doi:10.5194/gmd-2019-377 https://gmd.copernicus.org/preprints/gmd-2019-377/ eISSN: 1991-9603 Text 2020 ftcopernicus https://doi.org/10.5194/gmd-2019-377 2020-07-20T16:22:28Z The Taiwan Earth System Model (TaiESM) version 1 is developed based on Community Earth System Model version 1.2.2 of National Center for Atmospheric Research. Several innovated physical and chemical parameterizations, including trigger functions for deep convection, cloud macrophysics, aerosol, and three-dimensional radiation–topography interaction, as well as a one-dimensional mixed-layer model optional for the atmosphere component, are incorporated. The precipitation variability, such as diurnal cycle and propagation of convection systems, is improved in TaiESM. TaiESM demonstrates good model stability in the 500-year preindustrial simulation in terms of the net flux at the top of the model, surface temperatures, and sea ice concentration. In the historical simulation, although the warming before 1935 is weak, TaiESM well captures the increasing trend of temperature after 1950. The current climatology of TaiESM during 1979–2005 is evaluated by observational and reanalysis datasets. Cloud amounts are too large in TaiESM, but their cloud forcing is only slightly weaker than observational data. The mean bias of the sea surface temperature is almost zero, whereas the surface air temperatures over land and sea ice regions exhibit cold biases. The overall performance of TaiESM is above average among models in Coupled Model Intercomparison Project phase 5, particularly that the bias of precipitation is smallest. However, several common discrepancies shared by most models still exist, such as the double Intertropical Convergence Zone bias in precipitation and warm bias over the Southern Ocean. Text Sea ice Southern Ocean Copernicus Publications: E-Journals Southern Ocean
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description The Taiwan Earth System Model (TaiESM) version 1 is developed based on Community Earth System Model version 1.2.2 of National Center for Atmospheric Research. Several innovated physical and chemical parameterizations, including trigger functions for deep convection, cloud macrophysics, aerosol, and three-dimensional radiation–topography interaction, as well as a one-dimensional mixed-layer model optional for the atmosphere component, are incorporated. The precipitation variability, such as diurnal cycle and propagation of convection systems, is improved in TaiESM. TaiESM demonstrates good model stability in the 500-year preindustrial simulation in terms of the net flux at the top of the model, surface temperatures, and sea ice concentration. In the historical simulation, although the warming before 1935 is weak, TaiESM well captures the increasing trend of temperature after 1950. The current climatology of TaiESM during 1979–2005 is evaluated by observational and reanalysis datasets. Cloud amounts are too large in TaiESM, but their cloud forcing is only slightly weaker than observational data. The mean bias of the sea surface temperature is almost zero, whereas the surface air temperatures over land and sea ice regions exhibit cold biases. The overall performance of TaiESM is above average among models in Coupled Model Intercomparison Project phase 5, particularly that the bias of precipitation is smallest. However, several common discrepancies shared by most models still exist, such as the double Intertropical Convergence Zone bias in precipitation and warm bias over the Southern Ocean.
format Text
author Lee, Wei-Liang
Wang, Yi-Chi
Shiu, Chein-Jung
Tsai, I-chun
Tu, Chia-Ying
Lan, Yung-Yao
Chen, Jen-Ping
Pan, Hau-Lu
Hsu, Huang-Hsiung
spellingShingle Lee, Wei-Liang
Wang, Yi-Chi
Shiu, Chein-Jung
Tsai, I-chun
Tu, Chia-Ying
Lan, Yung-Yao
Chen, Jen-Ping
Pan, Hau-Lu
Hsu, Huang-Hsiung
Taiwan Earth System Model Version 1: Description and Evaluation of Mean State
author_facet Lee, Wei-Liang
Wang, Yi-Chi
Shiu, Chein-Jung
Tsai, I-chun
Tu, Chia-Ying
Lan, Yung-Yao
Chen, Jen-Ping
Pan, Hau-Lu
Hsu, Huang-Hsiung
author_sort Lee, Wei-Liang
title Taiwan Earth System Model Version 1: Description and Evaluation of Mean State
title_short Taiwan Earth System Model Version 1: Description and Evaluation of Mean State
title_full Taiwan Earth System Model Version 1: Description and Evaluation of Mean State
title_fullStr Taiwan Earth System Model Version 1: Description and Evaluation of Mean State
title_full_unstemmed Taiwan Earth System Model Version 1: Description and Evaluation of Mean State
title_sort taiwan earth system model version 1: description and evaluation of mean state
publishDate 2020
url https://doi.org/10.5194/gmd-2019-377
https://gmd.copernicus.org/preprints/gmd-2019-377/
geographic Southern Ocean
geographic_facet Southern Ocean
genre Sea ice
Southern Ocean
genre_facet Sea ice
Southern Ocean
op_source eISSN: 1991-9603
op_relation doi:10.5194/gmd-2019-377
https://gmd.copernicus.org/preprints/gmd-2019-377/
op_doi https://doi.org/10.5194/gmd-2019-377
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