Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5
Abstract In this study, the stochastic deep convection parameterization of Plant and Craig (PC) is implemented in the Community Atmospheric Model version 5 (CAM5) to incorporate the stochastic processes of convection into the Zhang‐McFarlane (ZM) deterministic deep convective scheme. Its impacts on...
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2016-12-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/2016MS000756 |
| _version_ | 1811274520297734144 |
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| author | Yong Wang Guang J. Zhang |
| author_facet | Yong Wang Guang J. Zhang |
| author_sort | Yong Wang |
| collection | DOAJ |
| description | Abstract In this study, the stochastic deep convection parameterization of Plant and Craig (PC) is implemented in the Community Atmospheric Model version 5 (CAM5) to incorporate the stochastic processes of convection into the Zhang‐McFarlane (ZM) deterministic deep convective scheme. Its impacts on deep convection, shallow convection, large‐scale precipitation and associated dynamic and thermodynamic fields are investigated. Results show that with the introduction of the PC stochastic parameterization, deep convection is decreased while shallow convection is enhanced. The decrease in deep convection is mainly caused by the stochastic process and the spatial averaging of input quantities for the PC scheme. More detrained liquid water associated with more shallow convection leads to significant increase in liquid water and ice water paths, which increases large‐scale precipitation in tropical regions. Specific humidity, relative humidity, zonal wind in the tropics, and precipitable water are all improved. The simulation of shortwave cloud forcing (SWCF) is also improved. The PC stochastic parameterization decreases the global mean SWCF from −52.25 W/m2 in the standard CAM5 to −48.86 W/m2, close to −47.16 W/m2 in observations. The improvement in SWCF over the tropics is due to decreased low cloud fraction simulated by the stochastic scheme. Sensitivity tests of tuning parameters are also performed to investigate the sensitivity of simulated climatology to uncertain parameters in the stochastic deep convection scheme. |
| first_indexed | 2024-04-12T23:21:35Z |
| format | Article |
| id | doaj.art-91e7c0f14ede4a228d3b9b3978508651 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-04-12T23:21:35Z |
| publishDate | 2016-12-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-91e7c0f14ede4a228d3b9b39785086512022-12-22T03:12:31ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662016-12-01841641165610.1002/2016MS000756Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5Yong Wang0Guang J. Zhang1Center for Earth System ScienceTsinghua UniversityBeijing ChinaScripps Institution of OceanographyLa Jolla California USAAbstract In this study, the stochastic deep convection parameterization of Plant and Craig (PC) is implemented in the Community Atmospheric Model version 5 (CAM5) to incorporate the stochastic processes of convection into the Zhang‐McFarlane (ZM) deterministic deep convective scheme. Its impacts on deep convection, shallow convection, large‐scale precipitation and associated dynamic and thermodynamic fields are investigated. Results show that with the introduction of the PC stochastic parameterization, deep convection is decreased while shallow convection is enhanced. The decrease in deep convection is mainly caused by the stochastic process and the spatial averaging of input quantities for the PC scheme. More detrained liquid water associated with more shallow convection leads to significant increase in liquid water and ice water paths, which increases large‐scale precipitation in tropical regions. Specific humidity, relative humidity, zonal wind in the tropics, and precipitable water are all improved. The simulation of shortwave cloud forcing (SWCF) is also improved. The PC stochastic parameterization decreases the global mean SWCF from −52.25 W/m2 in the standard CAM5 to −48.86 W/m2, close to −47.16 W/m2 in observations. The improvement in SWCF over the tropics is due to decreased low cloud fraction simulated by the stochastic scheme. Sensitivity tests of tuning parameters are also performed to investigate the sensitivity of simulated climatology to uncertain parameters in the stochastic deep convection scheme.https://doi.org/10.1002/2016MS000756stochastic parameterizationconvectionglobal climate impactsCAM5 |
| spellingShingle | Yong Wang Guang J. Zhang Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5 Journal of Advances in Modeling Earth Systems stochastic parameterization convection global climate impacts CAM5 |
| title | Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5 |
| title_full | Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5 |
| title_fullStr | Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5 |
| title_full_unstemmed | Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5 |
| title_short | Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5 |
| title_sort | global climate impacts of stochastic deep convection parameterization in the ncar cam5 |
| topic | stochastic parameterization convection global climate impacts CAM5 |
| url | https://doi.org/10.1002/2016MS000756 |
| work_keys_str_mv | AT yongwang globalclimateimpactsofstochasticdeepconvectionparameterizationinthencarcam5 AT guangjzhang globalclimateimpactsofstochasticdeepconvectionparameterizationinthencarcam5 |