A sensitivity analysis of cloud properties to CLUBB parameters in the single‐column Community Atmosphere Model (SCAM5)
In this study, we investigate the sensitivity of simulated shallow cumulus and stratocumulus to selected tunable parameters of Cloud Layers Unified by Binormals (CLUBB) in the single‐column version of Community Atmosphere Model version 5 (SCAM5). A quasi‐Monte Carlo (QMC) sampling approach is adopte...
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| Veröffentlicht in: | Journal of advances in modeling earth systems 2014-09, Vol.6 (3), p.829-858 |
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| container_title | Journal of advances in modeling earth systems |
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| creator | Guo, Zhun Wang, Minghuai Qian, Yun Larson, Vincent E. Ghan, Steven Ovchinnikov, Mikhail Bogenschutz, Peter A. Zhao, Chun Lin, Guang Zhou, Tianjun |
| description | In this study, we investigate the sensitivity of simulated shallow cumulus and stratocumulus to selected tunable parameters of Cloud Layers Unified by Binormals (CLUBB) in the single‐column version of Community Atmosphere Model version 5 (SCAM5). A quasi‐Monte Carlo (QMC) sampling approach is adopted to effectively explore the high‐dimensional parameter space and a generalized linear model is adopted to study the responses of simulated cloud fields to tunable parameters. One stratocumulus and two shallow cumulus cases are configured at both coarse and fine vertical resolutions in this study. Our results show that most of the variance in simulated cloud fields can be explained by a small number of tunable parameters. The parameters related to Newtonian and buoyancy‐damping terms of total water flux are found to be the most influential parameters for stratocumulus. For shallow cumulus, the most influential parameters are those related to skewness of vertical velocity, reflecting the strong coupling between cloud properties and dynamics in this regime. The influential parameters in the stratocumulus case are sensitive to the vertical resolution while little sensitivity is found for the shallow cumulus cases, as eddy mixing length (or dissipation time scale) plays a more important role and depends more strongly on the vertical resolution in stratocumulus than in shallow convections. The influential parameters remain almost unchanged when the number of tunable parameters increases from 16 to 35. This study improves understanding of the CLUBB behavior associated with parameter uncertainties and provides valuable insights for other high‐order turbulence closure schemes.
Key Points
Most variances in cloud fields can be explained by a small number of parameters
Results for stratocumulus are sensitive to vertical resolution
Critical parameters in shallow cumulus are related to vertical velocity skewness |
| doi_str_mv | 10.1002/2014MS000315 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_osti_</sourceid><recordid>TN_cdi_osti_scitechconnect_1168894</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1622604735</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4331-42287fb8542adcb4c81771ffcb8f94fe32cca5891b7ae90425bf3fd7bee90f093</originalsourceid><addsrcrecordid>eNp90cluFDEQANAWAomQcOMDLLgEKUO89nLstBIWzYhDyNlye8qMI7fd2O7A3PiEfCNfgqPhEHHIyeXSU6mWqnpD8AeCMT2nmPDNNcaYEfGsOiIdpyvK6_r5o_hl9SqlW4zruqbiqPrVowQ-2WzvbN4j5ZXbJ5tQMEi7sGzRHMMMMVtIKAc0rG8uLtCsopogQ0zIepR3gJL13x38-X2vg1smj4YwTYt_qNjnKaR5BxHQJmzBodProd-I9yfVC6Ncgtf_3uPq5ury2_Bptf768fPQr1eaM0ZWnNK2MWMrOFVbPXLdkqYhxuixNR03wKjWSrQdGRsFHeZUjIaZbTNC-RncsePq7aFuSNnKpG0GvdPBe9BZElK3bccLOj2gMu2PBVKWk00anFMewpIkqSmtMW-YKPTdf_Q2LLGsraiu9MZFJ1hRZwelY0gpgpFztJOKe0mwfLiVfHyrwtmB_7QO9k9a-aXfXJYEJuwvoGeVmA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1917745953</pqid></control><display><type>article</type><title>A sensitivity analysis of cloud properties to CLUBB parameters in the single‐column Community Atmosphere Model (SCAM5)</title><source>DOAJ Directory of Open Access Journals</source><source>Access via Wiley Online Library</source><source>Wiley Online Library Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Guo, Zhun ; Wang, Minghuai ; Qian, Yun ; Larson, Vincent E. ; Ghan, Steven ; Ovchinnikov, Mikhail ; Bogenschutz, Peter A. ; Zhao, Chun ; Lin, Guang ; Zhou, Tianjun</creator><creatorcontrib>Guo, Zhun ; Wang, Minghuai ; Qian, Yun ; Larson, Vincent E. ; Ghan, Steven ; Ovchinnikov, Mikhail ; Bogenschutz, Peter A. ; Zhao, Chun ; Lin, Guang ; Zhou, Tianjun ; Pacific Northwest National Lab. (PNNL), Richland, WA (United States)</creatorcontrib><description>In this study, we investigate the sensitivity of simulated shallow cumulus and stratocumulus to selected tunable parameters of Cloud Layers Unified by Binormals (CLUBB) in the single‐column version of Community Atmosphere Model version 5 (SCAM5). A quasi‐Monte Carlo (QMC) sampling approach is adopted to effectively explore the high‐dimensional parameter space and a generalized linear model is adopted to study the responses of simulated cloud fields to tunable parameters. One stratocumulus and two shallow cumulus cases are configured at both coarse and fine vertical resolutions in this study. Our results show that most of the variance in simulated cloud fields can be explained by a small number of tunable parameters. The parameters related to Newtonian and buoyancy‐damping terms of total water flux are found to be the most influential parameters for stratocumulus. For shallow cumulus, the most influential parameters are those related to skewness of vertical velocity, reflecting the strong coupling between cloud properties and dynamics in this regime. The influential parameters in the stratocumulus case are sensitive to the vertical resolution while little sensitivity is found for the shallow cumulus cases, as eddy mixing length (or dissipation time scale) plays a more important role and depends more strongly on the vertical resolution in stratocumulus than in shallow convections. The influential parameters remain almost unchanged when the number of tunable parameters increases from 16 to 35. This study improves understanding of the CLUBB behavior associated with parameter uncertainties and provides valuable insights for other high‐order turbulence closure schemes.
Key Points
Most variances in cloud fields can be explained by a small number of parameters
Results for stratocumulus are sensitive to vertical resolution
Critical parameters in shallow cumulus are related to vertical velocity skewness</description><identifier>ISSN: 1942-2466</identifier><identifier>EISSN: 1942-2466</identifier><identifier>DOI: 10.1002/2014MS000315</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Atmosphere ; Atmospheric models ; boundary layer ; Buoyancy ; Cloud Layers Unified by Binormals (CLUBB) ; Cloud properties ; Clouds ; Community Atmosphere Model ; Community Atmosphere Model version 5 (SCAM5) ; Cumulus clouds ; Damping ; Dynamics ; generalized linear model ; GEOSCIENCES ; Marine fishes ; Mixing length ; Parameters ; Properties ; quasi‐Monte Carlo sampling ; Resolution ; sensitivity analyses ; Sensitivity analysis ; shallow convection ; shallow cumulus and stratocumulus clouds ; Skewness ; Statistical methods ; stratocumulus ; Stratocumulus clouds ; Temperature ; Turbulence ; Velocity ; Vertical velocities</subject><ispartof>Journal of advances in modeling earth systems, 2014-09, Vol.6 (3), p.829-858</ispartof><rights>2014. The Authors.</rights><rights>2014. This work is published under http://creativecommons.org/licenses/by-nc-nd/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4331-42287fb8542adcb4c81771ffcb8f94fe32cca5891b7ae90425bf3fd7bee90f093</citedby><cites>FETCH-LOGICAL-c4331-42287fb8542adcb4c81771ffcb8f94fe32cca5891b7ae90425bf3fd7bee90f093</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2014MS000315$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2014MS000315$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,864,885,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids><backlink>$$Uhttps://www.osti.gov/servlets/purl/1168894$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Guo, Zhun</creatorcontrib><creatorcontrib>Wang, Minghuai</creatorcontrib><creatorcontrib>Qian, Yun</creatorcontrib><creatorcontrib>Larson, Vincent E.</creatorcontrib><creatorcontrib>Ghan, Steven</creatorcontrib><creatorcontrib>Ovchinnikov, Mikhail</creatorcontrib><creatorcontrib>Bogenschutz, Peter A.</creatorcontrib><creatorcontrib>Zhao, Chun</creatorcontrib><creatorcontrib>Lin, Guang</creatorcontrib><creatorcontrib>Zhou, Tianjun</creatorcontrib><creatorcontrib>Pacific Northwest National Lab. (PNNL), Richland, WA (United States)</creatorcontrib><title>A sensitivity analysis of cloud properties to CLUBB parameters in the single‐column Community Atmosphere Model (SCAM5)</title><title>Journal of advances in modeling earth systems</title><description>In this study, we investigate the sensitivity of simulated shallow cumulus and stratocumulus to selected tunable parameters of Cloud Layers Unified by Binormals (CLUBB) in the single‐column version of Community Atmosphere Model version 5 (SCAM5). A quasi‐Monte Carlo (QMC) sampling approach is adopted to effectively explore the high‐dimensional parameter space and a generalized linear model is adopted to study the responses of simulated cloud fields to tunable parameters. One stratocumulus and two shallow cumulus cases are configured at both coarse and fine vertical resolutions in this study. Our results show that most of the variance in simulated cloud fields can be explained by a small number of tunable parameters. The parameters related to Newtonian and buoyancy‐damping terms of total water flux are found to be the most influential parameters for stratocumulus. For shallow cumulus, the most influential parameters are those related to skewness of vertical velocity, reflecting the strong coupling between cloud properties and dynamics in this regime. The influential parameters in the stratocumulus case are sensitive to the vertical resolution while little sensitivity is found for the shallow cumulus cases, as eddy mixing length (or dissipation time scale) plays a more important role and depends more strongly on the vertical resolution in stratocumulus than in shallow convections. The influential parameters remain almost unchanged when the number of tunable parameters increases from 16 to 35. This study improves understanding of the CLUBB behavior associated with parameter uncertainties and provides valuable insights for other high‐order turbulence closure schemes.
Key Points
Most variances in cloud fields can be explained by a small number of parameters
Results for stratocumulus are sensitive to vertical resolution
Critical parameters in shallow cumulus are related to vertical velocity skewness</description><subject>Atmosphere</subject><subject>Atmospheric models</subject><subject>boundary layer</subject><subject>Buoyancy</subject><subject>Cloud Layers Unified by Binormals (CLUBB)</subject><subject>Cloud properties</subject><subject>Clouds</subject><subject>Community Atmosphere Model</subject><subject>Community Atmosphere Model version 5 (SCAM5)</subject><subject>Cumulus clouds</subject><subject>Damping</subject><subject>Dynamics</subject><subject>generalized linear model</subject><subject>GEOSCIENCES</subject><subject>Marine fishes</subject><subject>Mixing length</subject><subject>Parameters</subject><subject>Properties</subject><subject>quasi‐Monte Carlo sampling</subject><subject>Resolution</subject><subject>sensitivity analyses</subject><subject>Sensitivity analysis</subject><subject>shallow convection</subject><subject>shallow cumulus and stratocumulus clouds</subject><subject>Skewness</subject><subject>Statistical methods</subject><subject>stratocumulus</subject><subject>Stratocumulus clouds</subject><subject>Temperature</subject><subject>Turbulence</subject><subject>Velocity</subject><subject>Vertical velocities</subject><issn>1942-2466</issn><issn>1942-2466</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp90cluFDEQANAWAomQcOMDLLgEKUO89nLstBIWzYhDyNlye8qMI7fd2O7A3PiEfCNfgqPhEHHIyeXSU6mWqnpD8AeCMT2nmPDNNcaYEfGsOiIdpyvK6_r5o_hl9SqlW4zruqbiqPrVowQ-2WzvbN4j5ZXbJ5tQMEi7sGzRHMMMMVtIKAc0rG8uLtCsopogQ0zIepR3gJL13x38-X2vg1smj4YwTYt_qNjnKaR5BxHQJmzBodProd-I9yfVC6Ncgtf_3uPq5ury2_Bptf768fPQr1eaM0ZWnNK2MWMrOFVbPXLdkqYhxuixNR03wKjWSrQdGRsFHeZUjIaZbTNC-RncsePq7aFuSNnKpG0GvdPBe9BZElK3bccLOj2gMu2PBVKWk00anFMewpIkqSmtMW-YKPTdf_Q2LLGsraiu9MZFJ1hRZwelY0gpgpFztJOKe0mwfLiVfHyrwtmB_7QO9k9a-aXfXJYEJuwvoGeVmA</recordid><startdate>201409</startdate><enddate>201409</enddate><creator>Guo, Zhun</creator><creator>Wang, Minghuai</creator><creator>Qian, Yun</creator><creator>Larson, Vincent E.</creator><creator>Ghan, Steven</creator><creator>Ovchinnikov, Mikhail</creator><creator>Bogenschutz, Peter A.</creator><creator>Zhao, Chun</creator><creator>Lin, Guang</creator><creator>Zhou, Tianjun</creator><general>John Wiley & Sons, Inc</general><general>American Geophysical Union (AGU)</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>OIOZB</scope><scope>OTOTI</scope></search><sort><creationdate>201409</creationdate><title>A sensitivity analysis of cloud properties to CLUBB parameters in the single‐column Community Atmosphere Model (SCAM5)</title><author>Guo, Zhun ; Wang, Minghuai ; Qian, Yun ; Larson, Vincent E. ; Ghan, Steven ; Ovchinnikov, Mikhail ; Bogenschutz, Peter A. ; Zhao, Chun ; Lin, Guang ; Zhou, Tianjun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4331-42287fb8542adcb4c81771ffcb8f94fe32cca5891b7ae90425bf3fd7bee90f093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Atmosphere</topic><topic>Atmospheric models</topic><topic>boundary layer</topic><topic>Buoyancy</topic><topic>Cloud Layers Unified by Binormals (CLUBB)</topic><topic>Cloud properties</topic><topic>Clouds</topic><topic>Community Atmosphere Model</topic><topic>Community Atmosphere Model version 5 (SCAM5)</topic><topic>Cumulus clouds</topic><topic>Damping</topic><topic>Dynamics</topic><topic>generalized linear model</topic><topic>GEOSCIENCES</topic><topic>Marine fishes</topic><topic>Mixing length</topic><topic>Parameters</topic><topic>Properties</topic><topic>quasi‐Monte Carlo sampling</topic><topic>Resolution</topic><topic>sensitivity analyses</topic><topic>Sensitivity analysis</topic><topic>shallow convection</topic><topic>shallow cumulus and stratocumulus clouds</topic><topic>Skewness</topic><topic>Statistical methods</topic><topic>stratocumulus</topic><topic>Stratocumulus clouds</topic><topic>Temperature</topic><topic>Turbulence</topic><topic>Velocity</topic><topic>Vertical velocities</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Zhun</creatorcontrib><creatorcontrib>Wang, Minghuai</creatorcontrib><creatorcontrib>Qian, Yun</creatorcontrib><creatorcontrib>Larson, Vincent E.</creatorcontrib><creatorcontrib>Ghan, Steven</creatorcontrib><creatorcontrib>Ovchinnikov, Mikhail</creatorcontrib><creatorcontrib>Bogenschutz, Peter A.</creatorcontrib><creatorcontrib>Zhao, Chun</creatorcontrib><creatorcontrib>Lin, Guang</creatorcontrib><creatorcontrib>Zhou, Tianjun</creatorcontrib><creatorcontrib>Pacific Northwest National Lab. (PNNL), Richland, WA (United States)</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><jtitle>Journal of advances in modeling earth systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Zhun</au><au>Wang, Minghuai</au><au>Qian, Yun</au><au>Larson, Vincent E.</au><au>Ghan, Steven</au><au>Ovchinnikov, Mikhail</au><au>Bogenschutz, Peter A.</au><au>Zhao, Chun</au><au>Lin, Guang</au><au>Zhou, Tianjun</au><aucorp>Pacific Northwest National Lab. (PNNL), Richland, WA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A sensitivity analysis of cloud properties to CLUBB parameters in the single‐column Community Atmosphere Model (SCAM5)</atitle><jtitle>Journal of advances in modeling earth systems</jtitle><date>2014-09</date><risdate>2014</risdate><volume>6</volume><issue>3</issue><spage>829</spage><epage>858</epage><pages>829-858</pages><issn>1942-2466</issn><eissn>1942-2466</eissn><abstract>In this study, we investigate the sensitivity of simulated shallow cumulus and stratocumulus to selected tunable parameters of Cloud Layers Unified by Binormals (CLUBB) in the single‐column version of Community Atmosphere Model version 5 (SCAM5). A quasi‐Monte Carlo (QMC) sampling approach is adopted to effectively explore the high‐dimensional parameter space and a generalized linear model is adopted to study the responses of simulated cloud fields to tunable parameters. One stratocumulus and two shallow cumulus cases are configured at both coarse and fine vertical resolutions in this study. Our results show that most of the variance in simulated cloud fields can be explained by a small number of tunable parameters. The parameters related to Newtonian and buoyancy‐damping terms of total water flux are found to be the most influential parameters for stratocumulus. For shallow cumulus, the most influential parameters are those related to skewness of vertical velocity, reflecting the strong coupling between cloud properties and dynamics in this regime. The influential parameters in the stratocumulus case are sensitive to the vertical resolution while little sensitivity is found for the shallow cumulus cases, as eddy mixing length (or dissipation time scale) plays a more important role and depends more strongly on the vertical resolution in stratocumulus than in shallow convections. The influential parameters remain almost unchanged when the number of tunable parameters increases from 16 to 35. This study improves understanding of the CLUBB behavior associated with parameter uncertainties and provides valuable insights for other high‐order turbulence closure schemes.
Key Points
Most variances in cloud fields can be explained by a small number of parameters
Results for stratocumulus are sensitive to vertical resolution
Critical parameters in shallow cumulus are related to vertical velocity skewness</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/2014MS000315</doi><tpages>30</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Atmosphere Atmospheric models boundary layer Buoyancy Cloud Layers Unified by Binormals (CLUBB) Cloud properties Clouds Community Atmosphere Model Community Atmosphere Model version 5 (SCAM5) Cumulus clouds Damping Dynamics generalized linear model GEOSCIENCES Marine fishes Mixing length Parameters Properties quasi‐Monte Carlo sampling Resolution sensitivity analyses Sensitivity analysis shallow convection shallow cumulus and stratocumulus clouds Skewness Statistical methods stratocumulus Stratocumulus clouds Temperature Turbulence Velocity Vertical velocities |
| title | A sensitivity analysis of cloud properties to CLUBB parameters in the single‐column Community Atmosphere Model (SCAM5) |
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