Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations
A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipi...
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Veröffentlicht in: | Journal of advances in modeling earth systems 2019-03, Vol.11 (3), p.839-860 |
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description | A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipitation and the associated radiative effect, we perform climate simulations employing both the traditional diagnostic and new prognostic precipitation schemes, the latter also being tested with and without incorporating the radiative effect of snow. The prognostic precipitation, which maintains precipitation in the atmosphere across multiple time steps, models the ratio of accretion to autoconversion as being approximately an order of magnitude higher than that for the diagnostic scheme. Such changes in microphysical process rates tend to reduce the cloud water susceptibility as the autoconversion process is the only pathway through which aerosols can influence rain formation. The resultant anthropogenic aerosol effect is reduced by approximately 21% in the prognostic precipitation scheme. Modifications to the microphysical process rates also change the vertical distribution of hydrometeors in the manner that increases the fractional occurrence of single‐layered warm clouds by 38%. The new scheme mitigates the excess of supercooled liquid water produced by the previous scheme and increases the total mass of ice hydrometeors. Both characteristics are consistent with CloudSat/CALIPSO retrievals. The radiative effect of snow is significant at both longwave and shortwave (6.4 and 5.1 W/m2 in absolute values, respectively) and can alter the precipitation fields via energetic controls on precipitation. These results suggest that the prognostic precipitation scheme, with its radiative effects incorporated, makes an indispensable contribution to improving the reliability of climate modeling.
Key Points
Prognostic precipitation (both rain and snow) and its radiative effects are introduced into the MIROC6‐SPRINTARS aerosol‐climate model
Aerosol‐cloud‐precipitation‐climate interactions are evaluated using multisensor satellite data sets for different treatments of rain
The radiative effect of snow is significant for both longwave and shortwave radiation and must be included for more reliable climate modeling |
doi_str_mv | 10.1029/2018MS001596 |
format | Article |
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Key Points
Prognostic precipitation (both rain and snow) and its radiative effects are introduced into the MIROC6‐SPRINTARS aerosol‐climate model
Aerosol‐cloud‐precipitation‐climate interactions are evaluated using multisensor satellite data sets for different treatments of rain
The radiative effect of snow is significant for both longwave and shortwave radiation and must be included for more reliable climate modeling</description><identifier>ISSN: 1942-2466</identifier><identifier>EISSN: 1942-2466</identifier><identifier>DOI: 10.1029/2018MS001596</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Accretion ; Aerosol effects ; Aerosols ; aerosol‐cloud‐precipitation interactions ; Anthropogenic factors ; Atmospheric precipitations ; Climate ; Climate effects ; Climate models ; Cloud water ; GCM ; General circulation models ; Hydrometeors ; Microphysics ; Morphology ; Precipitation ; prognostic precipitation ; Rain ; Rain formation ; Remote sensing systems ; Satellite observation ; Snow ; Vertical distribution ; Websites</subject><ispartof>Journal of advances in modeling earth systems, 2019-03, Vol.11 (3), p.839-860</ispartof><rights>2019. The Authors.</rights><rights>2019. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (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-c4119-24b9d8cefa8b05ab3d30836c316abff86c51396f829326f5f0e2505e821cb73d3</citedby><cites>FETCH-LOGICAL-c4119-24b9d8cefa8b05ab3d30836c316abff86c51396f829326f5f0e2505e821cb73d3</cites><orcidid>0000-0002-1491-0297 ; 0000-0001-5315-2452 ; 0000-0003-2254-6429 ; 0000-0002-2859-6067</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2018MS001596$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2018MS001596$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids></links><search><creatorcontrib>Michibata, Takuro</creatorcontrib><creatorcontrib>Suzuki, Kentaroh</creatorcontrib><creatorcontrib>Sekiguchi, Miho</creatorcontrib><creatorcontrib>Takemura, Toshihiko</creatorcontrib><title>Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations</title><title>Journal of advances in modeling earth systems</title><description>A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipitation and the associated radiative effect, we perform climate simulations employing both the traditional diagnostic and new prognostic precipitation schemes, the latter also being tested with and without incorporating the radiative effect of snow. The prognostic precipitation, which maintains precipitation in the atmosphere across multiple time steps, models the ratio of accretion to autoconversion as being approximately an order of magnitude higher than that for the diagnostic scheme. Such changes in microphysical process rates tend to reduce the cloud water susceptibility as the autoconversion process is the only pathway through which aerosols can influence rain formation. The resultant anthropogenic aerosol effect is reduced by approximately 21% in the prognostic precipitation scheme. Modifications to the microphysical process rates also change the vertical distribution of hydrometeors in the manner that increases the fractional occurrence of single‐layered warm clouds by 38%. The new scheme mitigates the excess of supercooled liquid water produced by the previous scheme and increases the total mass of ice hydrometeors. Both characteristics are consistent with CloudSat/CALIPSO retrievals. The radiative effect of snow is significant at both longwave and shortwave (6.4 and 5.1 W/m2 in absolute values, respectively) and can alter the precipitation fields via energetic controls on precipitation. These results suggest that the prognostic precipitation scheme, with its radiative effects incorporated, makes an indispensable contribution to improving the reliability of climate modeling.
Key Points
Prognostic precipitation (both rain and snow) and its radiative effects are introduced into the MIROC6‐SPRINTARS aerosol‐climate model
Aerosol‐cloud‐precipitation‐climate interactions are evaluated using multisensor satellite data sets for different treatments of rain
The radiative effect of snow is significant for both longwave and shortwave radiation and must be included for more reliable climate modeling</description><subject>Accretion</subject><subject>Aerosol effects</subject><subject>Aerosols</subject><subject>aerosol‐cloud‐precipitation interactions</subject><subject>Anthropogenic factors</subject><subject>Atmospheric precipitations</subject><subject>Climate</subject><subject>Climate effects</subject><subject>Climate models</subject><subject>Cloud water</subject><subject>GCM</subject><subject>General circulation models</subject><subject>Hydrometeors</subject><subject>Microphysics</subject><subject>Morphology</subject><subject>Precipitation</subject><subject>prognostic precipitation</subject><subject>Rain</subject><subject>Rain formation</subject><subject>Remote sensing systems</subject><subject>Satellite observation</subject><subject>Snow</subject><subject>Vertical distribution</subject><subject>Websites</subject><issn>1942-2466</issn><issn>1942-2466</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</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>eNp90E9PwjAUAPDGaCKiNz9AE6-ibbfW1tsyETFMCMPz0pUOS-YGbcFw8yP4Gf0kTuaBk6f3kvd7f_IAuMToBiMibgnCPEkRwlSwI9DBIiQ9EjJ2fJCfgjPnlggxxgjtgPXE1ouqdt4oOLFamZXx0pu6gqaC_k3DZDgdx-z78yudTIcvs2iawkGc3MMH7ZQ1qz2V1Rz2t7LctJ3RQprKeZhKr8vSeA3HudN2u6-6c3BSyNLpi7_YBa-P_Vn81BuNB8M4GvVUiLFobs3FnCtdSJ4jKvNgHiAeMBVgJvOi4ExRHAhWcCICwgpaIE0oopoTrPK7RnfBVTt3Zev1RjufLeuNrZqVGSECERbSkDXqulXK1s5ZXWQra96l3WUYZb9PzQ6f2vCg5R-m1Lt_bfYcJX2COBXBD6gTeMI</recordid><startdate>201903</startdate><enddate>201903</enddate><creator>Michibata, Takuro</creator><creator>Suzuki, Kentaroh</creator><creator>Sekiguchi, Miho</creator><creator>Takemura, Toshihiko</creator><general>John Wiley & Sons, Inc</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>PRINS</scope><orcidid>https://orcid.org/0000-0002-1491-0297</orcidid><orcidid>https://orcid.org/0000-0001-5315-2452</orcidid><orcidid>https://orcid.org/0000-0003-2254-6429</orcidid><orcidid>https://orcid.org/0000-0002-2859-6067</orcidid></search><sort><creationdate>201903</creationdate><title>Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations</title><author>Michibata, Takuro ; Suzuki, Kentaroh ; Sekiguchi, Miho ; Takemura, Toshihiko</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4119-24b9d8cefa8b05ab3d30836c316abff86c51396f829326f5f0e2505e821cb73d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accretion</topic><topic>Aerosol effects</topic><topic>Aerosols</topic><topic>aerosol‐cloud‐precipitation interactions</topic><topic>Anthropogenic factors</topic><topic>Atmospheric precipitations</topic><topic>Climate</topic><topic>Climate effects</topic><topic>Climate models</topic><topic>Cloud water</topic><topic>GCM</topic><topic>General circulation models</topic><topic>Hydrometeors</topic><topic>Microphysics</topic><topic>Morphology</topic><topic>Precipitation</topic><topic>prognostic precipitation</topic><topic>Rain</topic><topic>Rain formation</topic><topic>Remote sensing systems</topic><topic>Satellite observation</topic><topic>Snow</topic><topic>Vertical distribution</topic><topic>Websites</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Michibata, Takuro</creatorcontrib><creatorcontrib>Suzuki, Kentaroh</creatorcontrib><creatorcontrib>Sekiguchi, Miho</creatorcontrib><creatorcontrib>Takemura, Toshihiko</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>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Journal of advances in modeling earth systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Michibata, Takuro</au><au>Suzuki, Kentaroh</au><au>Sekiguchi, Miho</au><au>Takemura, Toshihiko</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations</atitle><jtitle>Journal of advances in modeling earth systems</jtitle><date>2019-03</date><risdate>2019</risdate><volume>11</volume><issue>3</issue><spage>839</spage><epage>860</epage><pages>839-860</pages><issn>1942-2466</issn><eissn>1942-2466</eissn><abstract>A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipitation and the associated radiative effect, we perform climate simulations employing both the traditional diagnostic and new prognostic precipitation schemes, the latter also being tested with and without incorporating the radiative effect of snow. The prognostic precipitation, which maintains precipitation in the atmosphere across multiple time steps, models the ratio of accretion to autoconversion as being approximately an order of magnitude higher than that for the diagnostic scheme. Such changes in microphysical process rates tend to reduce the cloud water susceptibility as the autoconversion process is the only pathway through which aerosols can influence rain formation. The resultant anthropogenic aerosol effect is reduced by approximately 21% in the prognostic precipitation scheme. Modifications to the microphysical process rates also change the vertical distribution of hydrometeors in the manner that increases the fractional occurrence of single‐layered warm clouds by 38%. The new scheme mitigates the excess of supercooled liquid water produced by the previous scheme and increases the total mass of ice hydrometeors. Both characteristics are consistent with CloudSat/CALIPSO retrievals. The radiative effect of snow is significant at both longwave and shortwave (6.4 and 5.1 W/m2 in absolute values, respectively) and can alter the precipitation fields via energetic controls on precipitation. These results suggest that the prognostic precipitation scheme, with its radiative effects incorporated, makes an indispensable contribution to improving the reliability of climate modeling.
Key Points
Prognostic precipitation (both rain and snow) and its radiative effects are introduced into the MIROC6‐SPRINTARS aerosol‐climate model
Aerosol‐cloud‐precipitation‐climate interactions are evaluated using multisensor satellite data sets for different treatments of rain
The radiative effect of snow is significant for both longwave and shortwave radiation and must be included for more reliable climate modeling</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2018MS001596</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-1491-0297</orcidid><orcidid>https://orcid.org/0000-0001-5315-2452</orcidid><orcidid>https://orcid.org/0000-0003-2254-6429</orcidid><orcidid>https://orcid.org/0000-0002-2859-6067</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accretion Aerosol effects Aerosols aerosol‐cloud‐precipitation interactions Anthropogenic factors Atmospheric precipitations Climate Climate effects Climate models Cloud water GCM General circulation models Hydrometeors Microphysics Morphology Precipitation prognostic precipitation Rain Rain formation Remote sensing systems Satellite observation Snow Vertical distribution Websites |
title | Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations |
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