Sensitivity of climate models in relation to the “pool of inhibited cloudiness” over South of the Bay of Bengal
Realistic simulation of cloud variability and rainfall by the coupled models still remains a challenge particularly over the Asian Summer Monsoon (ASM). The simulation of the “pool of inhibited cloudiness” (hereafter referred to as PIC) and associated cloud variability have been analysed in the hist...
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creator | Roy, Kumar Mukhopadhyay, Parthasarathi Murali Krishna, Ravuri Phani Ganai, Malay Mahakur, Mata Narayana Rao, Thota Nair, Anish Kumar M. Ramakrishna, Surireddi Satya Venkata Siva |
description | Realistic simulation of cloud variability and rainfall by the coupled models still remains a challenge particularly over the Asian Summer Monsoon (ASM). The simulation of the “pool of inhibited cloudiness” (hereafter referred to as PIC) and associated cloud variability have been analysed in the historical run of 26 models which participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and it is shown that the current state of the art general circulation models (GCMs) still have difficulties in properly simulating the PIC. The pool covers an area greater than 1 million km2 between 3°–13°N and 77°–90°E over the southwest Bay of Bengal (BoB); persisting throughout the ASM and interestingly it is surrounded by the deep convective region. The majority of the models could not simulate cloud variability over the ASM. The performance of National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) version 2.0, which is being used for operational monsoon prediction over the Indian region, is also tested to evaluate its fidelity in capturing PIC. The moist convective process in the default CFSv2 is found to be one of the major sources of uncertainty in its monsoon prediction. In this study, evaluation of the default CFSv2 (CTRL) and suites of modified CFSv2 have been carried out. The default version of CFSv2 has the simplified Arakawa Schubert (SAS) as convection scheme and Zhao and Carr (ZC) as microphysics. In another modification, the SAS is changed to revised simplified Arakawa Schubert (RSAS) keeping the microphysics unchanged. Further, a more physically based cloud scheme (WRF Single Moment 6‐class microphysics—WSM6) is used with SAS and RSAS for comparison of simulation of PIC. Among the CMIP5 models, ACESS‐1‐0, GFDL‐CM3, HadGEM2‐CC, HadGEM2‐ES are able to represent the PIC reasonably well. CFSCR has shown an improved fidelity in comparison to the CTRL, CTRL‐WSM and RSAS and other CMIP5 models. The impact of cloud microphysics in CTRL‐WSM and CFSCR appears to play an important role in the simulation of the PIC.
Average seasonal mean (June–September) latitude altitude cross section of the frequency of occurrence of clouds (FALT, expressed in percentage) during ASM along 80–90°E obtained from CloudSat‐CALIPSO, 26 CMIP5 models and from three different versions of CFSv2 T126 (CTRL, CTRL‐WSM, RSAS and CFSCR). |
doi_str_mv | 10.1002/joc.6423 |
format | Article |
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Average seasonal mean (June–September) latitude altitude cross section of the frequency of occurrence of clouds (FALT, expressed in percentage) during ASM along 80–90°E obtained from CloudSat‐CALIPSO, 26 CMIP5 models and from three different versions of CFSv2 T126 (CTRL, CTRL‐WSM, RSAS and CFSCR).</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.6423</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Accuracy ; Climate ; Climate models ; Climate prediction ; Climate system ; cloud and convection process parameterization ; Cloud cover ; Cloud microphysics ; Cloudiness ; Clouds ; Computer simulation ; Convection ; fidelity of CMIP5 and CFSv2 models ; General circulation models ; Intercomparison ; Microphysics ; Monsoon forecasting ; Monsoons ; pool of inhibited cloudiness ; Rain ; Rainfall ; Simulation ; Summer monsoon ; Variability ; Wind</subject><ispartof>International journal of climatology, 2020-06, Vol.40 (8), p.3714-3730</ispartof><rights>2019 Royal Meteorological Society</rights><rights>2020 Royal Meteorological Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2933-608c3a9e71711f3f6cd0d7d5d7f08be6aca39aa709cfe7c0dbd802930ee0d4a73</citedby><cites>FETCH-LOGICAL-c2933-608c3a9e71711f3f6cd0d7d5d7f08be6aca39aa709cfe7c0dbd802930ee0d4a73</cites><orcidid>0000-0002-3092-033X ; 0000-0001-8770-9167</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjoc.6423$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.6423$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Roy, Kumar</creatorcontrib><creatorcontrib>Mukhopadhyay, Parthasarathi</creatorcontrib><creatorcontrib>Murali Krishna, Ravuri Phani</creatorcontrib><creatorcontrib>Ganai, Malay</creatorcontrib><creatorcontrib>Mahakur, Mata</creatorcontrib><creatorcontrib>Narayana Rao, Thota</creatorcontrib><creatorcontrib>Nair, Anish Kumar M.</creatorcontrib><creatorcontrib>Ramakrishna, Surireddi Satya Venkata Siva</creatorcontrib><title>Sensitivity of climate models in relation to the “pool of inhibited cloudiness” over South of the Bay of Bengal</title><title>International journal of climatology</title><description>Realistic simulation of cloud variability and rainfall by the coupled models still remains a challenge particularly over the Asian Summer Monsoon (ASM). The simulation of the “pool of inhibited cloudiness” (hereafter referred to as PIC) and associated cloud variability have been analysed in the historical run of 26 models which participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and it is shown that the current state of the art general circulation models (GCMs) still have difficulties in properly simulating the PIC. The pool covers an area greater than 1 million km2 between 3°–13°N and 77°–90°E over the southwest Bay of Bengal (BoB); persisting throughout the ASM and interestingly it is surrounded by the deep convective region. The majority of the models could not simulate cloud variability over the ASM. The performance of National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) version 2.0, which is being used for operational monsoon prediction over the Indian region, is also tested to evaluate its fidelity in capturing PIC. The moist convective process in the default CFSv2 is found to be one of the major sources of uncertainty in its monsoon prediction. In this study, evaluation of the default CFSv2 (CTRL) and suites of modified CFSv2 have been carried out. The default version of CFSv2 has the simplified Arakawa Schubert (SAS) as convection scheme and Zhao and Carr (ZC) as microphysics. In another modification, the SAS is changed to revised simplified Arakawa Schubert (RSAS) keeping the microphysics unchanged. Further, a more physically based cloud scheme (WRF Single Moment 6‐class microphysics—WSM6) is used with SAS and RSAS for comparison of simulation of PIC. Among the CMIP5 models, ACESS‐1‐0, GFDL‐CM3, HadGEM2‐CC, HadGEM2‐ES are able to represent the PIC reasonably well. CFSCR has shown an improved fidelity in comparison to the CTRL, CTRL‐WSM and RSAS and other CMIP5 models. The impact of cloud microphysics in CTRL‐WSM and CFSCR appears to play an important role in the simulation of the PIC.
Average seasonal mean (June–September) latitude altitude cross section of the frequency of occurrence of clouds (FALT, expressed in percentage) during ASM along 80–90°E obtained from CloudSat‐CALIPSO, 26 CMIP5 models and from three different versions of CFSv2 T126 (CTRL, CTRL‐WSM, RSAS and CFSCR).</description><subject>Accuracy</subject><subject>Climate</subject><subject>Climate models</subject><subject>Climate prediction</subject><subject>Climate system</subject><subject>cloud and convection process parameterization</subject><subject>Cloud cover</subject><subject>Cloud microphysics</subject><subject>Cloudiness</subject><subject>Clouds</subject><subject>Computer simulation</subject><subject>Convection</subject><subject>fidelity of CMIP5 and CFSv2 models</subject><subject>General circulation models</subject><subject>Intercomparison</subject><subject>Microphysics</subject><subject>Monsoon forecasting</subject><subject>Monsoons</subject><subject>pool of inhibited cloudiness</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Simulation</subject><subject>Summer monsoon</subject><subject>Variability</subject><subject>Wind</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kL1OwzAURi0EEqUg8QiWWFhSrpM0iUda8atKHQpz5No31FUaF9sp6tYHgZfrk-C0rEx3Oee70iHkmsGAAcR3SyMHWRonJ6THgOcRQFGckh4UnEdFyopzcuHcEgA4Z1mPuBk2Tnu90X5LTUVlrVfCI10ZhbWjuqEWa-G1aag31C-Q7nffa2PqDtbNQs-1RxU00yrdoHP73Q81G7R0Zlq_6KhOGonD-gibD1FfkrNK1A6v_m6fvD8-vI2fo8n06WV8P4lkzJMkyqCQieCYs5yxKqkyqUDlaqjyCoo5ZkKKhAuRA5cV5hLUXBUQTEAElYo86ZOb4-7ams8WnS-XprVNeFnGKYtDDQbDQN0eKWmNcxarcm1DA7stGZRd0mDJsksa0OiIfukat_9y5et0fOB_ASaFepg</recordid><startdate>20200630</startdate><enddate>20200630</enddate><creator>Roy, Kumar</creator><creator>Mukhopadhyay, Parthasarathi</creator><creator>Murali Krishna, Ravuri Phani</creator><creator>Ganai, Malay</creator><creator>Mahakur, Mata</creator><creator>Narayana Rao, Thota</creator><creator>Nair, Anish Kumar M.</creator><creator>Ramakrishna, Surireddi Satya Venkata Siva</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-3092-033X</orcidid><orcidid>https://orcid.org/0000-0001-8770-9167</orcidid></search><sort><creationdate>20200630</creationdate><title>Sensitivity of climate models in relation to the “pool of inhibited cloudiness” over South of the Bay of Bengal</title><author>Roy, Kumar ; Mukhopadhyay, Parthasarathi ; Murali Krishna, Ravuri Phani ; Ganai, Malay ; Mahakur, Mata ; Narayana Rao, Thota ; Nair, Anish Kumar M. ; Ramakrishna, Surireddi Satya Venkata Siva</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2933-608c3a9e71711f3f6cd0d7d5d7f08be6aca39aa709cfe7c0dbd802930ee0d4a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Climate</topic><topic>Climate models</topic><topic>Climate prediction</topic><topic>Climate system</topic><topic>cloud and convection process parameterization</topic><topic>Cloud cover</topic><topic>Cloud microphysics</topic><topic>Cloudiness</topic><topic>Clouds</topic><topic>Computer simulation</topic><topic>Convection</topic><topic>fidelity of CMIP5 and CFSv2 models</topic><topic>General circulation models</topic><topic>Intercomparison</topic><topic>Microphysics</topic><topic>Monsoon forecasting</topic><topic>Monsoons</topic><topic>pool of inhibited cloudiness</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Simulation</topic><topic>Summer monsoon</topic><topic>Variability</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Roy, Kumar</creatorcontrib><creatorcontrib>Mukhopadhyay, Parthasarathi</creatorcontrib><creatorcontrib>Murali Krishna, Ravuri Phani</creatorcontrib><creatorcontrib>Ganai, Malay</creatorcontrib><creatorcontrib>Mahakur, Mata</creatorcontrib><creatorcontrib>Narayana Rao, Thota</creatorcontrib><creatorcontrib>Nair, Anish Kumar M.</creatorcontrib><creatorcontrib>Ramakrishna, Surireddi Satya Venkata Siva</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Roy, Kumar</au><au>Mukhopadhyay, Parthasarathi</au><au>Murali Krishna, Ravuri Phani</au><au>Ganai, Malay</au><au>Mahakur, Mata</au><au>Narayana Rao, Thota</au><au>Nair, Anish Kumar M.</au><au>Ramakrishna, Surireddi Satya Venkata Siva</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensitivity of climate models in relation to the “pool of inhibited cloudiness” over South of the Bay of Bengal</atitle><jtitle>International journal of climatology</jtitle><date>2020-06-30</date><risdate>2020</risdate><volume>40</volume><issue>8</issue><spage>3714</spage><epage>3730</epage><pages>3714-3730</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>Realistic simulation of cloud variability and rainfall by the coupled models still remains a challenge particularly over the Asian Summer Monsoon (ASM). The simulation of the “pool of inhibited cloudiness” (hereafter referred to as PIC) and associated cloud variability have been analysed in the historical run of 26 models which participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and it is shown that the current state of the art general circulation models (GCMs) still have difficulties in properly simulating the PIC. The pool covers an area greater than 1 million km2 between 3°–13°N and 77°–90°E over the southwest Bay of Bengal (BoB); persisting throughout the ASM and interestingly it is surrounded by the deep convective region. The majority of the models could not simulate cloud variability over the ASM. The performance of National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) version 2.0, which is being used for operational monsoon prediction over the Indian region, is also tested to evaluate its fidelity in capturing PIC. The moist convective process in the default CFSv2 is found to be one of the major sources of uncertainty in its monsoon prediction. In this study, evaluation of the default CFSv2 (CTRL) and suites of modified CFSv2 have been carried out. The default version of CFSv2 has the simplified Arakawa Schubert (SAS) as convection scheme and Zhao and Carr (ZC) as microphysics. In another modification, the SAS is changed to revised simplified Arakawa Schubert (RSAS) keeping the microphysics unchanged. Further, a more physically based cloud scheme (WRF Single Moment 6‐class microphysics—WSM6) is used with SAS and RSAS for comparison of simulation of PIC. Among the CMIP5 models, ACESS‐1‐0, GFDL‐CM3, HadGEM2‐CC, HadGEM2‐ES are able to represent the PIC reasonably well. CFSCR has shown an improved fidelity in comparison to the CTRL, CTRL‐WSM and RSAS and other CMIP5 models. The impact of cloud microphysics in CTRL‐WSM and CFSCR appears to play an important role in the simulation of the PIC.
Average seasonal mean (June–September) latitude altitude cross section of the frequency of occurrence of clouds (FALT, expressed in percentage) during ASM along 80–90°E obtained from CloudSat‐CALIPSO, 26 CMIP5 models and from three different versions of CFSv2 T126 (CTRL, CTRL‐WSM, RSAS and CFSCR).</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.6423</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-3092-033X</orcidid><orcidid>https://orcid.org/0000-0001-8770-9167</orcidid></addata></record> |
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subjects | Accuracy Climate Climate models Climate prediction Climate system cloud and convection process parameterization Cloud cover Cloud microphysics Cloudiness Clouds Computer simulation Convection fidelity of CMIP5 and CFSv2 models General circulation models Intercomparison Microphysics Monsoon forecasting Monsoons pool of inhibited cloudiness Rain Rainfall Simulation Summer monsoon Variability Wind |
title | Sensitivity of climate models in relation to the “pool of inhibited cloudiness” over South of the Bay of Bengal |
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