Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2
The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the se...
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| Veröffentlicht in: | Journal of advances in modeling earth systems 2016-03, Vol.8 (1), p.96-120 |
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| creator | Saha, Subodh Kumar Pokhrel, Samir Salunke, Kiran Dhakate, Ashish Chaudhari, Hemantkumar S. Rahaman, Hasibur Sujith, K. Hazra, Anupam Sikka, D. R. |
| description | The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information‐based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead‐4 to lead‐2 and then decreases at lead‐1 followed by again increase at lead‐0. While the actual ISMR prediction skill is highest at lead‐3 forecast (second highest at lead‐1), the potential predictability is highest at lead‐2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño‐Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required.
Key Points:
Potential predictability of ISMR simulated by CFSv2 is estimated
In general, potential predictability increases with decrease in lead forecast time
Actual ISMR prediction skill is highest (second highest) with February (April) initial conditions |
| doi_str_mv | 10.1002/2015MS000542 |
| format | Article |
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Key Points:
Potential predictability of ISMR simulated by CFSv2 is estimated
In general, potential predictability increases with decrease in lead forecast time
Actual ISMR prediction skill is highest (second highest) with February (April) initial conditions</description><identifier>ISSN: 1942-2466</identifier><identifier>EISSN: 1942-2466</identifier><identifier>DOI: 10.1002/2015MS000542</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Boundary conditions ; Climate ; Climate system ; Drought ; El Nino ; El Nino phenomena ; Entropy ; General circulation models ; Indian summer monsoon ; Information theory ; Monsoon rainfall ; Monsoons ; Ocean basins ; Oceans ; Physics ; potential predictability ; Rain ; Sea surface ; Sea surface temperature ; Snow ; Soil ; Soil moisture ; Southern Oscillation ; Studies ; Summer ; Summer monsoon ; Surface temperature ; Variance analysis ; Wind</subject><ispartof>Journal of advances in modeling earth systems, 2016-03, Vol.8 (1), p.96-120</ispartof><rights>2015. The Authors.</rights><rights>2016. 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></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2015MS000542$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2015MS000542$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,1411,11541,27901,27902,45550,45551,46027,46451</link.rule.ids></links><search><creatorcontrib>Saha, Subodh Kumar</creatorcontrib><creatorcontrib>Pokhrel, Samir</creatorcontrib><creatorcontrib>Salunke, Kiran</creatorcontrib><creatorcontrib>Dhakate, Ashish</creatorcontrib><creatorcontrib>Chaudhari, Hemantkumar S.</creatorcontrib><creatorcontrib>Rahaman, Hasibur</creatorcontrib><creatorcontrib>Sujith, K.</creatorcontrib><creatorcontrib>Hazra, Anupam</creatorcontrib><creatorcontrib>Sikka, D. R.</creatorcontrib><title>Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2</title><title>Journal of advances in modeling earth systems</title><description>The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information‐based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead‐4 to lead‐2 and then decreases at lead‐1 followed by again increase at lead‐0. While the actual ISMR prediction skill is highest at lead‐3 forecast (second highest at lead‐1), the potential predictability is highest at lead‐2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño‐Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required.
Key Points:
Potential predictability of ISMR simulated by CFSv2 is estimated
In general, potential predictability increases with decrease in lead forecast time
Actual ISMR prediction skill is highest (second highest) with February (April) initial conditions</description><subject>Boundary conditions</subject><subject>Climate</subject><subject>Climate system</subject><subject>Drought</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>Entropy</subject><subject>General circulation models</subject><subject>Indian summer monsoon</subject><subject>Information theory</subject><subject>Monsoon rainfall</subject><subject>Monsoons</subject><subject>Ocean basins</subject><subject>Oceans</subject><subject>Physics</subject><subject>potential predictability</subject><subject>Rain</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Snow</subject><subject>Soil</subject><subject>Soil moisture</subject><subject>Southern Oscillation</subject><subject>Studies</subject><subject>Summer</subject><subject>Summer monsoon</subject><subject>Surface temperature</subject><subject>Variance analysis</subject><subject>Wind</subject><issn>1942-2466</issn><issn>1942-2466</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>BENPR</sourceid><recordid>eNpdkEFLwzAcxYMoOKc3P0DAi5dq8m-bNMdRNjfZdDA9hzRNJaNNZtMq_fZW5mF4eo_Hj8fjIXRLyQMlBB6B0HSzI4SkCZyhCRUJRJAwdn7iL9FVCHtCGGOQTtBy6zvjOqtqfGhNaXWnClvbbsC-witXWuVw6JvGtLjxLnjvcKusq1RdY-vwSz7f4nyx-4JrdDGGwdz86RS9L-Zv-TJavz6t8tk6-oiB8SgudVmYBAouTEYFM1oxUZgCsiIlqSppwqg2ptJapEQBUDCcZ7GqWCzGzTqeovtj76H1n70JnWxs0KaulTO-D5LyjAsuGBMjevcP3fu-deM6CSAIBZ4lMFLxkfq2tRnkobWNagdJifz9VJ5-Kp9nmzkQSHj8A8Kzaac</recordid><startdate>201603</startdate><enddate>201603</enddate><creator>Saha, Subodh Kumar</creator><creator>Pokhrel, Samir</creator><creator>Salunke, Kiran</creator><creator>Dhakate, Ashish</creator><creator>Chaudhari, Hemantkumar S.</creator><creator>Rahaman, Hasibur</creator><creator>Sujith, K.</creator><creator>Hazra, Anupam</creator><creator>Sikka, D. 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R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g3267-3dcdbe42b79e8196eca69beb28b505ad1461ceefcc950a2212e7783af639066c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Boundary conditions</topic><topic>Climate</topic><topic>Climate system</topic><topic>Drought</topic><topic>El Nino</topic><topic>El Nino phenomena</topic><topic>Entropy</topic><topic>General circulation models</topic><topic>Indian summer monsoon</topic><topic>Information theory</topic><topic>Monsoon rainfall</topic><topic>Monsoons</topic><topic>Ocean basins</topic><topic>Oceans</topic><topic>Physics</topic><topic>potential predictability</topic><topic>Rain</topic><topic>Sea surface</topic><topic>Sea surface temperature</topic><topic>Snow</topic><topic>Soil</topic><topic>Soil moisture</topic><topic>Southern Oscillation</topic><topic>Studies</topic><topic>Summer</topic><topic>Summer monsoon</topic><topic>Surface temperature</topic><topic>Variance analysis</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saha, Subodh Kumar</creatorcontrib><creatorcontrib>Pokhrel, Samir</creatorcontrib><creatorcontrib>Salunke, Kiran</creatorcontrib><creatorcontrib>Dhakate, Ashish</creatorcontrib><creatorcontrib>Chaudhari, Hemantkumar S.</creatorcontrib><creatorcontrib>Rahaman, Hasibur</creatorcontrib><creatorcontrib>Sujith, K.</creatorcontrib><creatorcontrib>Hazra, Anupam</creatorcontrib><creatorcontrib>Sikka, D. R.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</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><jtitle>Journal of advances in modeling earth systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Saha, Subodh Kumar</au><au>Pokhrel, Samir</au><au>Salunke, Kiran</au><au>Dhakate, Ashish</au><au>Chaudhari, Hemantkumar S.</au><au>Rahaman, Hasibur</au><au>Sujith, K.</au><au>Hazra, Anupam</au><au>Sikka, D. R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2</atitle><jtitle>Journal of advances in modeling earth systems</jtitle><date>2016-03</date><risdate>2016</risdate><volume>8</volume><issue>1</issue><spage>96</spage><epage>120</epage><pages>96-120</pages><issn>1942-2466</issn><eissn>1942-2466</eissn><abstract>The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information‐based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead‐4 to lead‐2 and then decreases at lead‐1 followed by again increase at lead‐0. While the actual ISMR prediction skill is highest at lead‐3 forecast (second highest at lead‐1), the potential predictability is highest at lead‐2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño‐Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required.
Key Points:
Potential predictability of ISMR simulated by CFSv2 is estimated
In general, potential predictability increases with decrease in lead forecast time
Actual ISMR prediction skill is highest (second highest) with February (April) initial conditions</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/2015MS000542</doi><tpages>25</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Boundary conditions Climate Climate system Drought El Nino El Nino phenomena Entropy General circulation models Indian summer monsoon Information theory Monsoon rainfall Monsoons Ocean basins Oceans Physics potential predictability Rain Sea surface Sea surface temperature Snow Soil Soil moisture Southern Oscillation Studies Summer Summer monsoon Surface temperature Variance analysis Wind |
| title | Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2 |
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