Relationships between subseasonal‐to‐seasonal predictability and spatial scales in tropical rainfall
Subseasonal to seasonal (S2S) tropical rainfall predictability is assessed both from an analysis of the spatial scales of observed rainfall variability data, as well as from an S2S model reforecast skill. Observed spatial scales are quantified from gridded observed daily rainfall data, in terms of t...
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description | Subseasonal to seasonal (S2S) tropical rainfall predictability is assessed both from an analysis of the spatial scales of observed rainfall variability data, as well as from an S2S model reforecast skill. Observed spatial scales are quantified from gridded observed daily rainfall data, in terms of the size (area) of daily contiguous wet grid‐points (referred to as ‘wet patches’), as well as from the spatial autocorrelations of 7–91‐day running averages of rainfall. Model S2S reforecast skill is measured using the anomaly correlation coefficient between observed and simulated weekly and monthly rainfall from an 11‐member ensemble of European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecasts (1998–2017). Both measures of S2S predictability are found to be systematically lower over land than sea, usually peaking at the start or end of the rainy season and decreasing during the core. Small spatial scales and low skill over equatorial/northern tropical Africa and western Amazonia coincide with small daily rainfall patch size and strong synoptic‐scale (≤7 days) variability there. Over most of South and SE Asia, daily wet patches are larger and strongly modulated by intraseasonal oscillations, boosting S2S rainfall predictability, while this is offset by large daily mean rainfall intensities that increase the noise. In consequence, S2S rainfall skill here generally remains low. Several land areas (as around Maritime Continent from the Philippines to Northern Australia, Eastern and Southern Africa, Eastern South America) exhibit larger spatial scales and skill, especially where the relative amplitude of SST‐forced interannual variations is strong. Most of the Maritime Continent illustrates such behaviour, but even here, the time‐averaged spatial scales and skill drop during the core of the rainy season.
Analysis of scales and skill of tropical rainfall, from daily to interannual time scales, using empirical estimates of ‘signal’ and ‘noise’ and numerical ensemble of S2S forecasts—larger scales and skill over ocean than over landmasses and for the start and ending stages of the rainy season than during its core
Variable combination of signal and noise explains the spatial modulation of scales and skill
Ordering of external variance and skill computed on running 31‐day periods belonging respectively to the start, core, and end of the rainy season across the landmasses. The start, core, and end stages of the rainy season are defined from the smoothed clim |
doi_str_mv | 10.1002/joc.7143 |
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Analysis of scales and skill of tropical rainfall, from daily to interannual time scales, using empirical estimates of ‘signal’ and ‘noise’ and numerical ensemble of S2S forecasts—larger scales and skill over ocean than over landmasses and for the start and ending stages of the rainy season than during its core
Variable combination of signal and noise explains the spatial modulation of scales and skill
Ordering of external variance and skill computed on running 31‐day periods belonging respectively to the start, core, and end of the rainy season across the landmasses. The start, core, and end stages of the rainy season are defined from the smoothed climatological daily mean. The core is the period around the maximum receiving 50% of the total amount. The start and end stages are defined as the increasing and decreasing slopes before and after the core. The colours in (a) show the six possible orderings—shown on the panel below the map—amongst the three stages. The lower panel (b,c) shows the percentage of area belonging to each ordering for the (a) external variance and (b) skill of ECMWF from the 8 to 38 day amount. This figure shows that skill and external variance usually peak either at the start and the end of the local‐scale rainy season rather than in its core.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.7143</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Annual variations ; climate change ; climate variability ; Correlation coefficient ; Correlation coefficients ; Daily ; Daily rainfall ; Hydrologic data ; Interannual variations ; Intraseasonal oscillation ; Monthly rainfall ; Ocean, Atmosphere ; Oscillations ; Rain ; Rainfall ; Rainfall data ; Rainfall forecasting ; Rainfall intensity ; Rainfall simulators ; Rainfall variability ; Rainy season ; Sciences of the Universe ; Sea surface ; Spatial analysis ; statistical climatology ; Surface temperature ; synoptic climatology ; Tropical climate ; Tropical rainfall ; Variability ; Weather forecasting ; Weekly ; Wet season</subject><ispartof>International journal of climatology, 2021-10, Vol.41 (12), p.5596-5624</ispartof><rights>2021 Royal Meteorological Society</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3273-926a2418920f4bff31102ca74e22b42c8552b506e53c448ca39a293fd1c26a7a3</citedby><cites>FETCH-LOGICAL-c3273-926a2418920f4bff31102ca74e22b42c8552b506e53c448ca39a293fd1c26a7a3</cites><orcidid>0000-0002-4981-9530</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.7143$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.7143$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03446954$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Moron, Vincent</creatorcontrib><creatorcontrib>Robertson, Andrew W.</creatorcontrib><title>Relationships between subseasonal‐to‐seasonal predictability and spatial scales in tropical rainfall</title><title>International journal of climatology</title><description>Subseasonal to seasonal (S2S) tropical rainfall predictability is assessed both from an analysis of the spatial scales of observed rainfall variability data, as well as from an S2S model reforecast skill. Observed spatial scales are quantified from gridded observed daily rainfall data, in terms of the size (area) of daily contiguous wet grid‐points (referred to as ‘wet patches’), as well as from the spatial autocorrelations of 7–91‐day running averages of rainfall. Model S2S reforecast skill is measured using the anomaly correlation coefficient between observed and simulated weekly and monthly rainfall from an 11‐member ensemble of European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecasts (1998–2017). Both measures of S2S predictability are found to be systematically lower over land than sea, usually peaking at the start or end of the rainy season and decreasing during the core. Small spatial scales and low skill over equatorial/northern tropical Africa and western Amazonia coincide with small daily rainfall patch size and strong synoptic‐scale (≤7 days) variability there. Over most of South and SE Asia, daily wet patches are larger and strongly modulated by intraseasonal oscillations, boosting S2S rainfall predictability, while this is offset by large daily mean rainfall intensities that increase the noise. In consequence, S2S rainfall skill here generally remains low. Several land areas (as around Maritime Continent from the Philippines to Northern Australia, Eastern and Southern Africa, Eastern South America) exhibit larger spatial scales and skill, especially where the relative amplitude of SST‐forced interannual variations is strong. Most of the Maritime Continent illustrates such behaviour, but even here, the time‐averaged spatial scales and skill drop during the core of the rainy season.
Analysis of scales and skill of tropical rainfall, from daily to interannual time scales, using empirical estimates of ‘signal’ and ‘noise’ and numerical ensemble of S2S forecasts—larger scales and skill over ocean than over landmasses and for the start and ending stages of the rainy season than during its core
Variable combination of signal and noise explains the spatial modulation of scales and skill
Ordering of external variance and skill computed on running 31‐day periods belonging respectively to the start, core, and end of the rainy season across the landmasses. The start, core, and end stages of the rainy season are defined from the smoothed climatological daily mean. The core is the period around the maximum receiving 50% of the total amount. The start and end stages are defined as the increasing and decreasing slopes before and after the core. The colours in (a) show the six possible orderings—shown on the panel below the map—amongst the three stages. The lower panel (b,c) shows the percentage of area belonging to each ordering for the (a) external variance and (b) skill of ECMWF from the 8 to 38 day amount. This figure shows that skill and external variance usually peak either at the start and the end of the local‐scale rainy season rather than in its core.</description><subject>Annual variations</subject><subject>climate change</subject><subject>climate variability</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Daily</subject><subject>Daily rainfall</subject><subject>Hydrologic data</subject><subject>Interannual variations</subject><subject>Intraseasonal oscillation</subject><subject>Monthly rainfall</subject><subject>Ocean, Atmosphere</subject><subject>Oscillations</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Rainfall data</subject><subject>Rainfall forecasting</subject><subject>Rainfall intensity</subject><subject>Rainfall simulators</subject><subject>Rainfall variability</subject><subject>Rainy season</subject><subject>Sciences of the Universe</subject><subject>Sea surface</subject><subject>Spatial analysis</subject><subject>statistical climatology</subject><subject>Surface temperature</subject><subject>synoptic climatology</subject><subject>Tropical climate</subject><subject>Tropical rainfall</subject><subject>Variability</subject><subject>Weather forecasting</subject><subject>Weekly</subject><subject>Wet season</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kM9KAzEQxoMoWKvgIyx40cPW_Nvd5FiKWqVQED2H2TRLU-JmTbaW3nwEn9EnMbXqzct8zMxvPmYGoXOCRwRjer3yelQRzg7QgGBZ5RgLcYgGWEiZC07EMTqJcYUxlpKUA7R8NA5669u4tF3MatNvjGmzuK6jgehbcJ_vH71P4TfPumAWVvdQW2f7bQbtIotd8kitqMGZmNk264PvbMqyALZtwLlTdJQkmrMfHaLn25unyTSfze_uJ-NZrhmtWC5pCTStKSlueN00jBBMNVTcUFpzqkVR0LrApSmY5lxoYBKoZM2C6DRZARuiq73vEpzqgn2BsFUerJqOZ2pXw4zzUhb8jST2Ys92wb-uTezVyq9DujEqWlSClCWhIlGXe0oHH2MwzZ8twWr38zSl1e7nCc336MY6s_2XUw_zyTf_BVvghLk</recordid><startdate>202110</startdate><enddate>202110</enddate><creator>Moron, Vincent</creator><creator>Robertson, Andrew W.</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><general>Wiley</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><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-4981-9530</orcidid></search><sort><creationdate>202110</creationdate><title>Relationships between subseasonal‐to‐seasonal predictability and spatial scales in tropical rainfall</title><author>Moron, Vincent ; Robertson, Andrew W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3273-926a2418920f4bff31102ca74e22b42c8552b506e53c448ca39a293fd1c26a7a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Annual variations</topic><topic>climate change</topic><topic>climate variability</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Daily</topic><topic>Daily rainfall</topic><topic>Hydrologic data</topic><topic>Interannual variations</topic><topic>Intraseasonal oscillation</topic><topic>Monthly rainfall</topic><topic>Ocean, Atmosphere</topic><topic>Oscillations</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Rainfall data</topic><topic>Rainfall forecasting</topic><topic>Rainfall intensity</topic><topic>Rainfall simulators</topic><topic>Rainfall variability</topic><topic>Rainy season</topic><topic>Sciences of the Universe</topic><topic>Sea surface</topic><topic>Spatial analysis</topic><topic>statistical climatology</topic><topic>Surface temperature</topic><topic>synoptic climatology</topic><topic>Tropical climate</topic><topic>Tropical rainfall</topic><topic>Variability</topic><topic>Weather forecasting</topic><topic>Weekly</topic><topic>Wet season</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moron, Vincent</creatorcontrib><creatorcontrib>Robertson, Andrew W.</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><collection>Hyper Article en Ligne (HAL)</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moron, Vincent</au><au>Robertson, Andrew W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Relationships between subseasonal‐to‐seasonal predictability and spatial scales in tropical rainfall</atitle><jtitle>International journal of climatology</jtitle><date>2021-10</date><risdate>2021</risdate><volume>41</volume><issue>12</issue><spage>5596</spage><epage>5624</epage><pages>5596-5624</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>Subseasonal to seasonal (S2S) tropical rainfall predictability is assessed both from an analysis of the spatial scales of observed rainfall variability data, as well as from an S2S model reforecast skill. Observed spatial scales are quantified from gridded observed daily rainfall data, in terms of the size (area) of daily contiguous wet grid‐points (referred to as ‘wet patches’), as well as from the spatial autocorrelations of 7–91‐day running averages of rainfall. Model S2S reforecast skill is measured using the anomaly correlation coefficient between observed and simulated weekly and monthly rainfall from an 11‐member ensemble of European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecasts (1998–2017). Both measures of S2S predictability are found to be systematically lower over land than sea, usually peaking at the start or end of the rainy season and decreasing during the core. Small spatial scales and low skill over equatorial/northern tropical Africa and western Amazonia coincide with small daily rainfall patch size and strong synoptic‐scale (≤7 days) variability there. Over most of South and SE Asia, daily wet patches are larger and strongly modulated by intraseasonal oscillations, boosting S2S rainfall predictability, while this is offset by large daily mean rainfall intensities that increase the noise. In consequence, S2S rainfall skill here generally remains low. Several land areas (as around Maritime Continent from the Philippines to Northern Australia, Eastern and Southern Africa, Eastern South America) exhibit larger spatial scales and skill, especially where the relative amplitude of SST‐forced interannual variations is strong. Most of the Maritime Continent illustrates such behaviour, but even here, the time‐averaged spatial scales and skill drop during the core of the rainy season.
Analysis of scales and skill of tropical rainfall, from daily to interannual time scales, using empirical estimates of ‘signal’ and ‘noise’ and numerical ensemble of S2S forecasts—larger scales and skill over ocean than over landmasses and for the start and ending stages of the rainy season than during its core
Variable combination of signal and noise explains the spatial modulation of scales and skill
Ordering of external variance and skill computed on running 31‐day periods belonging respectively to the start, core, and end of the rainy season across the landmasses. The start, core, and end stages of the rainy season are defined from the smoothed climatological daily mean. The core is the period around the maximum receiving 50% of the total amount. The start and end stages are defined as the increasing and decreasing slopes before and after the core. The colours in (a) show the six possible orderings—shown on the panel below the map—amongst the three stages. The lower panel (b,c) shows the percentage of area belonging to each ordering for the (a) external variance and (b) skill of ECMWF from the 8 to 38 day amount. This figure shows that skill and external variance usually peak either at the start and the end of the local‐scale rainy season rather than in its core.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.7143</doi><tpages>29</tpages><orcidid>https://orcid.org/0000-0002-4981-9530</orcidid></addata></record> |
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subjects | Annual variations climate change climate variability Correlation coefficient Correlation coefficients Daily Daily rainfall Hydrologic data Interannual variations Intraseasonal oscillation Monthly rainfall Ocean, Atmosphere Oscillations Rain Rainfall Rainfall data Rainfall forecasting Rainfall intensity Rainfall simulators Rainfall variability Rainy season Sciences of the Universe Sea surface Spatial analysis statistical climatology Surface temperature synoptic climatology Tropical climate Tropical rainfall Variability Weather forecasting Weekly Wet season |
title | Relationships between subseasonal‐to‐seasonal predictability and spatial scales in tropical rainfall |
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