{"id":749,"date":"2023-07-02T18:35:57","date_gmt":"2023-07-02T17:35:57","guid":{"rendered":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/?page_id=749"},"modified":"2023-07-02T18:35:57","modified_gmt":"2023-07-02T17:35:57","slug":"abstract032","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract032\/","title":{"rendered":"Abstract 032"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]Abstract ID:<\/strong> 032<\/p>\n

Assessing Decadal Variability of Subseasonal Predictability using Explainable Machine Learning<\/h2>\n

Lead Author:<\/strong> Marybeth Arcodia
\nColorado State University, United States of America
\n<\/span><\/p>\n

Keywords:<\/strong> machine-learning, tropical variability, subseasonal, predictability<\/p>\n

Abstract:<\/strong> The tropical MJO can influence midlatitude precipitation via tropical-extratropical teleconnections on S2S timescales, potentially resulting in life-threatening flooding and drought events. However, the field lacks an understanding of how the North American subseasonal precipitation predictability changes on low-frequency (~10-year) timescales. To assess low-frequency variability of predictability, we employ artificial neural networks to quantify how S2S prediction skill varies on decadal timescales along the West Coast of North America. The machine learning model uses tropical precipitation anomalies as a predictor of North American precipitation anomalies with a 3-week lead time to capture the subseasonal timescale of the tropical-extratropical teleconnection. Analysis of the prediction skill of the neural networks reveals fluctuations on decadal timescales, and the analysis is extended to observational data. The drivers of this low-frequency variability are investigated to understand why certain time periods have higher S2S predictability to identify forecasts of opportunity. Additionally, multiple eXplainable Artificial Intelligence (XAI) methods are used to identify sources of S2S precipitation predictability that are most strongly modulated on decadal timescales.<\/p>\n

Co-authors:
\n<\/strong>Elizabeth Barnes (Colorado State University)
\nKirsten Mayer (National Center for Atmospheric Research)[\/vc_column_text][vc_separator][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/6″][vc_single_image image=”344″ img_size=”full” onclick=”custom_link” link=”http:\/\/s2sprediction.net\/”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”345″ img_size=”full” onclick=”custom_link” link=”https:\/\/public.wmo.int\/en”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”346″ img_size=”full” onclick=”custom_link” link=”https:\/\/community.wmo.int\/activity-areas\/wwrp”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”347″ img_size=”full” onclick=”custom_link” link=”https:\/\/www.wcrp-climate.org\/”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”348″ img_size=”full”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”349″ img_size=”full” onclick=”custom_link” link=”https:\/\/www.reading.ac.uk\/”][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][vc_column_text]Abstract ID: 032 Assessing Decadal Variability of Subseasonal Predictability using Explainable Machine Learning Lead Author: Marybeth Arcodia Colorado State University, United States of America Keywords: machine-learning, tropical variability, subseasonal, predictability…Read More ><\/a><\/p>\n","protected":false},"author":145,"featured_media":0,"parent":528,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"__cvm_playback_settings":[],"__cvm_video_id":"","footnotes":""},"coauthors":[13],"class_list":["post-749","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nAbstract 032 - WWRP\/WCRP S2S Summit 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract032\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Abstract 032 - 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WWRP\/WCRP S2S Summit 2023","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract032\/","og_locale":"en_GB","og_type":"article","og_title":"Abstract 032 - WWRP\/WCRP S2S Summit 2023","og_description":"[vc_row][vc_column][vc_column_text]Abstract ID: 032 Assessing Decadal Variability of Subseasonal Predictability using Explainable Machine Learning Lead Author: Marybeth Arcodia Colorado State University, United States of America Keywords: machine-learning, tropical variability, subseasonal, predictability...Read More >","og_url":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract032\/","og_site_name":"WWRP\/WCRP S2S Summit 2023","twitter_card":"summary_large_image","twitter_misc":{"Estimated reading time":"2 minutes","Written by":"Robert Lee"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract032\/","url":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract032\/","name":"Abstract 032 - 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