{"id":199830,"date":"2024-06-26T10:00:48","date_gmt":"2024-06-26T20:00:48","guid":{"rendered":"https:\/\/www.hawaii.edu\/news\/?p=199830"},"modified":"2024-06-26T14:13:10","modified_gmt":"2024-06-27T00:13:10","slug":"el-nino-18-month-forecast","status":"publish","type":"post","link":"https:\/\/www.hawaii.edu\/news\/2024\/06\/26\/el-nino-18-month-forecast\/","title":{"rendered":"El Ni\u00f1o forecasts 18 months using new 糖心视频<\/abbr>-developed tool"},"content":{"rendered":"Reading time: <\/span> 2<\/span> minutes<\/span><\/span>
\"drought\"
El Niño Southern Oscillation can bring droughts. (Photo credit: Matt Palmer via Unsplash)<\/figcaption><\/figure>\n

As more attention is drawn to possible severe weather around the world scientists at the University of Hawaiʻi<\/span> at Mānoa are looking to improve planning for possible droughts, floods and other scenarios. Researchers from the School of Ocean and Earth Science and Technology<\/a> (SOEST<\/abbr>) created a new tool that will allow forecasting of El Niño Southern Oscillation (ENSO<\/abbr>) by up to 18 months.<\/p>\n

The findings, which meld insights into the physics of the ocean and atmosphere with predictive accuracy, were published in Nature<\/em><\/a>.<\/p>\n

“We have developed a new conceptual model—the so-called extended nonlinear recharge oscillator (XRO<\/abbr>) model—that significantly improves predictive skill of ENSO<\/abbr> events at over one year in advance, better than global climate models and comparable to the most skillful artificial intelligence [AI<\/abbr>] forecasts,” said Sen Zhao, lead author of the study and assistant researcher in SOEST<\/abbr>\u2019s Department of Atmospheric Sciences<\/a>. “Our model effectively incorporates the fundamental physics of ENSO<\/abbr> and ENSO<\/abbr>\u2019s interactions with other climate patterns in the global oceans that vary from season to season.”<\/p>\n

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ENSO<\/abbr> can cause crop failures and food shortages. (Photo credit: Tomasz Filipek via Unsplash)<\/figcaption><\/figure>\n

Scientists have been working for decades to improve ENSO<\/abbr> predictions given its global environmental and socioeconomic impacts. Traditional operational forecasting models have struggled to successfully predict ENSO<\/abbr> with lead times exceeding one year.<\/p>\n

Peering inside the ‘black box’<\/h2>\n

Recent advancements in AI<\/abbr> have pushed these boundaries, achieving accurate predictions up to 16–18 months in advance. However, the “black box” nature of AI<\/abbr> models has precluded attribution of this accuracy to specific physical processes. Not being able to explain the source of the predictability in the AI<\/abbr> models results in low confidence that these predictions will be successful for future events as the Earth continues to warm.<\/p>\n

“Unlike the ‘black box’ nature of AI<\/abbr> models, our XRO<\/abbr> model offers a transparent view into the mechanisms of the equatorial Pacific and its interactions with other climate patterns outside of tropical Pacific,” said Fei-Fei Jin, the corresponding author and professor of atmospheric sciences in SOEST<\/abbr>. “For the first time, we are able to robustly quantify their impact on ENSO<\/abbr> predictability, thus deepening our knowledge of ENSO<\/abbr> physics and its sources of predictability.”<\/p>\n

Climate model shortcomings, improvements<\/h2>\n

“Our findings also identify shortcomings in the latest generation of climate models that lead to their failure in predicting ENSO<\/abbr> accurately,” said Malte Stuecker, assistant professor of oceanography<\/a> in SOEST<\/abbr> and study co-author. “To improve ENSO<\/abbr> predictions, climate models must correctly capture the key physics of ENSO<\/abbr> and additionally, several compounding aspects of other climate patterns in the global oceans.”<\/p>\n

“Different sources of predictability lead to distinct ENSO<\/abbr> event evolutions,” said Philip Thompson, associate professor of oceanography in SOEST<\/abbr> and co-author of the study. “We are now able to provide skillful, long lead time predictions of this ‘ENSO<\/abbr> diversity,’ which is critical as different flavors of ENSO<\/abbr> have very different impacts on global climate and individual communities.”<\/p>\n","protected":false},"excerpt":{"rendered":"

糖心视频<\/abbr> researchers are able to forecast El Niño Southern Oscillation events up to 18 months ahead of time—significantly improving climate model forecasting.<\/p>\n","protected":false},"author":16,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[30],"tags":[745,93,1467,1363,107,158,92,9],"class_list":["post-199830","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","tag-atmospheric-sciences","tag-climate-change","tag-manoa-excellence-in-research","tag-manoa-research","tag-oceanography","tag-publication","tag-school-of-ocean-and-earth-science-and-technology","tag-uh-manoa","entry","has-media"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t