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UH Mānoa researchers develop tool to improve severe weather planning

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Researchers from the University of Hawai‘i at Mānoa created a new tool that they say will help improve planning for possible droughts, floods and other severe weather.

The conceptual model will help forecast El Niño Southern Oscillation, also known as ENSO, by up to 18 months. Their findings, which meld insights into the physics of the ocean and atmosphere with predictive accuracy, were published in Nature.

The nonlinear recharge oscillator (XRO) model was developed by researchers from the School of Ocean and Earth Science and Technology, also known as SOEST. According to Sen Zhao, lead author of the study and assistant researcher in SOEST’s Department of Atmospheric Sciences, the model’s predictive skills is better than global climate models and comparable to the most skillful artificial intelligence [AI] forecasts.

“Our model effectively incorporates the fundamental physics of ENSO and ENSO’s interactions with other climate patterns in the global oceans that vary from season to season,” Zhao said.


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

Recent advancements in AI have pushed these boundaries, achieving accurate predictions up to 16–18 months in advance. However, the AI models create a “black box” effect, meaning when data is inputted into the system, humans don’t really understand how AI arrived at the conclusions presented.

Because of this effect, researchers say it has precluded attribution of this accuracy to specific physical processes. Not being able to explain the source of the predictability in the AI models results in low confidence that these predictions will be successful for future events as the Earth continues to warm.


“Unlike the ‘black box’ nature of AI models, our XRO 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. “For the first time, we are able to robustly quantify their impact on ENSO predictability, thus deepening our knowledge of ENSO physics and its sources of predictability.”

Malte Stuecker, assistant professor of oceanography in SOEST and study co-author, said their findings also identify shortcomings in the latest generation of climate models that lead to their failure in predicting ENSO accurately.

“To improve ENSO predictions, climate models must correctly capture the key physics of ENSO and additionally, several compounding aspects of other climate patterns in the global oceans,” Stuecker said.


Philip Thompson, associate professor of oceanography in SOEST and co-author of the study, said they are now able to provide skillful, long lead time predictions of this ‘ENSO diversity,’ which is critical as different flavors of ENSO have different impacts on global climate and individual communities.

“By tracing model shortcomings and understanding these climate pattern interactions with our new conceptual XRO model, we can substantially refine our global climate models,” Stuecker said. “Such advancements are crucial for societal preparations and adaptations to climate-related hazards.”


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