As severe weather events draw increasing global attention, scientists at the University of Hawai at Mānoa are making strides in improving the forecasting of droughts, floods, and other climate scenarios. Researchers from the School of Ocean and Earth Science and Technology (SOEST) have developed a novel tool that allows for the prediction of El Niño Southern Oscillation (ENSO) events up to 18 months in advance.
The research, which integrates insights into the physics of the ocean and atmosphere with predictive accuracy, was recently published in the prestigious journal Nature.
“We have developed a new conceptual model the so called extended nonlinear recharge oscillator (XRO) model – that significantly improves predictive skill of ENSO events over one year in advance, better than global climate models and comparable to the most skillful artificial intelligence [AI] forecasts,” said Sen Zhao, lead author of the study and assistant researcher in SOEST’s Department of Atmospheric Sciences. “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.”
For decades, scientists have worked to enhance ENSO predictions due to its vast environmental and socioeconomic impacts worldwide. Traditional forecasting models have struggled to predict ENSO accurately with lead times exceeding one year. However, recent advancements in AI have achieved accurate predictions up to 16-18 months in advance. The “black box” nature of AI models, however, has limited the understanding of specific physical processes driving these predictions, leading to low confidence in their future applicability as the Earth’s climate continues to change.
“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 the tropical Pacific,” explained 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.”
The new model not only enhances prediction accuracy but also identifies shortcomings in the latest generation of climate models. These models often fail to capture the key physics of ENSO accurately. “Our findings also identify shortcomings in the latest generation of climate models that lead to their failure in predicting ENSO accurately,” said Malte Stuecker, assistant professor of oceanography in SOEST and study co-author. “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.”
Philip Thompson, associate professor of oceanography in SOEST and co-author of the study, highlighted the model’s ability to provide skillful, long lead-time predictions of ENSO diversity. “Different sources of predictability lead to distinct ENSO event evolutions,” Thompson noted. “We are now able to provide skillful, long lead-time predictions of this ‘ENSO diversity,’ which is critical as different flavors of ENSO have very different impacts on global climate and individual communities.”
This groundbreaking research signifies a significant advancement in climate science, promising better preparation and response strategies for severe weather events worldwide.
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