Chaopeng Ji, Mu Mu, Bo Qin, Tao Lian, Shijin Yuan, Jie Feng, Xunshu Song, Yuntao Wei, Guokun Dai, Jinyu Wang, Xianghui Fang
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引用次数: 0
Abstract
Forecasting super El Niño remains challenging, partly due to poor representation of westerly wind bursts (WWBs). We developed an artificial intelligence-based denoising diffusion probabilistic model (DDPM) to skillfully parameterize WWBs, capturing their joint modulation by oceanic and atmospheric processes. The DDPM-based scheme effectively captures observed WWBs’ characteristics (e.g., frequency, intensity, and spatial center). When implemented in the Community Earth System Model, it outperforms both the control (CTRL, without WWBs parameterization) and conventional warm pool eastern edge (WPEE)-dependent parameterization in predicting intensity and seasonal phase-locking for super El Niños (1982/83, 1997/98, 2015/16). This improvement stems from DDPM’s realistic WWBs representation, correcting CTRL and WPEE’s biases of overly weak and westward-shifted winds during El Niño growth. Consequently, DDPM produces more realistic eastern Pacific sea surface temperature anomaly warming patterns. These findings underscore WWB's accuracy as key to super El Niño prediction and demonstrate machine learning’s potential for WWB's parameterization.
期刊介绍:
npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols.
The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.