AI-Based Prediction of High-Impact Weather and Climate Extremes under Global Warming: A Perspective from The Large-Scale Circulations and Teleconnections

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Background

2021 has been the toughest year for the entire human being. With the COVID-19 pandemic raging on, various record-breaking weather and climate extremes swept through many countries of the world. In February, severe winter storms caused the most expensive power crisis in Texas’s history, affecting 4.5 million homes with more than 200 people killed. In June, a record-breaking heatwave scorched the Pacific Northwest United States and Canada, which led to the death toll exceeding 1,400 people. A few weeks later, Germany and Henan experienced their respective ‘once in a hundred years’ and ‘once in a thousand years’ torrential rainfall, and more than 100 were killed during both events. In December, a series of devastating tornadoes attacked nine states in the United States, producing severe to catastrophic damage in many towns with more than 100 deaths and numerous injuries. Following that, the super typhoon Rai displaced hundreds of thousands of people with more than 300 deaths.
The continuous and concentrated outbreaks of high-impact weather and climate extremes in 2021 raised the concern that the current state-of-the-art dynamical models may not make skillful prediction and projection of future climate, especially for the climate anomalies and extremes that bring in tremendous natural hazards. The thriving development of Artificial Intelligence (AI) has greatly advanced weather forecasting, climate monitoring and prediction through the reduction of human effort and more efficient use of computing power. However, the thorough understanding and representation of physical processes that modulate the weather and climate extremes are still lacking, especially the role that teleconnections, e.g., El Niño–Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), and Interdecadal Pacific Oscillation (IPO), which warrant further exploration.
We propose this Research Topic to leverage the understanding of the relationships among weather and climate extremes, large-scale circulation, and the associated teleconnections. In addition to utilizing the conventional analyses tools, the emerging state-of-the-art technologies of AI are especially encouraged, as they will bring new opportunities for the exploration and discoveries of the underlying mechanisms and causality. In this way, we can provide a more solid physical science basis for the connections between weather/climate extremes and large-scale circulation/teleconnections, and most importantly, reduce the uncertainties and establish our confidence in the future projection of those extremes.
We welcome studies addressing the following themes:
• The connection and causality analyses between weather/climate extremes and large-scale circulation/teleconnections.
• ML/AI-based pattern recognition and prediction of synoptic scenarios.
• Estimating weather and climate extremes from large-ensemble models.

Keywords: extreme weather and climate events, teleconnection, machine learning, ensemble prediction, climate projection

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