Researchers extend classical CNOP method for deep learning forecasting models with multi-time-slice-input structure.  It reveals when—not just where—input errors matter most in targeted observations. This improves forecasts for ocean-atmospheric variables, especially high-impact environmental events.

In an era of intensifying extreme weather, this review offers a clear message: to better project the future of tropical cyclones in a warmer climate, we must first understand the patterns of the warming seas.

Researcher from  Fudan University selected a global climate model, FGOALS-f3-L, to reveal the bias characteristics of CDV in this model. 

A newly published review  reveals that climate extremes are increasingly striking in combination—and their compounding impact is posing a growing threat to public health across China.