Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi-Member and Stochastic Parameterizations

Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic …

Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy

We review how the international modelling community, encompassing integrated assessment models, global and regional Earth system and climate models, and impact models, has worked together over the past few decades …

Simulated Impact of Time-Varying River Runoff and Greenland Freshwater Discharge on Sea Level Variability in the Beaufort Gyre Over 2005–2018

Global mean sea level has been rising at a rate of 3.25 ± 0.4 mm yr−1 over 1993–2018. Yet several regions are increasing at a much faster rate, such as …