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The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades.
Atmosphere-Ocean
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Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G (2018) Could machine learning break the convection parameterization deadlock? · 2018
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Journal of Advances in Modeling Earth Systems
Kooperman GJ, Pritchard MS, O’Brien TA, Timmermans BW (2018) Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present-Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model · 2018
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Journal of Advances in Modeling Earth Systems
Woelfle MD, Yu S, Bretherton CS, Pritchard MS (2018) Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1 · 2018
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