RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Paraskevi Georgakaki, Athanasios Nenes
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2024
• Subjects: secondary ice production; random forest parameterization; cloud radiative forcing; stratiform mixed‐phase clouds; regional‐climate simulations; model simplification; Physical geography; GB3-5030; Oceanography; GC1-1581; Arctic; Rime
• Online Access: https://doi.org/10.1029/2023MS003923; https://doaj.org/article/c47dffc939b948169d81d5e89e093332

Abstract Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties an...