RaFSIP: Parameterizing ice multiplication in models using a machine learning approach
Representing single or multi-layered mixed-phase clouds (MPCs) accurately in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Ice multiplication, or secondary ice production (SIP), can increase the ice crystal number concentration (ICNC) in MPCs by...
| Main Authors: | , |
|---|---|
| Format: | Other/Unknown Material |
| Language: | unknown |
| Published: |
Authorea, Inc.
2023
|
| Subjects: | |
| Online Access: | http://dx.doi.org/10.22541/essoar.170365383.34520011/v1 |