Open AccessMulticonvective Parameterizations as a Multimodel Proxy for Seasonal Climate Studies
Author(s) -
Timothy E. LaRow,
S. Cocke,
Dong Jik Shin
Publication year - 2005
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/jcli3448.1
Subject(s) - probabilistic logic , ensemble forecasting , climatology , proxy (statistics) , climate model , ensemble average , environmental science , forecast skill , precipitation , ensemble learning , meteorology , computer science , climate change , artificial intelligence , machine learning , geology , geography , oceanography
A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying the initial conditions.