In:
Journal of Climate, American Meteorological Society, Vol. 29, No. 24 ( 2016-12-15), p. 8965-8987
Abstract:
Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.
Type of Medium:
Online Resource
ISSN:
0894-8755
,
1520-0442
DOI:
10.1175/JCLI-D-16-0026.1
DOI:
10.1175/JCLI-D-16-0026.s1
Language:
English
Publisher:
American Meteorological Society
Publication Date:
2016
detail.hit.zdb_id:
246750-1
detail.hit.zdb_id:
2021723-7
