In:
Monthly Weather Review, American Meteorological Society, Vol. 143, No. 11 ( 2015-11-01), p. 4645-4659
Abstract:
This paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC). Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio in the LACC method, especially in the extratropical regions. The improved SST analysis also benefits the subsurface ocean temperature and low-level atmosphere temperature analyses through dynamic and statistical processes.
Type of Medium:
Online Resource
ISSN:
0027-0644
,
1520-0493
DOI:
10.1175/MWR-D-15-0088.1
Language:
English
Publisher:
American Meteorological Society
Publication Date:
2015
detail.hit.zdb_id:
2033056-X
detail.hit.zdb_id:
202616-8
SSG:
14
