Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM Experiments

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...

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Bibliographic Details
Published in:Monthly Weather Review
Main Authors: Lu, Feiyu, Liu, Zhengyu, Zhang, Shaoqing, Liu, Yun, Jacob, Robert
Other Authors: Lu, FY (reprint author), Ctr Climat Res, 1225 W Dayton St, Madison, WI 53706 USA., Univ Wisconsin, Nelson Inst Ctr Climat Res, Madison, WI USA., Univ Wisconsin, Dept Atmospher & Ocean Sci, Madison, WI USA., Peking Univ, Lab Climate Ocean & Atmosphere Studies, Beijing 100871, Peoples R China., NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA., Argonne Natl Lab, Div Math & Comp Sci, Argonne, IL 60439 USA., Ctr Climat Res, 1225 W Dayton St, Madison, WI 53706 USA.
Format: Journal/Newspaper
Language:English
Published: MONTHLY WEATHER REVIEW 2015
Subjects:
Mathematical and statistical techniques
Kalman filters
Models and modeling
Coupled models
Data assimilation
Ensembles
ENSEMBLE KALMAN FILTER
ATMOSPHERE-OCEAN DATA
CLIMATE MODEL
STATISTICAL ASSESSMENT
PARAMETER-ESTIMATION
DECADAL VARIABILITY
NORTH-ATLANTIC
SYSTEM
PERFORMANCE
HOLOCENE
North Atlantic
Online Access:https://hdl.handle.net/20.500.11897/415412
https://doi.org/10.1175/MWR-D-15-0088.1
Description
Summary: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. NSF [AGS-0968383]; Chinese MOST [2012CB955200] SCI(E) EI ARTICLE flu7@wisc.edu 11 4645-4659 143

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