An ensemble-based SST nudging method proposed for correcting the subsurface temperature field in climate model

Chen, Xingrong; Wang, Hui; Zheng, Fei; Cai, Qiufang

doi:10.1007/s13131-020-1568-2

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Different from the statistical method, the dynamic method uses dynamic constraints to transfer the sea surface information downward. Common dynamic methods include the nudging approach (Holland and Malanotte-Rizzoli, 1989;Chen et al, 2020) and the dynamic conservation technique (Haines, 1991;Cooper and Haines, 1996;Weaver et al, 2005). In the Nudging approach, a nudging term is added to the right side of the dynamic equation, and the assimilated sea surface observation information is transferred to the deep layer only through the model dynamic framework.…”

Section: Introductionmentioning

confidence: 99%

Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application

Chen

Wang²,

Bao³

et al. 2022

Front. Mar. Sci.

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Satellite observations play important roles in ocean operational forecasting systems, however, the direct assimilation of satellite observations cannot provide sufficient constraints on the model underwater structure. This study adopted the indirect assimilation method. First, we created a 3D temperature and salinity reconstruction model that took into account the advantage of the nonlinear regression of the generalized regression neural network with the fruit fly optimization (abbreviated as FOAGRNN). Compared with the reanalysis product and the WOA13 climatology data, the synthetic T/S (temperature and salinity) profiles had sufficient accuracy and could better describe the characteristics of mesoscale eddies. Then, the synthetic T/S profiles were assimilated into the Regional Ocean Model System (ROMS) using the Incremental Strong constraint 4D Variational (I4D-Var) data assimilation algorithm. The quantitative and qualitative analysis results indicated that compared with the direct assimilation of satellite observations, the root mean square errors (RMSEs) of temperature and salinity were reduced by 26.0% and 23.1% respectively by assimilating the synthetic T/S profiles. Furthermore, this method significantly improved the simulation effect of the model underwater structure, especially in the 300 m to 500 m water layer. Compared with the National Marine Data Center’s real-time analysis data, the machine learning-based assimilation system demonstrated a significant advantage in the simulation of underwater salinity structure, while showing a similar performance in the simulation of underwater temperature structure.

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Section: Introductionmentioning

confidence: 99%

Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application

Chen

Wang²,

Bao³

et al. 2022

Front. Mar. Sci.

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“…Accordingly, the emergence and application of these observations also have time sequences. The assimilation of sea surface temperature (SST) observations is usually the first step to compare assimilating other ocean observations in a coupled model [35][36][37][38]. In practical applications, most models assimilate as many different observations as possible to achieve a better assimilation effect.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Two Approaches for Correcting the Systematic Ocean Temperature Bias of CAS-ESM-C

Zheng

Zhu

et al. 2021

JMSE

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Currently, several ocean data assimilation methods have been adopted to increase the performance of air–sea coupled models, but inconsistent adjustments between the sea temperature with other oceanic fields can be introduced. In the coupled model CAS-ESM-C, inconsistent adjustments for ocean currents commonly occur in the tropical western Pacific and the eastern Indian Ocean. To overcome this problem, a new ensemble-based bias correction approach—a simple modification of the Ensemble Optimal Interpolation (EnOI) approach for multi-variable into a direct approach for a single variable—is proposed to minimize the model biases. Compared with the EnOI approach, this new approach can effectively avoid inconsistent adjustments. Meanwhile, the comparisons suggest that inconsistent adjustment mainly results from the unreasonable correlations between temperature and ocean current in the background matrix. In addition, the ocean current can be directly corrected in the EnOI approach, which can additionally generate biases for the upper ocean. These induced ocean biases can produce unreasonable ocean heat sinking and heat storage in the tropical western Pacific. It will generate incorrect ocean heat transmission toward the east, further amplifying the inconsistency introduced through the tropical air–sea interaction process.

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A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

Song

Zhang

et al. 2022

Acta Oceanol. Sin.

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An ensemble-based SST nudging method proposed for correcting the subsurface temperature field in climate model

Cited by 4 publications

References 26 publications

Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application

Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application

Comparative Analysis of Two Approaches for Correcting the Systematic Ocean Temperature Bias of CAS-ESM-C

A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

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