A New Ensemble‐Based Approach to Correct the Systematic Ocean Temperature Bias of CAS‐ESM‐C to Improve Its Simulation and Data Assimilation Abilities

Du, Mengjiao; Zheng, Fei; Zhu, Jiang; Lin, Renping; Yang, Haipeng; Chen, Quanliang

doi:10.1029/2020jc016406

Cited by 11 publications

(9 citation statements)

References 84 publications

(91 reference statements)

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“…for these purposes addressing the above limitations, could utilize other methods explicitly accounting for parameter-bias in the data-assimilation scheme to obtain a better accuracy (e.g., Dee and Da Silva, 1998;Sørensen and Madsen, 2004;Chepurin et al, 2005;Auligné et al, 2007;Li et al, 2009;Du et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

On Parameter Bias in Earthquake Sequence Models using Data Assimilation

Banerjee

Dinther²,

Vossepoel³

2022

Preprint

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Abstract. The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given uncertainties in both models and data. We investigate whether data assimilation can estimate current and future fault states, i.e., slip rate and shear stress, in the presence of a bias in the friction parameter. We perform state estimation as well as combined state-parameter estimation using a sequential importance resampling particle filter in a 0D generalization of the Burridge–Knopoff spring-block model with rate-and-state friction. Minor changes in the friction parameter epsilon can lead to different state trajectories and earthquake characteristics. The performance of data assimilation in estimating the fault state in the presence of a parameter bias in epsilon depends on the magnitude of the bias. A small parameter bias in epsilon (+3 %) can be compensated very well using state estimation (R2= 0.99), whereas an intermediate bias (-14 %) can only be compensated partly (R2= 0.47). When increasing particle spread by accounting for model error and an additional resampling step R2 increases to 0.61. However, when there is a large bias (-43 %) in epsilon, only state-parameter estimation can fully account for the parameter bias (R2= 0.97). Simultaneous state- and parameter estimation thus effectively separates error contributions from friction and shear stress to correctly estimate current and future shear stress and slip rate. This illustrates the potential of data assimilation for estimation of earthquake sequences and provides insight into its application in other non-linear processes with uncertain parameters.

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Section: Discussionmentioning

confidence: 99%

On Parameter Bias in Earthquake Sequence Models using Data Assimilation

Banerjee

Dinther²,

Vossepoel³

2022

Preprint

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“…However, there is also considerable bias in the assimilation system due to the inadequate consideration of the subsurface ocean state in the assimilation process. Recently, the assimilation system based on CAS‐ESM‐C has been improved through bias correction (Du et al., 2020) and SSH assimilation, as presented in this paper.…”

Section: Data Model and Experimental Designmentioning

confidence: 99%

“…C is a matrix whose columns/lines are correlation functions that is adopted to localize the background error covariance. Followed by our previous studies (Dong et al., 2016; Du et al., 2020; Lu et al., 2016), the Gaussian correlation function is used. The correlation length scale is set to 400 km which is the same all over the model domain.…”

Section: Data Model and Experimental Designmentioning

confidence: 99%

“…EnOI has been widely adopted in ocean assimilation and may be used to assimilate SLA, SST, and sea surface salinity (Dong et al., 2016; Du et al., 2020; Fu et al., 2009; Lin et al., 2016; Lu et al., 2016; Luo et al., 2017; Xie & Zhu, 2010). In this assimilation system, ocean state variables, including sea level ( h 0 ), temperature ( T ), salinity ( S ), and two‐dimensional ocean currents ( u and v ), were adjusted through the EnOI assimilation scheme.…”

Section: Data Model and Experimental Designmentioning

confidence: 99%

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A Reasonable Mean Dynamic Topography State on Improving the Ability of Assimilating the Altimetry Observations into a Coupled Climate System Model: An Example With CAS‐ESM‐C

et al. 2021

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In this study, based on an ocean data assimilation system for the coupled climate model CAS-ESM-C, how to reasonably assimilate altimetry data are explored. In sea surface height (SSH) assimilations, the mean dynamic topography (MDT) is an important factor that can coordinate the observed sea level anomalies with the modeled SSH. The SSH assimilation results are first compared through assimilation experiments using three different MDTs, including the observed reference height, the MDT from model control run, and the MDT from the assimilation experiment in CAS-ESM-C, with the climatological World Ocean Atlas (WOA) temperature assimilated into the coupled model. The results show that the third one can significantly improve the ability of the ocean data assimilation system to assimilate the SSH observations. Using this MDT, the SSH assimilation scheme of the ocean data assimilation system was established for the CAS-ESM-C, and a long-term SSH assimilation experiment from 1994 to 2017 was carried out. The results show that the SSH assimilation performs much better than the SST assimilation in reproducing the ocean states and seasonal-interannual variability. The improvements of SSH assimilation compared with SST assimilation may be due to the different properties of the two ocean variables. While SST is a thermodynamic variable used to evaluate the thermal condition of the ocean surface layer, SSH is a dynamic variable linked to the dynamical information of the whole ocean layer. Thus, SSH assimilation can constrain the ocean model with observed dynamic information, which is more important to the state estimation and temporal evolution of the ocean. Plain Language Summary Altimetry data are widely used in ocean assimilation. Compared with standalone ocean model assimilation, assimilation in a coupled model framework is more realistic due to consideration of the air-sea interaction processes and draws more attention from the modeling community. To reasonably assimilate altimetry data, mean dynamic topography (MDT) is an essential factor that can coordinate the observed sea level anomalies with the modeled sea surface temperature (SSH). In this study, to choose a best MDT for altimetry data assimilation in the coupled model (CAS-ESM-C), three different MDTs were used in SSH assimilation in CAS-ESM-C. We concluded that the MDT derived from assimilating the climatological World Ocean Atlas (WOA) temperature into the coupled model can significantly improve the ability of the ocean data assimilation system to assimilate the SSH observations. Then, using this selected MDT scheme, long-term SSH assimilation experiments were conducted and the performances of SSH assimilation were compared with those of sea surface temperature (SST) assimilation. In comparison with SST assimilation, SSH assimilation achieves better performances in reproducing the mean states and seasonal-interannual variability of the ocean. DONG ET AL.

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“…In the evaluation of the ocean data assimilation system in CAS-ESM-C, we found that adjustments in the current field are not significant when assimilating the SST [53], but when assimilating the Argo profiles (the configuration had been changed to suit 3D data, is not same as for SST), the adjusted current field and the model biases have a great influence on the assimilation, even resulting in a simulation that cannot be effectively improved by the assimilation. For this reason, we designed a new bias correction approach to deal with the effects of model biases on the assimilation of ocean observations [54]. Therefore, the main purpose of this study was to compare the effect of this new bias correction approach with the original EnOI approach for bias correction and investigate the reasons for the differences between these two approaches, especially the current inconsistent adjustments.…”

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 Ensemble‐Based Approach to Correct the Systematic Ocean Temperature Bias of CAS‐ESM‐C to Improve Its Simulation and Data Assimilation Abilities

Abstract: The persistent biases that are associated with the mean state in coupled or climate models can cause simulation problems and degrade the results of seasonal to decadal climate predictions by affecting the variability of the model (

Cited by 11 publications

References 84 publications

On Parameter Bias in Earthquake Sequence Models using Data Assimilation

On Parameter Bias in Earthquake Sequence Models using Data Assimilation

A Reasonable Mean Dynamic Topography State on Improving the Ability of Assimilating the Altimetry Observations into a Coupled Climate System Model: An Example With CAS‐ESM‐C

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

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