Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review

Zhang, Shaoqing; Liu, Zhengyu; Zhang, Xuefeng; Wu, Xinrong; Han, Guijun; Zhao, Yuxin; Yu, Xiaolin; Liu, Chang; Liu, Yun; Wu, Shu Hui; Lu, Feiyu; Li, Mingkui; Deng, Xiong

doi:10.1007/s00382-020-05275-6

Cited by 74 publications

(53 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we applied only this approach in correcting the sea temperature bias directly. Many additional strategies are available for reducing the model bias, such as increasing the model resolution and accuracy; in addition, a more advanced data assimilation approach is reviewed in Zhang et al (2020). Our approach has many aspects that merit exploration to identify the mechanisms of sea temperature mean states that affect the climate variations through air-sea interactions or teleconnection in the model.…”

Section: Summary and Discussionmentioning

confidence: 99%

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

Zheng

Zhu

et al. 2020

JGR Oceans

View full text Add to dashboard Cite

show abstract

Section: Summary and Discussionmentioning

confidence: 99%

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

Zheng

Zhu

et al. 2020

JGR Oceans

View full text Add to dashboard Cite

show abstract

“…Thus, ocean assimilation has been widely used in seasonal and decadal predictions (Molod et al, 2020;Molteni et al, 2011;Oschlies & Willebrand, 1996;Palmer et al, 2004;Saha et al, 2006;Segschneider et al, 2000;Wang et al, 2019;Zhu et al, 2017). In addition to standalone ocean model assimilation, coupled model assimilation is a rapidly developing direction of research and has proven to be an effective approach that has the potential to improve not only climate prediction but also seamless weather and climate predictions (Brunet et al, 2015;Fujii et al, 2009;Han et al, 2013;Zhang et al, 2007Zhang et al, , 2020aZhang et al, , 2020b.…”

Section: Research Articlementioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

“…In extensive practices, developing CDA system based on a complex coupled model and applying it in numerical prediction have become an important task for many operational and research centers. Generally, assimilation is either applied to each component of a coupled model independently (i.e., weakly CDA), or applied to several components simultaneously and treats these components as one single integrated system (i.e., strongly CDA) (Penny et al, 2017;Zhang et al, 2020). The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) developed an ocean-atmosphere coupled four-dimensional variational DA system and used it for seasonal and decadal predictions (Mochizuki et al, 2016;Sugiura et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Development of Coupled Data Assimilation With the BCC Climate System Model: Highlighting the Role of Sea‐Ice Assimilation for Global Analysis

Liu

Yao

et al. 2021

J Adv Model Earth Syst

View full text Add to dashboard Cite

The coupled data assimilation (CDA) system consisting of ocean, sea-ice, and atmosphere data assimilation components with the Beijing Climate Center (BCC) Climate System Model has been developed to provide reliable analyses of the atmosphere, ocean, and sea-ice states. It incorporates ocean temperature/salinity profiles, sea surface temperature, sea level height, and sea-ice concentration observations at a daily frequency, and atmosphere reanalysis at a 6-hourly frequency. Results show that the system is capable of realistically reproducing the climatology and variability of ocean, sea-ice, and atmosphere. The performances in analyzing ocean component are comparable to those of well-known ocean reanalyses that were once used to initialize the BCC model for climate predictions. A series of experiments with and without sea-ice observations in the CDA framework are designed to explore the role of sea-ice data assimilation (DA). The addition of sea-ice DA exerts very small influence to the analysis of upper ocean temperature over the Arctic area, but leads to an evident reduction of temperature error in the upper 1000 m of ocean south of 60°S. Particularly, only the inclusion of sea-ice DA can make the ocean/ocean-atmosphere DA effective in providing skillful analysis in the high-latitude Southern Ocean. Furthermore, we find that only on the basis of ocean DA, can the addition of sea-ice concentration assimilation improve the analysis of tropical tropospheric atmosphere, along with a better analysis of mid-and high-latitude stratospheric atmosphere. These results address the importance of coordination of sea-ice observations and ocean observations in CDA.Plain Language Summary Developing coupled data assimilation (CDA) technique has become an important task for many operational and research centers. Although CDA consisting of multiple assimilation components has made great progress in the past years, the impacts of each individual component, especially the sea-ice data assimilation (DA), are not fully addressed yet. In this study, we have developed a CDA system and implemented a coordinated assimilation scheme of ocean, sea-ice, and atmosphere data. This scheme shows reliable performances in analyzing the states of the ocean, sea-ice, and atmosphere. A series of experiments are conducted to examine the impacts of sea-ice DA on climate analysis. We stress that the importance of sea-ice DA can be highlighted only in a multicomponent coordinated assimilation framework. On one hand, on the basis of ocean/oceanatmosphere DA, addition of sea-ice DA can decrease the ocean temperature error in the upper 1000 m of the high-latitude Southern Ocean. On the other hand, adding sea-ice DA on the basis of ocean DA can improve the analysis of atmospheric variability in the tropical troposphere and mid-and high-latitude stratosphere. These findings call for more attentions of operational model developers to comprehensively describe the role of sea-ice DA in climate analysis and forecast. LIU ET AL.

show abstract

Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review

Cited by 74 publications

References 111 publications

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

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

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

Development of Coupled Data Assimilation With the BCC Climate System Model: Highlighting the Role of Sea‐Ice Assimilation for Global Analysis

Contact Info

Product

Resources

About