Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1)

Skachko, Sergey; Buehner, Mark; Laroche, Stéphane; Lapalme, Ervig; Smith, G. C. Moore; Roy, François; Surcel-Colan, Dorina; Bélanger, J R; Garand, Louis

doi:10.5194/gmd-12-5097-2019

Cited by 14 publications

(8 citation statements)

References 46 publications

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“…Moreover, assimilating only atmospheric observations into the atmosphere model in a coupled ocean–atmosphere system can better reproduce the intensity change of a typhoon (Kunii et al ., 2017). Compared to the uncoupled analysis, the weakly coupled ocean–atmosphere assimilation system provided an improved forecast for both the atmosphere and the ocean (especially for the sea‐surface temperaure prediction) by applying DA into both the ocean and the atmosphere (Browne et al ., 2019; Guiavarc'h et al ., 2019; Skachko et al ., 2019). Besides, climate variabilities were well simulated (Karspeck et al ., 2018) and most of the biases were corrected (Chang et al ., 2013) by such a coupled DA system.…”

Section: Introductionmentioning

confidence: 99%

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Tang

Sidorenko

et al. 2020

Quart J Royal Meteoro Soc

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An ensemble-based data assimilation framework for a coupled oceanatmosphere model is applied to investigate the influence of assimilating different types of ocean observations on the ocean and atmosphere simulation. The data assimilation is performed with the parallel data assimilation framework (PDAF) for the climate model AWI-CM. Observations of the ocean, namely satellite sea-surface temperature (SST) and temperature and salinity profiles, are assimilated into the ocean component. The atmospheric state is only influenced by the model dynamics. Different assimilation scenarios were carried out with different combinations of observations to investigate to what extent the assimilation into the coupled model leads to a better estimation of the state of the ocean as well as the atmosphere. The influence of the data assimilation is assessed by comparing the ocean prediction with dependent and independent ocean observations. For the atmosphere, the assimilation result is compared with the ERA-Interim atmospheric reanalysis data. The ocean temperature and salinity are improved by all the assimilation scenarios in the coupled system. The assimilation leads to a response of the atmosphere throughout the troposphere and impacts the global atmospheric circulation. Globally the temperature and wind speed are improved in the atmosphere on average. K E Y W O R D S coupled model, data assimilation, sea-surface temperature, temperature and salinity profiles 1 INTRODUCTION Traditionally, different components of the Earth system such as the ocean and the atmosphere are simulated by separate models with influences of other components being modelled as boundary conditions or forcings. However, the oceans and the atmosphere are connected and interact with each other. A consistent initial condition for these different components is required and is expected to provide a better forecast for both the ocean and the atmosphere. Earth system models simulate different components like the ocean, atmosphere, sea ice and land This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Tang

Sidorenko

et al. 2020

Quart J Royal Meteoro Soc

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“…The original source code and scripts corresponding to WRF and GSI/EnKF can be download from https://www2.mmm.ucar. edu/wrf/users/download/get_source.html (last access: 7 May 2021, Skamarock et al, 2019) and https://dtcenter.org/com-GSI/users/ downloads/index.php (last access: 15 April 2020, Shao et al, 2016), respectively. For the source code of FIO-AOW, please contact the authors of (Zhao et al, 2017;Wang et al, 2018).…”

Section: Correctness In Developing a Weakly Coupled Ensemble Da Systemmentioning

confidence: 99%

Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0

Sun

Liu

et al. 2021

Geosci. Model Dev.

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Abstract. Data assimilation (DA) provides initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even Earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, especially how to conveniently achieve efficient interaction between the ensemble of the coupled model and the DA methods. We first propose a new DA framework, DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1), for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model ensemble. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method without global communications and does not require users to develop extra code for implementing the data exchange functionality. Based on DAFCC1, we then develop an example weakly coupled ensemble DA system by combining an ensemble DA system and a regional atmosphere–ocean–wave coupled model. This example DA system and our evaluations demonstrate the correctness of DAFCC1 in developing a weakly coupled ensemble DA system and the effectiveness in accelerating an offline DA system that uses disk files as the interfaces for the data exchange functionality.

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“…The advantage of SCDA is that observations at a given time have instantaneous impacts across all components during all available analyses (Penny et al, 2017;Zhang et al, 2020b). Due to the difficulties in obtaining a high signal-to-noise ratio of the covariance between model components (Han et al, 2013), by now the WCDA is still the common choice for assimilating observations into coupled models (e.g., Laloyaux Browne et al, 2019;Skachko et al, 2019;Tang et al, 2020; and in the meanwhile some studies have also discussed the SCDA (e.g., Negar et al, 2020). In this study, we use WCDA.…”

Section: Introductionmentioning

confidence: 99%

An Online Ensemble Coupled Data Assimilation Capability for the Community Earth System Model: System Design and Evaluation

Sun

Jiang

Zhang

et al. 2021

Preprint

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Abstract. The Community Earth System Model (CESM) developed at the National Center of Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM’s capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented to both the finite-volume and spectral-element dynamical cores, and an online oceanic data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation”-constraints by ADA and ODA. The weakly CDA in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air-sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air-sea interface. A three-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. Results show that Ps RMSE is smaller than 20CR and SST RMSE is better than ERA-20C and close to CFSR. The success of the online CESM-ECDA system is the first step to implement a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.

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Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1)

Cited by 14 publications

References 46 publications

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0

An Online Ensemble Coupled Data Assimilation Capability for the Community Earth System Model: System Design and Evaluation

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