Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations

J. Geophys. Res 10/2008; 113(C10):C10017, doi:10.1029/2008JC004741, 2008. DOI: 10.1029/2008JC004741


A four-dimensional variational (4D-VAR) data assimilation system using a coupled ocean-atmosphere global model has been successfully developed with the aim of better defining the dynamical states of the global climate on seasonal to interannual scales. The application of this system to state estimations of climate processes during the 1996–1998 period shows, in particular, that the representations of structures associated with several key events in the tropical Pacific and Indian Ocean sector (such as the El Niño, the Indian Ocean dipole, and the Asian summer monsoon) are significantly improved. This fact suggests that our 4D-VAR coupled data assimilation (CDA) approach has the potential to correct the initial location of the model climate attractor on the basis of observational data. In addition, the coupling parameters that control the air-sea exchange fluxes of mass, momentum, and heat become well adjusted. Such an initialization using the 4D-VAR CDA approach allows us to make a roughly 1.5-year lead time prediction of the 1997–1998 El Niño event. These results demonstrate that our 4D-VAR CDA system has the ability to enhance forecast potential for seasonal to interannual phenomena.

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    • "The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has implemented a 4D-Var coupled assimilation system where the bulk adjustment factors of the latent heat, sensible heat and momentum fluxes are chosen as the control variables in addition to the oceanic initial condition. The 4D-Var method assimilates ocean and atmospheric 10-day mean values and produces a sequence of 10-day mean states that can be used to initialise seasonal predictions (Sugiura et al. 2008). "
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    ABSTRACT: A coupled data assimilation system has been developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), which is intended to be used for the production of global reanalyses of the recent climate. The system assimilates a wide variety of ocean and atmospheric observations and produces ocean-atmosphere analyses with a coupled model. Employing the coupled model constraint in the analysis implies that assimilation of an ocean observation has immediate impact on the atmospheric state estimate, and, conversely, assimilation of an atmospheric observation affects the ocean state. This covariance between atmosphere and ocean induced by the analysis method is illustrated with simple numerical experiments. Realistic data assimilation experiments based on the global observing system are then used to assess the quality of the assimilation method. Comparison with an uncoupled system shows overall a mostly neutral impact, with slightly improved temperature estimates in the upper ocean and the lower atmosphere. These preliminary results are considered of interest for the ongoing community efforts on coupled data assimilations.
    Quarterly Journal of the Royal Meteorological Society 07/2015; DOI:10.1002/qj.2629 · 3.25 Impact Factor
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    • "Finally, there are now new ways of simultaneously assimilating observations from different components (e.g. atmosphere and oceans) into climate models, in order to initialize them to make interannual–decadal predictions, for example the 4D-Var couple assimilation approach being developed by Awaji and co-workers in Japan (Mochizuki et al. 2007; Sugiura et al. 2008). "
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    ABSTRACT: Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.
    Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 03/2009; 367(1890):925-37. DOI:10.1098/rsta.2008.0178 · 2.15 Impact Factor
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    ABSTRACT: The potential for predicting natural internal climate variability on seasonal and decadal time scales resides mainly in information provided by the ocean initial conditions. While seasonal forecasting is a consolidated activity, with several operational centers around the world issuing routine seasonal forecasts, decadal forecasting is still in its infancy. This paper discusses the role of the ocean observing system in the initialization of seasonal and decadal predictions. It is shown that the assimilation of ocean observations reduces the uncertainty in the estimation of the upper ocean thermal structure and improves the skill of seasonal forecasts. Results from observing system experiments conducted with different seasonal forecast systems indicate that no observing system is redundant, highlighting their complementary nature. Results from synthetic observing system experiments conducted assuming sustainability of the current observing system indicate that Argo data has the potential for constraining the deep ocean variables responsible for decadal variability.
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