Polly Smith, University of Reading

An ensemble-variational data assimilation approach for the estimation of coupled atmosphere-ocean forecast error covariances.

Amos Lawless, Nancy Nichols


Strongly coupled atmosphere-ocean data assimilation treats the atmosphere and ocean as a single coherent system, applying a single assimilation system to a fully coupled model. This approach requires specification of the relationship between the errors in the atmosphere and ocean model forecasts. Unfortunately, the characterisation of the statistics of these errors is non-trivial and there is currently little knowledge of how to construct and implement coupled error covariances in variational data assimilation systems. The cross-covariance information must capture the correct physical structure of processes occurring across the air-sea interface as well as the different scales of evolution in the atmosphere and ocean; if prescribed correctly, it will allow observations in one fluid to positively influence the analysis in the other.

Here we investigate the nature and structure of the atmosphere-ocean forecast error cross correlations using an idealised strongly coupled single-column atmosphere-ocean 4D-Var assimilation system. We present results from a set of identical twin experiments that use an ensemble of coupled 4D-Var assimilations to derive estimates of the atmosphere-ocean error cross correlations. Our results show significant variation in the correlation structures within the atmosphere-ocean boundary layer between summer and winter, and also between day and night. These differences provide a valuable insight into the nature of coupled atmosphere-ocean correlations under different meteorological conditions and can be explained by considering the underlying model physics, forcing and known atmosphere-ocean feedback mechanisms.

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