Strongly Versus Weakly Coupled Data Assimilation in Coupled Systems With Various Inter‐Compartment Interactions

Abstract

Abstract Coupled data assimilation (CDA) has been attracting researchers' interests to improve Earth system modeling. The CDA methods are classified into two: weakly coupled data assimilation (wCDA), which considers cross‐compartment interaction only in a forecast phase, and strongly coupled data assimilation (sCDA), which additionally uses other compartment's information in an analysis phase. Although sCDA can theoretically provide better estimates than wCDA since sCDA fully uses inter‐compartment covariances, the potential of sCDA in practice is still in debate. We investigate conditions under which sCDA is effective by applying Local Ensemble Transform Kalman Filter (LETKF) to a coupled Lorenz96 model. Especially, we aim to generalize CDA's behavior on coupling strength basis. By continuously changing a cross‐compartment interaction in the coupled Lorenz96 model, we find that sCDA's superiority over wCDA is particularly evident with large cross‐compartment interactions which attenuates dynamics of each compartment (the increase of variables in one compartment induces the reduction of those in the other compartment) and does not intensify the chaoticity of the whole system. The performance of sCDA is more sensitive to the LETKF's hyperparameters (i.e., localization and inflation) than wCDA. Furthermore, through two imperfect model experiments in which model parameters are uncertain, we quantify sCDA's vulnerability to model inaccuracies. The findings of the relationship between the skill of sCDA and inter‐compartment interactions give implications for future CDA strategies.

Keywords

• coupled data assimilation
• cross‐domain interaction
• hyperparameter
• model bias