Preserving the coupled atmosphere–ocean feedback in initializations of decadal climate predictions

• Published in: WIREs Climate Change
• Main Authors: Brune, Sebastian, Baehr, Johanna
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2020
• Subjects: ddc:551.6; coupled data assimilation; decadal climate prediction; earth system model; large-scale atmosphere-ocean feedback; model-consistent initialization; North Atlantic
• Online Access: https://doi.org/10.1002/wcc.637; http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9369

On interannual to decadal time scales, memory in the Earth's climate system resides to a large extent in the slowly varying heat content of the ocean, which responds to fast atmospheric variability and in turn sets the frame for large-scale atmospheric circulation patterns. This large-scale coupled atmosphere–ocean feedback is generally well represented in today's Earth system models. This may fundamentally change when data assimilation is used to bring such models close to an observed state to initialize interannual to decadal climate predictions. Here, we review how the large-scale coupled atmosphere–ocean feedback is preserved in common approaches to construct such initial conditions, with the focus on the initialized ocean state. In a set of decadal prediction experiments, ranging from an initialization of atmospheric variability only to full-field nudging of both atmosphere and ocean, we evaluate the variability and predictability of the Atlantic meridional overturning circulation, of the Atlantic multidecadal variability and North Atlantic subpolar gyre sea surface temperatures. We argue that the quality of initial conditions for decadal predictions should not purely be assessed by their closeness to observations, but also by the closeness of their respective predictions to observations. This prediction quality may depend on the representation of the simulated large-scale atmosphere–ocean feedback. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models