Collaborative Research: Understanding Observed Low Frequency Variability of Sea-Surface Temperature (SST) in the North Atlantic

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Sea-surface temperatures (SST) in the North Atlantic fluctuate on interannual and on decadal timescales, with different characteristic spatial structures. The mechanisms responsible for these modes of variability remain poorly understood. Theories differ in the roles they ascribe to gyre and overturning circulations in the ocean, and, most importantly, in whether or not they involve feedbacks from the SST onto the circulation of the atmosphere.These issues will be addressed using a novel method; the interactive ensemble coupled global climate model (GCM), in which an ensemble of atmospheric models is coupled to a single realization of the oceanic model. Given a sufficiently large ensemble of atmospheric models, essentially all intrinsic atmospheric noise is removed by averaging, and the ocean is then driven by atmospheric forcing that includes only the deterministic response of the atmosphere to the SST. Intrinsic atmospheric variability denoted "weather noise" can then be reintroduced to the coupled simulations in a controlled way.An ensemble coupled version of the NCAR Community Climate System Model 3 (CCSM3) will be implemented, and a set of six hypothesis-testing experiments will be carried out. "Weather noise" will be evaluated using both observations and model output. The latter case is a so-called "perfect model" study, which will serve to evaluate the role of observational and model errors in using observationally derived weather noise. Similarly, the importance of model differences from observations can be evaluated by using weather noise derived from one GCM in the ensemble coupled version of a second GCM. Model-only studies will be valuable in studying modes of variability involving the meridional overturning circulation; as such modes occur on long timescales not readily captured by the extant observational record.Broader impacts are in making the ensemble coupled CCSM3 available to the community for application to climate prediction and to verifying and improving coupled models. Two graduate students will be trained in the application of coupled climate models.