The US National Science and Technology Council’s Joint Subcommittee on Ocean Science and Technology (JSOST) has identified the behavior of the Atlantic Meridional Overturning Circulation (AMOC) and its relationship to abrupt climate change as an important research priority. General circulation models (GCMs) play a crucial role in this endeavor. We propose to explore the interplay of deterministic and stochastic processes and their role in the predictability of the AMOC and Atlantic Climate, including the identification of systematic compensating model errors, in AMOC simulations in IPCC GCMs. We shall use novel dynamically-based statistical methods at multiple timescales, both in the frequency and in the temporal domains. We propose to apply Linear Inverse Modeling (LIM) to the output of each GCM to summarize nonlocal interactions between temperature and salinity resolved at the annual timescale, while estimating frequency-dependent transfer functions (transfer function analysis, or TFA) between these variables. Using these methods in combination aids us in separating forced-response multivariate phenomena from processes whose transient behaviors are coupled but operate on different timescales. Phase information from TFA and Fluctuation- Dissipation theory will be combined with LIM results to estimate the subscale forcing, both atmospheric and oceanic, required to maintain the AMOC as represented in each model. The proposed study will localize the sensitive regions affecting the AMOC in each model, identify sources of that sensitivity, diagnose compensating model errors, and allow comparison of results among the different models.
Relevance NOAA’s Next-Generation Strategic Plan and to ESS: This project satisfies all foci for the ESS Program, Priority 2, and is directly relevant to NOAA’s Next-Generation Strategic Plan. We shall study AMOC variability and compensating errors in IPCC models by estimating state-dependent tendencies as a function of variable and geographical location. Identifying rapidly varying processes (i.e., weather) that interact and/or maintain slower decadal variability (i.e., climate) will elucidate model dependence on those mechanisms. Thus, we may investigate the role of multi-scale interactions in both enhancing and destroying decadal predictability and improve the credibility of those models on which an informed society depends.