Project description
Better climate model predictions for the North Atlantic
Accurately predicting climate in the North Atlantic over an interannual-to-decadal timescale depends on models correctly simulating atmosphere and ocean processes, including the North Atlantic Oscillation (NAO) and the Atlantic Meridional Overturning Circulation (AMOC). The EU-funded DivPredSkill project will apply the novel idea of relating predictability to model biases via a better understanding of interactions between the NAO and AMOC and through linking findings from long, uninitialised simulations to initialised predictions, providing fresh insights for climate modelling and the prediction community. Specifically, the research fellow will identify model differences in the simulated NAO-AMOC relationship, assess the role of mean state biases for model differences, and link these findings with North Atlantic forecast skill.
Objective
Europe is vulnerable to climate extremes and climate change, making predictions valuable to political and economic stakeholders. The European winter climate depends a lot on sea level pressure differences between Iceland and the Azores, described by the North Atlantic Oscillation (NAO). Thus, many modelling centres working on predictions, focus on improving NAO predictions. Recent studies find the NAO could be highly predictable, much higher than suggested by individual models - calling for a better process understanding to improve models and predictions. The major source of our ability to make predictions for years and beyond are slow ocean processes, e.g. linked to the Atlantic Meridional Overturning Circulation (AMOC). Both, AMOC and NAO are considered key for North Atlantic decadal variability and predictability. They interact, especially via the NAO driving AMOC, but also vice versa. Consequently, prediction skill is reduced by uncertainties in the NAO-AMOC relationship, but also by severe mean state biases common among models. As of yet, not much effort has been made to understand skill difference across models - motivating the proposed research: The overarching research objective is to understand the causes for inter-model differences in interannual-to-decadal prediction skill - with a focus on the role of the model-dependent NAO-AMOC relationship and mean state biases. Specific objectives are to: (i) identify model differences in the NAO-AMOC interaction and their effect on differences of the potential to predict the North Atlantic; (ii) assess the role of mean state biases for model differences; (iii) link these findings with North Atlantic forecast skill. The proposed research comprises: the novel idea to relate predictability to model biases via NAO-AMOC interaction; and the innovative approach to link findings from long, uninitialised simulations to initialised predictions, enabling new insights valuable to the climate modelling and prediction community.
Fields of science
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
RG6 6AH Reading
United Kingdom