Descripción del proyecto
Mejores predicciones de los modelos climáticos del Atlántico Norte
El cálculo preciso del clima del Atlántico Norte a una escala temporal desde interanual hasta decenal depende de la capacidad de los modelos para simular de forma correcta los procesos atmosféricos y oceánicos, como la Oscilación del Atlántico Norte (NAO) y la Circulación de Retorno del Atlántico Meridional (AMOC). El proyecto financiado con fondos europeos DivPredSkill se servirá de la idea innovadora de relacionar la predictibilidad con los sesgos de los modelos mediante un estudio más a fondo de las interacciones entre la NAO y la AMOC y mediante la relación entre descubrimientos de simulaciones largas no iniciadas y predicciones iniciadas, lo que aportará información nueva para la modelización del clima y los profesionales dedicados a su predicción. En concreto, el investigador al cargo identificará diferencias en los modelos en las relaciones NAO-AMOC simuladas, evaluará la función de los sesgos del estado medio en busca de diferencias entre modelos y relacionará estos resultados con las capacidades de predicción del Atlántico Norte.
Objetivo
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.
Ámbito científico
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Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
RG6 6AH Reading
Reino Unido