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11.04.2024: Ocean Circulation and Climate Dynamics Colloquium
Prof. Maria Rugenstein, Colorado State University, USA: "The pattern effect: How radiative feedbacks depend on surface warming patterns and influence near-term projections and climate sensitivity"
When? Thursday, 11 April 2024 at 3 pm
Where? Lecture Hall, Building 8A, Wischofstr. 1-3 and
Zoom: https://geomar-de.zoom.us/j/84289388604?pwd=dGlpeTBUd1Nxem5Ec3dRYXh4NFpOUT09
Abstract:
Recent research has highlighted that radiative feedbacks — and thus climate sensitivity — are not constant in time but depend sensitively on sea surface temperature patterns (“pattern effect”). I will discuss three implications of this realization:
First, I will show how coupled climate models fail to reproduce observed surface warming patterns and global mean top of the atmosphere (TOA) radiation trends. We use large initial condition ensembles to compare observations and models on equal footing. For certain periods, not a single ensemble member can reproduce observed values of surface temperature trends and TOA radiation trends. Despite these astonishing observation-model discrepancies their global-mean temperatures are simulated well. This points to a common model problem in surface heat fluxes and ocean heat uptake.
Second, I will discuss the relevance of the pattern effect for climate change projections. Given that coupled climate models cannot recreate the observed pattern of surface warming in relevant regions, we should doubt the surface warming pattern evolution in projections. I will introduce “surface warming pattern storylines” starting from the observations and bridging to simulated future patterns in standard scenarios. We show that coupled climate models used ubiquitously for climate change projections, underestimate the uncertainty of possible global-mean temperature evolutions due to their surface warming patterns and that this new “pattern uncertainty” will be likely relevant throughout the 21st century.
Third, I will introduce how a convolutional neural network (CNN) can be trained to learn the pattern effect and predict global-mean TOA radiation from surface warming patterns. We use explainable artificial intelligence methods to quantify that the CNN draws its predictive skill from meaningful regions. Remarkably and different from traditional approaches, we can predict radiation under strong climate change from training the CNN on internal variability alone. This out-of-sample application works only when feedbacks are allowed to be non-linear or equivalent, changing in time, which is another, independent manifestation of the relevance of the pattern effect.
