Earth system modeling has fundamentally contributed to our understanding of past, present and future climate. Regional-scale multidecadal to centennial variability has been identified as a model blind spot, as across general circulation model generations they showed much lower levels of temperature variance than reconstructions, and underpredict regional state-dependency. In this talk I will discuss recent work on closing this gap, what this implies for projections of temperature extremes, and how TERRA aims to improve capacities to project global change impacts.

 
 

Deep learning emulates atmospheric reanalyses with high fidelity, enabling increasingly well-calibrated ensemble weather forecasts at progressively longer lead times. To extend these gains to climate-relevant horizons, AI prediction systems must produce credible forced responses to drivers of interest (e.g., greenhouse gases, land-use change). We propose a minimal, testable framework for AI climate modeling: (i) represent external forcings explicitly and restrict them to physically appropriate state tendencies; and (ii) stress-test robustness in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading climate emulators and hybrid physics-AI models, we identify coupling and development challenges and compare scaling with resolution and effective complexity. AI models do not appear intrinsically more efficient than GPU-ported dynamical models once complexity is accounted for, yet they can directly predict target variables at the desired grid without integrating the full high-frequency, multivariate state. Diverse ML downscaling strategies can partially substitute for explicit fine-scale resolution when observations are available, paving the way towards inexpensive, local risk assessment across prediction horizons

(1) tbd (2) tbd (3) Renewable Energy Systems in a Changing Climate(4) tbd

 
 

TBD

(1) tbd (2) tbd (3) tbd (4) tbd

(1) Predictability of the 2014 Pentecost storm (2) tbd (3) Added-Value of the Coming Decade ICONXPP Decadal Climate Predictions Ensemble for User-relevant Variables in Europe (4) tbd