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

Ice nucleating particles (INPs) strongly influence cloud phase, precipitation, and radiative properties, yet observations of their vertical distributions remain sparse. This presentation reviews recent advances in airborne INP measurements using drones, launched balloons, and tethered balloon systems, focusing on the development of the Profiling Upper altitudes For Ice Nucleation (PUFIN) sampler for routine altitude-resolved observations. Results from multiple deployments demonstrate that INP concentrations can vary substantially with height, season, aerosol source, and boundary layer structure, highlighting the limitations of relying solely on surface measurements. These vertically resolved observations provide critical constraints for understanding aerosol-cloud interactions and improving the representation of ice formation processes in atmospheric models.

(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 

Influences of aerosols on climate are qualitatively known, but the magnitude of their effects is uncertain with important implication for understanding past and future climate change. In this colloquium, I will explain some reasons for uncertainties in our understanding of aerosol effects on climate, and outline how we can make progress despite persistent multi-model spread in aerosol radiative forcing. We will see model-to-model differences for aerosol effects from global climate model simulations, and new CMIP aerosol data for use in climate simulations that inform the next IPCC assessment report of climate change. Looking ahead, I will introduce some of the plans for advancing the research field, e.g., through leading the new experiment protocol of the Aerosol and Chemistry Model Intercomparison Project, to which the most complex Earth system models currently available worldwide will contribute simulations.