Events

 
 

The complexity of clouds not only puzzles the casual observer but also challenges our understanding of the climate system. The processes that make clouds so hard to capture span a wide range of scales, from the formation of cloud droplets on aerosol particles to shifts of cloud regimes with large-scale conditions. This multiscale nature of clouds translates into persistently poor constraints on the cloud-mediated aerosol forcing and cloud feedbacks. We will focus on intermediate scales, where shallow cloud fields organize into mesoscale patterns. The evolution of these patterns is shaped by interactions with both smaller and larger scales. I will present examples of how this interplay leads to non-linear sensitivities, abrupt changes of patterns and memory effects. A particular focus will be on the role of aerosols in shaping all three. We will close by discussing implications of these results for numerical simulations and the analysis of satellite data.

Predicting atmospheric processes in complex terrain remains one of the greatest challenges in modern meteorology. The South Tyrolean Meteorological Service operates a dense observational network in a region defined by extreme orographic complexity. This presentation explores the synergy between our operational mandates and our participation in the international TEAMx program.
By integrating local observations into the TEAMx framework, we aim to improve the validation of kilometric-scale weather models and enhance the understanding convective events and sub-mesoscale flow dynamics in the Alps. This collaboration ensures that cutting-edge atmospheric research directly informs better forecasting tools for mountain regions.
 

(1) tbd (2) Partial Analysis Increments of the ‘Swabian MOSES 2023’ campaign in the ICON-D2 model (3) The representation of equatorial waves in data-driven weather prediction models (4) Assimilating Doppler wind lidar observations from ‘Swabian MOSES 2023’ reveals wind biases in the ICON-D2 model

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

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