Philip Stier

Abstract:

Fog and low stratus clouds (FLS) form as a result of complex interactions of multiple factors in the atmosphere and at the land surface and impact both the anthropogenic and natural environments. Here, we analyze the role of synoptic conditions and aerosol loading on FLS occurrence and persistence in the Po valley in northern Italy. By applying k‐means clustering to reanalysis data, we find that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Satellite‐based data on FLS persistence shows longer persistence of radiatively formed FLS events, likely due to air mass stagnation and a temperature inversion. Ground‐based aerosol optical depth observations further reveal that FLS event duration is significantly higher under high aerosol loading. The results underline the combined effect of topography, moisture advection and aerosol loading on the FLS life cycle in the Po valley.

Abstract:

We analyze the importance of cloud top temperature, dust aerosol, sea salt aerosol, and sea ice cover for the thermodynamic phase of low‐level, mid‐level, and mid to low‐level clouds observed by CloudSat/CALIPSO over the Arctic and the Southern Ocean using an explainable machine learning technique. As expected, the cloud top temperature is found to be the most important parameter for determining cloud phase. The results show also a predictive power of sea salt and sea ice on the phase of low‐level clouds, while in mid‐level clouds dust shows predictive power. Over the Southern Ocean, strong zonal winds coincide with the aerosol distribution. While they can produce high mixing ratios of sea spray at lower levels, the strong zonal winds may prevent the pole‐ward transport of dust. Sea ice may prevent the release of sea salt aerosols and marine organic aerosols leading to higher liquid fractions in clouds over sea ice.

Abstract:

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.