Atmospheric processes

Understanding the factors that shape climate and influence the isotopic composition of precipitation is crucial for paleoclimate reconstructions, especially in regions with Mediterranean climates where rainfall is influenced by both Atlantic and Mediterranean moisture sources. This study examines the relationship between moisture origins, climatic variables, and the stable isotopic composition of precipitation and cave drip water in the Orgnac and Chauvet caves, located in southern France, over a 20-year period. The research reveals notable seasonal variations in rainfall δ18O values, driven by temperature and Rayleigh distillation processes. As shown in our previous work in Villars Cave (SW-France), temperature changes alone cannot fully explain the observed isotopic variability. We observed that winter precipitation tends to have lower δ18O values due to longer transport distances from distant oceanic sources, while summer precipitation displays higher δ18O values due to shorter transport paths. Additionally, the study highlights the influence of sea surface wind speeds and evaporation rates on water vapor isotopes, further shaping the seasonal δ18O patterns. As rainwater infiltrates the soil and percolates into the karst system, the seasonal δ18O signal in drip water is often dampened due to mixing in the reservoirs above the caves, which typically reduces seasonality. The key findings include: (1) a multi-year increasing trend in drip water δ18O, likely associated with reduced local water excess and the effects of global warming, with significant implications for speleothem isotope records, and (2) moisture from the Mediterranean Sea contributes to 10% of the total precipitation source, despite the region's proximity to the sea, especially during intense storm events. This study provides new insights into the complex interactions between moisture sources, temperature, and isotopic signatures in Mediterranean climate regions, with implications for improving speleothem-based paleoclimate reconstructions.

Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures. We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs. Our approach outperforms state-of-the-art methods like U-Nets, and our geospatial encoding further improves prediction results, demonstrating the potential of SSL for cloud reconstruction.

Clouds are one of the largest sources of uncertainty in climate predictions. Global km-scale models need to simulate clouds and precipitation accurately to predict future climates. To isolate issues in their representation of clouds, models need to be thoroughly evaluated with observations. Here, we introduce multifractal analysis as a method for evaluating km-scale simulations. We apply it to outgoing longwave radiation fields to investigate structural differences between observed and simulated anvil clouds. We compute fractal parameters which compactly characterize the scaling behavior of clouds and can be compared across simulations and observations. We use this method to evaluate the nextGEMS ICON simulations via comparison with observations from the geostationary satellite GOES-16. We find that multifractal scaling exponents in the ICON model are significantly lower than in observations. We conclude that too much variability is contained in the small scales (<100 km) leading to less organized convection and smaller, isolated anvils.