the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model
Astrid B. Gjelsvik
Tim Carlsen
Franziska Hellmuth
Stefan Hofer
Zachary McGraw
Harald Sodemann
Trude Storelvmo
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W-shaped evolution during a 24 h land-falling atmospheric river event in southern Norway. We distinguish contributions from below-cloud processes, weather system characteristics, and moisture source conditions during different stages of the event. Rayleigh distillation models need to be expanded by additional processes to accurately predict isotopes in surface precipitation from stratiform clouds.
global dimmingas found in observations. Only model experiments with anthropogenic aerosol emissions display any dimming at all. The discrepancies between observations and models are largest in China, which we suggest is in part due to erroneous aerosol precursor emission inventories in the emission dataset used for CMIP6.
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Atmospheric delays affect global navigation satellite system (GNSS) signals. This study analyses the wet delay, a variable component caused by atmospheric water vapor, using a novel filtering method to examine small-scale turbulent variations. Case studies at five global stations revealed daily and seasonal turbulence patterns. This research will improve water vapour and cloud models, enhance nowcasting, and refine stochastic modelling for GNSS and very long baseline interferometry.
Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.