Brief communication: Storm Daniel flood impact in Greece in 2023: mapping crop and livestock exposure from synthetic-aperture radar (SAR) Natural Hazards and Earth System Sciences DOI 10.5194/nhess-24-2375-2024 22 July 2024 About 820 km2 of agricultural land was inundated in central Greece due to Storm Daniel. A detailed analysis revealed that the crop most affected by the flooding was cotton; the inundated area of more than 282 km2 comprised ~ 30 % of the total area planted with cotton in central Greece. In terms of livestock, we estimate that more than 14 000 ornithoids and 21 500 sheep and goats were affected. Consequences for agriculture and animal husbandry in Greece are expected to be severe. Read more
The risk of synoptic-scale Arctic cyclones to shipping Natural Hazards and Earth System Sciences DOI 10.5194/nhess-24-2115-2024 19 July 2024 The risk posed to ships by Arctic cyclones has seldom been quantified due to the lack of publicly available historical Arctic ship track data. This study investigates historical Arctic ship tracks, cyclone tracks, and shipping incident reports to determine the number of shipping incidents caused by the passage of Arctic cyclones. Results suggest that Arctic cyclones have not been hazardous to ships and that ships are resilient to the rough sea conditions caused by Arctic cyclones. Read more
Isotopomer labeling and oxygen dependence of hybrid nitrous oxide production Biogeosciences DOI 10.5194/bg-21-3215-2024 17 July 2024 Nitrous oxide, a potent greenhouse gas, accumulates in regions of the ocean that are low in dissolved oxygen. We used a novel combination of chemical tracers to determine how nitrous oxide is produced in one of these regions, the eastern tropical North Pacific Ocean. Our experiments showed that the two most important sources of nitrous oxide under low-oxygen conditions are denitrification, an anaerobic process, and a novel “hybrid” process performed by ammonia-oxidizing archaea. Read more
Coupled ice–ocean interactions during future retreat of West Antarctic ice streams in the Amundsen Sea sector The Cryosphere DOI 10.5194/tc-18-2653-2024 15 July 2024 A new ice–ocean model simulates future ice sheet evolution in the Amundsen Sea sector of Antarctica. Substantial ice retreat is simulated in all scenarios, with some retreat still occurring even with no future ocean melting. The future of small “pinning points” (islands of ice that contact the seabed) is an important control on this retreat. Ocean melting is crucial in causing these features to go afloat, providing the link by which climate change may affect this sector’s sea level contribution. Read more
Quantum data assimilation: a new approach to solving data assimilation on quantum annealers Nonlinear Processes in Geophysics DOI 10.5194/npg-31-237-2024 12 July 2024 Data assimilation is a crucial component in the Earth science field, enabling the integration of observation data with numerical models. In the context of numerical weather prediction (NWP), data assimilation is particularly vital for improving initial conditions and subsequent predictions. However, the computational demands imposed by conventional approaches, which employ iterative processes to minimize cost functions, pose notable challenges in computational time. The emergence of quantum computing provides promising opportunities to address these computation challenges by harnessing the inherent parallelism and optimization capabilities of quantum annealing machines. Read more
Elevation-dependent warming: observations, models, and energetic mechanisms Weather and Climate Dynamics DOI 10.5194/wcd-5-763-2024 10 July 2024 Observational data and numerical models suggest that, under climate change, elevated land surfaces warm faster than non-elevated ones. Proposed drivers of this “elevation-dependent warming” (EDW) include surface albedo and water vapour feedbacks, the temperature dependence of longwave emission, and aerosols. Yet the relative importance of each proposed mechanism both regionally and at large scales is unclear, highlighting an incomplete physical understanding of EDW. Read more
A downward-counterfactual analysis of flash floods in Germany Natural Hazards and Earth System Sciences DOI 10.5194/nhess-24-2147-2024 8 July 2024 To identify flash flood potential in Germany, we shifted the most extreme rainfall events from the last 22 years systematically across Germany and simulated the consequent runoff reaction. Our results show that almost all areas in Germany have not seen the worst-case scenario of flood peaks within the last 22 years. With a slight spatial change of historical rainfall events, flood peaks of a factor of 2 or more would be achieved for most areas. The results can aid disaster risk management. Read more
The impacts of elevated CO2 on forest growth, mortality, and recovery in the Amazon rainforest Earth System Dynamics DOI 10.5194/esd-15-763-2024 5 July 2024 Elevated CO2 concentration (eCO2) is critical for shaping the future path of forest carbon uptake, while uncertainties remain about concurrent carbon loss. Here, we found that eCO2 might amplify competition-induced carbon loss, while the extent of drought-induced carbon loss hinges on the balance between heightened biomass density and water-saving benefits. This is the first time that such carbon loss responses to ongoing climate change have been quantified separately over the Amazon rainforest. Read more
Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations Nonlinear Processes in Geophysics DOI 10.5194/npg-31-247-2024 3 July 2024 During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably. Read more
Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence Atmospheric Chemistry and Physics DOI 10.5194/acp-24-7041-2024 1 July 2024 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. Read more
