Representing glaciers in a regional climate model

A glacier parameterization scheme has been developed and implemented into the regional climate model REMO. The new scheme interactively simulates the mass balance as well as changes of the areal extent of glaciers on a subgrid scale. The temporal evolution and the general magnitude of the simulated glacier mass balance in the European Alps are in good accordance with observations for the period 1958-1980, but the strong mass loss towards the end of the twentieth century is systematically underestimated. The simulated decrease of glacier area in the Alps between 1958 and 2003 ranges from −17.1 to −23.6%. The results indicate that observed glacier mass balances can be approximately reproduced within a regional climate model based on simplified concepts of glacier-climate interaction. However, realistic results can only be achieved by explicitly accounting for the subgrid variability of atmospheric parameters within a climate model grid bo

Robust climate scenarios for sites with sparse observations: a two-step bias correction approach

Observed and projected climatic changes demand for robust assessments of climate impacts on various environmental and anthropogenic systems. Empirical-statistical downscaling (ESD) methods coupled to output from climate model projections are promising tools to assess impacts at regional to local scale. ESD methods correct for common model deficiencies in accuracy (e.g. model biases) and scale (e.g. grid vs point scale). However, most ESD methods require long observational time series at the target sites, and this often restricts robust impact assessments to a small number of sites. This paper presents a method to generate robust climate model based scenarios for target sites with short and (or) sparse observational data coverage. The approach is based on the well-established quantile mapping method and incorporates two major steps: (1) climate model bias correction to the most representative station with long-term measurements and (2) spatial transfer of bias-corrected model data to represent target site characteristics. Both steps are carried out using the quantile mapping technique. The resulting output can serve as end user–tailored input for climate impact models. The method allows for multivariate and multi-model ensemble scenarios and additionally enables to approximately reconstruct data for non-measured periods. The method's applicability is validated using (1) long-term weather stations across the topographically and climatologically complex territory of Switzerland and (2) sparse data sets from Swiss permafrost research sites located in challenging conditions at high altitudes. It is shown that the two-step approach performs well and offers attractive quality, even for extreme target locations. Uncertainties, however, remain and primarily depend on (1) data availability and (2) the considered variable. The two-step approach itself involves large uncertainties when applied to short reference data sets or spatially heterogeneous variables (e.g. precipitation, wind speed). For temperature, results are promising even when using very short calibration periods

Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications

We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations. Recommendation 1: Develop Hybrid AI-Physical Models: Emphasize the integration of AI and physical modeling for improved reliability, especially for longer prediction horizons, acknowledging the delicate balance between knowledge-based and data-driven components required for optimal performance. Recommendation 2: Emphasize Robustness in AI Downscaling Approaches, favoring techniques that respect physical laws, preserve inter-variable dependencies and spatial structures, and accurately represent extremes at the local scale. Recommendation 3: Promote Inclusive Model Development: Ensure Earth System Model development is open and accessible to diverse stakeholders, enabling forecasters, the public, and AI/statistics experts to use, develop, and engage with the model and its predictions/projections.Comment: 12 pages, 1 figure, submitted as part of the Swiss Academy of Engineering Sciences' 2024 whitepaper on "Artificial Intelligence for Climate Change Mitigation

Climate change scenarios in use: heat stress in Switzerland

Under hot conditions the human body is able to regulate its core temperature via sweat evaporation, but this ability is reduced when air humidity is high. These conditions of high temperature and high humidity invoke heat stress which is a major problem for humans, in particular for vulnerable groups of the population and people under physical stress (e.g. heavy duty work without appropriate cooling systems). It is generally expected that the frequency, duration and magnitude of such unfavorable conditions will increase with further climate warming. In this respect, climate services play a crucial role by putting together climatological information and adaptation solutions to reduce future heat stress. We here assess the recently developed CH2018 scenarios for Switzerland (https://www.climate-scenarios.ch) in terms of heat stress conditions including their future projections. For this purpose, we characterize future extreme heat conditions with the use of climate analogs. By doing so, we attempt to produce more accessible climate information which might foster the use and understanding of regional-scale climate scenarios. Here heat stress is expressed through the Wet Bulb Temperature (TW), which is a relatively simple proxy for heat stress on the human body and which depends non-linearly on temperature and humidity. It is assessed in terms of single-day events and heat stress spells. Projections based on the CH2018 scenarios indicate increasing heat stress over Switzerland, which is accentuated towards the end of the century. High heat stress conditions might be about 3?5 times more frequent for an emission scenario without mitigation (RCP 8.5) than for the mitigation scenario (RCP 2.6) by the end of the 21st century. The projected increase of heat stress results in more and longer heat stress spells, thus highlighting the importance of timely and precise prevention strategies in the context of heat-health action plans. Spatial climate analogs based on heat stress spells in Switzerland greatly vary depending on the emission scenario and are found in Central Europe under a mitigation scenario and in southern Europe under unmitigated warming.Financial support for this work is provided by the HEAT-SHIELD Project (European Commission HORIZON 2020, research and innovation programme under the grant agreement 668786). A.C. acknowledges support from Project COMPOUND (TED2021-131334A-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR