This study presents the detailed analysis of a novel and first-of-its-kind 10-year multi-model ensemble of kilometer-scale convection-permitting climate model (CPM) simulations over the Greater Alpine Region. The simulations were obtained by downscaling global climate model (GCM) projections using regional climate models (RCMs) and further downscaling to the kilometer scale using convection-permitting climate models (CPMs) . This study evaluates the CPMs and assesses their added value with respect to RCMs regarding basic and heavy precipitation characteristics. In addition, this study assesses projected changes for the end of the century. The analysis is performed for climatological seasons, for different temporal aggregations between 1 hour and 5 days, and for various precipitation indices.. ERA-Interim-driven and historical GCM-driven CPM simulations are compared against daily and hourly observational datasets, as well as their driving RCM counterparts to evaluate their performance and added value. Evaluation reveals that CPMs refine spatial patterns, reduce the overestimation of precipitation frequency and better capture intense precipitation characteristics, especially on the sub-daily scale and in summer. Climate change projections show an intensification of precipitation for all seasons and across all temporal aggregation levels. During summer, mean precipitation and precipitation frequency are projected to decrease, especially in the Mediterranean. In winter, an increase is projected across most parts of the Alps. CPMs and RCMs show agreement, with CPMs indicating slightly amplified signals and reduced model spread. The findings are consistent with previous studies using individual simulations, but provide one of the first multi-model assessment of projections in heavy precipitation over the Alps.

A document by Christoph Heim. Click on the document to view its contents.

The Hengduan Mountains (HM) are located on the southeastern edge of the Tibetan Plateau (TP) and feature high mountain ridges (> 6000 m a.s.l.) separated by deep valleys. The HM region also features an exceptionally high biodiversity, believed to have emerged from the topography interacting with the climate. To investigate the role of the HM topography on regional climate, we conduct simulations with the regional climate model COSMO at high horizontal resolutions (at ~12 km and a convection-permitting scale of ~4.4 km) for the present-day climate. We conduct one control simulation with modern topography and two idealised experiments with modified topography, inspired by past geological processes that shaped the mountain range. In the first experiment, we reduce the HM’s elevation by applying a spatially non-uniform scaling to the topography. The results show that, following the uplift of the HM, the local rainy season precipitation increases by ~25%. Precipitation in Indochina and the Bay of Bengal (BoB) also intensifies. Additionally, the cyclonic circulation in the BoB extends eastward, indicating an intensification of the East Asian summer monsoon. In the second experiment, we remove the deep valley by applying an envelope topography to quantify the effects of terrain undulation with high amplitude and frequency on climate. On the western flanks of the HM, precipitation slightly increases, while the remaining fraction of the mountain range experiences ~20% less precipitation. Simulations suggest an overall positive feedback between precipitation, erosion, and valley deepening for this region, which could have influenced the diversification of local organisms.

The long-existing double-ITCZ problem in Global Climate Models (GCMs) hampers accurate climate simulation. Using a regional climate model (RCM) over the tropical and sub-tropical Atlantic with a horizontal resolution of 12 km and explicit convection, we develop a bias-correction downscaling methodology to remove GCM biases. The methodology is adapted from the pseudo-global warming (PGW) approach, typically used to exert the climate-change signal to a reanalysis-driven RCM simulation. We show that the double ITCZ problem persists with conventional dynamical downscaling, but with our bias-corrected downscaling, the double ITCZ problem is removed. Detailed analysis attributes the main cause of the double ITCZ problem of the selected GCM to the sea surface temperature (SST) bias. Compared to the GCM’s AMIP simulations, RCMs with higher resolution allow explicit deep convection and enable a better simulation of tropical convection and clouds. The developed methodology is promising for constraining climate sensitivity by removing double-ITCZ biases.

Non-hydrostatic km-scale weather and climate models show significant improvements in simulating clouds, especially convective ones. However, even km-scale models need to parameterize some physical processes and are thus subject to the corresponding uncertainty of parameters. Systematic calibration has the advantage of improving model performance with transparency and reproducibility, thus benefiting model intercomparison projects, process studies, and climate-change scenario simulations. In this paper, the regional atmospheric climate model COSMO v6 is systematically calibrated over the Tropical South Atlantic. First, the parameters’ sensitivities are evaluated with respect to a set of validation fields. Five of the most sensitive parameters are chosen for calibration. The objective calibration then closely follows a methodology extensively used for regional climate simulations. This includes simulations considering the interaction of all pairs of parameters, and the exploitation of a quadratic-form metamodel to emulate the simulations. In the current set-up with 5 parameters, 51 simulations are required to build the metamodel. The model is calibrated for the year 2016 and validated in two different years using slightly different model setups (domain and resolution). Both years demonstrate significant improvements, in particular for outgoing shortwave radiation, with reductions of the bias by a factor of 3 to 4. The results thus show that parameter calibration is a useful and efficient tool for model improvement. Calibrating over a larger domain might help improve the overall performance, but could potentially also lead to compromises among different regions and variables, and require more computational resources.

Hail is a significant convective weather hazard, often causing considerable crop and property damage across the world. Although extremely damaging, hail still remains a challenging phenomenon to model and forecast, given the limited computational resolution and the gaps in understanding the processes involved in hail formation. Here, eight hailstorms occurring over the Alpine-Adriatic region are analyzed using Weather Research and Forecasting (WRF) and Consortium for Small Scale Modeling (COSMO) simulations, with embedded HAILCAST and Lightning Potential Index (LPI) diagnostics at kilometer-scale grid spacing (~2.2 km). In addition, a model intercomparison study is performed to investigate the ability of the different modeling systems in reproducing such convective extremes, and to further assess the uncertainties associated with simulations of such localized phenomena. The results are verified by hailpad observations over Croatia, radar estimates of hail over Switzerland and lightning measurements from the LINET network. The analysis revealed that both HAILCAST and LPI are able to reproduce the areas and intensities affected by hail and lightning. Moreover, the hail and lightning fields produced by both models are similar, although a slight tendency of WRF to produce smaller hail swaths with larger hailstones and higher LPI compared to COSMO is visible. It is found that these differences can be explained by systematic differences in vertical profiles of microphysical properties and updraft strength between the models. Overall, the promising results indicate that both HAILCAST and LPI could be valuable tools for real-time forecasting and climatological assessment of hail and lightning in current and changing climates.

Clouds over tropical oceans are an important factor in Earth’s response to increased greenhouse gas concentrations, but their representation in climate models is challenging due to the small-scale nature of the involved convective processes. We perform two 4-year-long simulations at kilometer-resolution (3.3 km horizontal grid spacing) with the limited-area model COSMO over the tropical Atlantic on a 9000x7000 km2 domain: A control simulation under current climate conditions driven by the ERA5 reanalysis, and a climate change scenario simulation using the Pseudo-Global Warming (PGW) approach. We compare these results to the changes projected in the CMIP6 scenario ensemble. We find a good representation of the annual cycle of albedo, in particular for trade-wind clouds, even compared to the ERA5 reanalysis. Also, the vertical structure and annual cycle of the marine intertropical convergence zone (ITCZ) is accurately simulated, and the simulation does not suffer from the double ITCZ problem commonly present in global climate models (GCMs). The ITCZ responds to warming through a vertical extension and intensification primarily at high levels, as well as a slight southward extension of the annual mean ITCZ, while the narrowing typically seen in GCMs is not visible.