Christian R. Steger

The EURO-CORDEX initiative provides a comprehensive set of regional climate simulations for the present and future European climate. We assess how well snow cover, a crucial component of the climate system, is represented in this model ensemble. We consider output from 11 regional climate models (RCMs) at 12 km resolution, driven by ERA-Interim and 10 different general circulation models, and compare it with model- and remote-sensing-based reference data sets for the periods 1990–2008 and 1985–2008, respectively. Elevation-resolved evaluations for four mountainous regions indicate that for mean winter snow water equivalent (SWE), the reanalysis-driven RCM ensemble mean agrees well with reference data. However, model spread becomes substantial (ca. ± 50%) at the highest elevations of the regions. For the number of snow-covered days (SCD), the agreement of individual models with reference data is generally better. With GCM-forcing, simulated mean winter SWE and SCD biases are typically larger in all regions except for Scandinavia. Part of this bias can likely be attributed to an overestimation of winter precipitation in the RCM runs. The analysis also reveals that some models indicate perennial snow accumulation at much lower elevations than suggested by proxy data. This bias is likely related to inaccuracies in atmospheric forcing (aforementioned precipitation biases) and shortcomings in the representation of snow cover in the RCMs. Overall, and after spatial masking of spurious grid cells, both the reanalysis-driven and the GCM-driven EURO-CORDEX 12 km ensemble are able to represent the main spatio-temporal characteristics of European snow coverage. The future changes in snow cover characteristics will be the subject of a follow-up study.

To better understand the landscape dynamics and changes in habitat connectivity influenced by glacial and interglacial oscillations over the biodiversity-rich Hengduan Mountains (HM) region, located at the south-eastern edge of the Tibetan Plateau (TP), high-resolution climate data for past periods are essential. We apply the non-hydrostatic limited-area model COSMO with a resolution of 12 km over East Asia for the Last Glacial Maximum (LGM), a period characterized by a generally colder and dryer climate compared to present-day conditions. We perform the downscaling with a novel approach for paleoclimate modelling, the Pseudo-Global Warming (PGW) method. The PGW method minimizes Global Climate Models' (GCMs') inherent biases and reduces computational demands. Despite its widespread use in future climate downscaling, this study represents the first application of the PGW method for paleoclimate simulation to our knowledge. The COSMO PGW simulation for the LGM shows that the regional climate model (RCM) replicates the large-scale dynamics of the driving GCM simulation in the colder climate. Both models suggest weaker Asian summer monsoon systems for this period. Consequently, regions such as the Bay of Bengal, the South China Sea, and the coastal region of China, which typically receive substantial monsoon rainfall, experienced significantly reduced precipitation. However, besides these model similarities, the high-resolution COSMO simulation exhibits distinctive differences on a smaller scale for variables like near-surface wind and precipitation — particularly over land. For instance, COSMO suggests a more significant southward shift of the jet stream during the LGM winter, with more pronounced annual cooling and a prolonged Meiyu season in southern China. Moreover, the COSMO simulation features increased LGM precipitation amounts for the majority of the HM despite the overall weaker summer monsoon circulation. Regarding snowfall, which is a crucial factor for the glaciation extent of the TP during LGM, COSMO suggests increases for certain sub-areas (e.g., central TP) during all seasons, while in the GCM, the increase is primarily constrained to summer and limited to a smaller region near the Himalayas. Furthermore, evaluation with proxy data indicates an improved representation of local climate by COSMO for specific regions. Our study suggests that the resource-saving PGW approach is a suitable method to bridge the gap between coarser climate data and regional climate impacts, also for past periods like the LGM.

Heavy and extreme precipitation and drought events are expected to increase in frequency and intensity as a result of climate change. Investigating the projected evolution of these events in terms of their spatio-temporal dynamics is important for understanding if certain regions are more susceptible to negative impacts of the changes in extremes. The spatio-temporal dynamics of extremes in complex terrain, such as in the Swiss Alps, is of particular interest as the same event might impact nearby catchments in different ways. Using climate model data at a horizontal resolution of 2.2km, dynamically downscaled with the regional climate model COSMO for the emission scenario RCP8.5, we explore projected extreme precipitation and dry spells for the end of the 21st century (2090-2099) relative to present conditions. We apply connected component labelling (CCL) to define precipitation clusters and identify the spatio-temporal changes in extreme precipitation events in alpine catchments of the southern Swiss Alps. In addition, we investigate changes between present and possible future drought conditions. The main aim is to determine if certain watersheds in the southern Alps are expected to experience different vulnerabilities to climate change-driven extreme precipitation and drought events and if the propensity to a certain type of extreme varies between different catchments. Preliminary results indicate that, relative to present-day conditions, the total amount of precipitation tends to decrease in the future scenario with increasing temperature across multiple sites. Initial assessment of the CCL results indicates that a higher overall number of extreme precipitation clusters may be found in the future summer season relative to present conditions, with weaker differences for the remaining seasons. We also expect to find shifts in the spatial range and duration of the precipitation clusters and dry spells between the present and end of century conditions.

Surface snow cover is an important and highly interactive component of global and regional climate systems and has already clearly responded to past warming trends in many regions of the world. Moreover, it is a key ingredient for tourism industry, water supply, irrigation, and hydro-power generation in many mountainous and high-latitude regions. Accurate information about the past, present and future evolution of snow cover is therefore of high importance.In this context, we here present and evaluate a newly developed gridded SWE climatology for Switzerland, available at daily resolution since 1961 and at a 1 km grid spacing. The climatology is based on a variant of the snow cover model of the Operational Snow Hydrological Service (OSHD) of Switzerland, driven by gridded atmospheric input and bias-adjusted towards in-situ snow depth measurements. In accordance with previous works, the analysis shows that the Swiss snow cover has changed strongly over the last decades. The comparison of two climatological long-term periods, 1962-1990 and 1991-2020, in terms of mean September-May SWE and the number of snow days (SWE > 10 mm) within the snow season, reveals a decrease in both indicators over the majority of the country. Low elevations < 1000 m show relative decreases larger than 50% of the mean SWE and larger than 30% regarding the mean number of snow days (about -22 days). The largest absolute difference of mean SWE is found at medium elevations between 1500 and 2000 m with a decrease of about 45 mm (about -26%).The validation of the new snow climatology indicates a high general agreement with in-situ observations and independent remote sensing products. Larger uncertainties and limitations are found at the highest elevations (> 3000 m). They originate from different sources, such as temporal inconsistencies in the gridded input data of the underlying OSHD snow model or the lack of stations at high elevations that are needed for the bias adjustment of the model. Nevertheless, the new snow climatology is able to provide adequate information on past snow cover for Switzerland as a whole and will, among others, serve as a reference for the development of future snow cover scenarios.

Thunderstorm-related severe weather, in particular hail, causes extensive damage to life and infrastructure in the Alpine region. However, changes in hail impact due to a warmer climate are still not fully understood. In the scClim project, convection-permitting regional climate simulations over Europe using the model COSMO with a ~2.2 km horizontal resolution have been conducted for present-day climate conditions (2011-2021) and a climate scenario with a 3°C global warming using a pseudo-global-warming approach. ERA5 reanalyses were used as boundary conditions and a CMIP6 simulation (MPI-ESM1-2-HR) to infer the large-scale climate-change signal. The integrated online diagnostic HAILCAST is used to calculate maximum hail size. The simulations provide total precipitation and maximum hail size estimates every 5 minutes, which allows for hail cell tracking in the climate simulations and the analysis of hail events in a warmer climate. Validation of the present-day simulation against observations of temperature, precipitation and hail shows an overall good model performance. For hail in particular, radar-based, station-based and crowd-sourced observations have been used to assess the model performance in simulating hail on spatial, diurnal and seasonal scales. The validation outcome encourages further study of the climate signal of hail as simulated with the pseudo-global-warming approach. We will show projected changes in the spatial distribution and seasonal cycle of hail over Europe as well as changes in lifetime, storm area and location of hail cells due to a 3°C global warming.

The Hengduan Mountains (HM) are located on the southeastern edge of the Tibetan Plateau and feature high mountain ridges (>6,000 m MSL) 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 period 2001–2005. We conduct one control simulation with modern topography and two idealized 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 deep valleys 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.

Terrain parameters like topographic horizon and sky view factor (SVF) are used in numerous fields and applications. In atmospheric and climate modelling, such parameters are utilised to parameterise the effect of terrain geometry on radiation exchanges between the surface and the atmosphere. Ideally, these parameters are derived from a high-resolution digital elevation model (DEM) because inferring them from coarser elevation data induces a smoothing effect. Computing topographic horizon with conventional algorithms, however, is slow because large amounts of non-local terrain data have to be processed. We propose a new and more efficient method, which is based on a high-performance ray-tracing library. The new algorithm can speed up horizon calculation by 2 orders of magnitude relative to a conventional approach. By applying terrain simplification to remote topography, the ray-tracing-based algorithm can also be applied with very high-resolution (<5 m) DEM data, which would otherwise induce an excessive memory footprint. The topographic horizon algorithm is accompanied by an SVF algorithm, which was verified to work accurately for all terrain – even very steep and complex terrain. We compare the computational performance and accuracy of the new horizon algorithm with two reference methods from the literature and illustrate its benefits. Finally, we illustrate how sub-grid SVF values can be efficiently computed with the newly derived horizon algorithm for a wide range of target grid resolutions (1–25 km).

Terrain parameters like topographic horizon and sky view factor (SVF) are used in numerous fields and applications. In atmospheric and climate modelling, such parameters are utilized to parameterise the effect of terrain geometry on radiation exchanges between the surface and the atmosphere. Ideally, these parameters are derived from a high-resolution digital elevation model (DEM), because inferring them from coarser elevation data induces a smoothing effect. Computing topographic horizon with conventional algorithms is however slow, because large amounts of non-local terrain data have to be processes. We propose a new and more efficient method, which is based on a high-performance ray tracing library. By applying terrain simplification to remote topography, this allows the application of the new algorithms also with very high-resolution (< 5 m) DEM data, which otherwise would induce an excessive memory footprint. The topographic horizon algorithm is accompanied by a SVF algorithm, which was verified to work accurately for all terrain – even very steep and complex one. We compare the computational performance and accuracy of the new horizon algorithm with two reference methods from literature and illustrate its benefits. Finally, we illustrate how sub-grid SVF values can be efficiently computed with the newly derived horizon algorithm for a wide range of target grid resolutions (1–25 km).

The Hengudan Mountains, located at the south-eastern fringe of the Tibetan Plateau, reveal exceptionally high biodiversity. It is believed that this feature is linked to past complex interactions between climate, land surface dynamics and plate tectonics in this region. Contemporary topography was formed by plate tectonics, causing surface uplift, and spatially heterogeneous erosion, which shaped the deep river valleys. The non-hydrostatic regional climate model COSMO is applied to study the impact of surface uplift and river incision. Decades-long simulations at horizontal resolutions of 12 and 4.4 km grid spacing are performed. To study the impact of local topography on climate, we consider two idealized experiments with terrains deviating from the present-day topography: In the first experiment, we reduce the topography in a spatially non-uniform way. This altered topography reflects a past potential state of the Hengduan Mountains. In the second experiment, we remove the deep valleys by applying an envelope topography to quantify the effects of deep valleys on the local climate. Both experiments assume that that the large-scale (continental) climate did not change, i.e., the experiments are driven by large-scale reanalysis data. Preliminary results from the coarse-resolution 12 km COSMO simulation indicate that the uplift of the Hengduan Mountains has a strong impact on the summer monsoon over South Asia caused by circulation changes around the uplifted region. The uplift of the Hengduan Mountains strengthens the westerly wind anomalies from the ocean in South Asia and markedly intensifies the precipitation in Indochina and southwestern China. Besides, the cyclonic circulation in the Bay of Bengal extends eastward, indicating an intensification of the East Asian summer monsoon. The diabatic heating in the eastern Tibetan Plateau increases in response to the regional uplift and it is coupled with the increased precipitation in summer through moist processes. On the contrary, the uplift has little impact on the strengthening of the winter monsoon. In the next stage, we will conduct the same simulations at a higher horizontal resolution of 4.4 km, which captures local terrain more accurate. These simulations will use explicit rather than parameterized convection, thereby providing more realistic estimates of heavy precipitation and erosion. Subsequently, we will run the same experiments for the envelop topography. We expect to relate the changes in the frequency and intensity of extreme precipitation to the changes in the local moisture transport and vertical movement in the high-resolution perspective. In the future, the two different topographies along with the modern topography will be used for simulations of two time periods in the past (i.e., the Last Glacial Maximum (21,000 years ago) and a phase in the Late Miocene (∼7 Ma)) and the future climate (2070–2100).

In mountainous regions, incoming surface radiation is strongly influenced by surrounding and local terrain. The direct beam part of incoming shortwave radiation depends both on local slope angle and azimuth as well as on neighbouring terrain, which can induce topographic shading. Shortwave radiation can be reflected (multiple times) by terrain, which leads to enhanced incoming diffuse shortwave radiation for locations with a reduced sky view factor (SVF) – particularly under snow-covered conditions when surface reflectivity is high. Finally, incoming longwave radiation can also be modulated by neighbouring terrain due to radiation exchange between facing slopes.Considering these effects in spatially distributed land surface models – either stand-alone or embedded in weather and climate models – typically requires the following topographic quantities: slope angle, slope aspect, terrain horizon and SVF. The first two quantities can be computed rapidly because they only depend on local terrain. The computation of the latter two quantities is however expensive, particularly for high-resolution (~30 m) digital elevation models (DEMs), because a large quantity of non-local DEM information has to be processed. We developed a new efficient algorithm for terrain horizon computation, which is based on a high-performance ray-tracing library. A benchmark against conventional algorithms confirmed its high performance – particularly for DEMs with very high resolution and for large terrain horizon search distances. Furthermore, due to the smooth representation of terrain by a triangle mesh, the new algorithm does not reveal artefacts in the computed horizon line in cases where the horizon is formed by proximal terrain. Finally, we demonstrate that the new algorithm is also eligible to compute sub-grid SVF for large spatial domains in a very efficient way. Sub-grid SVF is a useful quantity to parameterise above-mentioned topographic effects on surface radiation in weather and climate models applied on regional or even global scales.