To ensure accurate monitoring in high mountainous areas of mid-latitudes, we must establish a ground network of the highest quality standards.This monitoring will help us observe local meteoclimatic features and understand the impact of climate change on glacial, periglacial and cold temperate morphoclimatic environments. the last two years have been some of the hottest in the alpine chain over the past 150 years. As a result, also the glaciers above 4000m altitude have been significantly impacted, along with the density and degradation of interstitial permafrost, ultimately affecting the stability of rocky slopes. Despite the logistical and environmental challenges, national and local meteorological services, universities, environmental research centres, and associations of enthusiasts continuously install meteoclimatological survey stations at high altitudes. These organizations adhere to strict installation regulations, as indicated by the WMO. It is important to note that remote sensing surveys, while useful, have limites in resolution and accuracy and therefore do not significantly improve local information. During the summer of 2023, historical records starting from 1973, for the thermal zero degrees altitude and the geopotential height relative to the 500 hPa pressure have been repeatedly retouched in the free atmosphere of the Dolomites. In particular, from the data analysis of  soundings from the nearby Udine Rivolto meteorological station, on August 22nd at 14LT, we obtained respectively maximum values of 5197 m. and 5970 m. These measurements were taken during meteorological phases characterized by the presence of robust anticyclonic fields of subtropical continental matrix, which have a significant impact at mid-tropospheric elevation. The summer lasted longer than usual, with great weather conditions even in late autumn. On November 23rd at 1pm LT, the freezing point was approximately at 3306 m.  with a geopotential at 500 hPa of 5740 m. Over the past two years, the Meteotriveneto association has made significant progress in their microclimatic monitoring on the Dolomites Alps high elevation by installing new sensors at the summit measuring stations of Capanna Punta Penia (Marmolada Peak) - 3343 m., Cima Tofana - 3215 m. and Sass Pordoi - 2950 m. Comparing these measurements with those of existing official stations, often located a few tens of meters away and at very similar altitudes, moderate or strong differences were highlighted from a thermal point of view. These deviations cannot be solely attributed to measurement errors, but in particular to the local morphology. Thanks to the new availability of data, even in high mountain remote areas, we can now define evolutionary morphoclimatic aspects with greater precision at microscale. This will provide us with new resources to improve the inputs for the various forecasting climate models at different spatial resolution , resulting in better future simulations in environments that are increasingly fragile and less resilient to the climate crisis.

How to cite: Fazzini, M., Georgiadis, T., Cremonini, L., Tolin, F., and Zamperin, S.: Orographic complexity and local microclimatic variations in the Dolomites (north-eastern Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14933, https://doi.org/10.5194/egusphere-egu24-14933, 2024.

Several studies and observations suggest that global warming processes are more prevalent in mountain areas, showing higher rates of warming and more pronounced changes in precipitation than in average land data. Snow and ice are highly sensitive to variations in climate. The importance of mountain areas as water reservoirs for the surrounding land and valleys at lower altitudes justifies paying special attention to this type of behaviour and to the changes brought about by global warming. This interest is enhanced by the special environmental sensitivity of mountain ecosystems, and the difficult balance between these fragile ecosystems and their use as tourist resources or winter resorts.

In this paper we analyse climate data collected at mountain weather stations in Spain in time series up to 80 years. Stations in mountain areas are not numerous and are sometimes very scattered; nevertheless, we have selected the available data on temperatures, precipitation and other meteorological variables at stations located in the various mountain ranges throughout the Iberian Peninsula. For the selected stations, trends in temperatures (mean, maximum and minimum) have been studied and a seasonal analysis has been carried out. In addition, the data were processed with RClimDex, statistical and climatic software package, to evaluate Climate Extreme Indices. Cooling and warming patterns have been detected, and changes in precipitation have been analysed, trying to address the distinctive characteristics of mountain areas in the studies conducted. Monthly and seasonal assessments have also been carried out to detect changes in behaviour patterns. In general, good agreement with previously published data has been obtained, although not many studies have been carried out systematically in Spain, except in the Pyrenees area.

How to cite: Viloria, R. and Tricio, V.: Analysis of surface temperature and precipitation trends and climate indices in Spanish mountain areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19261, https://doi.org/10.5194/egusphere-egu24-19261, 2024.

The South American Altiplano is a high-altitude plateau (3800 m MSL) located in the central Andes between 15ºS and 22ºS. Bounded to the west by the coastal desert of Peru-Chile and to the east by the hyper-humid lowlands of Peru-Bolivia, the Altiplano exhibits a semi-arid climate, following a pronounced annual cycle, with over 70% of rainfall occurring during the austral summer (December-January-February). Associated with factors such as convective activity occurring west of the Amazon Basin, the generation of convective clouds over the central Andes occurs when eastward winds encounter the orographic barrier on the eastern slope of the Andes. This process represents the primary mechanism governing precipitation variability in the Altiplano. Previous studies analyzing future projections anticipate that the central Andes will become warmer during the 21st century, impacting the population, ecosystems, and glaciers of the South American Altiplano. This is particularly relevant since agriculture is the main economic activity in this region and depends directly on precipitation.

Summer precipitation over the Altiplano has shown a strong dependence on the magnitude of zonal flow in the free troposphere (200 - 300hPa). Nevertheless, General Circulation Models (GCMs) suggest a continuous increase in westerly flow over the central Andes, hindering moisture transport from the interior of the continent. Minvielle and Garreaud (2011) suggest a significant reduction (10%-30%) in Altiplano precipitation by the end of this century under moderate to strong greenhouse gas emission scenarios. More recently, Segura et al., (2020) found that precipitation variability in the Altiplano is also associated with upward motion over the western Amazon (WA). Thus, DJF precipitation over the Altiplano seems to respond directly and primarily to the upward motion over the WA, since the early 21st century.

Using a set of 13 GCMs, this study aims to explore possible future projections in precipitation processes under the SSP3-7.0 scenario. This study focuses on the evolution of two previously established mechanisms driving austral summer precipitation over the Altiplano: 1) easterly winds at upper levels over the central Andes, and 2) upward motion over the WA. As preliminary conclusions of this work, models indicate that both mechanisms appear to weaken for the future period analyzed (2050-2084), suggesting a reduction in summertime precipitation by the mid-21st century. Additionally, models project a more stable atmosphere over the central Andes for the future period, also indicating a reduction in precipitation in the region, reinforcing the initial conclusion.

How to cite: Agudelo, J., Espinoza, J.-C., Junquas, C., and Arias, P. A.: Future Projections of Summer precipitation-driving Mechanisms over the South American Altiplano, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13935, https://doi.org/10.5194/egusphere-egu24-13935, 2024.

The variation of near surface wind speed is a key dynamic parameter in the orographic effect of precipitation over eastern China. In this study, we used the latest high-resolution outputs from six GCMs in CMIP6-HighResMIP to evaluate the performance of high-resolution models in simulating the orographic precipitation characteristics of typical mountainous areas in summer over eastern China. Combined with observational results, the orographic precipitation under warming scenarios was projected and constrained. The results indicated that during the contemporary climate reference period (1979-2009), although the relationship between model-simulated near surface wind speed and the orographic light rain frequency was consistently stable, the sensitivity of the orographic light rain frequency to surface wind variability was generally underestimated, with a deviation approximately 24.1% lower than the observational values. Comparison of model-simulated wind speed with observational records showed that the negative bias of the sensitivity value was mainly contributed by the overestimated wind speed in models. Based on observed near-surface wind speed to constrain and correct the orographic light rain frequency, the constrained estimates revealed a 36.1% reduction in orographic light rain frequency under a 1.5°C warming scenario, which is 8.6 times greater than the original predictions (4.2%). The MRI-AGCM3-2-S model, with a longer dataset, demonstrated a relatively stable reduction in orographic light rain frequency under different warming scenarios (1.5°C, 2°C, 3°C, and 4°C) after wind speed constraints, all of which are exceeding the original predictions.

How to cite: Dong, X., Gong, D., and Shi, C.: The frequency of summer light rain projections in typical terrain over eastern China constrained by surface wind speed, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5260, https://doi.org/10.5194/egusphere-egu24-5260, 2024.

Air temperature is a pivotal factor influencing numerous chemical, physical, and biological processes. However, there is a notable scarcity of long-term data, especially at high elevations, exceeding 2000 m a.s.l. This study focuses on reconstructing the daily maximum, mean, and minimum temperatures at Jungfraujoch (3571 m a.s.l.) since 1864. The approach involves daily data from 10 meteorological stations within the ECA&D (6) and Meteo Swiss (4) databases. All selected stations are situated above 2000 m a.s.l. (in the range 2140-3109 m a.s.l.), providing uninterrupted observations spanning at least from 1961 to 2022. The methodology includes these steps: 1) for each meteorological station, in the calibration period 1980-1999, it was modeled the daily temperature at Jungfraujoch as the sum of the temperature at the selected station plus a deterministic and a stochastic component; the deterministic component is the product of the temperature lapse rate (TLR) and the elevation difference between the reference and selected station, and the stochastic component is a “noise” which comes from the statistical distribution of the residuals. The seasonality requires parameters with monthly variability which are different considering minimum, mean and maximum temperature. The calibration phase consists in the estimation of TLR and the statistical distribution of residuals (among Normal, GEV, Stable and Tlocscale distributions). The evaluation of model performances was based on the calculation of Pearson correlation coefficients (ρ_P) and Root mean squared errors (RMSE) within the two validation periods (1961-1979 and 2000-2022). High correlation coefficients (greater than 0.9 in both calibration and validation periods) and low values of RMSE (from 1.56°C to 3.32 °C in the calibration period and from 1.56°C to 3.42°C in the validation) confirm the model’s accuracy. The same high performances were found before (1961-1979) and after (2000-2022) the calibration period, for every meteorological stations. 2) Then the 10 simulated time series at Jungfraujoch were sorted according to the lowest values of the RMSE, and the first three was mediated to define an “ensemble” daily temperature time series, which was able to obtain these performances: (ρ_P=0.96,0.98,0.97; RMSE=1.97,1.46,1.68 °C respectively for max, mean and min temperature). The study was then extended from the year 1864. Comparing the results with the existing literature we highlighted: i) high performances without the need of modeling the observed trend due to the climate change (subjected to high uncertainty in the future), ii) very parsimonious model without the need of any other variables (relative humidity, cloud cover, wind velocity, weather patterns); iii) the importance of selecting high stations elevations (above 2000 m a.s.l.) rather than considering closer stations but subjected to the thermal inversion phenomena; iv) maximum temperature is affected by higher errors, especially from 2000-2022 which is probably due to the higher increasing of the summer and winter temperatures at high elevation accordingly to an elevation warming dependence; v) This method could be easily extended in many regions of the world and these results could be used to make a back ward analysis of many environmental processes (glacio-hydrological and permafrost), within the Jungfrau-Aletsch UNESCO World Heritage Site. 

How to cite: Bongio, M., De Michele, C., and Scotti, R.: An ensemble of meteorological stations for estimating daily air temperature time series at Jungfraujoch since 1864, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3382, https://doi.org/10.5194/egusphere-egu24-3382, 2024.

Regional imprints of global warming have to be investigated to predict the impact of climate change on a local scale and inform mitigation and adaptation policies. The rate of warming as a function of elevation in mountainous regions is yet to be fully characterized and understood. This study aims to identify elevation-dependent warming features in the Alps as well as its physical drivers, using MAR (Modèle Atmosphérique Régional) simulations with a 7kmx7km resolution over 1961-2100, under different climate scenarios. Different seasonal patterns have been found, most notably a maximum of warming at intermediate elevation (~1500m to 1800m) in Spring related to earlier snow melting in future projections. This maximum of warming moves towards higher elevations over the XXIst century. Elevation-dependent warming is found to be different in the free-atmosphere and along the slopes of the mountains, highlighting the major impact of surface processes, such as changes in albedo, in the drivers of climate change in the Alps.

How to cite: Castellanos, I., Ménégoz, M., Blanchet, J., and Beaumet, J.: Elevation-dependent warming in the Alps estimated from MAR simulations over 1961-2100, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1999, https://doi.org/10.5194/egusphere-egu24-1999, 2024.

As climate change advances, the Alps are expected to experience increasingly intense thunderstorms, which are likely to cause more damage due to floods and landslides. This study aims at evaluating extreme precipitation in weather models over complex terrains where orography causes the hardest challenges to precipitation prediction. 

Our preliminary analysis assessed which infrared and visible satellite channels are most effective in predicting precipitation, by examining the MSG satellite channels and radar-derived rain products. This assessment considered the influence of terrain by comparing data from flatlands and more complex topographies.

We will then use a combination of the selected channels to train a self-supervised machine learning (ML) algorithm for both observations and model outputs. We will exploit the space where cloud classes are identified, known as feature space, in two distinct ways to evaluate the ICON-GLORI model. Firstly, we pinpoint significant cases of extreme precipitation and simulate them using the ICON-GLORI model. This data is then input into the observation-trained ML algorithm to determine the cluster within the feature space where the simulated cases will be categorized. Secondly, we construct a feature space using the ensemble ICON-GLORI model. A showcase of the ML algorithm trained using the cloud optical thickness from 2015 imagery over Germany will demonstrate the potential of this approach.

How to cite: Corradini, D., Acquistapace, C., and Bigalke, P.: High-resolution model evaluation with self-supervised neural network approach targeted on severe storms over the Alps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16067, https://doi.org/10.5194/egusphere-egu24-16067, 2024.

Quantiative precipitation forecasting remains a major challenge even for kilometre-scale ensemble forecasating systems. However, operational high-resolution ensemble systems provide a large data-set from which - if combined with observational data - insight into systematic issues in the model physics can be gained. Here, we explore statistical methods to automatically identify systematic error patterns and their relation to the larger-scale conditions at example problem of precipitation at the Harz mountain range in northern Germany. For the analysis COSMO-D2-EPS forecasts for the years 2011-2018 are combined radar-derived and station-calibrated surface precipitation estimates provided by the German Weather Service (DWD). For the identification of common precipitation error patterns, empirical orthogonal function (EOF) analysis has been employed. For the winter season the leading order principal components show error features located on the elevated topography in the Harz region. Analysis of large-scale conditions (derived from ERA5) for each principal component shows systematic differences in upstream wind direction and speed, temperature, and specific humidity. In the summer seasons patterns are less localised, but some regional structure is maintained especially for the first principal component. Also the differentiation in large-scale conditions between EOFs is less. The challenges in summer are presumably related to a large contribution of convective precipitation. Overall, the leading 5 principal components explain 70,4% (48,2%) of the variance in winter (summer). To gain a better understanding of the relationship of error models to larger-scale conditions, as well as the physical mechanisms of model errors, simulations of precipitation at representative dates for principal components 1 and 2 were performed using the ICON-D2 model.

How to cite: Kazachkova, Y. and Miltenberger, A.: Automatic identification of systematic model failures in ensemble precipitation forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14519, https://doi.org/10.5194/egusphere-egu24-14519, 2024.

Complex terrain is often characterized by mechanical sources, which force the initiation of convection in unstable atmospheric conditions, resulting in severe weather such as thunderstorms and hail, etc. With respect to the synoptic prevailing wind, the terrain can also cause heavy precipitation by converging flow and stationary rain bands. Rain gauges can provide a direct measure of accurate precipitation at a point station. However, low accessibility and high maintenance costs limit the ability to install high-resolution rain gauge networks in mountainous regions. Thus, quantitative precipitation estimation (QPE) through remote sensing using weather radar plays an important role in securing observation information on rainfall amounts in mountainous areas. 
In this study, we first analyzed the statistics of hazardous weather events retrieved from radar-based rainfall estimates over the Korean Peninsula according to complex topography. In order to analyze the frequency of occurrence of each type of hazardous weather such as torrential rain, hail, and snowfall according to orographic conditions, we used radar-based QPE. We investigated the correlation between topographic characteristics such as terrain altitude, wind direction, and downwind side and the frequency of occurrence and development of hazardous weather phenomena. 
We also examined the accuracy of QPE relating to rainfall mechanisms including radar echo top height for each warm and cold season. We analyzed the accuracy of QPEs according to various methods using radar reflectivity, dual-polarization parameters, and radar attenuation. We analyzed precipitation estimation error factors that may be caused by terrain shielding and high radar beam height in mountainous areas. We explored QPE errors based on radar beam height and echo intensity to improve the accuracy of QPE. 
Furthermore, in order to provide hazardous weather information specialized for the mountainous region, we have planned to develop an algorithm to estimate the probability of severe weather due to topographic characteristics by merging terrain altitude, atmospheric instability, and radar echo intensity by calculating Froude number using radar-based three-dimensional wind (Wind Synthesis System using Doppler Measurements, WISSDOM). In conjunction with the technology for calculating stationary precipitation information using radar echo image processing techniques, we aim to strengthen the ability to respond to dangerous weather by providing information on possible areas of heavy rain, snowfall, and extreme wind due to complex terrain.

How to cite: Lee, S., Park, J.-W., Mo, S.-J., and Gu, J.-Y.: Statistical Analysis of Radar-based Quantitative Precipitation Estimation over Complex terrain in Korea and Development of User-oriented Services for Mountainous Regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14813, https://doi.org/10.5194/egusphere-egu24-14813, 2024.