We welcome contributions describing the influence of mountains on the atmosphere on meteorological and climate time scales, including terrain-induced airflow, orographic gravity waves, orographic precipitation, land-atmosphere exchange over mountains, forecasting, and predictability of mountain weather. We also encourage theoretical, modeling and observational studies on orographic gravity waves and their effects on the weather and the climate. Furthermore, we invite studies that investigate climate processes and climate change in mountain areas based on monitoring and modeling activities.
Particularly welcome are contributions that align with and address the interdisciplinary objectives of the Elevation-Dependent Climate Change (EDCC) working group of the Mountain Research Initiative, as well as the application and development of high-resolution (kilometer-scale) climate models over complex mountainous terrain, including advances in model design, challenges, and observational gaps needed for robust model evaluation and improvement.
Orals: Wed, 6 May, 08:30–12:30 | Room L1
Although the concept of enhanced mountain warming has been around for several decades, it was not until just over a decade ago that the concept of elevation-dependent warming, whereby warming rates may be stratified by elevation, was widely identified by the scientific community as an important phenomenon. Unlike Arctic amplification, which is broadly homogenous, elevation dependent warming (EDW) is more complex, and although systematic changes in warming rates over the elevation gradient are often present, the pattern of the elevation profile is often non-linear and it can change with season, time of day and location. This is probably because there are a wide variety of drivers which can be responsible for contrasting warming rates, including patterns of surface albedo change (often driven by retreating snow cover and/or vegetation changes), aerosol loadings (and deposition on snow), changes in the free atmospheric lapse rate, Planck feedback and moisture controls on downward longwave emission (DLR) and clouds. In any one season or location, one or more of these drivers may have a dominant impact, leading to contrasting elevation patterns of change.
Over recent years there has been an acknowledgement that elevation dependent changes involve broad adjustments in the climate system, which includes vertical gradients of precipitation, condensation, wind speed and shear, humidity and clouds. There has been a change in emphasis from EDW towards EDCC (elevation-dependent climate change). However our understanding of elevation dependent changes in variables other than temperature is in its infancy, in part because of lack of reliable observations at high elevations. Mountain precipitation (rain and snow) is particularly hard to measure accurately, and gridded datasets often interpolate to higher elevations based on limited observations.
Future developments in EDCC research must involve both improving high elevation observations and learning from the new tranche of convection permitting models which can explicitly resolve more atmospheric processes such as mountain slope winds and small scale convection. Particular questions concern how orographic precipitation gradients may change, both for widespread stratiform precipitation and more intense localised convective storm development (often in summer). How the frequency and intensity of extreme events in mountain regions will change is also an important unanswered question, in particular how enhanced hourly precipitation extremes and heatwaves will be impacting high elevation regions. How EDCC will interact with the rate of snow loss and cryospheric change is also a major area of future concern, including impacts on downstream water supply. Other areas of EDCC research which have so far received relatively little attention include teleconnections with large scale circulation features such as the jet stream and Asian Monsoons, and interactions with ecological zonation and habitat hypsometry. The impact on mountain micro-climates, including the frequency, intensity and location of cold air pools is also not well understood. Thus, there are still numerous unanswered questions about climate change in mountain regions and at high elevations.
How to cite: Pepin, N.: A decade of research in elevation dependent climate change (EDCC): A review of past discoveries and perspectives on future developments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5737, https://doi.org/10.5194/egusphere-egu26-5737, 2026.
Elevation-dependent warming (EDW) has been reported in observations and climate models, yet its magnitude and controlling mechanisms remain uncertain, particularly due to the complexity of mountain regions. However both theoretical studies and climate simulations indicate a reduction in lapse rates and enhanced tropospheric warming under climate change. In this study, we examine EDW, and its relationship to tropospheric warming, in atmosphere-only experiments. This is done through the comparison between a historical control simulation and a perturbed climate state driven by uniform 4K warming in the prescribed sea surface temperatures. These simulations were performed with the ICON model in its Sapphire configuration at ∼10 km horizontal grid spacing. This setup offers an improved representation of high-elevation terrain compared to common climate change simulations, key to adequate analysis of EDW, together with a strong free-tropospheric warming, important for understanding its role in shaping EDW.
The simulation exhibits a robust, statistically significant increase in surface warming with elevation, ranging from ∼4.9 K below 500 m to almost 7 K above 5500 m, corresponding to a global EDW slope of 0.317 K km-1. Regional contrasts are most pronounced at low elevations, while at intermediate and high elevations the surface warming profiles converge toward the tropospheric warming profile. Seasonal variations suggest an influence from snow-related processes, yet the majority of the seasonal variability in surface warming can be explained by seasonal variations in tropospheric warming.
A direct comparison of binned surface and tropospheric temperature changes at corresponding heights reveals a tight coupling, with small deviations possibly resulting from radiative processes near the surface. These results indicate that, under strong free-tropospheric warming, EDW can be approximated to first order by the vertical structure of tropospheric warming, with surface energy-balance processes largely providing secondary modulation. The sensitivity of this coupling to different forcing magnitudes and climate states warrants further investigation.
How to cite: Martuzzi, P. and Giorgetta, M. A.: Coupling between Free Tropospheric Warming and Elevated Surface Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11637, https://doi.org/10.5194/egusphere-egu26-11637, 2026.
Due to the thermal homogeneity of the tropics, the rapidly retreating glaciers in Eastern Africa, such as at the summit of Kilimanjaro, are predominantly influenced by moisture and precipitation variability. Several case studies have shown that significant snowfall events with durations of just a few days can lead to deep snow cover that can persist for several months on the glaciers, with significant impacts on their long-term mass balance. However, the large-scale phenomena that influence this intraseasonal variability at high elevations remain poorly understood. Here, we use a unique dataset of daily surface height observations from Kilimanjaro’s Northern Ice Field and the ERA5 reanalysis to investigate the large-scale weather patterns that are associated with snowfall at the summit of Kilimanjaro from 2000-2022. We highlight that over 50% of surface height increase on the glacier was associated with the recently identified phenomenon known as westerly moisture transport events (WMTEs), atmospheric river like features that bring moisture into Eastern Africa from the Congo basin and can lead to enhanced precipitation in Eastern Africa. This work develops understanding of the processes that influence the mass balance of East Africa’s glaciers, which will help to improve the interpretation of these glaciers’ unique proxy record of the sparsely observed tropical mid-troposphere.
How to cite: Peal, R., Collier, E., and Hardy, D.: Large scale atmospheric drivers of intraseasonal snowfall variability on Kilimanjaro's glaciers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20304, https://doi.org/10.5194/egusphere-egu26-20304, 2026.
Understanding the behaviour of future extreme precipitation in the European Alps is a major adaptation challenge, as these events often cause flooding and severe impacts on infrastructure and society. Convection-permitting models (CPMs) have recently emerged as a key tool to better represent extreme precipitation processes in complex Alpine terrain, overcoming limitations of regional climate models (RCMs). While previous studies have analysed future changes in hourly and daily precipitation extremes using CPMs, it remains unclear how extremes will evolve under known impactful atmospheric circulation patterns, such as deep Mediterranean cyclones or persistent southerly flow regimes associated with major Alpine flood events.
Here, we investigate future precipitation changes conditioned on circulation types associated with observed high-impact events. We build on 6 impactful historical circulation types derived from the circulation classification scheme proposed in Lemus-Canovas et al. (2025). To identify circulation and precipitation patterns analogous to these target circulation types, we apply a combined circulation–precipitation analogue framework. Candidate days are required to belong to the 10% closest circulation analogues, defined by the joint similarity of daily sea-level pressure and 500 hPa geopotential height fields simulated by each of the five EURO-CORDEX RCMs relative to the corresponding ERA5 circulation-type composite, quantified using a root-mean-square distance over the European domain. In addition, these candidate days must exhibit high precipitation-pattern agreement, defined as correlations exceeding the 90th percentile between CPM-simulated daily precipitation and an Alpine-wide observational precipitation dataset. Note that CPM outputs are first aggregated from hourly to daily resolution for the purpose of analogue selection. The final analogue dates are retained when basin-averaged precipitation exceeds the 90th percentile—computed separately for each experiment (Historical: 1996–2005; RCP8.5: 2090–2099) and weather type—if either 1-hour or 24-hour accumulated precipitation exceed the threshold in the most affected Alpine basins.
Our results show a precipitation intensification of autumn Mediterranean-origin weather types across all accumulation steps by the end of the century. For these circulation types, hourly precipitation extremes in CPMs scale with temperature at or above the Clausius–Clapeyron rate (~7%/K), while weaker scaling is found at daily timescales. In contrast, summer-dominated weather types exhibit slight intensity increases at hourly scales but decreases at daily accumulations. These findings highlight strong circulation-dependent and scale-dependent changes in Alpine precipitation extremes and are particularly relevant for future risk management in the Alps.
References:
Marc Lemus-Canovas, Manuela Irene Brunner, Massimiliano Pittore, et al. Spatio-temporal patterns and drivers of high-impact precipitation events in the European Alps (1961-2022). ESS Open Archive . September 12, 2025. https://doi.org/10.22541/essoar.175767109.93227583/v1
How to cite: Lemus-Canovas, M., Crespi, A., and Brunner, M.: Future alpine precipitation extremes under high-impact atmospheric circulation patterns, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4515, https://doi.org/10.5194/egusphere-egu26-4515, 2026.
Convection-permitting regional climate models (CPRCMs) at kilometer scale can better represent intense precipitation, yet their added value for flood-risk applications is still limited and often inconsistent. A key reason is the presence of systematic biases in precipitation and temperature over complex terrain, which may strongly affect hydrological response. To address whether bias correction is necessary when using CPRCM forcing for flood modelling in complex terrain, we run WRF-Hydro with raw and bias-corrected 3-km HCLIM3 precipitation and temperature for two contrasting basins spanning coastal to mountainous terrain in western Norway: Røykenes (coastal, rainfall-driven floods) and Bulken (mountainous, snowmelt-influenced floods). We further compare two widely used bias-correction approaches, i.e., Quantile Mapping (QM) and Distribution Delta Mapping (DDM), applied to precipitation and temperature prior to the hydrological simulations.
The results show that bias correction reduces mean biases in both variables, but its effectiveness depends on basin type and metric. In Røykenes basin, QM does not adequately correct annual maximum 1-hour precipitation, whereas DDM provides a better adjustment of extreme precipitation. For temperature, the correction reduces absolute bias relative to raw HCLIM3 but also shifts the bias from cold to warm. In terms of hydrological performance, raw HCLIM3 forcing already yields a small flood-peak bias in Røykenes basin (~3% underestimation), while bias-corrected forcing can further worse this peak underestimation. In Bulken basin, temperature correction improves both flood peaks and flood seasonality, underscoring the strong sensitivity of snowmelt-influenced floods to temperature errors. By contrast, precipitation correction in this mountainous basin degrades flood-simulation skill. Overall, our results show that CPRCM forcing can be highly informative for flood simulations, but the benefits depend on process regime: temperature correction is critical for snowmelt-dominated basins, while precipitation correction over mountains requires particular caution.
How to cite: Li, L., Xie, K., Chen, H., Sobolowski, S. P., Paasche, Ø., and Xu, C.: Is bias correction necessary for CPRCM-driven flood simulation in mountainous region?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14380, https://doi.org/10.5194/egusphere-egu26-14380, 2026.
In this study, we perform a thorough validation of precipitation estimates from the high-resolution WRF model, run with a 4-km horizontal grid spacing over Colombia during 2000-2022, using in-situ data from the Colombia weather, climate and hydrology service (IDEAM) at annual, monthly, and diurnal scales. Model outputs were validated against IDEAM rain gauge data using multiple statistical metrics, including Spearman correlation, p-value, RMSE, ME, MAE, and BIAS. Results show that the model is able to capture the main precipitation regimes, with notable contrasts between coastal (Caribbean and Pacific) and low-lying and plain regions (Orinoco and Amazon), and over the Andes cordillera. While the model generally tends to overestimate rainfall throughout most of the country, the error metrics are smaller over the Andean regions, where the spatial and seasonal variability are better represented. Comparisons across regions at monthly, interannual, and diurnal scales highlight significant differences between model estimates over the Pacific region and those over the Andes. The analysis includes the incidence of both phases of ENSO (El Niño and La Niña), showing positive and negative precipitation anomalies ranging between -300 mm and 350 mm per month, with higher anomalies during El Niño. Results of the validation at monthly and diurnal timescales highlight characteristic nighttime precipitation peaks consistent with the literature. These results confirm that, although the model effectively reproduces high-rainfall regions and their seasonal and diurnal variability, systematic biases remain, especially in the wettest periods (MAM and SON) underscoring the need for further calibration to improve its accuracy and practical applicability.
How to cite: Huertas, W., Poveda, G., Ikeda, K., and Rasmussen, R.: Ground validation of high-resolution WRF model precipitation estimates over Colombia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15938, https://doi.org/10.5194/egusphere-egu26-15938, 2026.
Mountain regions are particularly vulnerable to climate change, as warming reduces snow and ice reserves, thus amplifying positive temperature feedbacks. These processes also have consequences for the hydrological cycle and, therefore, having wide-ranging impacts on society by altering ecosystem services and products. This highlights the importance of understanding how climate change affects mountain areas. However, the limited availability of long-term climate records at high elevations, due to adverse weather conditions, makes high-resolution regional climate models essential for studying complex terrain.
The CIMAs (Climate Research Iniciative for Iberian Mountain Areas) project is focused on analyzing climate variability and the impact of climate change on the Central System of the Iberian Peninsula. The studied area is the largest mountain range of the peninsula, reaching 2.592 m at its highest point (Almanzor Peak) and includes surrounding areas with lowest altitudes.
CIMAs data is gathered from several institutions in Portugal and Spain and distributes over the domain of interest. It was used to asses the accuracy of two regional climate models: the WRF and the HCLIM models at 4 and 1 km horizontal resolution. Both were configured as convection permitting to allow for explicitly simulating convection. In addition, both models were driven by the same boundary conditions provided by the ERA5 reanalysis, which was also used to evaluate the added value of increased resolution by each regional model.
Results show how increasing resolution improves the simulation of temperature at high elevations and allow for better understanding of the climatology of temperature in this mountain range. The comparison of the WRF and HCLIM simulations with observations highlights differences, mostly in the reproduction of extremes.
How to cite: Madera Sánchez, S., González Rouco, F., García Bustamante, E., Navarro Montesinos, J., Vegas Cañas, C., Rodríguez Guisado, E., Rodríguez Camino, E., Sánchez Perrino, J. C., Prieto Rico, I., Greciano Zamorano, E., Cardoso Tavares, R. M., and Cardoso dos Santos, L.: Assessment of temperature variability over the Central System of the Iberian Peninsula: Multi-resolution model evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10030, https://doi.org/10.5194/egusphere-egu26-10030, 2026.
Arctic mountainous environments show pronounced spatial and temporal variability in near-surface air temperature (Tair), driven by complex terrain, frequent temperature inversions, seasonal snow cover, and strong seasonal contrasts in solar radiation. Local atmospheric and surface processes, such as cold-air pooling, can cause rapid temperature changes over short distances and timescales. These dynamics are important for understanding Arctic ecosystem change and climate sensitivity, but remain difficult to quantify using sparse in situ temperature observations alone. Satellite-derived land surface temperature (LST) provides spatially continuous information on surface thermal conditions and has increasingly been explored as a proxy for Tair. However, LST-Tair relationships in Arctic mountain environments are highly variable, complicating the application of satellite LST for characterising fine-scale Tair patterns.
This study uses a unique in situ Tair dataset from the Kevo valley in northern Finland (26.88–27.05°E, 69.72–69.78°N), which is characterised by strong topographic shading, seasonal snow cover and frequent temperature inversions, and is subjected to the polar night and continuous summer daylight. The dataset comprises 65 stations spanning elevations from 74 to 330 m and recording hourly Tair since 2007. These observations are used to evaluate satellite‑derived LST and to develop models for mapping local Tair using Landsat LST combined with terrain and surface variables, including elevation, slope orientation, snow cover and vegetation indices. We analyse higher spatial resolution LST from Landsat sensors together with coarser resolution LST from MODIS Terra/Aqua and Sentinel-3 SLSTR, examining how terrain, snow cover and surface properties influence LST-Tair relationships and the ability of different LST products to represent microclimate variability across the valley. A focused case study examines high-resolution thermal patterns during nighttime and polar-night conditions using Landsat 8/9 LST acquired from October 2024 to August 2025. Preliminary results indicate that strong apparent LST-Tair agreement is largely driven by the seasonal cycle, with correlations in MODIS LST decreasing from ~0.95 to ~0.74 after deseasonalisation. For Landsat, performance is highly sensitive to data quality, with good‑quality data aligning closely with Tair and poorer‑quality data producing large scatter and a cold bias.
How to cite: Mo, Y., Pepin, N., and Lovell, H.: Mapping Arctic mountain microclimates using satellite land surface temperature: insights from the Kevo valley, northern Finland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10471, https://doi.org/10.5194/egusphere-egu26-10471, 2026.
Coastal Santa Barbara is among the most wildfire-prone communities in Southern California. Downslope, dry, and gusty windstorms frequently occur along the south-facing slopes of the east–west-oriented Santa Ynez Mountains (SYM), which separate the Pacific Ocean from the Santa Ynez Valley. These winds, known as Sundowner Winds, typically peak after sunset and often persist overnight. They represent the most critical fire-weather phenomenon in the region.
The Sundowner Winds Experiment (SWEX), conducted from 1 April to 15 May 2022, integrated airborne and ground-based observations to examine interactions between continental and marine atmospheric boundary layers (ABLs), assess mountain waves and hydraulic jumps and their influence on surface winds and dew point, and evaluate forecasting challenges in mesoscale models.
This study analyzes two Sundowner events—IOP-2 (April 5–6) and IOP-10 (May 12–13)—affecting the eastern SYM. IOP-2 occurred during a heat wave, with temperatures reaching the 95th percentile, whereas IOP-10 reflected typical spring conditions.
During IOP-2, observations revealed sharp elevated inversions near the SYM, with mountain waves propagating across these layers. The free atmosphere was extremely dry, and strong horizontal winds were confined near inversion height. On the lee side, a large-amplitude lee wave evolved into a hydraulic jump, followed by wave breaking and a downslope jet. Despite strong offshore forcing, a shallow sea breeze developed over the eastern foothills, while nighttime marine boundary layer (MBL) intrusion—capped by a strong inversion—played a key role in the Sundowner cycle. Descending wave structures and rotor circulations produced reversed flows and enhanced surface winds. A nocturnal mid-channel eddy over the Santa Barbara Channel further stratified the MBL and decoupled it from the downslope jet. WRF simulations at 1-km resolution underestimated ridgetop and lee slope winds and overestimated coastal winds, with biases linked to misrepresentation of ABL height, inversion strength, and delayed MBL advection.
IOP-10 was investigated using ground-based instruments and radiosondes. It featured the second-largest observed mean sea level pressure difference between Santa Barbara and Bakersfield during SWEX. However, winds exceeding 20 m/s occurred on eastern slopes hours before peak pressure differences. LiDAR detected vertical motions near 6 m/s, associated with lifting of the lee-slope jet and weakening of surface winds—evidence of mountain wave activity influencing wind intermittency. Similar to IOP-2, the nocturnal mid-channel eddy contributed to lifting the lee jet and terminating Sundowners near the surface.
These findings emphasize the need for accurate representation of inversion structure and height, as well as marine–continental ABL interactions, in mesoscale models. Realistic simulation of complex flow dynamics—such as mountain waves and hydraulic jumps—is essential to improve forecasts of downslope winds in coastal environments. The SWEX campaign provided unique measurements to evaluate these features.
How to cite: Carvalho, L. M. V.: Downslope Windstorms in Coastal Mountains: Observations and Modeling during the Sundowner Wind Experiment (SWEX), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8550, https://doi.org/10.5194/egusphere-egu26-8550, 2026.
Thermally-driven valley circulation governs heat, momentum, and pollutant transport in mountains and is affected by the valley topography, large-scale weather, surface properties, and thermal forcing. Aerosols alter the heat distribution in the atmosphere through absorption and scattering of the incoming solar radiation, influencing the boundary layer (BL) development. From studies considering urban BL over flat terrain, it is known that depending on the radiative properties and vertical distribution of the aerosol population, aerosols can either enhance or suppress the buoyancy and mixing in BL, and cause simultaneous cooling and warming at different altitudes within BL. The impact of aerosols on the thermally-driven valley circulation remains poorly understood, a shortcoming addressed by this study.
This study examines how the absorption of incoming solar radiation by black carbon (BC) affects the daytime valley and slope winds in high-resolution idealised simulations using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). The simulations have an idealised valley topography that has a sinusoidal shape in the cross-valley direction and is 100 km long, 20 km wide, and 2 km deep. The study consists of two simulations: one including realistic BC concentrations interacting with the meteorological fields through absorption of shortwave radiation, and a reference simulation without BC. Heat and momentum budgets for the valley volumes are computed to understand the mechanisms behind the differences in the winds between the two simulations.
BC absorption acts to warm the upper BL and cool the lower levels during daytime, enhancing stability and reducing surface heating. Consequently, up-slope winds are weaker and confined to a shallower layer in the BC simulation. In the afternoon the up-valley winds are stronger in the BC simulation, although BC weakens the daytime temperature difference between the valley atmosphere and the BL above the plain. Based on the classic valley wind theory, the stronger temperature difference, hence a stronger pressure-gradient force, should lead to stronger up-valley winds. The average up-valley wind speed in the afternoon is 2.6 m s-1 in the BC simulation and 2.3 m s-1 in the simulation without BC. However, in the evening when the up-valley winds peak in magnitude, the maximum wind speed is stronger in the simulation without BC with a 0.5 m s-1 margin.
Momentum budget analysis shows that in the simulation without BC the pressure-gradient force is indeed stronger than in the BC simulation, which is in line with the stronger temperature difference. The advection term shows that the vertical export of along-valley momentum out from the valley by the cross-valley circulation, which is seen in the simulation without the BC, is suppressed or even absent in the BC simulation. This occurs likely due to the weaker up-slope winds which allow the stronger up-valley winds to develop in the afternoon despite the weaker pressure-gradient forcing. These results show that realistic BC concentrations can affect the thermally-driven valley circulation and fluxes of heat and momentum, revealing a pathway through which absorbing aerosols can modify the daytime slope and valley wind characteristics.
How to cite: Mikkola, J., Sinclair, V. A., Ciarelli, G., Gohm, A., and Bianchi, F.: Impact of black carbon on slope and valley winds in idealised simulations , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9157, https://doi.org/10.5194/egusphere-egu26-9157, 2026.
Air pollution in mountain ecosystems has recently received particular attention. The peculiar and complex topography of such regions, combined with region-specific heating practices, has been shown to significantly reduce air quality levels, particularly in locations and communities situated on mountain valley floors.
The Khumbu Valley, located in the Himalayan ridge, connects the Indo-Gangetic Plain to the Nepal Climate Observatory – Pyramid (NCO-P) observation site at the foothills of Mount Everest (5079 m a.s.l). It often experiences high levels of particulate matter, including carbonaceous aerosols species (e.g. black carbon), which are largely modulated by the typical mountain valley circulation. These aerosols can be transported into the Khumbu valley from the Indo-Gangetic plain through thermally driven up-valley flows. However, the extent to which such circulation is directly impacted by absorbing and scattering aerosol compounds is currently unknown.
In this study, we conducted a one-month regional chemical transport model (CTM) simulation using the WRF-CHIMERE model at 1 km horizontal grid spacing, centered over the Khumbu Valley. The resolution was chosen to best account for the valley wind circulation typical of the region, while maintaining a trade-off with computational demands. We evaluated the impact of aerosols on meteorology due to aerosol-radiation interactions (ARI) over the Khumbu Valley and quantified its overall absolute magnitude. The pre-monsoon month of April was chosen as the period when transport of particulate matter from the Indo-Gangetic Plain is at its peak. Our results indicated that the model was able to reproduce the influx of particulate matter from the Indo-Gangetic Plain, with the modelled midday average peak in line with measurements at the NCO-P site. Accounting for ARI in the meteorological host model indicated a statistically significant cooling of the valley induced by aerosols, with potential implications for valley wind circulation. Given the extent of the Himalayan range, the results presented here may have implications for future climate scenarios, as aerosol-radiation interactions are often not resolved in coarse Earth system model applications.
How to cite: Ciarelli, G., Di Antonio, L., Mikkola, J., Sinclair, V. A., Cholakian, A., Bessagnet, B., Madina, T., Marinoni, A., Tuccella, P., and Bianchi, F.: Radiative impacts of particulate matter in a Himalayan valley: A modelling case study of the Khumbu Valley, Nepal., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11787, https://doi.org/10.5194/egusphere-egu26-11787, 2026.
Due to the close proximity of large urban areas to mountainous environments, air pollution can pose a serious threat to sensitive ecosystems through rapid transport driven by advection and mountain–valley circulation. Almaty (Kazakhstan), frequently ranked among the most polluted cities globally, is situated at the foothills of the Ile Alatau (part of the northern Tien Shan mountains). The city’s urban area located about 15-35 km from the major glacial systems, that have experienced a substantial decrease over the past years. In this study, we investigated the impact of locally emitted black carbon (BC) from Almaty on the surrounding mountain areas using the WRF-CHIMERE regional chemistry-transport model with three nested domains up to 1 km resolution for periods representative of winter and summer conditions (i.e. January and July of 2023, respectively).
Simulation results indicated that during winter, BC concentrations remained trapped over the Almaty basin, at the lower elevations north of the city, and along the main valleys, due to stable atmospheric conditions and limited vertical mixing. In contrast, in summer, despite lower anthropogenic emissions arising from the city, BC was found to reach the mountain tops more effectively (up to 4000 m a.s.l.), likely due to increased vertical mixing and enhanced mountain–valley circulation. The peak BC concentrations at the mountain stations occurred approximately 5 (in July) – 8 (in January) hours after the maximum values in the city, suggesting faster upslope transport from the city in summer than in winter.
Additionally, model runs with and without online exchange between meteorology and chemistry were conducted to quantify the effect of BC concentrations on the radiative fluxes. Estimates of BC direct radiative effect (DRE) confirmed that the presence of BC over Almaty decreases solar radiation at the bottom of the atmosphere (BOA, BC DREBOA up to -1.20 W m-2) and enhances absorption within the atmosphere (BC DREATM up to +1.33 W m-2). Analysis of the potential temperature gradients in both months indicated, on average, no significant effect of BC concentrations on vertical atmospheric mixing, which in January can be attributed to strong temperature inversions over the region.
This research represents the first assessment of dynamics, transport and radiative effects of BC over the mountainous regions in Central Asia and highlights the need for further analysis extending to transitional periods (spring, autumn) when the temperature inversions are weaker or absent, but emissions rates remain high.
How to cite: Tursumbayeva, M., Ciarelli, G., Di Antonio, L., Bettineschi, M., and Baimatova, N.: Modelling the black carbon dynamics over Almaty, Kazakhstan, during winter and summer seasons., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16598, https://doi.org/10.5194/egusphere-egu26-16598, 2026.
The spatial resolution and accuracy of land-cover datasets used in numerical models can have a significant impact on the modeled mountain boundary layer. The land-surface cover influences the surface-energy budget through, for example, the effect of albedo on net shortwave radiation and roughness length on the turbulent exchange between the surface and the atmosphere. Local heating and cooling of the near-surface valley atmosphere are thus equally affected by the land-surface cover, which in turn influences the development of thermally driven slope and valley winds. In addition, the roughness length impacts near-surface turbulent momentum transport and flow fields, which may be of particular importance for shallow slope winds.
We have performed a series of WRF simulations for the Inn Valley, Austria, using three different land-use datasets and three idealized land-cover distributions. The two standard WRF land-use datasets MODIS and USGS strongly overestimate the amount of forest cover in the valley compared to the newer and better resolved CORINE Land Cover (CLC18) dataset. To further analyze the impact of this overestimation in forest cover, semi-idealized simulations are performed with a prescribed amount of forest cover across the model domain. The presentation will show the impact of the land cover on the local surface-energy budget and near-surface atmosphere as well as on the bulk valley atmosphere. Differences in the local sensible heat flux averaged over the surface of the valley are linked to total heating of the valley and the resulting valley-wind circulation.
How to cite: Lehner, M. and Simonet, G.: The impact of forest cover on the modeled valley atmosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6720, https://doi.org/10.5194/egusphere-egu26-6720, 2026.
Near surface air temperature is a key meteorological parameter with high implications for the understanding and modelling of snow and water resource in mountain regions. Yet, it is hard to estimate and forecast accurately in these environments due to observational scarcity and model limitations in complex terrain.
In the present study, we analyze whether structural inhomogeneities in observational networks for temperature in mountain regions contribute to errors in their representations in numerical weather prediction (NWP) systems. Taking the case of the Arome-France NWP system over the French Alps, we analyze in particular the effects of the disparity in height above ground of the temperature sensors, of the inhomogeneous geographical distribution of stations that are preferentially located in valleys, and of the frequent altitude mismatch between stations’ real location and model grid points. We evaluate the consequences of these inhomogeneities in terms of model evaluation and data assimilation.
We especially show that measurement height is of high impact for model evaluation, providing a strong incentive to revisit model scores in mountain regions. It also carries strong implications for the assimilation, leading in the case of Arome-France to a negative impact of the assimilation of high-altitude temperature data if their height above ground is not properly considered. Inhomogeneities in data density between mountains and valleys also play a role that can be modulated depending on the assimilation system. This work paves the way for a better use of high-altitude near-surface observations within models deployed over mountain regions.
How to cite: Gouttevin, I., Préaux, D., Etchevers, I., and Seity, Y.: On the proper use of near-surface temperature observations in atmospheric models deployed over mountain regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7635, https://doi.org/10.5194/egusphere-egu26-7635, 2026.
In mid-latitude regions, seasonal snow cover is predominantly distributed over high mountain areas characterized by complex terrain. Wind-driven snow transport is a key process controlling snow redistribution, accumulation patterns, and surface mass balance in these environments. However, a gap exists between the accurate representation of drifting snow processes, which requires boundary-layer wind fields at hundred-meter scales, and the coarse horizontal resolution of most atmospheric models on the order of 10 km, leading to large uncertainties in simulations of snow–atmosphere interactions in mountainous regions. In this study, multi-level nested simulations are performed using the WRF–LES framework to resolve boundary-layer horizontal wind fields across a range of spatial scales (from 9 km to 111 m) relevant to drifting snow. Wind speed statistics at different resolutions are analyzed, and their relationships with an integrated topographic factor are systematically quantified. Based on these analyses, a topography- and scale-dependent statistical downscaling scheme is developed to bridge the gap between coarse-resolution atmospheric forcing and fine-scale wind fields governing snow erosion, transport, and deposition. The result is also evaluated using in situ observations from a snow monitoring station in the Qilian Mountains, demonstrating an improved representation of near-surface wind characteristics, which are critical for snow redistribution.
How to cite: Li, G., Feng, Y., Yu, H., and Wang, C.: A scale-adaptive parameterization of the horizontal wind field in the mountainous boundary layer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16295, https://doi.org/10.5194/egusphere-egu26-16295, 2026.
The Yarlung Zsangbo Grand Canyon (YGC) acts as a critical water vapor channel for the Tibetan Plateau, profoundly influencing regional and downstream hydrometeorology. Significant research progress has recently been made in understanding the complex precipitation processes within this unique corridor, integrating multi-source observations, satellite retrieval evaluation, and model simulations.
A core finding is the systematic underestimation of precipitation over the eastern Himalayas by widely used products like GPM IMERG, which has been quantitatively reduced through improved algorithms informed by dense in-situ gauge data. Comprehensive investigations utilizing a novel multi-platform observational network have elucidated the complete three-dimensional structure and life cycle of precipitation systems within the YGC. This network, combining ground-based radars, disdrometers, and radiosondes, has revealed distinct seasonal shifts in precipitation microphysics. Notably, mixed-phase and ice-phase processes play a key role in these seasonal transitions, with significant differences identified between the southeastern Tibetan Plateau and lower-altitude regions. Furthermore, two dominant types of heavy precipitation events have been classified and their distinct dynamic and thermodynamic mechanisms have been established.
Research also highlights the challenges of reanalysis accuracy in complex terrain, while providing pathways for improvement. Leveraging these mechanistic insights, recent efforts have successfully improved the forecasting of heavy precipitation in the YGC through optimized model physics, specifically by integrating enhanced cumulus and turbulent orographic form drag (TOFD) parameterization schemes. Collectively, these studies advance the quantitative understanding of precipitation processes in this major water vapor channel, offering crucial insights for hydrological modeling, climate studies, and numerical weather prediction in high-altitude complex terrain.
How to cite: Chen, X.: Research progress of precipitation process in the water vapor channel of Yarlung Zsangbo Grand Canyon, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1414, https://doi.org/10.5194/egusphere-egu26-1414, 2026.
Extreme summer precipitation events pose significant challenges, particularly in regions with complex topography such as the European Alps. Furthermore, the pronounced vulnerability of this region to climate change underscores the need to better understand its precipitation dynamics and processes at different spatial and temporal scales.
To in-depth investigate the spatial and temporal characteristics of these events, this study employs high-resolution regional climate simulations from the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Studies (CORDEX-FPS) on convection over the Alps and the Mediterranean region. Focusing specifically on elevation-dependent patterns and sub-daily variability, we analyze the spatial distribution of summer precipitation extremes and the underlying processes associated with these events.
The results identify key hotspots of precipitation intensity and frequency, providing valuable insights for risk assessment, management, and adaptation strategies in mountainous regions. They also demonstrate how topography and other static factors, together with dynamic processes, affect the distribution of extreme precipitation events.
How to cite: Napoli, A., Ban, N., Pasquero, C., and Zardi, D.: Exploring the complex dynamic of summer extreme events in the European Alpine Region using the high-resolution CORDEX-FPS ensemble, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6869, https://doi.org/10.5194/egusphere-egu26-6869, 2026.
Kilometer-scale (k-scale) simulations, with explicit treatment of convection at sub-grid scales, are useful for understanding precipitation characteristics. Such simulations with their high spatiotemporal resolution can be particularly valuable in complex topographies like the Hindu Kush Himalayas (HKH), where sparse observations and uncertainties in coarse-resolution datasets pose challenges. This study evaluates a regional AMIP-style k-scale (1 km) simulation, initialised from the ECMWF IFS analysis, for winter mean and extreme precipitation (December 2018-February 2019) in the HKH region, using high-resolution gridded precipitation datasets from multiple sources. The model realistically depicts the spatial distribution of precipitation, particularly the ridge-valley variations, often missed in coarser products. In general, it aligns more with reanalysis datasets but closely matches station observations too. Mean precipitation exhibits sensitivity to elevation, and the highest rates occur at about 2500 m in most of the reference products (observations/reanalysis), which the k-scale model represents well. The diurnal cycle depicts sub-daily precipitation maxima in the local afternoon and early morning hours. The analysis for precipitation extremes indicates the model’s close fidelity with reanalysis products in capturing higher-intensity and prolonged precipitation events in the western Himalayas. Radiosonde profiles and atmospheric thermodynamic characteristics highlight a highly saturated and unstable environment during extremes, which is favourable for enhanced convective developments and heavy precipitation. The model captures these atmospheric conditions well and represents the localized variations and intensifications in valley wind flows during extremes, which are often missed in coarser-resolution and parameterized ERA5 data. Our findings highlight the added value of k-scale convection-permitting models over coarser-resolution, parameterized models in resolving subgrid-scale processes, particularly in complex terrains like the HKH, without the need for convective parameterization.
How to cite: Attada, R., Sharma, N., Hunt, K., and Anantharaj, V.: Kilometer-Scale Convection-Permitting Simulations in Representing Winter Precipitation over the Indian Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2245, https://doi.org/10.5194/egusphere-egu26-2245, 2026.
The persistent spring precipitation over East Asia, with a notable drying trend in recent decades, poses substantial impacts on the regional hydrological cycle and socio-economy. This study investigates the climatology and long-term trend of East Asian spring precipitation during 1980-2014 simulated from CMIP6 HighResMIP coupled models, focusing on the role of model horizontal resolution. Our results show that high-resolution models outperform their low-resolution counterparts in simulating the spatial pattern and intensity of East Asian spring mean precipitation, owing to improved representations of low-level winds and moisture transport. However, many high-resolution models in HighResMIP fail to reproduce the long-term variation of East Asian spring precipitation and associated remote influencing factors (e.g., tropical Pacific and North Atlantic sea surface temperature) while only two models (FGOALS-f3-H and EC-Earth3P-HR) show improved performance for this unique climate phenomenon. Particularly, the high-resolution FGOALS-f3-H model exhibits the best skill in simulating this regional climatic change, increasing a regional mean drying trend from -0.10 in its low-resolution version to -0.33 mm day-1 decade-1 (observed: -0.43). This remarkable improvement in FGOALS-f3-H stems from more realistic representations of both the weakening Western North Pacific Anticyclone and strengthening Mongolia High, which are key regional circulation drivers of the East Asian spring drying trend, as well as its improved simulation of the weakening vertical velocity over East Asia. By contrast, five out of all seven high-resolution models show degraded performance in reproducing this precipitation trend, even showing amplified simulation biases in precipitation trend and improper relationships with remote and regional influencing factors relative to their low-resolution counterparts. This study suggests that the simultaneous improvement of horizontal resolution and physical parameterizations governing precipitation-related interannual variability in climate models is critical for simulating East Asian climatic change.
Keywords: East Asia, spring precipitation, high-resolution models, CMIP6
How to cite: Zi, P., Li, J., Yang, R., Liu, Y., Yang, Z., Li, T., He, B., and Bao, Q.: East Asian Spring Precipitation and its Dry Trend revealed by CMIP6 High-Resolution Coupled Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8916, https://doi.org/10.5194/egusphere-egu26-8916, 2026.
The spatiotemporal variation of summer precipitation on the Tibetan Plateau (TP) is complex. In this study, we propose that there exist visible differences in the dominant modes of the interannual variability of eastern TP (ETP) precipitation between early (June) and peak (July–August) summers during 1979–2022. A north-south dipole pattern of the precipitation interannual variability appears in early summer, but in peak summer, the dominant mode is changed to be a monopole pattern. This phenomenon is mainly due to the intraseasonal transition of the dominant atmospheric circulation patterns over the TP and surrounding areas. In early summer, the north-south dipole pattern of the interannual variability of ETP precipitation is associated with the upper-level anomalous anticyclonic circulation over the western TP, which is primarily forced by the convective heating of South Asian summer monsoon. Under the control of anomalous northerlies on the eastern side of the anticyclonic circulation, the precipitation on the northern ETP is suppressed by both negative moist enthalpy advection and negative moisture advection. While in peak summer, the monopole pattern of the interannual variability of ETP precipitation is mainly regulated by the large-scale meridional displacement of the subtropical westerly jet. When the westerly jet shifts southward, the strengthened westerlies control the entire plateau and create unified positive moist enthalpy advection over the ETP, finally resulting in anomalous upward motions and increased precipitation; and vice versa. This study provides an insight that further investigations on the ETP summer precipitation should consider the intraseasonal difference.
How to cite: Liu, E., Yang, S., Luo, H., Xie, J., and Wang, Z.: Differences in the Dominant Modes of the Interannual Variability of Eastern Tibetan Plateau Precipitation between Early and Peak Summers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10268, https://doi.org/10.5194/egusphere-egu26-10268, 2026.
Reanalysis products, like ERA5-Land, offer user-friendly, high-resolution gridded climate data (9 km) by combining ground observations, remote sensing, and model estimates. However, they inevitably contain uncertainties due to data gaps and modelling. Validating these datasets with land-based measurements is essential, though these observations also suffer from errors and inconsistencies. For this reason, this study validates ERA5-Land Temperature over the Extended European Alpine Region using the EEAR-Clim dataset, which includes only observational data records that meet strict reliability and temporal-consistency criteria.
The validation process involves 159 land-based meteorological stations, along with their corresponding nearest grid points in the ERA5-Land dataset. These grid points meet two criteria: a maximum elevation difference of ±100 meters and a maximum horizontal distance of ±0.5°. The selection procedure is designed to avoid repetition. The 159 grid points are different from each other. The stations are located between 504 and 2,965 meters above sea level and cover the period 1980–2020. We compared the daily temperature probability distributions for each station, grouping the stations into five elevation bands as well as considering the entire dataset. Our analysis examined temperature bimodality, the autocorrelation function, the ‘near-0°C probability’, and the ongoing issue of elevation-dependent warming trend.
The analysis shows that ERA5-Land generally underestimates temperature, with a global mean bias of –0.94 °C, and overestimates the standard deviation by +0.24 °C. The mean absolute error ranges from +1.37 °C in the lowest elevation band to +2.19 °C in the highest. The EEAR-Clim dataset provides clear evidence that low-elevation stations exhibit a bimodal temperature probability distribution, while stations above 1,500 m show a transition toward a unimodal distribution. ERA5-Land does not reproduce this transition, as even the highest grid points retain two main modes. The autocorrelation function of the observations decreases with elevation, whereas ERA5-Land shows increasing errors in its estimates, particularly at high elevations. The ‘near-0 °C probability’ is overestimated at low elevations and underestimated at high elevations. Despite this, the two datasets show good agreement in their estimates of the mean annual temperature trend rate, irrespective of elevation. However, the EEAR-Clim dataset indicates that lower elevations have warmed faster than the highest ones. These results are influenced by high variability and the limited number of stations above 2,000 m, which may affect or obscure the true temperature behavior. This underscores the urgent need for additional instrumentation, particularly at high elevations.
How to cite: Bongio, M., Sangiorgio, M., and De Michele, C.: ERA5L Temperature validation in the Extended European Alpine Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6475, https://doi.org/10.5194/egusphere-egu26-6475, 2026.
The Climate research initiative for Iberian Mountain Areas (CIMAs) is a collaborative framework involving several Spanish institutions: the Spanish Meteorological Office (AEMET), Complutense University of Madrid (UCM), Institute of Geosciences (IGEO, CSIC-UCM) and CIEMAT. The main goal of the initiative is to advance the characterization and understanding of climate variability and change in the Central System of the Iberian Peninsula. Mountain regions are particularly sensitive to climate change, however observational data in these environments remain scarce, heterogeneous and difficult to maintain. CIMAs addresses this challenge by integrating multi-source meteorological datasets from institutions with different measurement protocols, temporal resolutions and data formats, such as AEMET, the Guadarrama Monitoring Network (GuMNet), the Portuguese Meteorological Office (IPMA), hydrological agencies operating Automatic Hydrological Information Systems in Spain (SAIH Duero, SAIH Tajo) and the Portuguese National Water Resources Information System (SNIRH).
In this work, the development of the CIMAs observational database is presented. The workflow includes harmonization of formats and units, metadata consolidation, systematic quality control, temporal aggregation and a version-controlled architecture that ensures traceability and facilitates future updates. The temperature and precipitation databases are currently operational, incorporating station records distributed across Spain and Portugal. As part of the evaluation of the current data releases, spatial summaries of data availability and temporal coverage are also presented, together with preliminary climatological fields used to assess the internal consistency of the integrated datasets. The system additionally provides web-based tools for data visualization and access. Ongoing developments include the integration of wind and snow products and the coupling of the observational database with simulations.
The CIMAs framework provides a structured and interoperable basis for integrating climate observations across high-mountain areas of the Iberian Peninsula. Its aim is to improve data accessibility, consistency and usefulness for scientific and operational purposes. In addition, it offers an observational basis for assessing simulation performance and for the development of climate-service applications.
How to cite: Vegas Cañas, C., González Rouco, J. F., Rodríguez Guisado, E., Rodríguez Camino, E., Cardoso, R. M., Santos, L. C., Navarro Montesino, J., García Bustamante, E., Pereira, C., Luna, Y., Morata, A. B., Robles Martínez, G., and Hinojal, J. A.: CIMAs: A multi-source climate dataset for high-mountain environments in the Iberian Central System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15032, https://doi.org/10.5194/egusphere-egu26-15032, 2026.
Southwest Asia (SWA) is a climatically sensitive region where water resources are determined by the complex interactions between the Indian Summer Monsoon and Mediterranean winter systems. Coarse-resolution Global Climate Models (GCMs) have difficulties in capturing the arid-to-semi-arid hydroclimate of the region, which is characterized by high variability and orographically intensified precipitation. This study evaluates the added value of dynamical downscaling in representing mean and extreme precipitation in SWA. We use the Weather Research and Forecasting (WRF) model at a resolution of 10 km, driven by boundary conditions from the Community Earth System Model (CESM v1.2.2). For evaluating the models, we compare the native CESM (~2° resolution), the downscaled WRF simulation, and the ERA5 reanalysis for the common period 1950-2002. Our analysis reveals two outcomes for regional downscaling. First, the downscaled WRF simulation significantly improves the representation of the annual cycle, closely agreeing with ERA5, while the original CESM overestimates precipitation during summer. This overestimation can also be seen in the extreme precipitation values of CESM, especially in the south part of our region. Second, in areas of complex orography, like the Zagros Mountains, WRF tends to exaggerate precipitation compared to ERA5. Spatial differences between WRF and ERA5 precipitation in these complex regions can be attributed to the higher resolution of WRF. The extreme precipitation pattern generally agrees between WRF and ERA5 even though we observe the aforementioned spatial differences. The findings point out that dynamical downscaling can accurate simulate topographically forced precipitation, reducing large-scale GCM biases. This offers an important baseline for improved representation of precipitation in complex mountainous regions with low observational data availability, such as SWA.
How to cite: Bouchorikou, M., Nguyen, T. Q. T., and Raible, C.: From Global to Regional: The Added Value of High-Resolution Dynamical Downscaling for Precipitation in Southwest Asia's Complex Terrain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4921, https://doi.org/10.5194/egusphere-egu26-4921, 2026.
High-altitude mountains play a key role in modulating regional weather and climate. The tropical Andes in South America are characterized by strong climatic diversity and complex orography. In this region, identifying atmospheric circulation patterns (CPs) that control the meteorological extremes across different altitudinal and latitudinal gradients remains challenging. Using unique, quality-controlled hourly air temperature observations from four automatic weather stations located above 4700 m a.s.l. in the Peruvian Andes, this study links local extreme air temperature events to large-scale CPs during 2013-2024. CPs were identified using a k-means clustering algorithm applied to the standardized anomalies of the daily 200-hPa wind field from the ERA5 reanalysis over South America (10° N-30° S, 90°-30° W) for the 1980-2024 climatological period. Nine CPs were identified and classified into dry (D1-D4), wet (W1-W3), and transitional (T1-T2) circulation types, consistent with the regional seasonal cycle. Results show that warm nights (daily minimum air temperature exceeding the 90th percentile) are closely related to the occurrence of the transitional (dry-to-wet season) CP T1. This pattern is linked to warmer-than-normal conditions relative to the daily climatology, with a high frequency of warm nights observed from April to November. The 200-hPa circulation associated with T1 exhibits an upper-level ridge extending down to 500-hPa, resembling the Bolivian High. This circulation enhances easterly flow, favoring the advection of warm and moist air into the Andes and increasing nighttime and early-morning cloud cover. These conditions inhibit nocturnal radiative cooling and maintain elevated minimum air temperatures during a climatologically cold period in the Andes. During the 2023-2024 El Niño event, warm nights increased markedly compared to the previous years, while cold events became less frequent. This behavior appears to be primarily linked to an increased frequency of the T1 pattern, reaching up to 35%, particularly during July-October 2023 and April-July 2024. These findings provide a framework for future analyses of changes in this circulation regime under future climate scenarios and its role in modulating warm temperature extremes over the tropical glaciers.
How to cite: Ita Vargas, T., Sicart, J. E., Zin, I., Condom, T., Suarez, W., Quispe, K., Junquas, C., and Espinoza, J.-C.: Recent high-altitude observations (2013-2024) of extreme air temperatures and associated atmospheric circulation patterns in the tropical Andes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13515, https://doi.org/10.5194/egusphere-egu26-13515, 2026.
Gridded near-surface air temperature datasets are essential for environmental and climate applications, providing spatially continuous information beyond point measurements. In mountain regions, however, accurately representing temperature is particularly challenging. Strong spatial variability, frequent departures from simple elevation-based gradients, and cold-air pooling driven by nocturnal cooling and drainage flows lead to complex temperature patterns that are generally underrepresented when interpolating temperature observations from sparse weather stations. These limitations can reduce the accuracy in capturing extreme conditions, such as hot spells in the valley bottoms and urban areas or cold spells and strong thermal inversions. High-resolution dynamical models offer a complementary, physically based perspective by explicitly resolving terrain and atmospheric processes, improving representation of temperature gradients, diurnal cycles, and local circulations. Yet, near-surface temperatures in complex terrain remain sensitive to model resolution and surface-atmosphere coupling. The distinct strengths and limitations of these approaches raise the question of how different methods perform in representing local temperature patterns in complex terrain. In this study, we compare a 1-km dataset of daily near-surface air temperature produced through an interpolation scheme with high-resolution fields from dynamical modelling to assess the abilities to represent temperature variability in a complex mountainous terrain like the one of the Adige River catchment in Eastern Italian Alps. The interpolation method estimates the vertical temperature structure through a daily fitted, non-linear temperature-elevation profile based on more than 600 station observations at multiple altitudes and accounts for topographic complexity (Frei, 2014). Model-based products include the km-scale reanalysis VHR-REA_IT (Raffa et al., 2022) obtained by a dynamical downscaling of ERA5 for Italy at approximately 2-km resolution and the Copernicus European Regional ReAnalysis (CERRA). The comparison is conducted over the period 1990-2020 and focuses on the representation of temperature extremes and their spatial variability, e.g., cold-air pooling and heatwaves, and on the description of daily vertical profiles. Interpolated fields capture local extremes and cold-air pools where observations are available but are limited in resolving broader spatial variability and vertical thermal structure. In contrast, high-resolution reanalyses provide a more physically consistent depiction of thermal gradients, although systematic differences in describing extremes emerge. Our results will illustrate how the complementarity of approaches can guide the appropriate use and integration of temperature products in mountainous regions to support temperature-related hazard monitoring and risk assessment.
How to cite: Maines, E., Crespi, A., Campalani, P., Pittore, M., and Zebisch, M.: Representing local-scale temperature patterns in complex terrain: performance of high-resolution datasets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13277, https://doi.org/10.5194/egusphere-egu26-13277, 2026.
Reconstructing high-resolution geophysical fields from sparse observations is a central challenge for environmental sensing and model evaluation in complex terrain. While high-resolution climate models provide detailed insights, they are computationally expensive and difficult to validate in remote mountainous regions. This work adapts a data-driven sparse sensor placement framework [1] to identify optimized meteorological station locations for an arbitrary number of sensors in complex terrain.
Applied to a mountainous region in northern Norway, our approach can help hydrologists, glaciologists, and climate scientists determine where to place sensors to obtain independent streams of data, supporting a comprehensive representation of variables such as wind speed, humidity, or snow depth. We generalize the original framework by introducing a spatial weighting formulation, allowing users to prioritize specific sub-regions or account for physical constraints such as inaccessible terrain. In addition, prevailing wind patterns are incorporated into the selection criteria, guiding sensor placement toward configurations that capture the most frequent and impactful flow regimes. An orthogonal component approach is further introduced to integrate existing stations, ensuring that newly deployed sensors capture complementary information rather than redundant data. Ongoing work explores the use of the same framework to reconstruct missing or partially degraded measurements when stations are temporarily unavailable, using information from the remaining network.
A key advantage of the framework is its transparency. In contrast to many data-driven or machine-learning-based downscaling approaches, the reconstruction relies on explicit linear algebra operations, providing a traceable link from point observations to a domain-wide target field. For operational safety applications such as monitoring airport winds or avalanche hazards, this offers a computationally efficient and flexible alternative when high-resolution simulations are unavailable.
[1] Xihaier Luo, Ahsan Kareem, and Shinjae Yoo. “Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing”. In: Journal of Building Engineering 75 (2023), p. 106855. issn: 2352-7102.
How to cite: Poltronieri, A. and Olson Aksamit, N.: Optimizing Meteorological Station Placement for High-Resolution Field Reconstruction in Mountainous Terrain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12250, https://doi.org/10.5194/egusphere-egu26-12250, 2026.
SPARTACUS (Spatial Climate Observation Dataset for Austria) version 3 is the latest iteration of the main gridded dataset used by Geosphere Austria for operational climate monitoring. It provides daily values of temperature (mean, minimum, and maximum), precipitation sum, and sunshine duration at 1 km resolution for the territory of Austria and for selected surrounding regions (catchment areas of relevant rivers), covering the period from 1961 to the present. SPARTACUS is based solely on in-situ measurements of the Austrian network and of neighboring countries, which are interpolated by adapting statistical methods specifically developed for mountainous regions (e.g., Frei, 2014).
The most important addition with respect to the previous version (v2.1) is the calculation of the actual daily mean temperature (based on 24 hourly measurements) that replaces the arithmetic averages of maximum and minimum temperature. For the years preceding the automation of the measurements (when only three measurements per day are available) station-specific corrections were calculated by means of multi-linear regression to take into account a network-wide change of the observation times that took place in 1971 (Hiebl et al., 2025). In general, the temporal homogeneity of the input data has improved. Moreover, the number of ingested stations has been increased.
We demonstrate the improved suitability of the new version for climate‑change analyses compared to its predecessor (with particular focus on elevation-dependent climate change), examine the remaining issues, and offer an outlook on forthcoming developments.
References:
Frei, C. (2014), Interpolation of temperature in a mountainous region using nonlinear profiles and non-Euclidean distances. Int. J. Climatol., 34: 1585-1605. https://doi.org/10.1002/joc.3786
Hiebl, J., Rohrböck, A., and Haslinger, K. (2025), Correcting breaks in temperature and humidity observations: Implications for climate variability analysis in Austria. Int. J. Climatol., e70214. https://doi.org/10.1002/joc.70214
How to cite: Brugnara, Y., Höfler, A., Rohrböck, A., and Romatschke, U.: SPARTACUS version 3: An improved gridded climate dataset for Austria at daily resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16406, https://doi.org/10.5194/egusphere-egu26-16406, 2026.
Anabatic winds are thermally-driven flows that develop over heated mountain slopes. These upslope winds develop when the air near the slope rises due to the along-slope component of the buoyancy force, driven by the horizontal temperature contrast between the heated slope-adjacent air and the cooler ambient air at the same elevation. Due to the temperature difference, a horizontal pressure gradient forces the air to rise along the slope. Anabatic flows have a distinct vertical structure, with a near-surface wind maximum and a jet-like profile.
According to Prandtl’s analytical model and data from numerical simulations, the strength and depth of the anabatic flow layer are sensitive to the slope angle. The slope angle has also been suspected as a potential driver of turbulence anisotropy based on measurement results. The impact of the slope angle on turbulence anisotropy, however, has not been investigated in numerical simulations so far. To address this gap, we used the Cloud Model 1 (CM1) to conduct high-resolution large-eddy simulations of anabatic flows above idealized ridges to evaluate the influence of ridge height, slope angle and slope curvature on turbulence anisotropy. In total, 10 simulations have been conducted so far, consisting of 7 simulations for sinusoidal ridges of different heights, widths and slope angles and three simulations for ridges with the same constant slope angle but different ridge heights. The simulations were initialized with a constant potential temperature gradient throughout the domain and a constant surface heat flux of 0.12 K m s-1 and ran with a grid spacing of 10 m horizontally and 5 m vertically.
First results suggest that steeper slopes lead to more anisotropic turbulence. Apart from the slope angle itself, terrain curvature has a pronounced effect on the degree of anisotropy, as turbulence is more isotropic above slopes with constant slope angles compared to concave slopes of sinusoidal ridges. This is expected since upslope flow along a concave slope implies concave streamlines, and concave streamlines enhance shear stress and the momentum flux according to to the streamline curvature analogy. To gain further insights into the processes causing anisotropic turbulence, we plan to also investigate potential correlations between the degree of anisotropy and individual terms in the turbulent kinetic energy budget.
How to cite: Rauchöcker, A., Stiperski, I., and Gohm, A.: Anabatic Flows over Idealized Mountain Ridges and the Relation between Slope Angle and Turbulence Anisotropy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8111, https://doi.org/10.5194/egusphere-egu26-8111, 2026.
Current generation climate and global numerical weather prediction models still must parameterize
the effects of subrgid-scale orography, which they cannot explicitly resolve. One of the effects are
the orography gravity waves that affect the dynamics and transport throughout the atmosphere due
to flux convergences during their dissipation. Complicating the problem further is the interplay with
the turbulence parameterization schemes, which influence the dynamics and mixing near the
surface and then aloft in unstable regions in the free atmosphere.
In this work, we study the life cycle of orography gravity waves numerically under background
conditions and set-ups ranging from idealistic to realistic. A hierarchy of idealized three-
dimensional simulations of mountain–flow interaction is developed for various orographic shapes,
atmospheric conditions and model settings (with turbulence parameterizations or in large-eddy
resolving mode) to address the coupling between orographic gravity waves and turbulence. The
ultimate goal of the study is to provide constraints for parameterized mixing in climate models and
establish foundations for coupling the turbulence and gravity wave parameterizations.
How to cite: Bartoň, Š. and Šácha, P.: Systematic analysis of flow-orography interactionin idealized numerical simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3384, https://doi.org/10.5194/egusphere-egu26-3384, 2026.
Various studies have shown that low-level drag in the atmosphere is parametrised inconsistently across the world’s numerical weather prediction and climate models, ultimately due to a lack of constraints on the underlying physical processes and the overlap in scale between them. Trapped lee waves (TLWs) are not parametrised in most models but have been shown in theoretical and case studies to produce significant drag (necessarily at low levels) on the atmosphere under the right conditions. To investigate whether TLWs contribute to low-level drag consistently, the resolved momentum fluxes in the archived analyses of the TLW-resolving UKV model are calculated and compared to the resolved plus parametrised gravity wave fluxes in the coarse-resolution, global version of the Met Office Unified Model (MetUM), which does not resolve TLWs.
The comparison between the models reveals that gravity wave momentum fluxes in the UKV model are about double that of the global MetUM in the mid-troposphere and up to four times that in the boundary layer. Only a portion of this discrepancy in momentum fluxes can be explained by the presence of trapped lee wave modes, which are found using a numerical solver of the Taylor-Goldstein equation. The other part is likely to be caused by orographic gravity waves that are reflected due to the general decrease of the Scorer parameter with altitude (and are distinct from the resonant TLWs). This work therefore demonstrates that the inclusion of the drag produced by both reflected and trapped lee waves would alleviate the current issues with low-level drag parametrisation.
How to cite: Houtman, H., Teixeira, M., Gray, S., Sheridan, P., Vosper, S., and van Niekerk, A.: Missing drag due to orographic gravity waves in a global numerical weather prediction model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10153, https://doi.org/10.5194/egusphere-egu26-10153, 2026.
Extreme precipitation is crucial for hydrological cycle and water resources, and has increased over many regions in recent decades. However, simulating and projecting precipitation extremes remain challenging over complex terrains, such as the Tibetan Plateau (TP). In this study, we evaluate the performance of the kilometer-scale (3.3 km) convection-permitting ICON model in simulating summer daily precipitation characteristics and extremes over the TP and project its future changes, focusing on the comparison with coarser-resolution CMIP6 models. ICON reasonably reproduces the observed daily precipitation characteristics, reducing the bias by ~80–95% for dry day frequency and precipitation-event persistence compared to ERA5 and the CMIP6 ensemble, and substantially lowering biases in extreme precipitation. For future projections, both ICON and CMIP6 project qualitatively consistent signals, including increasing extreme precipitation over almost the entire TP and, over the southeastern TP, increasing dry-day frequency and more frequent but shorter precipitation events. Despite consistent signs, ICON suggests an overall drier future over the southeastern TP than CMIP6, characterized by larger increases in dry days, smaller increases in extreme precipitation and event frequency, and a larger reduction in event duration. The systematic drier future in ICON compared to CMIP6 are linked to projected weakened low-level southwesterlies south of the TP, which suppress moisture transport into the interior southeastern TP and thus, reduce both daily and extreme precipitation. As water from southeastern TP affects downstream populations closely, these results are expected to provide more reliable projections for future risk assessments.
How to cite: Chen, Y., Zhang, W., Zou, L., and Zhou, T.: Convection-Permitting Projections of Summer Extreme Precipitation Over the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4643, https://doi.org/10.5194/egusphere-egu26-4643, 2026.
Complex terrain poses significant challenges for Numerical Weather Prediction (NWP) models, particularly in capturing localized boundary layer phenomena such as thermal circulations, katabatic flows, and temperature inversions. This study focuses on the Pyrenees mountain range, a region where accurate high-resolution forecasting is critical for understanding local weather extremes and variability, especially during synoptically quiescent conditions.
As part of a doctoral research project integrating Artificial Intelligence with high-resolution NWP, this work presents the foundational optimization of the Weather Research and Forecasting (WRF) model (v4.6.1). The modeling setup utilizes a one-way nested domain configuration bridging synoptic scales down to turbulence-resolving resolutions (333 m and 111 m LES), driven by ERA5 and GFS boundary conditions. We hypothesize that standard static input data provided by default in the WRF Preprocessing System (WPS) are insufficient to resolve the intricate surface heterogeneity of the Pyrenees. To address this, we conduct sensitivity experiments comparing the default USGS/MODIS configurations against enhanced high-resolution static datasets: 1-arc-second (~30 m) SRTM topography and the 100 m Copernicus Global Land Cover (CGLS-LC100). We evaluate the model’s performance in reproducing key local effects, focusing on surface wind fields, valley-floor cold pools, and thermal gradients under stable stratification.
Preliminary results quantify the bias reduction achieved by updating surface boundary conditions, establishing a robust baseline configuration. These findings are a prerequisite for subsequent full Large Eddy Simulations (LES) and the development of AI-driven bias correction schemes aimed at reducing computational costs while preserving accuracy in complex terrain.
This research has been funded by projects ARTEMIS (PID2021-124253OB-I00) and LIFE22-IPC-ES-LIFE PYRENEES4CLIMA.
How to cite: Toledano Rubí, A.: High-resolution WRF modeling in the Pyrenees: Sensitivity to static data for complex terrain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21196, https://doi.org/10.5194/egusphere-egu26-21196, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot 5
EGU26-9211 | ECS | Posters virtual | VPS2
Kilometer Scale Climate Modeling of Extremes: Evaluation of the NonHydrostatic Regional Climate Model (NHRCM) over PakistanMon, 04 May, 14:30–14:33 (CEST) vPoster spot 5
Rising global warming is accelerating climate extremes at regional scales worldwide. Capturing these extremes at the regional level requires high resolution climate modeling capable of representing complex topography and strong land atmosphere interactions. In this study, a high-resolution Non-Hydrostatic Regional Climate Model (NHRCM) is configured at a kilometer scale resolution (5 km) through dynamic downscaling of the MRI AGCM 3.2 outputs developed by the Meteorological Research Institute of Japan (MRI). Daily precipitation and temperature (maximum and minimum) data are generated at 5 km resolution for the historical period (1980–2000) and the future period (2081–2100) under the high emission climate scenario SSP585. The performance of the downscaled climate variables is evaluated against ERA5 Land data after resampling to the same resolution as the NHRCM. Statistical metrics and extreme climate indices are used to quantify model skill and biases at regional and sub regional scales over Pakistan. The results reveal a strong correlation in high elevation regions of Pakistan compared to the plains. After evaluating model performance, precipitation and temperature extreme indices are calculated for both historical and future periods. The findings indicate an increase in precipitation in the southern regions of Pakistan, accompanied by rising temperatures. These trends are also associated with an increase in short-duration intense rainfall events during summer and prolonged dry conditions in winter. Furthermore, the frequency of heatwaves is expected to rise by the end of the century across Pakistan, along with increasing temperatures in snow fed regions. Overall, this study highlights the added value of high-resolution nonhydrostatic regional climate modeling in understanding and assessing climate extremes over Pakistan, providing a robust foundation for future climate impact assessments and adaptation planning.
How to cite: Mamoor, M., Rahim, A., Yaseen, M., Naz, R., Kosar, T., Tariq, N., and Akif, A.: Kilometer Scale Climate Modeling of Extremes: Evaluation of the NonHydrostatic Regional Climate Model (NHRCM) over Pakistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9211, https://doi.org/10.5194/egusphere-egu26-9211, 2026.
