Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world’s water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded datasets (observations, reanalyses and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency towards enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high vs. low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded datasets (CRU, GISTEMP, GPCC, ERA5, CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in mid-latitudes. Agreement on elevation-dependent changes between gridded datasets is weak for temperature but stronger for precipitation.

We study the properties of precipitation in the Hindu‐Kush Karakoram Himalaya (HKKH) region using currently available data sets. We consider satellite rainfall estimates (Tropical Rainfall Measuring Mission), reanalyses (ERA‐Interim), gridded in situ rain gauge data (Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, Climate Research Unit, and Global Precipitation Climatology Centre), and a merged satellite and rain gauge climatology (Global Precipitation Climatology Project). The data are compared with simulation results from the global climate model EC‐Earth. All data sets, despite having different resolutions, coherently reproduce the mean annual cycle of precipitation in the western and eastern stretches of the HKKH. While for the Himalaya only a strong summer precipitation signal is present, associated with the monsoon, the data indicate that the Hindu‐Kush Karakoram, which is exposed to midlatitude “western weather patterns”, receives water inputs in winter. Time series of seasonal precipitation confirm that the various data sets provide a consistent measurement of interannual variability for the HKKH. The longest observational data sets indicate a statistically significant decreasing trend in Himalaya during summer. None of the data sets gives statistically significant precipitation trends in Hindu‐Kush Karakoram during winter. Precipitation data from EC‐Earth are in good agreement with the climatology of the observations (rainfall distribution and seasonality). The evolution of precipitation under two different future scenarios (RCP 4.5 and RCP 8.5) reveals an increasing trend over the Himalaya during summer, associated with an increase in wet extremes and daily intensity and a decrease in the number of rainy days. Unlike the observations, the model shows an increasing precipitation trend also in the period 1950–2009, possibly as a result of the poor representation of aerosols in this type of GCMs.

The enhancement of warming rates with elevation, so-called elevation-dependent warming (EDW), is one of the regional, still not completely understood, expressions of global warming. Sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming trends in the recent decades, leading to serious impacts on mountain ecosystems and downstream. In this paper we use a state-of-the-art Global Climate Model (EC-Earth) to investigate the impact of model spatial resolution on the representation of this phenomenon and to highlight possible differences in EDW and its causes in different mountain regions of the Northern Hemisphere. To this end we use EC-Earth climate simulations at five different spatial resolutions, from \(\sim\) 125 to \(\sim\) 16 km, to explore the existence and the driving mechanisms of EDW in the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau–Himalayas. Our results show that the more frequent EDW drivers in all regions and seasons are the changes in albedo and in downward thermal radiation and this is reflected in both daytime and nighttime warming. In the Tibetan Plateau-Himalayas and in the Greater Alpine Region, an additional driver is the change in specific humidity. We also find that, while generally the model shows no clear resolution dependence in its ability to simulate the existence of EDW in the different regions, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions. Moreover, we find that the role of internal climate variability can be significant in modulating the EDW signal, as suggested by the spread found in the multi-member ensemble of the EC-Earth experiments which we use.

The results of a comparison exercise of radiative transfer models (RTM) of various international research groups for Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) viewing geometry are presented. Besides the assessment of the agreement between the different models, a second focus of the comparison was the systematic investigation of the sensitivity of the MAX-DOAS technique under various viewing geometries and aerosol conditions. In contrast to previous comparison exercises, box-air-mass-factors (box-AMFs) for different atmospheric height layers were modelled, which describe the sensitivity of the measurements as a function of altitude. In addition, radiances were calculated allowing the identification of potential errors, which might be overlooked if only AMFs are compared. Accurate modelling of radiances is also a prerequisite for the correct interpretation of satellite observations, for which the received radiance can strongly vary across the large ground pixels, and might be also important for the retrieval of aerosol properties as a future application of MAX-DOAS. The comparison exercises included different wavelengths and atmospheric scenarios (with and without aerosols). The strong and systematic influence of aerosol scattering indicates that from MAX-DOAS observations also information on atmospheric aerosols can be retrieved. During the various iterations of the exercises, the results from all models showed a substantial convergence, and the final data sets agreed for most cases within about 5%. Larger deviations were found for cases with low atmospheric optical depth, for which the photon path lengths along the line of sight of the instrument can become very large. The differences occurred between models including full spherical geometry and those using only plane parallel approximation indicating that the correct treatment of the Earth's sphericity becomes indispensable. The modelled box-AMFs constitute an universal data base for the calculation of arbitrary (total) AMFs by simple convolution with a given trace gas concentration profile. Together with the modelled radiances and the specified settings for the various exercises, they can serve as test cases for future RTM developments.

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003-2011, and the RainFARM technique to generate downscaled synthetic rainfall fields from regional climate model projections for the 14-year calibration period 2002-2015, and for the 40-year projection period 2010-2049. Using a specific algorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dry periods of no or negligible amount of rain, from the measured and the synthetic rainfall series. Then, we used the selected rainfall events to forcethe Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model TRIGRS v. 2.1. We analyzed the results in terms of variations (or lack of variations) in the rainfall thresholds for the possible initiation of landslides, in the probability distribution of landslide size (area), and in landslide hazard. Results showed that the downscaled rainfall fields obtained by RainFARM can be used to single out rainfall events, and to force the slope stability model. Results further showed that while the rainfall thresholds for landslide occurrence are expected to change in future scenarios, the probability distribution of landslide areas are not. We infer that landslide hazard in the study area is expected to change in response to the projected variations in the rainfall conditions. We expect our results to contribute to regional investigations of the expected impact of projected climate variations on slope stability conditions and on landslide hazards.

We use the output of twenty-seven Global Climate Models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5) to investigate the temperature changes and their dependence on the elevation in the Tibetan Plateau, Himalaya and Karakoram mountains and in the surrounding areas in historical model simulations and in future projections. The aim of this study is to explore if and to what extent the CMIP5 models show elevation-dependent warming (EDW) in this part of the globe and to investigate what are the driving factors at play and their relative importance. Our results indicate that the models show enhanced rates of warming at higher elevations in the Tibetan Plateau-Himalayan region in the twentieth century, and this phenomenon is projected to strengthen by the end of the twenty-first century under a high-emission scenario. We find a nonlinear relationship between the warming rates and the elevation, for both the minimum and the maximum temperature: regions with temperatures below the freezing level of water show more warming than the regions with temperatures above, likely suggesting a key role of mechanisms involving water phase changes, the presence/absence of snow and the snow-albedo feedback. We consider the main variables simulated by the CMIP5 models whose change may be related to temperature changes at higher elevations. We find that changes in surface albedo, atmospheric humidity and downward longwave radiation are relevant factors for EDW in the Tibetan Plateau-Himalayas, with surface albedo being the leading driver.