. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of
snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany,
Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a
principal component analysis and k -means clustering, we identified five main modes of variability and five regions which match the climatic
forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between
1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to
May) mean snow depth was − 8.4  % per decade, for seasonal maximum snow depth − 5.6  % per decade, and for seasonal snow
cover duration − 5.6  % per decade. Stronger and more significant trends were observed for periods and elevations where the transition
from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same
elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of
station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

/pdf/observed-snow-depth-trends-in-the-european-alps-1971-to-2019-2llg8yq7up.pdf

Observed snow depth trends in the European Alps: 1971 to 2019

/pdf/a-multi-century-meteo-hydrological-analysis-for-the-adda-1ofq4vdgeq.pdf

A multi‐century meteo‐hydrological analysis for the Adda river basin (Central Alps). Part I: gridded monthly precipitation (1800–2016) records

/pdf/high-resolution-monthly-precipitation-climatologies-over-405kjcswdi.pdf

High‐resolution monthly precipitation climatologies over Norway (1981–2010): Joining numerical model data sets and in situ observations

Weather forecasts over mountainous terrain are challenging due to the complex topography that is necessarily smoothed by actual local-area models. As complex mountainous territories represent 20% of the Earth’s surface, accurate forecasts and the numerical resolution of the interaction between the surface and the atmospheric boundary layer are crucial. We present an assessment of the Weather Research and Forecasting model with two different grid spacings (1 km and 0.5 km), using two topography datasets (NASA Shuttle Radar Topography Mission and Global Multi-resolution Terrain Elevation Data 2010, digital elevation models) and four land-cover-description datasets (Corine Land Cover, U.S. Geological Survey land-use, MODIS30 and MODIS15, Moderate Resolution Imaging Spectroradiometer land-use). We investigate the Ortles Cevadale region in the Rhaetian Alps (central Italian Alps), focusing on the upper Forni Glacier proglacial area, where a micrometeorological station operated from 28 August to 11 September 2017. The simulation outputs are compared with observations at this micrometeorological station and four other weather stations distributed around the Forni Glacier with respect to the latent heat, sensible heat and ground heat fluxes, mixing-layer height, soil moisture, 2-m air temperature, and 10-m wind speed. The different model runs make it possible to isolate the contributions of land use, topography, grid spacing, and boundary-layer parametrizations. Among the considered factors, land use proves to have the most significant impact on results.

/pdf/land-use-improvements-in-the-weather-research-and-37tgqch1vu.pdf

Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes

Fine-resolution precipitation mapping over Syria using local regression and spatial interpolation

Mountain environments are extremely influenced by climate change but are also often affected by the lack of long and high-quality meteorological data, especially in glaciated areas, which limits the ability to investigate the acting processes at local scale. For this reason, we checked a method to reconstruct high-resolution spatial distribution and temporal evolution of precipitation. The study area is centred on the Forni Glacier area (Central Italian Alps), where an automatic weather station is present since 2005. We set up a model based on monthly homogenised precipitation series and we spatialised climatologies and anomalies on a 30-arc-second-resolution DEM, using Local Weighted Linear Regression (LWLR) and Regression Kriging (RK) of precipitation versus elevation, in order to test the most suitable approach for this complex terrain area. The comparison shows that LWLR has a better reconstruction ability for winter while RK slightly prevails during summer. The results of precipitation spatialisation were compared with station observations and with data collected at the weather station on Forni Glacier, which were not used to calibrate the model. A very good agreement between observed and modelled precipitation records was pointed out for most station sites. The agreement is lower, but encouraging, for Forni Glacier station data.

/pdf/high-resolution-monthly-precipitation-fields-1913-2015-over-3uw230z8ih.pdf

Pelfini Manuela

Papers

High-Resolution Monthly Precipitation Fields (1913–2015) over a Complex Mountain Area Centred on the Forni Valley (Central Italian Alps)