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Abstract. This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA). The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics. However, the database generated has great potential for understanding other environmental processes from a hydrometeorological or ecological perspective in which snow dynamics play a determinant role.

Although the mean environmental lapse rate (MELR) value (a linear decrease of −6.5 °C/km) is the most widely used, near‐surface (i.e., non‐free atmosphere) air temperature lapse rates (NSLRs; measured at ~1.5 m height) are variable in space and time because of their dependence on topography and meteorological conditions. In this study we conducted the first analysis of the spatial and temporal variability of NSLRs for continental Spain and their relationship to synoptic atmospheric circulation (circulation weather types [CWTs]), focusing on major mountain areas including the Pyrenees, Cantabrian, Central, Baetic, and Iberian ranges. The results showed that the NSLR varied markedly at spatial and seasonal scales and depended on the dominant atmospheric conditions. The median NSLR values were weaker (less negative) than the MELR for the mountain areas (Pyrenees −5.17 °C/km; Cantabrian range −5.22 °C/km; Central range −5.78 °C/km; Baetic range −4.83 °C/km; Iberian range −5.79 °C/km) and for the entire continental Spain (−5.28 °C/km). For the entire continental Spain the steepest NSLR values were found in April (−5.80 °C/km), May (−5.58 °C/km), and October (−5.54 °C/km) because of the dominance of northerly and westerly advections of cold air. The weakest NSLR values were found in July (−4.67 °C/km) and August (−4.78 °C/km) because of the inland heating, and in winter because of the occurrence of thermal inversions. As the use of the MELR involves the assumption of large errors, we propose 1 zonal, 12 monthly, 11 CWTs, and 132 hybrid monthly–CWTs NSLRs for each of the mountain ranges and for the entire continental Spain. More regional studies are urgently needed to accurately assess the NSLR as a function of atmospheric circulation conditions.

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: instrumental bias and variability induced by observers. Hydrological Processes.

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

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The presence of a seasonal snowpack determines the hydrology, geomorphology and ecology of wide parts of the Iberian Peninsula, with strong implications for the economy, transport and risk management. Thus, reliable information on snow is necessary from a scientific and operational point of view. This is the case of the Iberian Peninsula where, lack of observation has impeded proper analysis of snowpack duration, magnitude and interannual variability. In this study, we present the first snow climatology of the entire Iberian Peninsula. The scarcity of in situ observations has been overcome, using a newly developed remote sensing snow database from MODIS satellite sensors for the period 2000–2014 and a physically based snow model (Factorial Snow Model—FSM), driven by a regional atmospheric model (Weather Research and Forecast model—WRF) over the Iberian Peninsula for the period 1980–2014. The snowpack of the main mountain areas (Pyrenees, Cantabrian, Central, Iberian range and Sierra Nevada) are described, estimated from the generated databases. The information has been processed using a k‐means cluster algorithm, looking for similarities in snow indices at different elevation bands. Results show four different types of snowpack in terms of depth, duration and interannual variability, lying over different elevation bands in the different ranges, proving the variability of the snowpack over Iberia. Analyses reveal areas characterized by ephemeral snowpacks, while in some sectors snowpack lasts, on average, 198 days per year with 3.02 m of peak snow depth. The coefficient of variation of interannual peak snow depth oscillated between 35.2 and 162.4%. All the analysed indices show that at common elevations the Cantabrian range and the Pyrenees host the deepest and longest lasting snowpacks, followed by the Central and Iberian ranges. The Sierra Nevada exhibits the shortest, shallowest snowpack and more year‐to‐year variability.

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