This study investigated the temporal variability and changes in snow cover duration and the average snow depth from December to April in the Pyrenees at 1,500 and 2,100 m a.s.l. for the period 1958–2017. This is the first such analysis for the entire mountain range using SAFRAN‐Crocus simulations run for this specific purpose. The SAFRAN‐Crocus simulations were evaluated for the period 1980–2016 using 28 in situ snow depth data time series, and for the period 2000–2017 using MODIS observations of the snow cover duration. Following confirmation that the simulated snow series satisfactorily reproduced the observed evolution of the snowpack, the Mann–Kendall test showed that snow cover duration and average depth decreased during the full study period, but this was only statistically significant at 2,100 m a.s.l. The temporal evolution in the snow series indicated marked differences among massifs, elevations, and snow variables. In general, the most western massifs of the French Pyrenees underwent a greater decrease in the snowpack, while in some eastern massifs the snowpack did not decrease, and in some cases increased at 1,500 m a.s.l. The results suggest that the trends were consistent over time, as they were little affected by the start and end year of the study period, except if trends are computed only starting after 1980, when no significant trends were apparent. Most of the observed negative trends were not correlated with changes in the atmospheric circulation patterns during the snow season. This suggests that the continuous warming in the Pyrenees since the beginning of the industrial period, and particularly the sharp increase since 1955, is a major driver explaining the snow cover decline in the Pyrenees.

Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open-source, community-driven project dedicated to snow data assimilation, which makes it difficult to compare existing algorithms and fragments development efforts. Here we introduce a new data assimilation toolbox, the Multiple Snow Data Assimilation System (MuSA), to help fill this gap. MuSA was developed to fuse remotely sensed information that is available at different timescales with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e., the number of particles and their prior covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (central Pyrenees). Furthermore, we conducted a joint-assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single-cell experiment. In light of these experiments, we discuss the pros and cons of the assimilation algorithms, including their computational cost.

Pyrenean glaciers are the largest in southern Europe. Their survival is threatened by climate change, highlighting the significance of their study. This research presents an assessment of changes in the glacierized area and thickness of Pyrenean glaciers from 2011 to 2020, using high‐resolution optical satellite, airborne lidar and UAV images. The total glacierized area has shrunk by 23.2% and thickness has decreased on average by 6.3 m. These two variables show no correlation for individual glaciers. Although climatic conditions do not vary much among glaciers, their evolution was heterogeneous during the study period. The smaller glaciers (10 ha) have a more homogeneous response. This can be attributed to the generally larger influence of local topography on the response of the smaller Pyrenean glaciers. There is no sign of slowdown in glacier shrinkage respect to previous decades.

This paper analyzes the evolution of the Monte Perdido Glacier, the third largest glacier in the Pyrenees, from 1981 to the present. We assessed the evolution of the glacier's surface area by analysis of aerial photographs from 1981, 1999, and 2006, and changes in ice volume by geodetic methods with digital elevation models (DEMs) generated from topographic maps (1981 and 1999), airborne lidar (2010) and terrestrial laser scanning (TLS, 2011, 2012, 2013, and 2014) data. We interpreted the changes in the glacier based on climate data from nearby meteorological stations. The results indicate that the degradation of this glacier accelerated after 1999. The rate of ice surface loss was almost three times greater during 1999–2006 than during earlier periods. Moreover, the rate of glacier thinning was 1.85 times faster during 1999–2010 (rate of surface elevation change = −8.98 ± 1.80 m, glacier-wide mass balance = −0.73 ± 0.14 m w.e. yr⁻¹) than during 1981–1999 (rate of surface elevation change = −8.35 ± 2.12 m, glacier-wide mass balance = −0.42 ± 0.10 m w.e. yr⁻¹). From 2011 to 2014, ice thinning continued at a slower rate (rate of surface elevation change = −1.93 ± 0.4 m yr⁻¹, glacier-wide mass balance = −0.58 ± 0.36 m w.e. yr⁻¹). This deceleration in ice thinning compared to the previous 17 years can be attributed, at least in part, to two consecutive anomalously wet winters and cool summers (2012–2013 and 2013–2014), counteracted to some degree by the intense thinning that occurred during the dry and warm 2011–2012 period. However, local climatic changes observed during the study period do not seem sufficient to explain the acceleration of ice thinning of this glacier, because precipitation and air temperature did not exhibit statistically significant trends during the study period. Rather, the accelerated degradation of this glacier in recent years can be explained by a strong disequilibrium between the glacier and the current climate, and likely by other factors affecting the energy balance (e.g., increased albedo in spring) and feedback mechanisms (e.g., heat emitted from recently exposed bedrock and debris covered areas).