Detection of precipitation and snow cover trends in the the European Alps over the last century using model and observational data

2020 ◽  
Author(s):  
Julien Beaumet ◽  
Martin Menegoz ◽  
Hubert Gallée ◽  
Vincent Vionnet ◽  
Xavier Fettweis ◽  
...  
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Observational Data ◽  
Snow Depth ◽  
European Alps ◽  
Temperature Changes ◽  
In Situ Observations ◽  
Precipitation Changes ◽  
High Elevations

<p><span>The European Alps are particularly sensitive to climate change. Compared to temperature, changes in precipitation are more challenging to detect and attribute to ongoing anthropic climate change </span><span>mainly </span><span>as a result of large inter-annual variability, </span><span>lack of reliable measurements at high elevations</span><span> and opposite signals depending on the season or the elevation considered. However, changes in precipitation and snow cover have significant socio-environmental impact mostly trough water resource availability. These changes are investigated within the framework of the Trajectories initiative (</span><span><span></span></span><span>). The variability and changes in precipitation and snow cover in the European Alps has been simulated with the MAR regional climate model at a 7 km horizontal resolution driven by ERA20C (1902-2010) and ERA5 (1979-2018) reanalyses. </span></p><p><span>For precipitation, MAR outputs were compared with EURO-4M, SAFRAN, SPAZM and E-OBS reanalyses as well as in-situ observations. The model was shown to reproduce correctly seasonal and inter-annual variability. The spatial biases of the model have the same order of magnitude as the differences between the three observational data sets. Model experiment has been used to detect precipitation changes over the last century. An increase in winter precipitation is simulated over the North-western part of the Alps at high altitudes (>1500m). Significant decreases in summer precipitation were found in many low elevation areas, especially the Po Plain while no significant trends where found at high elevations. Because of large internal variability, precipitation changes are significant (pvalue<0.05) only when considering their evolution over long period, typically 60-100 years in both model and observations.</span></p><p><span>Snow depth and water equivalent (SWE) in the French Alps simulated with MAR have been compared to the SAFRAN-Crocus reanalyses and to in-situ observations. MAR was found to simulate a realistic distribution of SWE as function of the elevation in the French Alpine massifs, although it underestimates SWE at low elevations in the Pre-Alps. Snow cover over the whole European Alps is evaluated using MODIS satellite data. Finally, trends in snow cover and snow depth are highlighted as well as their relationships with the precipitation and temperature changes over the last century. </span></p>

scholarly journals Spatial–Temporal Variability of Snow Cover and Depth in the Qinghai–Tibetan Plateau

2017 ◽  
Vol 30 (4) ◽  
pp. 1521-1533 ◽  
Author(s):  
Wenfang Xu ◽  
Lijuan Ma ◽  
Minna Ma ◽  
Haicheng Zhang ◽  
Wenping Yuan
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Snow Cover ◽  
Temporal Variability ◽  
Snow Depth ◽  
Initial Increase ◽  
Spatial And Temporal Variability ◽  
Important Challenge ◽  
In Situ Observations

Abstract Changes in snow cover over the Qinghai–Tibetan Plateau have attracted much attention in recent years owing to climate change. Because of the limitations of in situ observations, only a few studies have analyzed the dynamics of snow cover. Using observations from 103 meteorological stations across the Qinghai–Tibetan Plateau, this study investigated the spatial and temporal variability of snow depth and the number of snow-cover days. The results show a very weak negative trend for the snow depth and the number of snow-cover days in spring and winter from 1961 to 2010, but two different trends were found: an initial increase followed by a decrease. In summer and autumn, snow depth and the number of snow-cover days show a significant decreasing trend for most sites. The duration of snow cover exhibits a significant decreasing trend (−3.5 ± 1.2 days decade−1), which was jointly controlled by a later snow starting time (1.6 ± 0.8 days decade−1) and an earlier snow ending time (−1.9 ± 0.8 days decade−1) consistent with a response to climate change. This study highlights the competing effects of rising temperatures and changing precipitation, which remain an important challenge in understanding and interpreting the observed changes in snow depth and the number of snow-cover days for the Qinghai–Tibetan Plateau.

Future Climate Change in the European Alps

2020 ◽  
Author(s):  
Andreas Gobiet ◽  
Sven Kotlarski
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Precipitation Extremes ◽  
Convective Precipitation ◽  
Future Climate Change ◽  
European Alps ◽  
Strong Wind ◽  
Alpine Area ◽  
Natural Snow ◽  
Precipitation Changes

The analysis of state-of-the-art regional climate projections indicates a number of robust changes of the climate of the European Alps by the end of this century. Among these are a temperature increase in all seasons and at all elevations and a significant decrease in natural snow cover. Precipitation changes, however, are more subtle and subject to larger uncertainties. While annual precipitation sums are projected to remain rather constant until the end of the century, winter precipitation is projected to increase. Summer precipitation changes will most likely be negative, but increases are possible as well and are covered by the model uncertainty range. Precipitation extremes are expected to intensify in all seasons. The projected changes by the end of the century considerably depend on the greenhouse-gas scenario assumed, with the high-end RCP8.5 scenario being associated with the most prominent changes. Until the middle of the 21st century, however, it is projected that climate change in the Alpine area will only slightly depend on the specific emission scenario. These results indicate that harmful weather events in the Alpine area are likely to intensify in future. This particularly refers to extreme precipitation events, which can trigger flash floods and gravitational mass movements (debris flows, landslides) and lead to substantial damage. Convective precipitation extremes (thunderstorms) are additionally a threat to agriculture, forestry, and infrastructure, since they are often associated with strong wind gusts that cause windbreak in forests and with hail that causes damage in agriculture and infrastructure. Less spectacular but still very relevant is the effect of soil erosion on inclined arable land, caused by heavy precipitation. At the same time rising temperatures lead to longer vegetation periods, increased evapotranspiration, and subsequently to higher risk of droughts in the drier valleys of the Alps. Earlier snowmelt is expected to lead to a seasonal runoff shift in many catchments and the projected strong decrease of the natural snow cover will have an impact on tourism and, last but not least, on the cultural identity of many inhabitants of the Alpine area.

scholarly journals Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations

2019 ◽  
Vol 13 (8) ◽  
pp. 2221-2239 ◽  
Author(s):  
Yvan Orsolini ◽  
Martin Wegmann ◽  
Emanuel Dutra ◽  
Boqi Liu ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Operating Conditions ◽  
The Tibetan Plateau ◽  
Critical Surface ◽  
High Altitudes ◽  
Medium Range ◽  
In Situ Observations

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.

scholarly journals Evaluation of snow depth and snow-cover over the Tibetan Plateau in global reanalyses using in-situ and satellite remote sensing observations

2019 ◽  
Author(s):  
Yvan Orsolini ◽  
Martin Wegmann ◽  
Emanuel Dutra ◽  
Boqi Liu ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Global Climate ◽  
Operating Conditions ◽  
The Tibetan Plateau ◽  
Critical Surface ◽  
High Altitudes ◽  
In Situ Observations

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole and, is the world highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impacts on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric re-analyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global re-analyses with satellite and in-situ data. Yet, snow in re-analyses provides critical surface information for forecast systems from the medium to sub-seasonal time scales. Here, snow depth and snow cover from 5 recent global reanalysis products are inter-compared over the TP region, and evaluated against a set of 33 in-situ station observations, as well as against the Interactive Multi-sensor Snow and Ice Mapping System (or IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in-situ observations provides confidence in the station data despite the relative paucity of in-situ measurement sites and the harsh operating conditions. While several re-analyses show a systematic over-estimation of the snow depth or snow cover, the reanalyses that assimilate local in-situ observations or IMS snow-cover are better capable of representing the shallow, transient snowpack over the TP region. The later point is clearly demonstrated by examining the family of re-analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF), of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. One missing process in the re-analyses is the blown snow sublimation, which seems important in the dry, windy and cold conditions of the TP. By incorporating a simple parametrisation of this process in the ECMWF land re-analysis, the positive snow bias is somewhat alleviated. Future snow reanalyses that optimally combine the use of satellite snow cover and in-situ snow-depth observations over the Tibetan Plateau region in the assimilation and analysis cycles, along with improved representation of snow processes, have the potential to substantially improve weather and climate prediction and water resources applications.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Mediterranean Sea climatic indices: monitoring long term variability and climate changes

2018 ◽  
Author(s):  
Athanasia Iona ◽  
Athanasios Theodorou ◽  
Sarantis Sofianos ◽  
Sylvain Watelet ◽  
Charles Troupin ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Mediterranean Sea ◽  
Climate Changes ◽  
Salt Content ◽  
Climatic Indices ◽  
In Situ Observations ◽  
The Mediterranean

Abstract. We present a new product composed of a set of thermohaline climatic indices from 1950 to 2015 for the Mediterranean Sea such as decadal temperature and salinity anomalies, their mean values over selected depths, decadal ocean heat and salt content anomalies at selected depth layers as well as their long times series. It is produced from a new high-resolution climatology of temperature and salinity on a 1/8° regular grid based on historical high quality in situ observations. Ocean heat and salt content differences between 1980–2015 and 1950–1979 are compared for evaluation of the climate shift in the Mediterranean Sea. The spatial patterns of heat and salt content shifts demonstrate in greater detail than ever before that the climate changes differently in the several regions of the basin. Long time series of heat and salt content for the period 1950 to 2015 are also provided which indicate that in the Mediterranean Sea there is a net mean volume warming and salting since 1950 with acceleration during the last two decades. The time series also show that the ocean heat content seems to fluctuate on a cycle of about 40 years and seems to follow the Atlantic Multidecadal Oscillation climate cycle indicating that the natural large scale atmospheric variability could be superimposed on to the warming trend. This product is an observations-based estimation of the Mediterranean climatic indices. It relies solely on spatially interpolated data produced from in-situ observations averaged over decades in order to smooth the decadal variability and reveal the long term trends with more accuracy. It can provide a valuable contribution to the modellers' community, next to the satellite-based products and serve as a baseline for the evaluation of climate-change model simulations contributing thus to a better understanding of the complex response of the Mediterranean Sea to the ongoing global climate change. The product is available here: https://doi.org/10.5281/zenodo.1210100.

scholarly journals Validation of MODIS snow cover images over Austria

2006 ◽  
Vol 3 (4) ◽  
pp. 1569-1601 ◽  
Author(s):  
J. Parajka ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Classification Accuracy ◽  
Snow Depth ◽  
Seasonal Pattern ◽  
Acquisition Time ◽  
Spatial And Temporal Variability ◽  
Temporal Shift ◽  
Early Afternoon ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon).

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