scholarly journals The Swiss snow climatology as seen by reanalyses and further gridded datasets

2021 ◽  
Author(s):  
Monika Goeldi ◽  
Stefanie Gubler ◽  
Christian Steger ◽  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Data Sets ◽  
Data Set ◽  
Climate Services ◽  
Snow Cover Extent ◽  
Snow Water

<p>Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.</p><p>In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.</p><p>The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations – e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.</p>

scholarly journals Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

scholarly journals Snow observations in Mount-Lebanon (2011–2016)

2017 ◽  
Author(s):  
Abbas Fayad ◽  
Simon Gascoin ◽  
Ghaleb Faour ◽  
Pascal Fanise ◽  
Laurent Drapeau ◽  
...  
Keyword(s):  
Snow Cover ◽  
Mediterranean Climate ◽  
Snow Water Equivalent ◽  
Current Model ◽  
High Mountain ◽  
Snow Density ◽  
Data Set ◽  
Study Region ◽  
Snow Cover Extent ◽  
Snow Water

Abstract. We present a unique meteorological and snow observational dataset in Mount-Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of: (1) continuous meteorological and snow height observations; (2) snowpack field measurements; and (3) medium resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA 2296 m, LAQ 1840 m, and CED 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 minute intervals for the snow seasons (November–June) between 2011 and 2016 for MZA and 2014–2016 for CED and LAQ. Precipitation data was filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons 2014–2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011–2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount-Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m−3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200–2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this data set bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the Eastern Mediterranean. The doi for the data is: 10.5281/zenodo.291405.

Links between spring snow cover and long-term alpine vegetation change

2020 ◽  
Author(s):  
Shengwei Zong ◽  
Christian Rixen
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Plant Species ◽  
Climate Warming ◽  
Vegetation Change ◽  
Alpine Vegetation ◽  
Alpine Ecosystems ◽  
Snow Cover Extent ◽  
The Relationship

<p><span>Snow is an important environmental factor determining distributions of plant species in alpine ecosystems. During the past decades, climate warming has resulted in significant reduction of snow cover extent globally, which led to remarkable alpine vegetation change. Alpine vegetation change is often caused by the combined effects of increasing air temperature and snow cover change, yet the relationship between snow cover and vegetation change is currently not fully understood. To detect changes in both snow cover and alpine vegetation, a relatively fine spatial scales over long temporal spans is necessary. In this study in alpine tundra of the Changbai Mountains, Northeast China, we (1) quantified spatiotemporal changes of spring snow cover area (SCA) during half a century by using multi-source remote sensing datasets; (2) detected long-term vegetation greening and browning trends at pixel level using Landsat archives of 30 m resolution, and (3) analyzed the relationship between spring SCA change and vegetation change. Results showed that spring SCA has decreased significantly during the last 50 years in line with climate warming. Changes in vegetation greening and browning trend were related to distributional range dynamics of a dominant indigenous evergreen shrub <em>Rhododendron aureum</em>, which extended at the leading edge and retracted at the trailing edge. Changes in <em>R. aureum</em> distribution were probably related to spring snow cover changes. Areas with decreasing <em>R. aureum</em> cover were often located in snow patches where probably herbs and grasses encroached from low elevations and adjacent communities. Our study highlights that spring SCA derived from multi-source remote sensing imagery can be used as a proxy to explore relationship between snow cover and vegetation change in alpine ecosystems. Alpine indigenous plant species may migrate upward following the reduction of snow-dominated environments in the context of climate warming and could be threatened by encroaching plants within snow bed habitats.</span></p>

scholarly journals Spatiotemporal variation of snow depth in the Northern Hemisphere from 1992 to 2016

2019 ◽  
Author(s):  
Xiongxin Xiao ◽  
Tingjun Zhang ◽  
Xinyue Zhong ◽  
Xiaodong Li ◽  
Yuxing Li
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Snow Water ◽  
Microwave Sensors ◽  
Global Surface

Abstract. Snow cover is an effective best indicator of climate change due to its effect on regional and global surface energy, water balance, hydrology, climate, and ecosystem function. We developed a long term Northern Hemisphere daily snow depth and snow water equivalent product (NHSnow) by the application of the support vector regression (SVR) snow depth retrieval algorithm to historical passive microwave sensors from 1992 to 2016. The accuracies of the snow depth product were evaluated against observed snow depth at meteorological stations along with the other two snow cover products (GlobSnow and ERA-Interim/Land) across the Northern Hemisphere. The evaluation results showed that NHSnow performs generally well with relatively high accuracy. Further analysis were performed across the Northern Hemisphere during 1992–2016, which used snow depth, total snow water equivalent (snow mass) and, snow cover days as indexes. Analysis showed the total snow water equivalent has a significant declining trends (~ 5794 km3 yr−1, 12.5 % reduction). Although spatial variation pattern of snow depth and snow cover days exhibited slight regional differences, it generally reveals a decreasing trend over most of the Northern Hemisphere. Our work provides evidence that rapid changes in snow depth and total snow water equivalent are occurring beginning at the turn of the 21st century with dramatic, surface-based warming.

scholarly journals Long-Term Records of Snow Cover Water Equivalent in the Swiss Alps

1994 ◽  
Vol 25 (1-2) ◽  
pp. 53-64 ◽  
Author(s):  
M. B. Rohrer ◽  
L. N. Braun ◽  
H. Lang
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Swiss Alps ◽  
Special Investigation ◽  
Quality Aspects ◽  
Snow Water ◽  
Ranking Order ◽  
Seasonal Discharge

The snow-water equivalent (SWE) of the seasonal snow cover is an important component of the water cycle in the Swiss Alps. It is used for predicting seasonal discharge, for short-range discharge forecasts and also for assessing water quality aspects. The SWE has been measured every two weeks at about 50 stations located between 860 and 2,540 m a.s.l. for more than 30 years. In addition there are special investigation areas with stations located between 600 m and 2,900 m a.s.l. where SWE is measured once per winter. The main characteristics of temporal and spatial SWE distributions are analyzed. The variations of SWE values depend in ranking order on elevation, on the year-to-year variations, on the region and on the exposition. The standardized SWE-values depend mostly on the year-to-year variations and on the region.

scholarly journals Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

2015 ◽  
Vol 17 (1) ◽  
pp. 153-170 ◽  
Author(s):  
Ally M. Toure ◽  
Matthew Rodell ◽  
Zong-Liang Yang ◽  
Hiroko Beaudoing ◽  
Edward Kim ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Average Bias ◽  
Community Land Model ◽  
Model Version ◽  
Snow Cover Extent ◽  
Moderate Resolution ◽  
Snow Water ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30°N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of −1.54 × 102 km2. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow–no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044 m (0.19 m) and −0.010 m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

scholarly journals Recent advances in remote sensing of seasonal snow

2010 ◽  
Vol 56 (200) ◽  
pp. 1141-1150 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Analysis Techniques ◽  
Seasonal Snow ◽  
The Past ◽  
Snow Cover Extent ◽  
Snow Water ◽  
Instruments And Techniques

AbstractRemote sensing offers local, regional and global observations of seasonal snow, providing key information on snowpack processes. This brief review highlights advancements in instrumentation and analysis techniques that have been developed over the past decade. Areas of advancement include improved algorithms for mapping snow-cover extent, snow albedo, snow grain size, snow water equivalent, melt detection and snow depth, as well as new uses of instruments such as multiangular spectroradiometers, scatterometry and lidar. Limitations and synergies of the instruments and techniques are discussed, and remaining challenges such as multisensor mapping, scaling issues, vegetation correction and data assimilation are identified.

scholarly journals The Response of Areal Snow Cover to Climate Change in a Snowmelt—Runoff Model

1997 ◽  
Vol 25 ◽  
pp. 232-236 ◽  
Author(s):  
A. Rango
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Major Influence ◽  
Snowmelt Runoff ◽  
Snow Cover Extent ◽  
Physically Based ◽  
Depletion Rate ◽  
Snow Water ◽  
Runoff Model

The cryosphere is represented in some hydrological models by the arcal extent of snow cover, a variable that has been operationally available in recent years through remote sensing. In particular, the snowmelt runoff model (SRM) requires the remotely sensed snow-cover extent as a major input variable. The SRM is well-suited for simulating the hydrological response of a basin to hypothetical climate change because it is a non-calibrated model. In order to run the SRM in a climate-change mode, the response of the areal snow cover to a change in climate is critical, and must be calculated as a function of elevation, precipitation, temperature, and snow-water equivalent. For the snowmelt-runoff season, the effect of climate change on conditions in the winter months has a major influence. In a warmer climate, winter may experience more rain vs snow events, and more periods of winter snowmelt that reduce the snow water equivalent present in the basin at the beginning of spring snow melt. As a result, the spring snowmelt runoff under conditions of climate warming will be affected not only by different temperatures and precipitation, but also by a different snow cover with a changed depletion rate. A new radiation-based version of the SRM is under development that will also take changes in cloudiness and humidity into account, making climate-change studies of the cryosphere even more physically based.

scholarly journals Evaluation of snow cover and snow water equivalent in the continental Arctic in CMIP5 models

2020 ◽  
Vol 55 (11-12) ◽  
pp. 2993-3016
Author(s):  
María Santolaria-Otín ◽  
Olga Zolina
Keyword(s):  
Snow Cover ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Spatial And Temporal Patterns ◽  
Multiple Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
Intercomparison Project

Abstract Spatial and temporal patterns of snow cover extent (SCE) and snow water equivalent (SWE) over the terrestrial Arctic are analyzed based on multiple observational datasets and an ensemble of CMIP5 models during 1979–2005. For evaluation of historical simulations of the Coupled Model Intercomparison Project (CMIP5) ensemble, we used two reanalysis products, one satellite-observed product and an ensemble of different datasets. The CMIP5 models tend to significantly underestimate the observed SCE in spring but are in better agreement with observations in autumn; overall, the observed annual SCE cycle is well captured by the CMIP5 ensemble. In contrast, for SWE, the annual cycle is significantly biased, especially over North America, where some models retain snow even in summer, in disagreement with observations. The snow margin position (SMP) in the CMIP5 historical simulations is in better agreement with observations in spring than in autumn, when close agreement across the CMIP5 models is only found in central Siberia. Historical experiments from most CMIP5 models show negative pan-Arctic trends in SCE and SWE. These trends are, however, considerably weaker (and less statistically significant) than those reported from observations. Most CMIP5 models can more accurately capture the trend pattern of SCE than that of SWE, which shows quantitative and qualitative differences with the observed trends over Eurasia. Our results demonstrate the importance of using multiple data sources for the evaluation of snow characteristics in climate models. Further developments should focus on the improvement of both dataset quality and snow representation in climate models, especially ESM-SnowMIP.

Five decades of warming: impacts on snow cover in Norway

2017 ◽  
Vol 49 (3) ◽  
pp. 670-688 ◽  
Author(s):  
Jonathan Rizzi ◽  
Irene Brox Nilsen ◽  
James Howard Stagge ◽  
Kjersti Gisnås ◽  
Lena M. Tallaksen
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Land Area ◽  
Air Temperatures ◽  
Northern Latitudes ◽  
Snow Cover Extent ◽  
The World ◽  
Snow Water ◽  
Seasonal Analysis ◽  
High Elevations

Abstract Northern latitudes are experiencing faster warming than other regions in the world, which is partly explained by the snow albedo feedback. In Norway, mean temperatures have been increasing since the 1990s, with 2014 being the warmest year on record, 2.2 °C above normal (1961–1990). At the same time, a concurrent reduction in the land area covered by snow has been reported. In this study, we present a detailed spatial and temporal (monthly and seasonal) analysis of trends and changes in snow indices based on a high resolution (1 km) gridded hydro-meteorological dataset for Norway (seNorge). During the period 1961–2010, snow cover extent (SCE) was found to decrease, notably at the end of the snow season, with a corresponding decrease in snow water equivalent except at high elevations. SCE for all Norway decreased by more than 20,000 km2 (6% of the land area) between the periods 1961–1990 and 1981–2010, mainly north of 63° N. Overall, air temperature increased in all seasons, with the highest increase in spring (particularly in April) and winter. Mean monthly air temperatures were significantly correlated with the monthly SCE, suggesting a positive land–atmosphere feedback enhancing warming in winter and spring.

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