scholarly journals The Changing Cryosphere: Pan-Arctic Snow Trends (1979–2009)

2011 ◽  
Vol 24 (21) ◽  
pp. 5691-5712 ◽  
Author(s):  
Glen E. Liston ◽  
Christopher A. Hiemstra
Keyword(s):  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Reanalysis Data ◽  
The Arctic ◽  
Regional Variability ◽  
Time Step ◽  
Snow Season ◽  
Regional Trends ◽  
Dominant Feature

Abstract Arctic snow presence, absence, properties, and water amount are key components of Earth’s changing climate system that incur far-reaching physical and biological ramifications. Recent dataset and modeling developments permit relatively high-resolution (10-km horizontal grid; 3-h time step) pan-Arctic snow estimates for 1979–2009. Using MicroMet and SnowModel in conjunction with land cover, topography, and 30 years of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, a distributed snow-related dataset was created including air temperature, snow precipitation, snow-season timing and length, maximum snow water equivalent (SWE) depth, average snow density, snow sublimation, and rain-on-snow events. Regional variability is a dominant feature of the modeled snow-property trends. Both positive and negative regional trends are distributed throughout the pan-Arctic domain, featuring, for example, spatially distinct areas of increasing and decreasing SWE or snow season length. In spite of strong regional variability, the data clearly show a general snow decrease throughout the Arctic: maximum winter SWE has decreased, snow-cover onset is later, the snow-free date in spring is earlier, and snow-cover duration has decreased. The domain-averaged air temperature trend when snow was on the ground was 0.17°C decade−1 with minimum and maximum regional trends of −0.55° and 0.78°C decade−1, respectively. The trends for total number of snow days in a year averaged −2.49 days decade−1 with minimum and maximum regional trends of −17.21 and 7.19 days decade−1, respectively. The average trend for peak SWE in a snow season was −0.17 cm decade−1 with minimum and maximum regional trends of −2.50 and 5.70 cm decade−1, respectively.

scholarly journals Simulation of Snow Water Equivalent (SWE) Using Thermodynamic Snow Models in Québec, Canada

2009 ◽  
Vol 10 (6) ◽  
pp. 1447-1463 ◽  
Author(s):  
A. Langlois ◽  
J. Kohn ◽  
A. Royer ◽  
P. Cliche ◽  
L. Brucker ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Regional Scale ◽  
Correlation Coefficients ◽  
Reanalysis Data ◽  
Regional Variability ◽  
The North ◽  
Snow Water ◽  
Regional Reanalysis

Abstract Snow cover plays a key role in the climate system by influencing the transfer of energy and mass between the soil and the atmosphere. In particular, snow water equivalent (SWE) is of primary importance for climatological and hydrological processes and is a good indicator of climate variability and change. Efforts to quantify SWE over land from spaceborne passive microwave measurements have been conducted since the 1980s, but a more suitable method has yet to be developed for hemispheric-scale studies. Tools such as snow thermodynamic models allow for a better understanding of the snow cover and can potentially significantly improve existing snow products at the regional scale. In this study, the use of three snow models [SNOWPACK, CROCUS, and Snow Thermal Model (SNTHERM)] driven by local and reanalysis meteorological data for the simulation of SWE is investigated temporally through three winter seasons and spatially over intensively sampled sites across northern Québec. Results show that the SWE simulations are in agreement with ground measurements through three complete winter seasons (2004/05, 2005/06, and 2007/08) in southern Québec, with higher error for 2007/08. The correlation coefficients between measured and predicted SWE values ranged between 0.72 and 0.99 for the three models and three seasons evaluated in southern Québec. In subarctic regions, predicted SWE driven with the North American Regional Reanalysis (NARR) data fall within the range of measured regional variability. NARR data allow snow models to be used regionally, and this paper represents a first step for the regionalization of thermodynamic multilayered snow models driven by reanalysis data for improved global SWE evolution retrievals.

scholarly journals Quantifying and Diagnosing Sources of Uncertainty in Midcentury Changes in North American Snowpack from NARCCAP

2019 ◽  
Vol 20 (11) ◽  
pp. 2229-2252 ◽  
Author(s):  
Rachel R. McCrary ◽  
Linda O. Mearns
Keyword(s):  
Climate Change ◽  
North America ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
The Arctic ◽  
Climate Change Signal ◽  
Future Projections ◽  
Climate Change Uncertainty ◽  
Snow Season ◽  
Snow Cover Extent

Abstract The NARCCAP RCM–GCM ensemble is used to explore the uncertainty in midcentury projections of snow over North America that arise when multiple RCMs are used to downscale multiple GCMs. Various snow metrics are examined, including snow water equivalent (SWE), snow cover extent (SCE), snow cover duration (SCD), and the timing of the snow season. Simulated biases in baseline snow characteristics are found to be sensitive to the choice of RCM and less influenced by the driving GCM. By midcentury, domain-averaged SCE and SWE are projected to decrease in all months of the year. However, using multiple RCMs to downscale multiple GCMs inflates the uncertainty in future projections of both SCE and SWE, with projections of SWE being more uncertain. Spatially, the RCMs show winter SWE decreasing over most of North America, except north of the Arctic rim, where SWE is projected to increase. SCD is also projected to decrease with both a later start and earlier termination of the snow season. For all metrics considered, the magnitude of the climate change signal varies across the RCMs. The ensemble spread is large over the western United States, where the RCMs disagree on the sign of the change in SWE in some high-elevation regions. Future projections of snow (both magnitude and spatial patterns) are more similar between simulations performed with the same RCM than the simulations driven by the same GCM. This implies that climate change uncertainty is not sufficiently explored in experiments performed with a single RCM driven by multiple GCMs.

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

scholarly journals Trends and regime shifts in climatic conditions and river runoff in Estonia during 1951–2015

2017 ◽  
Vol 8 (4) ◽  
pp. 963-976 ◽  
Author(s):  
Jaak Jaagus ◽  
Mait Sepp ◽  
Toomas Tamm ◽  
Arvo Järvet ◽  
Kiira Mõisja
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Large Scale ◽  
Regime Shifts ◽  
The Arctic ◽  
Dry Period ◽  
Snow Cover Duration ◽  
Large Scale Atmospheric Circulation

Abstract. Time series of monthly, seasonal and annual mean air temperature, precipitation, snow cover duration and specific runoff of rivers in Estonia are analysed for detecting of trends and regime shifts during 1951–2015. Trend analysis is realised using the Mann–Kendall test and regime shifts are detected with the Rodionov test (sequential t-test analysis of regime shifts). The results from Estonia are related to trends and regime shifts in time series of indices of large-scale atmospheric circulation. Annual mean air temperature has significantly increased at all 12 stations by 0.3–0.4 K decade−1. The warming trend was detected in all seasons but with the higher magnitude in spring and winter. Snow cover duration has decreased in Estonia by 3–4 days decade−1. Changes in precipitation are not clear and uniform due to their very high spatial and temporal variability. The most significant increase in precipitation was observed during the cold half-year, from November to March and also in June. A time series of specific runoff measured at 21 stations had significant seasonal changes during the study period. Winter values have increased by 0.4–0.9 L s−1 km−2 decade−1, while stronger changes are typical for western Estonia and weaker changes for eastern Estonia. At the same time, specific runoff in April and May have notably decreased indicating the shift of the runoff maximum to the earlier time, i.e. from April to March. Air temperature, precipitation, snow cover duration and specific runoff of rivers are highly correlated in winter determined by the large-scale atmospheric circulation. Correlation coefficients between the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices reflecting the intensity of westerlies, and the studied variables were 0.5–0.8. The main result of the analysis of regime shifts was the detection of coherent shifts for air temperature, snow cover duration and specific runoff in the late 1980s, mostly since the winter of 1988/1989, which are, in turn, synchronous with the shifts in winter circulation. For example, runoff abruptly increased in January, February and March but decreased in April. Regime shifts in annual specific runoff correspond to the alternation of wet and dry periods. A dry period started in 1964 or 1963, a wet period in 1978 and the next dry period at the beginning of the 21st century.

scholarly journals Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

2014 ◽  
Vol 15 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Jiarui Dong ◽  
Mike Ek ◽  
Dorothy Hall ◽  
Christa Peters-Lidard ◽  
Brian Cosgrove ◽  
...  
Keyword(s):  
United States ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Probability Of Detection ◽  
Joint Analysis ◽  
Snow Cover Area ◽  
Fractional Snow Cover ◽  
Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

Representation of Snow in the Canadian Seasonal to Interannual Prediction System. Part I: Initialization

2016 ◽  
Vol 17 (5) ◽  
pp. 1467-1488 ◽  
Author(s):  
Reinel Sospedra-Alfonso ◽  
Lawrence Mudryk ◽  
William Merryfield ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Initial Conditions ◽  
Reanalysis Data ◽  
Prediction System ◽  
Snow Water ◽  
System Part ◽  
Surface Scheme

Abstract The ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) to provide realistic forecast initial conditions for snow cover is assessed using in situ measurements and gridded snow analyses. Forecast initial conditions for snow in CanCM3 and CanCM4 employed by CanSIPS are determined by the response of the Canadian Land Surface Scheme (CLASS) used in both models to forcing from model atmospheric fields constrained by assimilation of 6-hourly reanalysis data. These snow initial conditions are found to be representative of the daily climatology of snow water equivalent (SWE) as well as interannual variations in maximum SWE and the timing of snow onset and snowmelt observed at eight in situ measurement sites located across Canada. The level of this agreement is similar to that of three independent gridded snow analyses (MERRA, the European Space Agency’s GlobSnow, and an offline forced version of CLASS). Total Northern Hemisphere snow mass generated by the CanSIPS initialization procedure is larger for both models (especially CanCM3) than in MERRA, mostly because of higher SWE in regions of common snow cover. Globally, the interannual variability of initial SWE is found to correlate highly with that of MERRA in locations with appreciable snow. These initial values are compared to SWE in freely running CanCM3 and CanCM4 simulations produced without data assimilation of atmospheric fields. Differences in climatological SWE relative to MERRA are similar in the freely running and assimilating CanCM3 and CanCM4 simulations, suggesting that inherent model biases are a major contributor to biases in CanSIPS snow initial conditions.

scholarly journals Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

2016 ◽  
Vol 64 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Pavel Krajčí ◽  
Michal Danko ◽  
Jozef Hlavčo ◽  
Zdeněk Kostka ◽  
Ladislav Holko
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Flood Forecasting ◽  
Snow Accumulation ◽  
Small Scale ◽  
Climatic Data ◽  
Tatra Mountains ◽  
Snow Water

AbstractSnow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m2, mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at short-time (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

scholarly journals Changes in Snow Storage in the Upper Hron River Basin (Slovakia)

2014 ◽  
Vol 10 (2) ◽  
pp. 145-160
Author(s):  
Katarína Kotríková ◽  
Kamila Hlavčová ◽  
Róbert Fencík
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Satellite Images ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Kendall Test ◽  
Measured Data ◽  
Time Step ◽  
Snow Storage ◽  
Snow Water

Abstract An evaluation of changes in the snow cover in mountainous basins in Slovakia and a validation of MODIS satellite images are provided in this paper. An analysis of the changes in snow cover was given by evaluating changes in the snow depth, the duration of the snow cover, and the simulated snow water equivalent in a daily time step using a conceptual hydrological rainfall-runoff model with lumped parameters. These values were compared with the available measured data at climate stations. The changes in the snow cover and the simulated snow water equivalent were estimated by trend analysis; its significance was tested using the Mann-Kendall test. Also, the satellite images were compared with the available measured data. From the results, it is possible to see a decrease in the snow depth and the snow water equivalent from 1961-2010 in all the months of the winter season, and significant decreasing trends were indicated in the months of December, January and February

scholarly journals Long-term ice-rich permafrost coast sensitivity to air temperatures and storm influence: lessons from Pullen Island, N.W.T.

2021 ◽  
Author(s):  
H. Bay Berry ◽  
Dustin Whalen ◽  
Michael Lim
Keyword(s):  
Air Temperature ◽  
The Arctic ◽  
Climate Trends ◽  
Time Step ◽  
Erosion Rates ◽  
Air Temperatures ◽  
Ground Survey ◽  
Storm Duration ◽  
Cliff Retreat

Response of erosive mechanisms to climate change is of mounting concern on Beaufort Sea coasts, which experience some of the highest erosion rates in the Arctic. Collapse of intact permafrost blocks and slumping within sprawling retrogressive thaw complexes are two predominant mechanisms that manifest as cliff retreat in this region. Using aerial imagery and ground survey data from Pullen Island, N.W.T., Canada, from 13 time points between 1947 and 2018, we observe increasing mean retreat rates from 0 ± 4.8 m/a in 1947 to 12 ± 0.3 m/a in 2018. Mean summer air temperature was positively correlated with cliff retreat over each time step via block failure (r2 = 0.08; p = 0.5) and slumping (r2 = 0.41; p = 0.05), as was mean storm duration with cliff retreat via block failure (r2 = 0.84; p = 0.0002) and slumping (r2 = 0.34; p = 0.08). These data indicate that air temperature has a greater impact in slump-dominated areas, while storm duration has greater control in areas of block failure. Increasingly heterogeneous cliff retreat rates are likely resulting from different magnitudes of response to climate trends depending on mechanism, and on geomorphological variations that prescribe occurrences of retrogressive thaw slumps.

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