Snow and albedo climate change impacts across the United States Northern Great Plains

Fassnacht, S. R.; Cherry, Mikaela; Venable, N. B. H.

doi:10.5194/tcd-9-3331-2015

Cited by 8 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, snow cover changes can be difficult to predict in areas where temperature increases are accompanied by changes in precipitation (Dahe et al, 2006;Adam et al, 2009). Moreover, decreases in the duration of snowpack cause negative feedbacks due to changes in albedo, which leads to increased absorption of solar radiation, intensification of warming trends, and further reductions in snowpack (Fassnacht et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Changes in Andes snow cover from MODIS data, 2000–2016

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract. The Andes span a length of 7000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP) as the fraction of time with snow cover for each year between 2000 and 2016 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors (500 m, 8-day maximum snow cover extent). This analysis is conducted between 8 and 36 • S due to high frequency of cloud (> 30 % of the time) south and north of this range. We ran Mann-Kendall and Theil-Sens analyses to identify areas with significant changes in SP and snowline (the line at lower elevation where SP = 20 %). We evaluated how these trends relate to temperature and precipitation from Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) and University of Delaware datasets and climate indices as El Niño-Southern Oscillation (ENSO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO). Areas north of 29 • S have limited snow cover, and few trends in snow persistence were detected. A large area (34 370 km 2 ) with persistent snow cover between 29 and 36 • S experienced a significant loss of snow cover (2-5 fewer days of snow year −1 ). Snow loss was more pronounced (62 % of the area with significant trends) on the east side of the Andes. We also found a significant increase in the elevation of the snowline at 10-30 m year −1 south of 29-30 • S. Decreasing SP correlates with decreasing precipitation and increasing temperature, and the magnitudes of these correlations vary with latitude and elevation. ENSO climate indices better predicted SP conditions north of 31 • S, whereas the SAM better predicted SP south of 31 • S.

show abstract

Section: Introductionmentioning

confidence: 99%

Changes in Andes snow cover from MODIS data, 2000–2016

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Herder observations of snowfall quantities are most likely based on snow amounts on the ground; thus, the balance between snowfall gauge undercatch and snowpack losses may provide a better comparison to IKS than bias-corrected station data [56], even when there is a temporal disconnect in the time step of meteorological data [84]. This same method was used by Fassnacht et al [7].…”

Section: Data Considerationsmentioning

confidence: 99%

“…The station trends computed herein represent a point in space, but even in homogeneous terrains, station trends may not be spatially consistent [6,7]. This work also used a perspective on herder observations (indigenous knowledge) through a range of time periods to evaluate change.…”

Section: Data Considerationsmentioning

confidence: 99%

See 1 more Smart Citation

Merging Indigenous Knowledge Systems and Station Observations to Estimate the Uncertainty of Precipitation Change in Central Mongolia

et al. 2018

Self Cite

View full text Add to dashboard Cite

Across the globe, station-based meteorological data are analyzed to estimate the rate of change in precipitation. However, in sparsely populated regions, like Mongolia, stations are few and far between, leaving significant gaps in station-derived precipitation patterns across space and over time. We combined station data with the observations of herders, who live on the land and observe nature and its changes across the landscape. Station-based trends were computed with the Mann-Kendall significance and Theil-Sen rate of change tests. We surveyed herders about their observations of changes in rain and snowfall amounts, rain intensity, and days with snow, using a closed-ended questionnaire and also recorded their qualitative observations. Herder responses were summarized using the Potential for Conflict Index (PCI 2 ), which computes the mean herder responses and their consensus. For one set of stations in the same forest steppe ecosystem, precipitation trends were similar and decreasing, and the herder-based PCI 2 consensus score matched differences between stations. For the other station set, trends were less consistent and the PCI 2 consensus did not match well, since the stations had different climates and ecologies. Herder and station-based uncertainties were more consistent for the snow variables than the rain variables. The combination of both data sources produced a robust estimate of climate change uncertainty.

show abstract

“…Furthermore these regions are ecologically vital zones (Schlosser, 1995;Lowe and Likens, 2005) that impact downstream water quality (Peterson et al, 2001;Alexander et al, 2007). The dynamics of headwaters are quite variable and are projected to change in the future (Adam et al, 2009;Clow, 2010;Harpold et al, 2012;Fassnacht and Hultstrand, 2015;Fassnacht et al, 2016;Musselman et al, 2017), thus it is important to better understand the functioning of these systems for future planning and management of natural resources (Bales et al, 2006).…”

Section: Introductionmentioning

confidence: 99%