Spatial variability and trends in observed snow ... - Wiley Online Library

GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L16503, doi:10.1029/2006GL027258, 2006

Spatial variability and trends in observed snow depth over North America Jamie L. Dyer1 and Thomas L. Mote2 Received 21 June 2006; revised 21 July 2006; accepted 27 July 2006; published 31 August 2006.

[1] This study uses a gridded dataset of daily U.S. and Canadian surface observations from 1960 – 2000 to study regional spatial and temporal variability and trends in snow depth across North America. Analysis shows minimal change in North American snow depth through January, with regions of decreasing snow depths beginning in late January. These regional decreases grow in intensity and extent through March and into April, implying an earlier onset of spring melt. The region showing the greatest decreases in snow depth occurs in central Canada, along a line from the Yukon Territory in northwestern Canada to the Great Lakes region. The regional decreases in spring snow depth across central Canada are likely a result of more rapid melt of shallower winter snowpacks, evident through shallower snow cover (2 – 10 cm) during May and October and a decrease in extent of deeper snowpacks (>40 cm) through March and April. Citation: Dyer, J. L., and T. L. Mote (2006), Spatial variability and trends in observed snow depth over North America, Geophys. Res. Lett., 33, L16503, doi:10.1029/2006GL027258.

1. Introduction [2] Snow cover is a vital component of the Earth’s climate system due to its interactions with energy and moisture budgets from the local to the global scale. Most notably, it displays important linkages to the radiative balance at higher latitudes [Groisman et al., 1994], and is sensitive to variations in air temperature and precipitation [Karl et al., 1993; Brown and Goodison, 1996]. As a result, snow cover is considered to be a useful indicator of climate change. [3] Over the Northern Hemisphere, satellite data have indicated that mean snow cover extent (SCE) has been considerably less extensive after the mid-1980s [Robinson et al., 1993; Robinson, 1999; Robinson and Frei, 1997; Groisman et al., 1994; Karl et al., 1993]. Robinson et al. [1993] noted that the greatest negative anomalies occurred in the spring and early summer. Based on satellite snow cover data over North America, the variability of SCE is similar to the hemispheric records, with decreasing SCE in the 1980s [Frei and Robinson, 1999; Frei et al., 1999; Brown, 2000]. Using surface observations of snow cover, several investigators have shown an increase in North American winter snow cover over most of the 20th Century [Brown and Goodison, 1996; Hughes and Robinson, 1996]. 1 Department of Geosciences, Mississippi State University, Mississippi State, Mississippi, USA. 2 Department of Geography, University of Georgia, Athens, Georgia, USA.

Copyright 2006 by the American Geophysical Union. 0094-8276/06/2006GL027258

Although this seems counter to results obtained using satellite information, the difference is most likely attributable to the longer time series of the surface measurements. [4] Snow depth provides an additional dimension for snow cover studies by providing information relevant to water resources, surface energy and moisture budgets, soil processes, and ecological systems. Trends in snow depth have been analyzed for Canada [Brown and Braaten, 1998] and Eurasia [Ye et al., 1998], and several studies have looked at snow depth in relation to climate variability [Aguado et al., 1992; Changnon et al., 1993; Cayan, 1996], but these studies focus on regions within North America and not the continent as a whole. Brown et al. [2003] developed a snow depth climatology over North America from 1979 to 1996; however, no studies of interannual snow depth variability with North America as the inclusive study region have been published over a period at least as long as the satellite record (starting in 1966). [5] The primary objective of this project is to analyze the spatial and temporal trends of North American snow depth using a combined U.S. and Canadian climatological surface observation network. Regions showing regionally significant annual trends in snow depth from 1960 – 2000 are outlined, and possible climatological forcings associated with the snow variability are discussed. Additionally, a hybrid snow cover extent (SCE)/snow depth analysis shows the behavior of the North American snowpack during the snow season. Overall, this project provides a comprehensive understanding of the historical trends and patterns of snow depth over North America.

2. Snow Depth Data and Gridding Methodology [6] The snow depth data used in this project include daily surface observations from the United States (National Climatic Data Center, TD-3200 surface summary of the day, http://www.ncdc.noaa.gov) and Meteorological Service of Canada [Braaten, 1996; R.D. Brown, Canadian snow depth data, personal communication, 2003]. Records are available at some U.S. and Canadian sites since the late 1800s; however, a majority of the US data is available beginning in 1948 due to the modernization of the cooperative observer network and digitization of historical data. The increase in the number of US stations after 1948 is concentrated along the eastern and western seaboards of the US and the Great Plains region of the Midwest. In Canada prior to 1960, daily snow depth observing stations were concentrated along the US-Canada border and the west coast of British Columbia; however, by 1960 the sites were more evenly distributed, including stations in the far northern reaches of the country (Figure 1). The Cryospheric System to Monitor Global Change in Canada (CRYSYS)

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DYER AND MOTE: TRENDS IN OBSERVED SNOW DEPTH

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Figure 1. Density (number per 1° 1° area) of daily snow depth observation sites over North America for beginning and end of the 1960 – 2000 study period. program [Goodison and Brown, 1997], initiated in 1995, added approximately 400 stations with varying lengths of record from 1900 – 1990 to the existing Digital Archive of Canadian Climate Data [Brown and Braaten, 1998], further improving the station distribution. As a result of station density issues before 1960, the study period for this project begins in 1960 and extends through 2000. [7] Snow depth values represent an average of a series of manual ruler measurements taken from a given area, with care taken to include all snow and ice layers and to avoid snow drifts; however, some local variability does exist. In addition, snow depth observations are normally taken in clearings; therefore, observations are unlikely to be fully representative of surrounding terrain and vegetation cover. The most apparent limitation of snow depth observations at the continental scale is that station locations frequently follow population patterns, resulting in observations that are often biased to lower elevations, and in the case of the Canadian network, to lower latitudes. Nevertheless, it is possible for individual snow depth observations to demonstrate regional-scale consistency, especially in regions characterized by relatively homogeneous surface characteristics (i.e., the southern plains of Canada and the Great Plains of the U.S). 2.1. Quality Control [8] The reasonableness and internal consistency of the U.S. and Canadian snow depth data were tested by subjecting all observations to a quality control routine following Robinson [1989], regardless of individual station record length. This process involved comparing a time series of daily snow depth measurements for a given station with associated extreme daily snow depth, snow fall, maximum and minimum temperature, and precipitation data for the associated state or province. These extreme values were chosen based on existing quality controlled extreme event climate data (Environment Canada, Canadian climate normals or averages 1971 –2000, available at http://climate. weatheroffice.ec.gc.ca/climate_normals/index_e.html; National Climatic Data Center, Local climatological data annual summary, http://www.ncdc.noaa.gov). Any snow depth data that did not meet the quality control criteria were flagged as inconsistent and not included in the analysis. Overall, 3.3% of all potential snow depth measure-

ments were recorded as missing in the original datasets, with less than 1.2% of the remaining non-missing data flagged by the QC routines and removed. 2.2. Grid Generation [9] North American snow depth grids were created using the U.S. and Canadian daily snow depth observations. An inverse-distance interpolation algorithm was employed to interpolate the grids on a spherical surface before projecting them onto a two-dimensional Cartesian plane [Willmott et al., 1984]. Using observations from a minimum of five stations, 0.25° 0.25° grids were produced for each day of the 1960 – 2000 study period, with the bounded grid region including those areas between 53° – 168°W longitude and 20°– 71°N latitude. The maximum search radius was changed dynamically to incorporate up to 25 observations to find an optimal interpolated value, which is advantageous in areas with a high station density such as the eastern US. In data sparse regions, if less than five observations were located within 100 km of the center of a grid point, only those observations were used for interpolation; however, if no observations were located within 100 km, the maximum search radius was set as the distance to the nearest observation. In this way all nearby stations could be used to calculate the estimated grid point value while minimizing the error due to interpolation from stations far removed from the respective grid centers. [10] The 0.25° 0.25° snow depth grids were spatially averaged to create the final 1° 1° data set by taking an average of four adjacent 0.25° 0.25° grid points and assigning that value to the location corresponding to the intersection of those four grid cells. This was done to minimize the effect of inconsistent snow depth observations on interpolated grid values, wherein a missing observation for a single or several day period would effectively lower the interpolated grid value. The primary setbacks associated with this methodology are terrain effects within mountainous regions, where snow depth is highly dependent on slope and altitude, as well as the effects of low station density on interpolated values. In general, areas in North America east of the Rocky Mountains contain an adequate station density to employ the gridding methodology described above, and the lack of extreme topographic variability allows the issue of terrain to not be directly considered. Eastern North

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Figure 2. Trend in snow depth for select pentads (p < 0.05) from 1960 – 2000. Crosshatched and dotted regions denote increasing and decreasing snow depths, respectively. America, namely the Canadian and US prairie regions, is the primary area of research within this study; therefore no additional measures were taken to address the topographical shortcomings in the 1° 1° snow depth grids. [11] Once the daily snow depth grids were complete, five-day means (a.k.a., pentads) were computed. This was done to minimize high frequency temporal variations in the data resulting from the interpolation procedures while still maintaining a reasonably detailed temporal resolution. The pentads were calculated relative to the first day of each year such that the first pentad includes data for January 1– 5, with the 12th pentad (February 25– March 1) including February 29 on leap years. This time resolution was used instead of monthly or weekly averages because of the superior temporal resolution relative to monthly data, and because using weekly values (7-day means, e.g. NOAA snow charts) makes multi-annual analysis difficult due to the offset time periods between years.

3. North American Snow Depth Analysis 3.1. Snow Depth Trends [12] A linear regression analysis was performed on each 1° 1° grid cell in the study region for each pentad average to identify regional trends in snow depth over North America from 1960 – 2000. A two-sample difference of means test was computed for each regression to determine if the resulting trend was significantly different than zero. In areas where significant linear regression trends were recognized, sample snow depth time series were taken to verify that each trend was not largely affected by zero-value occurrences. Results of the regional regression analysis generally show little change in snow depth from pentad 60 (October 23– 27) through pentad 70 (December 12– 16), except for smaller areas showing locally significant decreases in northwestern Canada (>0.25 cm yr 1; p < 0.05; Figure 2). However, the extent of these regions is

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minimal, implying little change in the patterns of fall and early winter snow accumulation over the study period. [13] As the winter progresses through the turn of the year (pentads 1 – 5), regions with significant negative regional trends greater than 1 cm yr 1 (p < 0.05) increase in extent northwest of the Great Lakes and in northwestern Canada. This pattern continues through March (pentad 15), with areas of significant negative regional trends in snow depth (>1 cm yr 1; p < 0.05) extending to include a large area of central Canada from the Yukon Territory southeast to the Great Lakes. The roughly linear arrangement of decreasing snow depth regions during the winter corresponds to an area of relatively high extratropical cyclone frequencies, with cyclogenesis occurring between 50°N–55°N and tracking southeast towards the Great Lakes [Isard et al., 2000]. A change in the frequency and/or intensity of systems following this track could alter winter temperature and precipitation characteristics of the central Canadian Prairies. [14] Isard et al. [2000] suggested that cyclone frequency in North America is sensitive to the Pacific-North American (PNA) teleconnection, with a positive index correlating to a decreasing trend in cyclone frequency. PNA during the winter months (December – February) was in a predominantly positive phase from the late 1970s through 2000 (0.33 standardized units above 1948 –2004 mean), which agrees with the argument that the decrease in snow depth could be a result of a change in the frequency of extratropical cyclones. However, due to the known sensitivity of snow melt to the radiative balance [Groisman et al., 1994], surface energy fluxes [Leathers et al., 2004; Dyer and Mote, 2002; Kuusisto, 1986], and air temperature variations [Karl et al., 1993; Brown and Goodison, 1996], a general increase in energy available for snow melt would also lead to a decrease in snow depth. Stewart et al. [2005] showed an increase in spring temperatures over western North America along with an earlier onset of snow melt runoff, which could explain the change in snow depth over western Canada. More than likely the change in snow depth over North America is a combination of storm track and surface energy balance variability, leading to the specific pattern of snow depth variability over the study area. [15] From pentad 15 (March 12– 16) through pentad 25 (May 1 –5), the band of significant negative regional snow depth trends through central Canada becomes more pronounced as it slowly shifts northwards during the progression of spring melt. Within the U.S. the regional trends in snow depth are minimal from pentad 60 (October 23– 27) through pentad 10 (February 15 –19; 0.05) negative trends in snow depth at all threshold levels during the fall and winter, the strengthening of the trends during the late winter and spring indicates a transition toward an earlier onset of melt and a more rapid melt season. [19] To better illustrate the changes in snow depth, the 1960 – 2000 study period was separated into two equal periods, 1960 – 1980 and 1980 –2000, within which snow

Timing of Max Change, pentad 25 25 23 19 19 19 15 15 15 15 15

Max Change, % of Mean 0.39 0.40 0.51 0.71 0.98 1.28 1.69 2.05 2.44 2.99 3.42

depth at pentads 15, 20, and 25 was examined. These pentads correspond to the spring melt season, and also the timing of maximum negative trends in snow depth at all snow depth threshold levels. For pentad 15 (March 12– 16; Figure 3), the greatest changes in snow depth occurred within central Canada, where depths between 40– 60 cm reduce to a much narrower coverage relative to 1960 – 1980 patterns, while snow depths less than 20 cm from 1980– 2000 decreased northward toward the Northern Plains region of South Dakota and Iowa. A similar pattern exists for pentad 20 (Apr 6 – 10) when snow depths deeper than 40 cm decreased substantially throughout central and northern Canada, while the 40 cm depth line remained relatively stable. Shallower snow depths continue to migrate northward, especially along the leeward side of the Rocky Mountains into Canada. Pentad 25 (May 1 – 5) from 1980– 2000 shows a more rapid decrease in snow covered

Figure 3. Mean pentad snow depth from 1960 – 1980 period and 1980 –2000 periods. Pentads correspond to times of greatest SCE trends at select snow depth thresholds.

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area relative to the 1960 – 1980 period in the Canadian prairies and the Yukon Territories.

4. Conclusions [20] This project identifies regional spatial and temporal trends in snow depth over North America, and quantifies the magnitude and frequency of the defined interannual variations. Analysis is done using a gridded dataset composed of daily snow depth measurements from United States cooperative observer sites (National Climatic Data Center, TD3200 surface summary of the day, http://www.ncdc.noaa. gov) and Meteorological Service of Canada observation sites [Braaten, 1996] over the period 1960 – 2000. [21] Regression analysis of regional North American snow depth over the study period shows little change from November through January, with the exception of smaller areas of regional decreases in central Quebec and the northern Mackenzie River basin. Through February these areas expand to include much of central Canada. This large area of significant decreases in snow depth (>1 cm yr 1, p < 0.05) reached a maximum extent in March, at which point regions within the northern U.S. showed decreasing trends in snow depth coinciding with the initiation of the melt season, implying an earlier onset of spring thaw. The same is true of central Canada, although the maximum negative snow depth trends occurred later in April in this area because of the lag in spring temperature increases. The more rapid decrease in snow depth in the spring across central Canada is likely a result of shallower winter snow packs due to temperature and precipitation variations, caused by a decrease in the frequency of extratropical cyclones traveling from northwestern Canada southeast across the Great Lakes combined with a general increase in spring temperatures. [22] Seasonal SCE analysis shows the 2 cm snow depth shield reached its maximum extent in early January, while the 100 cm snow depth shield did not reach maximum extent until early March. This is expected since the deepest snowpacks should exist just before the onset of the spring melt period; however, trend analysis shows that SCE at all depths is decreasing most rapidly during early spring, with the greatest change occurring in the 40 cm snow depth shield in late March and early April. This supports the argument of an early onset of spring melt, and that snow depth is decreasing substantially over North America as well as SCE. [23] The results of this study shed light on the spatial patterns and trends of North American snow depth, and provide evidence that localized changes in snow depth are occurring. These changes will have repercussions in regional hydrologic systems due to a change in the availability and release of snowmelt runoff, effecting water resources and freshwater flux into the Arctic Ocean [Barnett et al., 2005]. Additionally, the radiative balance may be affected at higher latitudes by reduced continental snow depths due to variations in net radiation resulting from shallower snowpacks. The fact that the greatest changes in snow depth occurred in central Canada, where winter snow cover is a dominate factor in both climatic and hydrologic systems, is particularly relevant. Future research should be performed on the climatological causes and effects of

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changes in North American snow depth to better understand the associated mechanisms responsible for the documented trends. This includes analysis of temperature and precipitation fields, synoptic storm tracks, and global teleconnection patterns. In addition, because of the greater availability of SCE data but the greater detail of snow depth data, correlations between the two at a regional scale should be carried out such that changes in SCE can be used to infer associated changes in snow depth. [24] Acknowledgments. The authors would like to thank Andrew Grundstein, Vernon Meentemeyer, John Knox, and Todd Rasmussen at the University of Georgia for their helpful comments and suggestions, as well as Dan Leathers at the University of Deleware. They would also like to thank Josh Durkee at the Climatology Research Laboratory at the University of Georgia. This work was supported by NASA grant NAG5-11592 and NOAA grant NA04OAR4310169 to the University of Georgia. Data processing was partially supported by SHRMC through USDA grant SRS 03-CA-11330136-003. The authors would also like to thank two anonymous reviewers for their helpful comments and suggestions on the manuscript.

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Robinson, D. A. (1999), Northern Hemisphere snow extent during the satellite era, paper presented at Fifth Conference on Polar Meteorology and Oceanography, Am. Meteorol. Soc., Dallas, Tex. Robinson, D. A., and A. Frei (1997), A Northern Hemisphere snow cover climatology using satellite information, paper presented at 10th Conference on Applied Climatology, Am. Meteorol. Soc., Reno, Nev. Robinson, D. A., K. F. Dewey, and R. R. Heim (1993), Global snow cover monitoring: An update, Bull. Am. Meteorol. Soc., 74, 1689 – 1696. Stewart, I. T., D. R. Cayan, and M. D. Dettinger (2005), Changes toward earlier streamflow timing across western North America, J. Clim., 18, 1136 – 1155.

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Willmott, C. J., C. M. Rowe, and W. D. Philpot (1984), Spheremap, software, Cent. for Clim. Res., Dep. of Geogr., Univ. of Del., Newark. Ye, H., H. R. Cho, and P. E. Gustafson (1998), Changes in Russian winter snow accumulation during 1936 – 83 and its spatial patterns, J. Clim., 11, 856 – 863. J. L. Dyer, Department of Geosciences, Mississippi State University, P.O. Box 5448, Mississippi State, MS 39762, USA. ([email protected]) T. L. Mote, Department of Geography, University of Georgia, 210 Field St., Rm. 204, Athens, GA 30602, USA. ([email protected])

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