Scottish snow cover dependence on the North Atlantic Oscillation index

Forecasting seasonal snow cover is useful for planning resources and mitigating natural hazards. We present a link between the North Atlantic Oscillation (NAO) index and days of snow cover in Scotland between winters beginning from 1875 to 2013. Using broad (5 km resolution), national scale data sets like UK Climate Projections 2009 (UKCP09) to extract nationwide patterns, we support these findings using hillslope scale data from the Snow Survey of Great Britain (SSGB). Currently collected snow cover data are considered using remotely sensed satellite observations, from moderateresolution imaging spectroradiometer; but the results are inconclusive due to cloud. The strongest correlations between theNAO index and snow cover are found in eastern and southern Scotland; these results are supported by both SSGB andUKCP09 data. Correlations betweenNAO index and snowcover are negative with the strongest relationships found for elevations below 750 m. Four SSGB sites (two in eastern Scotland, two in southern Scotland) were modelled linearly with resulting slopes between 6 and 16 days of snow cover per NAO index integer value. This is the first time the relationship between NAO index and snow cover duration has been quantified and mapped in Scotland. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/nh.2016.085 s://iwaponline.com/hr/article-pdf/47/3/619/368053/nh0470619.pdf Michael Spencer (corresponding author) Richard Essery School of GeoSciences, University of Edinburgh, Grant Institute, James Hutton Road, King’s Buildings, Edinburgh EH9 3FE, UK E-mail: m.spencer@ed.ac.uk


INTRODUCTION
Snow is important in Scotland for water resources, e.g., the largest instrument-measured flow in Scotland's largest catchment, the River Tay, was partly caused by snowmelt (Black & Anderson ). Dunn et al. () showed that snow can contribute to river baseflow until July, as melted snow takes a generally slower sub-surface pathway to a water course. Also, Gibbins et al. ()  west of the UK, but eastern catchments had a weaker correlation (e.g., River Tweed: 0.38). Harrison et al. () suggested that an association between snow cover and NAO phase is likely. Trivedi et al. () found snow cover in the Ben Lawers region north of Loch Tay, at 300 m and below to be significantly (P < 0.05) negatively correlated with NAO index, between À0.55 and À0.38, with lower elevations having a stronger relationship. Trivedi et al. () also found no correlation between NAO index and falling snow, perhaps because it is often cold enough for snow to fall during a Scottish winter, irrespective of NAO phase, but during positive NAO phases the warmer air causes snow to melt and only with the colder temperatures associated with negative NAO indices does snow lie for longer. There has been more research on snow cover links to the NAO index in continental Europe, where snow cover has a greater impact (e.g., Beniston ; Bednorz ; Scherrer et al.  We hypothesise that snow cover in Scotland is negatively correlated with the NAO index. We establish this by looking at nationwide snow cover data sets, before further investigating relationships at a hillslope scale, using case studies with more detailed data available. Our paper is laid out as follows: methods and data, results, discussion and conclusion. The methods and results sections are split by data set.

DATA AND METHODS
We used NAO index data from the Climate Research Unit University of East Anglia (undated) and Osborn (undated) as these comprise a long and definitive record ( Table 1).
The longest data series of Scottish snow cover is from UK Met Office stations which record snow presence at a given point at 09:00 hours UTC each morning; the longest of these is Braemar which has recorded since 1927 (Harrison et al. ). Ninety-six per cent of UK Met Office snow recording stations lie below 300 m elevation (Spencer et al. ) and so are unrepresentative of the 31% of Scottish landmass that is higher (Spencer et al. ). These UK Met Office station data are used by proxy via the UK Climate Projections 2009 (UKCP09) snow cover data set (Met Office undated). Table 1 shows a non-definitive list of Scottish snow cover data sets, which are all used within this study.
Snow in Scotland is often ephemeral and so metrics like average snowline and maximum snow cover extent are meaningless because each winter can see many snow accumulation and melt cycles. We solved this by using a count of the days of snow cover during a given time period. We define a winter period for snow cover as November to April to help differentiate the snowiest winters, while being short enough to not discount many Snow Survey of Great Britain (SSGB) records, as some are missing (Spencer  (Figure 2) and statistically using an analysis of variance (ANOVA) and Tukey honest significant differences (HSD) (Yandell ) tests, the latter to account for family-wise analysis (Table 2).

UKCP09
The UKCP09 snow data set comprises a 5 km resolution raster image for each month, where each grid value   The resulting Pearson correlation is plotted (Figure 3) to show spatial patterns.
Stations from Table 3, judged by eye to have a LOESS close to a straight line, are plotted in Figure 9 with linear models, showing the Pearson correlation value and line parameters (slope and intercept). This allows us to relate a given NAO index to an expected number of days snow cover duration for a high or low elevation.

Moderate-resolution imaging spectroradiometer
There are two main methods for remote sensing of snow:

Data comparison
To relate SSGB station and national results, Pearson correlations from SSGB, MODIS and UKCP09 are compared.
Values from MODIS and UKCP09 rasters were extracted at SSGB station locations and are shown together in Table 4.

SSGB stations Crathes, Eskdalemuir, Forrest Lodge and
Whitehillocks have been plotted with linear regression lines ( Figure 9). Line slopes vary from À7 to À14 days for higher elevations and from À6 to À16 days for lower elevations. As can be seen in Figures 5-8 Differences from UKCP09 and SSGB results are most likely because of the frequency of cloud, as it is difficult for visible remote sensing to see through cloud. The problem is illustrated in Figure 10(b), which shows cloud cover as  This will have an impact on seeing spatial snow cover trends; if we expect the east to get more days of snow cover when there is a negative NAO index, a corresponding increase in cloud cover will obscure snow observations.

Data comparison
A comparison of correlations from different data sets can be seen in Those stations that showed a more easily defined relationship with a LOESS have had linear models fitted ( Figure 9), with Pearson correlation values, from À0.29 to À0.5. This range of results could be explained by microclimates having a bigger impact on snow cover than longterm weather patterns. This would be especially true on the east side of the Cairngorms, where wind (predominantly westerly) driven snow often accumulates on eastern slopes and can take a long time to melt. These spatial local discrepancies can also be temporal, given that the SSGB sites did not all observe the same winters, and some may have been more closely correlated with the NAO index than others.
The obvious solution is to consider the results from Figure 5, which average over a greater number of SSGB stations, helping to reduce uncertainty.

CONCLUSION
Spatial variability of snow cover is a big challenge and is difficult to observe and quantify. This is typified by the contrasting results of UKCP09 snow and MODIS data correlations. We have overcome this by using disparate snow cover data sets, encompassing anecdotal type data (Bonacina index), interpolated ground observed data (UKCP09), the SSGB and satellite observations (MODIS).
With the exception of the MODIS analysis, these have all shown the same results: that Scottish snow cover is generally negatively correlated with the NAO index, with stronger correlations at lower elevations and in southern and eastern Scotland. Results from individual SSGB stations and UKCP09 grids correlate well demonstrating the value of UKCP09 data for national scale assessment of spatial trends.

At sample locations, snow lying between November and
April increase by 6 to 16 days for each unit reduction in the NAO index. These estimates could be used in conjunction with seasonal NAO forecasts in preparation for upcoming winters by groups like highways and local authority planners and snow sports industries.
As new snow data sets become available, particularly from satellite and reanalysis products, it will be worthwhile revisiting and updating this research to help constrain uncertainty. This will be particularly pertinent if predictions of a more volatile NAO index come to pass, as we will be able to better link snow cover to climate variability, helping our understanding of snow cover in a changing climate.