1. Poleward amplification of Northern Hemisphere weekly snowcover extent trends Stephen Déry & Ross Brown ENSC 454/654 – “Snow and Ice”

2. Outline Background Motivation & Goals Data, Methods & Data Issues Results Discussion Conclusions

3. Background Northern Hemisphere snowcover extent (SCE) varies between 4-46 x 10 6 km 2. Its distinct properties makes snow a key component of global climate. Snow responds to changes in surface air temperatures & precipitation, thus providing another indicator of climate change.

4. Previous studies reveal a 5% per decade decline in Northern Hemisphere SCE (Frei & Robinson 1999). Declining mountain snowpacks & earlier spring freshets have been observed in recent decades over western North America (Mote et al. 2005; Stewart et al. 2005). Changes in snow depth & snowcover duration have also been recorded (Brown and Braaten 1998; Stone et al. 2000). Background

5. Motivation & Goals In light of near-record warmth in 2006 & the recent changes observed in the cryosphere, there is an urgent need to better understand SCE trends. Objective: To develop & interpret weekly trends in Northern Hemisphere (NH), North American (NA) & Eurasian (EU) SCE for the period 1972-2006.

6. Weekly values of SCE from January 1972 to December 2006 from Rutgers University. Monotonic trends in weekly SCE assessed with Mann-Kendall test (MKT) over NH, NA (excluding Greenland) & EU. MKT assumes a linear trend in the form: S = mt + b (1) Where S is SCE, t is time (year) & m is the slope of the linear trend given by: Data & Methods

7. m k = (S j – S i )/(t j – t i ), k = 1, 2, …, n(n-1)/2 i = 1, …, n-1 j = 2, 3, …, n All of the slopes m k are then ranked, with the median value representing the slope m of the linear trend. The coefficient b is found by substituting the median values of SCE & time in Eq. (1) & solving for b.

8. Strength of the MKT

10. Trends expressed in absolute values (× 10 6 km 2 ), as a % from initial (1972) values, in standardized units, & insolation-weighted anomalies. Time series of weekly SCE data (S i ) are standardized by: SS i = (S i – S i )/σ i, (i =1-53) Insolation-weighted anomalies are computed by multiplying the absolute values of SCE by the ratio of the average & maximum weekly incoming solar radiation at 60 o N.

12. Data Issues Continental snowcovers exhibit temporal persistence. This implies positive autocorrelation of SCE values, meaning that time series of subsequent weekly SCE values do not form independent datasets. Thus methodologies must be developed to reduce/remove the effect of serial correlation on trend analyses. Trends & correlations are considered statistically-significant when p < 0.05.

13. Number of weeks with significant autocorrelations of continental SCE

14. Year-to-year autocorrelation in continental SCE

15. Results

18. Summary of Trend Analysis StatisticN.H.N.A.Eurasia Mean SCE (×10 6 km 2 ) 23.38.414.9 SCE Trend (×10 6 km 2 )/35 years -1.32-0.80-0.49 Positive significant trends 204 Negative significant trends 242320

26. Discussion

27. Coherent variability & signal Correlation between standardized NA & EU weekly SCE is r = 0.41 (p < 0.001). Standardized weekly SCE are of the same sign 64% of the time (88% when greater than ±1 standard deviation). Correlation between NA & EU trends in standardized SCE is r = 0.83 (p < 0.001). This implies a hemispheric-scale process may be acting on continental snowcovers.

28. Poleward amplification of trends Linear regressions on standardized SCE trends (January to early August) yield correlation coefficients of -0.89 to -0.96. This suggests a poleward amplification of SCE anomalies owing to persistence in the cryospheric system. Negative trends in early spring SCE amplify during late spring & summer, with implications to the growing season, vegetation growth, species composition, …

29. Poleward Amplification

30. Snow-albedo feedback Trends in insolation-weighted SCE values show greatest changes near the summer solstice. This feature, in addition to the spatial coherence of the intercontinental snowcovers & temporal persistence on weekly & annual time scales, are possible manifestations of snow-albedo feedback.

31. Conclusions Strong negative trends in NH, NA, & EU weekly SCE (1972-2006) are observed. These trends are influenced by temporal persistence (i.e. serial correlation) in the cryospheric system. Similar behaviour in NA & EU snowcovers, including covariability, persistence, & amplified trends in spring/summer provides evidence of the snow-albedo feedback acting on a hemispheric-scale.

32. Postscript: Autocorrelation