1. CanSISE East meeting, CIS, 10 February 2014 Seasonal forecast skill of Arctic sea ice area Michael Sigmond (CCCma) Sigmond, M., J. Fyfe, G. Flato, V. Kharin, W. Merryfield, GRL, 2013 (CanSIPS) Merryfield, W., W. Lee, W. Wang, M. Chen and A. Kumar, GRL, 2013 (CanSIPS+CFSv2)

2. Increased interest in seasonal predictions Sept. 2012 Sept. 1980 Number of commercial vessels through NE passage: 2009: 2 2012: 46 Sept. 2013: first commercial vessel through NW passage

3. Statistical models: Until recently, forecasts were made exclusively with statistical models (MLR, etc) Based on observed statistical relationships between: - T, circulation, SST, sea ice etc. in month X (predictor) - sea ice cover in month X+1,2,3,…. (predictand) But: Relationships depend on the mean state of the climate Begin year: 1979 Holland and Stroeve (2011) Correlation AO winter and SIE in September

4. Statistical models: Until recently, forecasts were made exclusively with statistical models Based on observed statistical relationships between: - T, circulation, SST, sea ice etc. in month X (predictor) - sea ice cover in month X+1,2,3,…. (predictand) But: Relationships depend on the mean state of the climate → statistical models may have large errors → Need to develop new tools

5. Dynamical models: Models based on laws of physics (like climate models) Require substantially more computational power than statistical models Have been used operationally to produce seasonal forecast of temperature, precipitation But only a few operational seasonal forecast systems include an interactive sea ice component Not yet clear how skillful forecasts of sea ice are Geophys. Res. Lett., 2013

6. Canadian Seasonal to Inter-annual Prediction System (CanSIPS) Environment Canada’s seasonal forecasting system Based on two coupled climate models (CanCM3/CanCM4) Initial conditions (including sea ice area) constrained to be close to observations (20 ensemble members) But: Sea ice thickness not initialized (instead: climatology of previous model version) Re-forecasts initialized in each month between January 1979 and December 2009 (12 month duration)

7. September forecasts:

8. Sep 1 Oct 1 Aug 1Jul 1 time Jun 1May 1 Nov 1 012 3 4 11 Lead (months)

9. September forecasts: Sep 1 Oct 1 Aug 1Jul 1 time Jun 1May 1 Nov 1 012 3 4 11 Lead (months)

10. September forecasts: Sep 1 Oct 1 Aug 1Jul 1 time Jun 1May 1 Nov 1 012 3 4 11 Lead (months)

11. September forecasts: Sep 1 Oct 1 Aug 1Jul 1 time Jun 1May 1 Nov 1 012 3 4 11 Lead (months) ?

12. September forecasts: Sep 1 Oct 1 Aug 1Jul 1 time Jun 1May 1 Nov 1 012 3 4 11 Lead (months) ?

13. September forecasts (detrended): Sep 1 Oct 1 Aug 1Jul 1 time Jun 1May 1 Nov 1 012 3 4 11 Lead (months)

14. September forecasts (detrended): No skill for predicting deviations from trend when initialized prior to June 1 Several studies have shown that winter/spring sea ice thickness good predictor for September sea ice (‘preconditioning’)  Skill may be enhanced by initializing sea ice thickness

15. Forecasts for other months:

16. Correlation Skill Score (TOTAL, not detrended): Sigmond et al. (2013)

17. TOTAL TREND + DE-TRENDED Sigmond et al. (2013) Decomposition Correlation Skill Score

18. TOTAL TREND + DE-TRENDED Sigmond et al. (2013) Decomposition Correlation Skill Score ? ?

19. ● Consistent with potential predictability studies ( Holland et al, 2010 ) ● Explanation: winter sea ice edge closely related convergence of ocean heat fluxes (predictable on longer timescales) DE-TRENDED Trend-independent skill: OBSERVED LAG COR

20. Trend-independent skill: ● Good news: we understand seasonal dependency of skill ● Potentially bad news: Similarity suggest that all skill is due to persistence  Does our model outperform a persistence forecast? DE-TRENDED OBSERVED LAG COR Sigmond et al. (2013)

21. Skill relative to persistence (detrended) ● Model outperforms persistence for forecasts initialized in January and June ● Averaged over all months and lead times, enhancement is statistically significant (p<0.01) Merryfield et al. (2013)

22. Skill relative to persistence (detrended) CanSIPS performs slightly better than CFSv2 for detrended anomalies

23. Skill relative to persistence (Total anomalies) CanSIPS performs substantially worse than CFSv2 because: ● Underestimation of trend: 1) SIC initialization: dataset used (HadISST) underestimates trend → Large skill increase expected just by changing initialization dataset 2) SIT not initialized: (does not decrease with time as in observations) → Further skill increase expected by initializing sea ice thickness

24. Does a multi-system ensemble outperform single systems?

25. Skill relative to persistence Total anomalies: Detrended: Merryfield et al. (2013)

26. Conclusions: Initial examination of forecast skill of sea ice area in CanSIPS, which forms a baseline for improvements to be achieved by CanSISE Substantial skill, but most of the skill is due to strong downward trend in observations Forecast skill of detrended anomalies for longer lead times is generally small except for January/February Trend-independent forecast skill exceeds that of an anomaly persistence forecast Forecast skill for sea ice can be increased by combining multiple forecasting systems

27. Future research: Do we get skill on regional and local scales? Will model and initialization improvements lead to enhanced skill? Multi-model study on impact of sea ice initialization on prediction of 2007, 2008, 2011 and 2012 September minima (SPECS)