1. Seasonal Arctic sea ice in the NMME
Kirstin Harnos, Michelle L’Heureux, Qin Zhang, and Qinghua Ding

4. Current State of Sea Ice
Images courtesy of National Snow Ice Data Center

5. Current Events: Northwest Passage
Images courtesy of National Snow Ice Data Center

6. Previous Studies: Sea Ice Research & Prediction
General
Sea Ice extent is decreasing, with trends steepening
Initialization
Better initial conditions (including thickness) = better skill at longer leads
Trend
Highest skill from ability to capture trend
Multi-model
Multi-model ensembles better than individual models

7. How well does NMME predict sea ice?
NMME and Sea Ice
How well does NMME predict sea ice?
Sea ice extent (SIE) = Total area ≥ 15% concentration
Skill metrics:
Model Bias
Anomaly Correlation
Root Mean Square Error
Trend and Variability
Total SIE
Year-to-year SIE
hindcast climatology, 1 to 9 month lead
Observations:
NASA Bootstrap gridded sea ice concentrations

8. NMME Sea Ice Contributions
only using 16 of the members following past CFSv2 sea ice publications
complete hindcast records on the NCAR NMME archive

9. Climatology

10. Total SIE Bias
Less Ice
More Ice
[106 km2]

11. Year-to-Year SIE Root Mean Square Error

12. Total SIE Anomaly Correlation

13. Year-to-Year SIE Anomaly Correlation

14. NMME reduces total SIE bias
consequence of large opposite biases in individual models?
Y2Y largest errors during fall/winter (SIE minimum)
NMME slight improvement during shorter leads in fall/winter
Trend dominates ACC values
consistent with past studies
little to no Y2Y skill beyond 5 months

15. Trend

16. The Trend Problem
Comparison of linear trends in September sea ice extent for the period 1979–1996 and for 1997–2014. The smoothed nonlinear trend line is calculated using locally weighted scatterplot smoothing. Linear trends are calculated using least-squares regression.
Mark C. Serreze, and Julienne Stroeve Phil. Trans. R. Soc. A 2015;373:
© 2015 The Author(s) Published by the Royal Society. All rights reserved.

17. Observations:
Observations:

18. Observations: -13.4 % per decade

19. Observations: -13.4 % per decade NMME: -5.7% to -3.9% per decade
Blue: NMME Ensemble mean spread

20. NMME September Root Mean Square Error Observed SIE Anomaly [106 km2]
1982
1985
1988
1991
1994
1998
2001
2004

21. General: Sea Ice trends non linear and steepening
September NMME trends less than observed
Increase in September RMSE in most recent years
model inability to capture steepening trends?
Increase trends in observations = increase variance = increase RMSE

22. Take Home NMME reduces total SIE bias High skill associated with trend
SIE trends are steepening, models need to monitor and adjust to capture changing trends