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)

Increased interest in seasonal

predictions Sept. 2012Sept. 1980

• Number of commercial vessels through NE passage:

2009: 2

2012: 46

• Sept. 2013: first commercial vessel through

NW passage

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

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

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

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)

September forecasts:

September forecasts:

Sep 1O

ct 1

Aug 1

Jul 1

time

Jun 1

May 1

Nov 1

0123411Lead(months)

September forecasts:

Sep 1O

ct 1

Aug 1

Jul 1

time

Jun 1

May 1

Nov 1

0123411Lead(months)

September forecasts:

Sep 1O

ct 1

Aug 1

Jul 1

time

Jun 1

May 1

Nov 1

0123411Lead(months)

September forecasts:

Sep 1O

ct 1

Aug 1

Jul 1

time

Jun 1

May 1

Nov 1

0123411Lead(months)

?

September forecasts:

Sep 1O

ct 1

Aug 1

Jul 1

time

Jun 1

May 1

Nov 1

0123411Lead(months)

?

September forecasts (detrended):

Sep 1O

ct 1

Aug 1

Jul 1

time

Jun 1

May 1

Nov 1

0123411Lead(months)

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

Forecasts for other months:

Correlation Skill Score

(TOTAL, not detrended):

Sigmond et al. (2013)

TOTAL

TREND

+

DE-TRENDED

Sigmond et al. (2013)

Decomposition Correlation Skill Score

TOTAL

TREND

+

DE-TRENDED

Sigmond et al. (2013)

Decomposition Correlation Skill Score

?

?

● 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

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

Sigm

ond et al. (2013)

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)

Skill relative to persistence (detrended)

CanSIPS performs slightly better than CFSv2 for detrended anomalies

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

Does a multi-system ensemble outperform

single systems?

Skill relative to persistence

Total anomalies:

Detrended:

Merryfield et al. (2013)

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

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)