1. An Approach to Enhance Credibility of Decadal-Century Scale Arctic
Climate Change Projections: Transient Climate Sensitivity Analysis
Xiangdong Zhang
International Arctic Research Center, University of Alaska Fairbanks

2. Rapid reduction of sea ice cover in the Arctic Ocean
Extreme Event
Summer 2007
acceleration
Zhang et al, 2012

3. (IPCC AR4)
Multi-model and multi-model-ensemble mean

4. Modeled multiyear (summer minimum) sea ice area anomaly
Zhang and Walsh, J. Climate, 2006

5. Transient climate sensitivity/feedback analysis
Questions:
Can we get physically-based information to improve understanding of model uncertainties, beyond conventional visual data-model comparison or statistical analysis?
measured by sensitivity/feedback term
Can we exclude natural variability in the recently-observed acceleration of Arctic sea ice reduction?
no need to answer this question
Can we develop a criterion matrix to help constrain or reduce the uncertainties in the model projections of future Arctic sea ice cover change?
select a subset based on sensitivity
Approach:
Transient climate sensitivity/feedback analysis

6. Global climate sensitivity analysis (e.g., Gregory et al. 2004):
N = Q - Y*ΔTs
where the net energy flux imbalance of climate system (N) is related to the external radiative forcing (Q), a climate feedback term (Y), and global averaged surface air temperature change (ΔTs).
Arctic sea ice sensitivity analysis:
ΔVice = -Y*ΔTs
ΔAice: a proxy of ΔVice
ΔAice = -Y*ΔTs

7. Seasonality of the sensitivity to surface air temperature change:
Correlation between Summer Sea Ice Area and Arctic regionally averaged surface air temperature.
Summer sea ice area is strongly correlated with melting season, not freezing season, Arctic regionally averaged surface air temperature.

8. Comparison of modeled and observed surface air temperature:
Arctic Regionally Averaged Melting Season (April-September) Surface Air Temperatures – Model Outputs and Observations

9. Comparison of modeled and observed sea ice area:
Arctic Regionally Averaged Melting Season (April-September) Surface Air Temperatures – Model Outputs and Observations

10. CMIP3 models as a whole underestimated observed sensitivity

11. Individual CMIP3 models either underestimated or overestimated observed sensitivity

12. Sensitivity-constrained projection of summer minimum sea ice changes: Uncertainties are much reduced
Ice-Free:

13. Sensitivity-constrained projection of winter and summer surface air temperature changes

14. Comparison of CMIP3 and CMIP5 models

15. CMIP5 CMIP3 Comparison of CMIP3 and CMIP5 models
Arctic Regionally Averaged Melting Season (April-September) Surface Air Temperatures – Model Outputs and Observations
CMIP5
CMIP3

16. CMIP5 CMIP3 Comparison of CMIP3 and CMIP5 models
Arctic Regionally Averaged Melting Season (April-September) Surface Air Temperatures – Model Outputs and Observations
CMIP5
CMIP3

17. Summary:
Large uncertainties in the Arctic sea ice cover changes result from the spread of sensitivities or overall feedback strengths involved in the model computations.
Initial condition can impact evolution of feedback strength in the model integration. Direct ensemble mean cannot enhance credibility of model projections.
The Sensitivity-constrained selection of subset of the model runs substantially reduced uncertainties.
Compared to CMIP3 models, the sensitivity has been greatly increased in CMIP5 models, overestimating the sensitivity in the observations.
The increased sensitivity in the CMIP5 models is due to over-tuned sea ice. The cold bias of surface air temperature continually exist in the models.

18. Thank You!