3. Expected Changes in the Climate Forcing of Alaskan Waters in Late Summer/Early Fall Nicholas A. Bond 1 James E. Overland 2 and Muyin Wang 1 1 University of Washington/JISAO 2 NOAA/PMEL

4. Analysis of Model Output Ascertain parameters crucial to the ecosystem and/or the species of interest Use monthly mean model output for direct projections (when appropriate based on type and scale of parameter) Determine proxies or empirical relationships for parameters that must be projected indirectly

5. Models Contributed to IPCC AR4

6. Bayesian Model Averaging (BMA) Considers an ensemble of plausible models Forecast PDF estimated through weighting the PDFs of the individual models, with weights determined by posterior model probabilities BMA possesses a range of properties optimal from a theoretical point of view; works well in short-term weather prediction

7. Procedure Retain IPCC models that replicate the PDO in their 20th century hindcasts Select parameter(s) and criteria (mean, variance, trend?) for the region/ecosystem of interest Compute errors (“distances”) between observations and hindcasts for the latter half of the 20th century Compute weights based on W i = exp(-D i /D m ) Calculate PDFs of projections (ensemble weighted means and variances)

8. Zooplankton on the Bering Sea Shelf - Coyle et al. (2008) Compared water properties and zooplankton abundance and community structure between a cold and warm year 19992004 ------------------------------------------------------------------ Upper Temp 7.0 12.6 (deg. C) Lower Temp 2.0 3.2 Oithona 348 1633 (#/m3) Pseudocalanus 404 1211 Calanus 44 ~0 Thysanoessa 0.33 0.05

9. CCMAT_63 MIROC_M MRI UKHAD_C GFDL20 GFDL21

10. Predictive Variance Predicted Std. Dev.

12. SLP Anomalies Strong Wind Mixing Years SLP Anomalies Weak Wind Mixing Years

13. Change in SLP: 2040s vs 2000s

14. Coastal Gulf of Alaska Focus on Alaska Coastal Current (ACC) Transports related to along-coast winds Baroclinity related to runoff of freshwater

15. Weighted Ensemble Mean

16. Downwelling Upwelling Weighted Ensemble Mean

17. Summary Present generation coupled GCMs are starting to be used for making projections for Alaskan marine populations Bayesian model averaging represents a method for constructing model means and uncertainties Bering Sea shelf - SST warming of ~2 C by 2050; Weak trend of greater storminess Coastal Gulf of Alaska - Minimal net change in dynamic forcing of runoff; Weak trend of greater upwelling

18. Final Remarks From present to mid-21st century, climate change liable to be dominated by thermodynamic effects as opposed to dynamic effects (e.g., winds) Hybrid model simulations using present-day physics (modified to account for warming) and dynamic biology may be feasible. The results from these kinds of simulations should complement those from vertically- integrated numerical models with full dynamics.