# Presentation: 828748

## Slide 1

## Slide 2

## Expected Changes in the Climate Forcing of Alaskan Waters in Late Summer/Early Fall  		

- Nicholas A. Bond1
- James E. Overland2 and Muyin Wang1
- 1 University of Washington/JISAO
- 2 NOAA/PMEL

## 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

## Models Contributed to IPCC AR4 

- Models Contributed to IPCC AR4

## 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

## 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 Wi = exp(-Di/Dm)
- Calculate PDFs of projections (ensemble weighted means and variances)

## 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**
**	****	****	****1999****	****	****2004**
**------------------------------------------------------------------**
**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**

## CCMAT_63

**CCMAT_63**

**MIROC_M**

**MRI**

**UKHAD_C**

**GFDL20**

**GFDL21**

## Predicted

- Predictive
-  Variance

**Predicted**
** ****Std. Dev.**

## Slide 11

## SLP Anomalies

- Strong Wind
- Mixing Years
- Weak Wind
- Mixing Years

## Change in SLP: 2040s vs 2000s

**Change in SLP: 2040s vs 2000s**

**Notes:**
Miub Model - Outlier in upper right-hand corner

## Coastal Gulf of Alaska 

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

## Weighted Ensemble Mean

**Weighted Ensemble Mean**

## Weighted Ensemble Mean

**Downwelling**

**Upwelling**

**Weighted Ensemble Mean**

## 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

## 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.