PROGNOSTIC DISCUSSION FOR 6 TO 10 AND 8 TO 14 DAY OUTLOOKS NWS CLIMATE PREDICTION CENTER CAMP SPRINGS, MD 300 PM EDT FRI AUGUST THE OPERATIONAL 00Z AND 06Z GFS MODEL SOLUTIONS BEGIN TO BREAK DOWN THE RIDGE OVER THE PACIFIC NORTHWEST WHILE THE HIGH RESOLUTION 00Z ECMWF MAINTAINS A STRONG RIDGE THERE. TELECONNECTIONS FROM THE UPSTREAM TROUGH OVER THE GULF OF ALASKA AND WESTERN ALEUTIAN RIDGE BOTH SUPPORT RIDGING OVER WESTERN NORTH AMERICA WHICH AGREES MORE WITH THE 00Z ECMWF SOLUTION. THIS, IN COMBINATION WITH VERY HIGH 500-HPA ANOMALY CORRELATIONS EXHIBITED DURING THE PAST 60 DAYS, RESULTED IN THE ECMWF-BASED SOLUTIONS BEING FAVORED IN TODAYS OFFICIAL 500-HPA HEIGHT BLEND CHART. TODAY'S OFFICIAL 500-HPA BLEND CONSISTS OF 10% OF TODAY'S OPERATIONAL 6Z GFS CENTERED ON DAY % OF TODAY'S GFS SUPERENSEMBLE MEAN CENTERED ON DAY % OF TODAY'S OPERATIONAL 0Z ECMWF CENTERED ON DAY % OF TODAY'S 0Z ECMWF ENSEMBLE MEAN CENTERED ON DAY 8...AND 10% OF TODAY'S 0Z CMC ENSEMBLE MEAN CENTERED ON DAY 8.

Generic Levels of EM Uncertainty – Lessons from geophysical modeling and current studies of climate impacts Nicholas Bond 1 Kerim Aydin 2, Anne Hollowed 2, James Overland 3 and Muyin Wang 1 1 University of Washington/JISAO 2 NOAA/AFSC 3 NOAA/PMEL

Techniques for Incorporating Model Ensembles Simple means Means w/ individual bias corrections Means w/ collective bias corrections Regularization via EOFs Bayesian techniques

Forecast error (24-h Temp.) Rixen et al. (J. Mar. Sys., 2009)

Time series of uncertainty

Models Contributed to IPCC AR4

Walsh et al. (2007)

Bayesian Model Averaging (BMA) Considers an ensemble of plausible models Key Idea - The models vary in their skill, and calibration of this skill produces better forecasts 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

p(y) is the forecast PDF; f k is the kth forecast model; w k is the posterior probability of forecast k being the best; g k (y | f k ) is the PDF conditional on f k being the best forecast. Weighted ensemble mean of parameter y

Estimating Weights by Maximum Likelihood Yields parameter values (weights) that make observed data most likely Likelihood function maximized over time and space through determination of model weights for a particular parameter Method uses expectation-maximization (EM) algorithm, which resembles a “hotter-colder” game Final weights related to how often a particular model constitutes the best model Training data set consists of last 40 days of short- term weather forecasts

Ensemble Model Projections for North Pacific Marine Ecosystems Initial Selection - Pick models that replicate the observed character of the PDO in their 20th century hindcasts (12 of 22 pass test) Regional Perspective - Examine specific parameter(s) in region of interest; consider means, variances, seasonality, etc. Model projections - Use quasi-Bayesian method based on “distance” between hindcasts and observations; form weighted ensemble means Uncertainty/Confidence - Estimate based on a combination of inter-model and intra-model variances in projections

Present Application Limited statistics for evaluation (there has been a single outcome for the past climate) Compare 20th century hindcast simulations by the climate models to observations on a regional basis Observations based on NCEP Reanalysis; good match of spatial scales Consider mean, variance, and other measures (trend, seasonality, etc.) if appropriate Estimate weights for projections following a scheme developed for objective analysis of weather observations, W k = exp(-D k /D m ) Apply intra-model variance from models with 5+ runs to models with fewer runs

Parameters Evaluated Bering Sea - Flow through Unimak Pass (Nutrient supply); Spring Winds (Larval flatfish transports); Summer SST & Wind Mixing (Sustained productivity) Gulf of Alaska - Along-coast winds (Larval fish distribution and abundance); Precipitation (Upper ocean baroclinity and eddy generation) NE Pacific - Coastal upwelling (Productivity); Zonal winds (LTL communities); Pycnocline depth; Upper-ocean transports; Temperature cross-sections

MIROC_hi MRI GFDL21 GFDL20 MPI CCCMA_t63 MIROC_med UKHadCM3 ECHO-G CCCMA_t47 UKHadGEM1

Downwelling Upwelling Weighted Ensemble Mean

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 Upper Temp (deg. C) Lower Temp Oithona (#/m3) Pseudocalanus Calanus 44 ~0 Thysanoessa

CCCMA_t63 MRI UKHadGEM1 MIROC_med UKHadCM3 MIROC_hi CCSM3 ECHO_G CCCMA_t47 GFDL21 GFDL20

SLP Anomalies Strong Wind Mixing Years SLP Anomalies Weak Wind Mixing Years

Sea Ice Area Anomaly for Bering Sea Overland and Wang (2007)

Flatfish in the SE Bering Larval Transport & Recruitment

MRI MIROC_med GFDL21 UKHadCM3 MIROC_hi GFDL20 CCCMA_t47 CCCMA_t63 ECHO-G

Hollowed et al. (2009)

Weighted Mean

2 FEAST Higher trophic level model NPZ-B-D Lower trophic level ROMS Physical Oceanography Economic/ecological model Climate scenarios BSIERP Integrated modeling Observational Data Nested models BEST

JELLYFISH EUPHAUSIIDS CALANUS NEOCALANUS SMALL MICROZOOPLANKTON LARGE MICROZOOPLANKTON SMALL PHYTOPLANKTON LARGE PHYTOPLANKTON NITRATE AMMONIUM DETRITUS BENTHIC INFAUNA BENTHIC DETRITUS IRON ICE ALGAE NITRATEAMMONIUM PSEUDOCALANUS Excretion + Respiratio n ICE OCEAN BENTHOS BSIERP NPZ model Mortality Predation Egestion Molting Mortality Predation Egestion Molting

* Both *Z*Z * *Z*Z *Z*Z * Transport Transport * Temperature X-Section

Newport Alaska Peninsula Vancouver Is. PAPA Seward Line *

*

SODA ECHAM5 PCM1 MirocM MirocH MRI Feb Aug Average Temperatures

SODA CCCMAT47_1 CCCMAT47_2 CCCMAT47_3 CCCMAT47_4 CCCMAT47_5 Feb Aug Average Temperatures

Absolute Model Errors

Final Remarks Global climate model simulations are being used for a host of regional applications There does not seem to be any single “best” method, but the protocol should include evaluation of model hindcast simulations of key parameters in the region of interest. Multi-model ensembles represent a key tool for seasonal climate forecasts, and are being used increasingly for short-term weather prediction. On long time horizons, model structural uncertainty dominates initial condition sensitivity.

From present to mid-21st century, climate change liable to be dominated by thermodynamic effects as opposed to dynamic effects (e.g., winds). The latter will be prone to interannual to decadal natural variability. The output from global climate models (perhaps subject to statistical downscaling) can complement that from vertically- integrated numerical models with full dynamics.