Potential Approaches Empirical downscaling: Ecosystem indicators for stock projection models are projected from IPCC global climate model simulations.

Slides:



Advertisements
Similar presentations
Individual-based Models Three Examples
Advertisements

Modelling Vertebrates Beth Fulton End to End Model.
Chesapeake Bay Environmental Model Package A coupled system of watershed, hydrodynamic and eutrophication models The same package used for the 2002 load.
Seasonal and Interannual Variability of Peruvian anchovy Population Dynamics --progress report-- Yi Xu and Fei Chai June 2007.
(Mt/Ag/EnSc/EnSt 404/504 - Global Change) Climate Models (from IPCC WG-I, Chapter 8) Climate Models Primary Source: IPCC WG-I Chapter 8 - Climate Models.
An individual-based population dynamic model of seas scallop, with application to Georges Bank Rucheng Tian Department of Fisheries Oceanography SMAST,
The Response of Atlantic Cod (Gadus morhua) to Future Climate Change
Population Interactions Competition for Resources: –Exploitative competition: Both organisms competing for the same resource(s). –Interference competition.
0 OCEAN LITERACY Essential Principles & Fundamental Concepts of Ocean Science PRINCIPLE 5.
Bond. Climate Downscaling. Aydin. Upper trophic level (FEAST) Haynie. Economic and spatial fishery predictions Gibson. Lower trophic level (NPZ) Harvey.
Change in Ocean Surface Thermal Habitat in a Continental Shelf Marine Ecosystem and Its Affect on Lower Trophic Level Organisms Kevin Friedland, Joe Kane,
WP12. Hindcast and scenario studies on coastal-shelf climate and ecosystem variability and change Why? (in addition to the call text) Need to relate “today’s”
Zooplankton processes Puget Sound Oceanography Jan. 28, 2011.
Importance of Protecting Lake Trout. 250,000 lakes in Ontario 1% of these contain lake trout Central/eastern Ontario has >1/3 of lakes Provincial responsibility.
Barents Sea fish modelling in Uncover Daniel Howell Marine Research Institute of Bergen.
Energy Flow & Nutrient Cycle
COLLABORATORS: P. Estrade, S. Herbette, C. Lett, A. Peliz, C. Roy, B. Sow, C. Roy EDDY-DRIVEN DISPERSION IN COASTAL UPWELLING SYSTEMS California Canary.
Climate, Ecosystems, and Fisheries A UW-JISAO/Alaska Fisheries Science Center Collaboration Jeffrey M. Napp Alaska Fisheries Science Center NOAA Fisheries.
Food Webs in the Ocean Andrew W Trites Marine Mammal Research Unit University of British Columbia Who eats whom and how much?
US GLOBEC Fundamental Discoveries and Surprises David Mountain.
Ecological processes in a changing climate: winners and losers Third US GLOBEC Pan Regional Workshop 20 February 2009 J. Runge, presenter.
Role of IOOS in fisheries science and management? Power of IOOS data Models of fish distribution & abundance Models useful for management Future applications.
Physical effects on plankton and productivity on the Faroe Plateau E. Gaard, B. Hansen, S. K. Eliasen and K. M. H. Larsen Faroese Fisheries Laboratory.
Prince William Sound Resurrection Bay Knight Island Passage Middleton Island The physical model is run in three dimensions and the data are used to drive.
Spatial Fisheries Values in the Gulf of Alaska Matthew Berman Institute of Social and Economic Research University of Alaska Anchorage Ed Gregr Ryan Coatta.
Stratification on the Eastern Bering Sea Shelf, Revisited C. Ladd 1, G. Hunt 2, F. Mueter 3, C. Mordy 2, and P. Stabeno 1 1 Pacific Marine Environmental.
Centre for Ecological and Evolutionary Synthesis ICES/NAFO Decadal Symposium Santander, Spain May 12th 2011 The serial recruitment failure to North Sea.
Arctic Operational Oceanography at IMR Einar Svendsen Arctic GOOS planning meeting, September 2006 at NERSC, Bergen.
Module 2 Biocomplexity of the North Dactylica arctica Algae under Arctic sea ice Xanthoria elegens Poripidia flavocaerulescens.
Chapter 15.3 Oceanic Productivity. Marine organisms are connected through food production and consumption. Producers in the ocean are phytoplankton, larger.
Predicting right whale distributions from space Andrew J. Pershing University of Maine/ Gulf of Maine Research Institute.
GOA Retrospective analysis Model use: hypothesis testing The system, the stories, and the “data” The model: Elseas; like Ecosim but more flexible for our.
Water as an Environment Light Water Movements Part 3.
Jennifer M. Marsh M.S. Fisheries Student School of Fisheries and Ocean Sciences University of Alaska Fairbanks.
Science Behind Sustainable Seafood Solving the Ecosystem Problem Alaska Fisheries Science Center.
Yvette H. Spitz Oregon State University, CEOAS Carin J. Ashjian (1), Robert G. Campbell (2), Michael Steele (3) and Jinlun Zhang (3) (1) Woods Hole Oceanographic.
1 1 Morten D. Skogen WP10: Hindcast and scenario studies on coastal- shelf climate and ecosystem variability and change Overview and plans ECOOP annual.
First results of recently performed scenario simulations for the Baltic Sea for ECOSUPPORT co-workers Annual General Assembly 15 Oct 2009 H.E.
B. Question 1 - Climate Impacts How does climate forcing affect the target forage species in terms of timing, distribution, abundance, and species composition?
NOAA’s Climate Prediction Center & *Environmental Modeling Center Camp Springs, MD Impact of High-Frequency Variability of Soil Moisture on Seasonal.
Experience with ROMS for Downscaling IPCC Climate Models 2008 ROMS/TOMS European Workshop, Grenoble, 6-8 October Bjørn Ådlandsvik, Paul Budgell, Vidar.
"The Gulf of Alaska Seward Line & 2006 Russell R. Hopcroft, Kenneth O. Coyle, Tomas J. Weigngartner, Terry E. Whitledge Institute.
Ecosystem Research Initiative (ERI) for the Gulf of Maine Area (GoMA)
Ocean-scale modelling of Calanus finmarchicus
US GLOBEC NWA Program Phase 4B Synthesis Workshop 10/2-3/2006 – 507 Clark Laboratory, WHOI Logistics Logistics – Continental breakfast today & tomorrow.
Doney, 2006 Nature 444: Behrenfeld et al., 2006 Nature 444: The changing ocean – Labrador Sea Ecosystem perspective.
The Influence of Spatial Dynamics on Predation Mortality of Bering Sea Walleye Pollock Pat Livingston, Paul Spencer, Troy Buckley, Angie Greig, and Doug.
Atlantic Herring Conservation Lauren Keyes Yu Kawakami Brigette Jones.
Analysis of Walleye Growth, Movement and Habitat Quality in Lake Erie, Wang, H.-Y., 1 Rutherford, E.S., 2 Haas, R.C., and 3 Schwab, D. J. Lake.
OEAS 604: Final Exam Tuesday, 8 December 8:30 – 11:30 pm Room 3200, Research Innovation Building I Exam is cumulative Questions similar to quizzes with.
GLOBEC NWA Program: Phase 4B Synthesis FVCOM-NPZD- Copepod Dynamics Calanus Diapause Larval Fish Dynamics Basin-scale Calanus IBM Data/model synthesis.
The influence of climate on cod, capelin and herring in the Barents Sea Dag Ø. Hjermann (CEES, Oslo) Nils Chr. Stenseth (CEES, Oslo & IMR, Bergen) Geir.
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.
Benthic Fauna.
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.
THE BC SHELF ROMS MODEL THE BC SHELF ROMS MODEL Diane Masson, Isaak Fain, Mike Foreman Institute of Ocean Sciences Fisheries and Oceans, Canada The Canadian.
Tracking life history of each particle Particles could be divided into three groups (Fig. 7) The red group’s period of copepodite stage shortened when.
Critical and Compensation Depths (refer to handouts from 9/11/17)
Sea Surface Temperature as a Trigger of Butterfish Migration: A Study of Fall Phenology Amelia Snow1, John Manderson2, Josh Kohut1, Laura Palamara1, Oscar.
Plankton Ecology: Primary production, Phytoplankton and Zooplankton
Climate change research in the Gulf of Alaska
Ken Coyle, Russ Hopcroft & Alexei Pinchuk
Ocean Water & Life.
Critical and Compensation Depths Spring bloom and seasonal cycle
The effect of ship Nox deposition on cyanobacteria blooms
Relationship Between NO3 and Salinity:
Projected changes to tuna stocks
Secondary Productivity
Supervisor: Eric Chassignet
OCEAN WATER & OCEAN LIFE
Presentation transcript:

Potential Approaches Empirical downscaling: Ecosystem indicators for stock projection models are projected from IPCC global climate model simulations. Dynamical downscaling: IPCC simulations form the boundary conditions for regional bio- physical numerical models with higher trophic level feedbacks. Fully coupled bio-physical models that operate at time and space scales relevant to regional domains (impractical at present).

Predation Spawning Early larvae (spring) Late larvae (fall) Age-1 recruits Spatial distribution Biomass Consumption rate Prey composition Spring conditions (Late) summer conditions Prey Timing of ice retreat Spring SST Prey Summer SST Wind mixing Stability

Estimated effects of summer SST & predation on log-recruitment R 2 =0.44 P = Prediction interval Simulate effect of increase in average SST on recruitment at three levels of predation Low Med High

Dynamical Modeling Physical Forcing (Wind, temp, sun) Nutrients NO 3, NH 4… Primary Producers (Phytoplankton) Secondary Producers (Zooplankton) Higher trophic levels (Pollock etc.)

Horizontal resolution: ~10km, vertical resolution: 60 layers Computes physical properties i.e. temperature, salinity currents BEST-NPZ model coupled to ROMS at every grid point and time-step ROMS Physical Oceanography Model

EUPHAUSIIDS LARGE COPEPODS MICROZOOPLANKTON SMALL PHYTOPLANKTON LARGE PHYTOPLANKTON NITRATE AMMONIUM Slow sinking DETRITUS IRON SMALL COPEPODS Excretion + Respiration WATER Mortality Predation Egestion JELLYFISH Fast sinking DETRITUS Inexplicit food source ICE ALGAE NITRATEAMMONIUM BENTHIC FAUNA BENTHIC DETRITUS ICE BENTHOS

Model Validation: Data availability Location of nitrate data used: All months, all years

Model Validation: Primary Production Observations from Rho, Whitledge and Goering (1997) Simulated Observed Monthly mean daily primary production: Middle Shelf Simulated Observed

Zooplankon Biomass Day Microzooplankton5.916 Small Copepods Large Copepods Euphausiids E-5 Compares ‘reasonably’ well to Coyle data … – but will the fish have enough to eat ?

Model Predictions: Ecosystem Projections Euphausiid production: Annual average for shelf break A single projection CCC MA Ensemble of runs will define upper and lower limits of projection g C m Zooplankton biomass: Depth integrated at M2 mooring

FEAST model for forage species and predators Bioenergetics of feeding, growth, spawning Focus on data-driven functional response between predator and prey Use allometric relationships for rates Diet preferences based on stomach data Movement (towards prey concentrations, away from poor conditions, migration for spawning) Currently includes pollock, cod, and arrowtooth flounder

Diet fitting by region Prey Type (proportion in diet) by pollock body length (0-80cm) region 3 size classes of copepod in model summed for fitting amphipods, shrimp stomachs sampled by pollock length by region

Combined BTS+Acoustic survey vs FEAST

FEAST age-0 seasonal forage potential and stock-assessment estimate of year-class strength Colors: stock- assessment year-class strength Blue weakest Red strongest Domain 8 (outer northwest shelf) Week of year Age 0 foraging potential