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International Workshop on Climate and Oceanic Fisheries, Rarotonga, Cook Islands
21st Century Climate Change Impacts on Marine FisheriesAnne B. Hollowed, NOAA, NMFS, Alaska Fisheries Science Center,
Seattle, WA USA
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Key References
2010 Stock et al. Progress in oceanography 88(1-4): 1-27
2011 ICES Journal of Marine Science 68(6)
Century‐scale physical climate model projections
Objectives:– Simulate and understand the causes of historical climate change (1860‐present)
– Make global projections of climate change over the next century, including and estimate of uncertainty.
Atmosphere
Land OceanIce
Climate models agree on many broad‐scale climate changes over the next century
Precipitation change, A1B, 2080-2099 – 1980-1999
Stippling in places where at least 80% of models agree on sign of change
Meehl et al., Chapter 10, IPCC AR4 WG1 Report
Substantial biases may exist at regional scales. C. Stock (GFDL, American Fisheries Society Annual Meeting, Seattle, 2011)
Bias corrections and focusing on changes in features provide ways forward, but it will take continued improvements to climate model dynamics to solve
Two pronged approach, applied with caution!
Refined resolution AOGCMs (Stock, AFS 2011)• Could fundamentally improve the resolution of shelf‐scale
processes and basin‐shelf interactions in climate models• Computational costs increase with the cube of horizontal grid
refinement• Processes that were once sub‐grid scale are now resolved:
parameterizations must be reformulated• May address some biases, but not all biases rooted in
resolution.
While more refined‐resolution simulations (~1/8‐1/4 degree) will be available in IPCC AR5, most will have resolutions similar to those in IPCC AR4.
Regional NPZ-B-D
Lower trophiclevel
Regional Higher trophiclevel model
Regional Economic/ecological
model
Land physicsand hydrology
Ocean ecology andBiogeochemistry
Atmospheric circulation and radiation
Interactive CO2
Ocean circulation
Plant ecology andland use
Sea Ice
Regional Economic/ecological
model
Regional Higher trophiclevel model
Regional NPZ-B-D
Lower trophiclevel
Regional Economic/ecological
model
Regional Higher trophiclevel model
Regional NPZ-B-D
Lower trophiclevel
CCSGOA
EBS
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Predicting Responses of Predicting Responses of Living Marine ResourcesLiving Marine Resources•• Shifting zoogeographic distributions Shifting zoogeographic distributions •• PhenologyPhenology (match(match--mismis--match)match)•• Changing vital rates (growth, mortality, maturity Changing vital rates (growth, mortality, maturity
schedules)schedules)•• Adaptive flexibility (genetic diversity, flexibility in life Adaptive flexibility (genetic diversity, flexibility in life
history (spawning distribution, food habits))history (spawning distribution, food habits))•• Species interactions (predatorSpecies interactions (predator--prey, competition)prey, competition)
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PhenologyPhenology Example:Example:Loss of Sea Ice in ArcticLoss of Sea Ice in ArcticWassmannWassmann (2011) (2011) ProgProg. . OceanogrOceanogr..
AprMay
JunJul
Aug
SepSnow
Sea icePhytoplankton Bloom
Snow
Sea ice
AprMay
JunJul
Aug
Sep
Ice algae
Ice algae
Phytoplankton Bloom
StratificationBASIS Survey
Bottom TemperatureBottom Trawl
Example of Current Environmental Tolerances: Eastern Bering Sea Forage Fish, Hollowed et al. In Review, DSR II
Example of Current Environmental Tolerances:Eastern Bering Sea Forage FishHollowed et al. In Review, DSR II
Cold years 2006-2009
Warm years (2004-2005)
Age – 0 pollock Age-1 pollock Capelin
General Additive Model predicted spatial surfaces of fish density.Dotted line is 2o
C isotherm, solid lines are 50m and 100m isobaths.
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Projection Modelling ApproachesProjection Modelling Approaches
•• Statistical downscaling: IPCC scenarios Statistical downscaling: IPCC scenarios downscaled to local regions and ecosystem downscaled to local regions and ecosystem indicators incorporated into stock projection indicators incorporated into stock projection models.models.
•• Dynamical downscaling: IPCC scenarios Dynamical downscaling: IPCC scenarios downscaled to local regions and coupled to biodownscaled to local regions and coupled to bio--physical models with higher physical models with higher trophictrophic level level feedbacks. feedbacks.
•• Fully coupled bioFully coupled bio--physical models that operate at physical models that operate at time and space scales relevant to coastal time and space scales relevant to coastal domains.domains.
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Qualitative Vulnerability AssessmentQualitative Vulnerability AssessmentDawDaw et et ealeal 2009 FAO Report 530 Climate change 2009 FAO Report 530 Climate change
and capture fisheries: potential impacts adaptation and capture fisheries: potential impacts adaptation and mitigationand mitigation
•• ExposureExposure•• SensitivitySensitivity•• Potential impactPotential impact•• Adaptive capacityAdaptive capacity
Elements of Stock Projection Models
FisheriesOceanography
Fisheries Management
Policies
Demand forfish
FisheriesEconomics
Fisheries Enhancement
YieldForecast
DownscaledIPCC
modeloutput
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1960 1970 1980 1990 2000
-2-1
01
23
Year
Link torecruitment
Statistical Example:Climate Impacts on Productivity
Age-structured model
Management Strategy
TACData
ClimateDecision ruleYears for
defining thecurrent regime
Climatedata
Modified from A’mar et al. 2009, IJMS
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1960 1970 1980 1990 2000
-2-1
01
23
Year
Link torecruitment
Statistical Example:Climate Impacts on Productivity
Age-structured model
Management Strategy
TACData
ClimateDecision rule
Modified from A’mar et al. 2009, IJMS
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Climate variability – GOA pollock spawning biomass A’mar et al. 2009 ICES J. Mar. Sci.
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 ccsm31
Year
Spaw
ning
bio
mas
s (m
illion
mt)
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 gfdl201
Year
Spaw
ning
bio
mas
s (m
illion
mt)
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 gfdl211
Year
Spaw
ning
bio
mas
s (m
illion
mt)
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 mirocH1
Year
Spaw
ning
bio
mas
s (m
illion
mt)
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 mirocM1
Year
Spaw
ning
bio
mas
s (m
illion
mt)
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 mirocM2
Year
Spaw
ning
bio
mas
s (m
illion
mt)
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 mirocM3
Year
Spaw
ning
bio
mas
s (m
illion
mt)
2010 2020 2030 2040 2050
0.0
0.1
0.2
0.3
0.4
0.5 ukhadcm31
YearSp
awni
ng b
iom
ass
(milli
on m
t)
SB40%SB20%
EBS pollock recruitment study ‐Retrospective study to Identify
mechanisms
7 8 9 10 11 12
67
89
1011
Summer SST
log(
Rec
ruitm
ent)
Mueter et al. (ICES Journal of Marine Science 68(6)
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1. Select models that perform well in region (9 models)
2. Select scenarios: B1(low), A1B (intermediate) and A2 (high) CO2
3. Create 82 SST scenarios4. Apply SST to recruitment
mechanism to create future production scenarios.
Ianelli et al. ICES Journal of Marine Science 68(6)
EBS Walleye Pollock Spawning Biomass
SSTs based on 82 climate-change scenarios
NPZ‐NEMURO.FISH
Understanding ecosystem feedbacks and linkages
ECOSIMFood-web
IndividualBased Models withBioenergetics
SpatialGradient Tracking“happiness”
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Individual‐basedEggs & Larvae
“PassiveParticles“
Batch SpawningAdult Energy
Allocation
DynamicEnergy Budgets
Post‐larval to adult Habitat Utilization
1) Hydrodynamic &Ecosystem modelsprey field, water currents
CoupledModels(End to End)
Projecting Climate Impacts using Coupled Models
Hufnagl and Peck ICES Journal Marine Science 68(6)
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2
FEAST Higher trophiclevel model
NPZ-B-DLower trophic
level
ROMSPhysical
Oceanography
Economic/ecological model
Climate scenarios
BSIERP Integrated modeling
Observational DataNested mod
els
BEST
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Statistical StockProjection Model
NEMURO-FISH Bioenergetic
ECOSIMFood-web
NPRB –BEST-BSIERP
#Species <~5-10 Multispecies or single species
1-10 Bottom up with dominant fish
100s Bottom up and top down with dominant fish and fisheries
Ecosystem Feedbacks
One Way One way (some two way)
One way Two way
Biological Realism
Minimally realistic
Moderately realistic
Minimal w/Ecosystem feedbacks
Reasonably realistic
Computational Requirements
Moderate High Moderate Very high
Capability to Perform Sensitivity Analyses to Track Sources of Error
High Moderate Moderate Low
Treatment of Uncertainty
Excellent Moderate Moderate Minimal
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Global Assessments:Global Assessments:How much fish in the future?How much fish in the future?2006 2006 ––
–– Capture fisheries stabilized at 85Capture fisheries stabilized at 85--95 95 mmtmmt. . –– Aquaculture ~ 40 Aquaculture ~ 40 mmtmmt and increasingand increasing–– 33 33 mmtmmt used for oil and animal feed, rest used for oil and animal feed, rest
consumed.consumed.•• 2050 2050 ––
–– Population projected to increases to 9 billion (UN Population projected to increases to 9 billion (UN Human Population Prospectus)Human Population Prospectus)
–– If fish stays 20% percent of dietary protein, 20% of If fish stays 20% percent of dietary protein, 20% of 365 365 mmtmmt ~ 75 ~ 75 mmtmmt tonnes MORE fishtonnes MORE fish
Rice and Garcia (ICES Journal Marine Science 68(6)
Coupled marine social‐ecological systems (Perry et al., 2010; In:Barange et al., Marine ecosystems and global change. OUP).
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• GCOMS (Global Coastal Ocean Modelling System). • Model components:
– POL-Coastal Ocean Modelling System– ERSEM (European Regional Seas Ecosystem
Model)• 1/10°resolution • Includes important shelf
processes: Tides, upwelling, Benthic/pelagic recycling
• Geographically linked to LME : ocean governance scale
• Although global, the models are regional
Development of Regional Shelf Seas Modelling
Barange et al. 2010
2012
SICCME Marine
Ecosystem Modeling & Analysis
IPCC Synthesis & Reporting
IPCC Earth System Modeling & Analysis
Symposiumvolume
Symposium Symposium
Symposiumvolume
SICCME Synthesis & Comparative Research
IPCC Earth System Modeling &Analysis
2015
SICCME Model Update & Revision
SICCME Synthesis & Comparative Research
Symposiumvolume
20172013 2014 2016
Symposium
Workshop
Workshop
UN Millennium Climate Report
Human Dimensions
Regional Synthesis
2018
IPCC Synthesis & ReportingW
orkshop
Wor
ksho
p
Earth Ecosystems
NorthernHemisphereMarine Ecosystems
TrainingSimulation
Tools and Models
TrainingSimulation
Tools and Models
SICCME Marine
Ecosystem Modeling & Analysis
2019
Ocean Monitoring ProgramOcean Monitoring Program
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SummarySummary
•• Models have inherent strengths and weaknessesModels have inherent strengths and weaknesses•• Multiple model ensembles currently under development.Multiple model ensembles currently under development.•• Coupling NPZ into Coupling NPZ into GCMsGCMs or or ESMsESMs holds great promiseholds great promise•• Coupling to fish and fisheries may be computationally too Coupling to fish and fisheries may be computationally too
complex complex •• Developing future policy frameworks is needed and will Developing future policy frameworks is needed and will
require integration of stakeholders and policy makers. require integration of stakeholders and policy makers. •• A global perspective is needed to project longA global perspective is needed to project long--term term
trends in fisheries. trends in fisheries. •• Coordinated monitoring and assessment needed to Coordinated monitoring and assessment needed to
support global models. support global models. •• Uncertainty must be communicatedUncertainty must be communicated