dynamical seasonal forecasting for decision support in ......feb 09, 2017 · eveson et al (2015)...
TRANSCRIPT
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Dynamical seasonal forecasting for decision support in marine fisheries and aquaculture
Claire Spillman, Alistair Hobday, Paige Eveson, Jason Hartog & Grant SmithNOAA Science Seminar, 9 February 2017, Silver Spring, MD, USA
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Fig 3.2 in Rhein et al 2013. Climate Change 2013:
The Physical Science Basis. WG1 IPCC AR5
How to best manage our marine resources under
climate change?
Observation-based estimates of annual global mean upper ocean heat content
Warming oceans
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ERSSTv4 SST data: 1961-1990 climatology
Recent marine heatwave
• Highest SST on record for JFM 2016 for GBR & Tasmania
26.5
27.0
27.5
28.0
28.5
29.0
(°C) Summer (JFM) SST: GBR
Hughes et al 2016: The Conversation
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In marine industries, coping with climate variability is “business as usual” to many…
Coping with climate variability is responsive adaptationCost effective?
Does it allow for “opportunity” to be recognised?
Climate change is a new factor for a range of businesses Can it just be managed as for climate variability?
Anticipating climate variability & change is proactive adaptation
Climate variability vs climate change
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Weather timescales: 1-7 days
Minimal warning time
Reactive management
Seasonal timescales: 2 weeks-9 months
Early window for implementation of strategies to minimise
impacts
Climate forecasting: 10 – 100s years
Long term planning
Seasonal timescale most useful for proactive management.Business performance and industry resilience could be
improved with predictions about future conditions.
Tim
e
Management decision timescales
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• Seasonal timescale most useful for marine managers
• Provides an early window for implementation of management strategies to minimise impacts
• Dynamical models can incorporate climate change signals unlike statistical models
• Better managed marine resources have improved resilience under climate change
Surveying bleached corals
Spillman & Alves 2009, Spillman 2011Image Commonwealth of Australia (GBRMPA)
Seasonal forecasting
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Usefulness depends on the timing of both the management decision to be made and that of the critical environmental period affecting the decision, together with forecast accuracy at that time.
When the lead-time required to make a decision is such that the forecast skill for the critical environmental period is adequate, a seasonal forecast may be useful (a). Hobday et al 2016
When is seasonal forecasting useful?
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Predictive Ocean Atmosphere Model for Australia The Bureau of Meteorology's global dynamical coupled
ocean-atmosphere seasonal prediction system
Spillman & Alves 2009, Spillman 2011
Atmospheric ModelAtmosphere Initial Conditions
E 180o W90o E
Coupler
90o W 0o
Atmosphere &
Land Initial
Conditions
Ocean ModelOcean Initial Conditions
POAMA
• Forecast out to 9 months• Run operationally in real
time twice a week• Atmospheric grid: ~250 km• Ocean grid: 0.5-1.5o x 2o
• Temperature, sea level, salinity, currents & atmospheric variables
http://poama.bom.gov.au
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ACCESS-S
• ACCESS-S1 to replace POAMA as Bureau operational system in 2017
• Global coupled model• UKMO collaboration• S1 operational in 2017• S2 operational in 2018-19• Run daily in real-time
Australian Community Climate & Earth SystemSimulator
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POAMA-2 ACCESS-S1
Atmospheric model
Bureau BAM (~10 years old) Latest UKMO atmospheric model (GC2)
Atmospheric resolution
Horizontal: 250 km (T47)Vertical: 17 levels
Horizontal: 60 km in the mid latitudes (N216)Vertical: 85 levels
Ocean model MOM version 2 (~13 years old) Latest European ocean model NEMO
Ocean resolution Horizontal: ~200 km x 100 km Vertical: 15- 1000 m levels
Horizontal: 25 km (eddy permitting)Vertical: 1-200 m levels
Land surface model
Simple bucket model State-of-the-art land surface model JULES
Sea ice model No sea ice model Latest sea ice model CICE (UK & USA)
Hindcast set 1982-2010 (33 ensembles) 1990-2012 (11 ensembles)
POAMA vs ACCESS-S
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ACCESS-SPOAMA-2
Ocean model resolution
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Define management
or industry
need
Determine critical
variables &
decision timescales
Verification data
A. Assess needs
Assess seasonal
model skill
Produce habitat
distribution forecast
Forecast products
+
B. Development
User feedbackHobday et al 2016
C. Implementation
End user decision
Forecast delivery
Support &
education
Seasonal forecast tool development
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A. Assess needs
• Define management need� What is the management issue?� What management decisions are made and when?� Which thresholds trigger management action?
• What forecast information is needed?� Variable of interest?� Spatial and temporal resolution?� Lead time required?� Minimum skill?
• What validation data is available?
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Habitat models
aerial
surveys
farm data
catch
data
tag
data
200m
B. Development
Observations
0
10
20
30
40
50
Fre
qu
ency
(%
)
NOV 2000 (Issued: NOV 2000 L=0)
-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
Bin size: 0.50Observed climModel climModel fcstEnsemble mean
0
10
20
30
40
50
DEC 2000 (Issued: NOV 2000 L=1)
-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
Farm Temperature Anomaly oC
0
10
20
30
40
50
Fre
qu
ency
(%
)
JAN 2001 (Issued: NOV 2000 L=2)
-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 Farm Temperature Anomaly oC
0
10
20
30
40
50
FEB 2001 (Issued: NOV 2000 L=3)
-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
NOV 2000 [L=0]Emn:14.67oC Clim:13.85
oC
DEC 2000 [L=1]Emn:15.61oC Clim:15.14
oC
JAN 2001 [L=2]Emn:16.74oC Clim:16.45
oC
FEB 2001 [L=3]Emn:17.25oC Clim:16.81
oC
TERCILES
Lower
Middle
Upper
Probabilityof falling in each
tercile basedon 33 members
0.00.20.40.60.81.0
Forecast issued: 20001101
Forecast products
+
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-31 JAN [Lead=Month 0]
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-28 FEB [Lead=Month 1]
e2.4abc SSTA: Australia 20110101 Start: 01 JAN (1982-2010)
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-31 MAR [Lead=Month 2]
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-30 APR [Lead=Month 3]
0.00.20.40.60.81.0degC
-1.0
-0.90
-0.80
-0.70
-0.60
-0.50
-0.40
-0.30
-0.20
-0.10
0.0
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.50.00.20.40.60.81.0
0.00.20.40.60.81.0degC
Realtime Forecasts
Model skill
lead, location, season
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Hobday et al 2011, Spillman & Hobday 2014, Spillman et al 2015, Eveson et al 2015
• Online forecast delivery
• Emailed reports
• Meetings & presentations
• Support & education
• Industry feedback
• Farm visits very successful
• Industry award
C. Implementation
Matt West, Australian Prawn Farms
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Essential ingredients: Very useful ingredients:
• Strong industry engagement• Clear understanding of end
user skills & requirements• Skilful model forecasts• Appropriate forecast delivery• Industry feedback
• Industry advocate• Face-to-face user meetings
Lessons learned
Critical information: Very useful information:
• Relevant spatial & temporal scales?
• Minimum skill level required?• Types of management
decisions made?
• Which threshold initiates management actions?
• The economic cost/benefit value of forecasts to industry?
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Coral bleaching risk
Commercial wild fisheries
Aquaculture
Sea level extremes
Heatwaves
ENSO
Images: C
om
monw
ealth
of A
ustra
lia, w
ww
.dis
cove
rwest.c
om
.au, N
atio
nal
Geogra
phic
, AB
C
Seasonal marine applications
Use forecasts of seasonal ocean
temperature and sea level
anomalies up to 6 months ahead
Operational & experimental
products
Spillman (2011), Spillman & Hobday (2014), Hobday et al (2012), Eveson et al. (2015), Miles et al (2014), Alves et al (2011), Shi et al (2012)
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Prawns - QueenslandRainfall & air temperatureSpillman et al (2015)
Salmon –TasmaniaSea surface temperatureSpillman & Hobday (2014)
Tuna – Eastern AustraliaTemperature at depthHobday et al (2011)Tuna – GAB
TemperatureEveson et al (2015)
Marine seasonal forecasting case studies
Hobday et al (2016), Tommasi et al (accepted). Images: Leal et al 2014
Sea level – West PacificSea levelMiles et al (2014)
Coral – Great Barrier ReefSea surface temperatureSpillman et al (2009,2011,2012)
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Great Barrier ReefA$6B
Great Barrier Reef Marine Park Authority (GBRMPA)
Extreme ocean temps for bleaching
Inform management activities
Information 2 weeks to 6 months ahead
Summer SST though year round
1. Coral bleaching
Spillman et al (2009, 2011, 2012)
• First operational dynamical seasonal forecasts for coral bleaching risk
• Component of GBRMPA Early Warning System
• Brief government, tourist operators, general public
• 3 year project to upgrade bleaching risk product suite to ACCESS-S ($510K)
Real-time forecast products• Spatial maps of multiweek SSTA• GBR index based on mean SSTA • Probabilistic SSTA forecasts > thresholds• Degree Heating Months
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Eastern AustraliaSBT
A$40M
Australian Fisheries Management Authority (AFMA)
Reduce non-quota capture
Inform spatial zoning to regulate access
Information 2 weeks to 6 months ahead
Winter ocean temp(0-200m)
2a. Southern bluefin tuna
Hobday et al (2011)
+
0m
POAMA forecast
Observed temperature preference by depth
200m
Catch dataTuna tags
Management decision
Information to allow authorities to set management zones
Probability maps across
depth
SBT habitat forecast products
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Great AustralianBight SBT
A$60M
Peak industry body and fishers
Changing SBT distributions
Improve industry efficiency
Information up to 1-2 months ahead
Summer SST
Eveson et al (2015)
2b. Southern bluefin tuna
Forecasts were skilful 2 months ahead in the fishing season (Dec-Mar). Information about future habitat distributions in upcoming months allows fishers to better plan their port departures and operational activities.
SBT habitat distribution forecast products
SBT data for habitat model
+Meetings & education Industry feedback
Forecasts online
http://www.cmar.csiro.au/gab-
forecasts/index.html
POAMA ocean forecasts
Habitat distribution model
Habitat distribution forecasts
120 125 130 135 140
-40
-38
-36
-34
-32
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Habitat preference forecast: January 2013 Issued 01 Dec 2012
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Tasmanian SalmonA$500M
Salmon Growers Association
Salmon growntowards upper thermal limit
Reduce vulnerability to temp extremes
Information 1 month to a season ahead
Summer SST
3. Salmon aquaculture
Spillman & Hobday (2014)
Tailored salmon farm forecast products
Farm temp data & monthly climatologies
+
NOV 2000 [L=0]Emn:14.67oC Clim:13.85
oC
DEC 2000 [L=1]Emn:15.61oC Clim:15.14
oC
JAN 2001 [L=2]Emn:16.74oC Clim:16.45
oC
FEB 2001 [L=3]Emn:17.25oC Clim:16.81
oC
TERCILES
Lower
Middle
Upper
Probabilityof falling in each
tercile basedon 33 members
0.00.20.40.60.81.0
Forecast issued: 20001101
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-31 JAN [Lead=Month 0]
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-28 FEB [Lead=Month 1]
e2.4abc SSTA: Australia 20110101 Start: 01 JAN (1982-2010)
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-31 MAR [Lead=Month 2]
110E 120E 130E 140E 150E 160E45S
35S
25S
15S
5S
1-30 APR [Lead=Month 3]
0.00.20.40.60.81.0degC
-1.0
-0.90
-0.80
-0.70
-0.60
-0.50
-0.40
-0.30
-0.20
-0.10
0.0
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.50.00.20.40.60.81.0
0.00.20.40.60.81.0degC
Monthly forecast reports
0
1
2
3
Lead
time (
month
s)
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
a0.00.20.40.60.81.0
0.00.20.40.60.81.0acc
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.0
0
1
2
3
Lead
time (
month
s)
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
b
Forecast accuracy information
Meetings & education
Industry feedback
Farm specific forecasts
Regional ocean forecasts
Forecasts were skilful up to 3
months ahead in summer months
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4. Prawn aquaculture
Queensland prawnsA$70M
Australian Prawn
Farmers Association and
individual farms
Optimize prawn growth
& yield; target markets
Reduce vulnerability to
temp & rainfall extremes
Information 2 weeks to a
season ahead
Air temperature &
rainfall year round
Spillman et al (2015)
Tailored prawn farm forecast packages
Farm visitsIndustry feedbackcatch
data
catch
data
National ENSO & TC outlooksNational ENSO & TC outlooks
catch
data
National ENSO & TC outlooks
Forecast skillForecast skill
QLD1: Tmin below lower tercile
F1
F2
M
S
J F M A M J J A S O N D
QLD2: Tmin below lower tercile
0.00.20.40.60.81.0
Regional multiweek rainfall & air temp forecasts
Regional multiweek rainfall & air temp forecasts
Forecast skill
QLD1: Tmin below lower tercile
F1
F2
M
S
J F M A M J J A S O N D
QLD2: Tmin below lower tercile
0.00.20.40.60.81.0
Regional multiweek rainfall & air temp forecasts
Seasonal forecast information allows farm managers to implement strategies to optimise prawn growth, whilst reducing vulnerability to climate extremes.
Farm forecastsFarm forecastsF1 (20120809-20120822)Emn: 11.7oC Clm: 14.4oC Obs:12.0oC
F2 (20120823-20120905)Emn: 15.0oC Clm: 15.8oC Obs:16.4oC
Australian Prawn Farms: QLD2 TMIN + Stn 033119 TEMP clim
M1 (20120901-20120930)Emn: 17.1oC Clm: 17.0oC Obs:16.6oC
S1 (20120901-20121130)Emn: 19.7oC Clm: 19.5oC Obs:18.9oC
TERCILES
Lower
Middle
Upper
Probabilityof falling in each
tercile basedon 33 members
0.00.20.40.60.81.0
Forecast issued: 20120809
Forecasts onlineForecasts online
Farm forecastsF1 (20120809-20120822)Emn: 11.7oC Clm: 14.4oC Obs:12.0oC
F2 (20120823-20120905)Emn: 15.0oC Clm: 15.8oC Obs:16.4oC
Australian Prawn Farms: QLD2 TMIN + Stn 033119 TEMP clim
M1 (20120901-20120930)Emn: 17.1oC Clm: 17.0oC Obs:16.6oC
S1 (20120901-20121130)Emn: 19.7oC Clm: 19.5oC Obs:18.9oC
TERCILES
Lower
Middle
Upper
Probabilityof falling in each
tercile basedon 33 members
0.00.20.40.60.81.0
Forecast issued: 20120809
Forecasts online
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Seasonalvariability
+Global SL
rise
Tuvalu
Fiji
Future projections
Regionalvariability
Weather+
Tides
• Seasonal sea level anomaly forecasts provided to Pacific Island Nations
• First dynamical sea level forecasts
• Assist planning and improve resilience under climate change in Western Pacific
• NOAA collaboration MME experiment
Miles et al. 2014, Mcintosh et al. 2015. Images: http://news.com.auPacific-Australia Climate Change Science Adaptation Program
5. Sea level extremesTuvalu
Fiji
Future projections..
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Does thinking more about the future lead to better long term skills?
Skill in using seasonal forecasts
Long-t
erm
pla
nnin
g a
bili
ty Ho
Engaging at a relevant timescale…
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• A risk-based management strategy that can be used by industries exposed to both short-term environmental variability and long-term change.
• Long–term climate projections provide insight into when current locations may no longer be suitable for industry.
• In the meantime, seasonal forecasting can be beneficial in helping plan ahead to reduce impacts in poor years & maximise opportunities in good years.
• Use of seasonal forecasting could extend the period of time in which industry can cope in a location as environmental suitability declines due to climate change.
Climate proofing
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Decision tree
Hobday et al. submitted
Decision tree to guide climate-proofing approach by aquaculture businesses
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Case study:Southern Australia
Projected SST change over the period 2081-2100 relative to 1986-2005 for southern Australia based on output from the CSIRO-Downscaling project (Hobday et al submitted).
Marine industries in southern AustraliaMarine industries in southern Australia
Southern bluefintuna
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Case study: How long do we have?
ExampleidealSSTrange
2016
• First and permanent emergence times for summer (red dots)
• February SST maps for the year of first exceedance; summer SST below the threshold values are shaded.
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Benefit of seasonal forecasting
Using seasonal forecasts to provide information on future conditions, businesses should be able to reduce costs and increase profits, relative to no forecast such that they can remain profitable under less suitable environmental conditions for longer (until t2).
Beyond this point, conditions are such that relocation (or another adaptation option) is necessary.
Hobday et al. submitted
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Inter-annual SST variability
Persist skill
Model skill
Best approach at this time
Climate-proof?
1 Low NR NR Climatology No
2 High Low Low Difficult –uncertain environment for business
No
3 Low/High High Low Use real-time obs.
No
4 Low/High Low High Dynamic model forecast.
Yes
5 Low/High High High Dynamic model or persistence forecast.
Yes
Managing environmental variability approaches
Hobday et al. submitted
Only dynamical model forecasts provide a viable option for managing environmental risk where climate change is reducing environmental suitability
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Jones & Mearns 2005
• Climate change is leading to a future where past experience is of reduced value – outside coping range?
• Need to make reasonable decisions even if the details change, based on the best information available at the time
• Risk management approach
The future will be different..
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• Dynamical seasonal forecast products valuable tools for proactive marine management
• Probabilistic forecasts important for risk analysis & management
• Potential for a variety of marine and climate applications
• Improves efficiency and enhances resilience of industry to climate variability and change
• Adoption takes time…. need to be in it for the long run!
Image courtesy of Michele Benoy-Westmorland
(www.photomediaonline.com)
Looking ahead
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Dr Claire SpillmanSenior Research Scientist
+61 3 9669 [email protected]/research
• POAMA: http://poama.bom.gov.au
• Hobday et al (submit) Frontiers in Marine Science• Hobday et al. (2016) Fisheries Oceanography• Vanhalato et al. (2016) Ocean Modelling• Spillman et al. (2015) Aquaculture• Eveson et al. (2015) Fisheries Research • McIntosh et al. (2015) Geophysical Res Letters• Spillman & Hobday (2014) Climate Risk Managemt• Miles et al. (2014) Climate Dynamics • Spillman et al. (2012) International J Climatology• Hobday et al. (2011) CJFAS• Spillman et al. (2011) Monthly Weather Review• Spillman (2011) J Operational Oceanography
Thank you
Front image courtesy of Commonwealth of Australia (GRBMPA)