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

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

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

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

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

• 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

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?

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

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

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

ACCESS-SPOAMA-2

Ocean model resolution

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

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?

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

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0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.0

1.1

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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

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

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?

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)

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)

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

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

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

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

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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

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

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..

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…

• 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

Decision tree

Hobday et al. submitted

Decision tree to guide climate-proofing approach by aquaculture businesses

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

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.

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

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

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..

• 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

Dr Claire SpillmanSenior Research Scientist

+61 3 9669 8105claire.spillman@bom.gov.auwww.bom.gov.au/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)