Communicating climate risk: Choices chances and chocolate wheels - - PowerPoint PPT Presentation

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Communicating climate risk: Choices chances and chocolate wheels - - PowerPoint PPT Presentation

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Communicating climate risk: Choices chances and chocolate wheels Peter Hayman SARDI Climate Applications Farming is risky Returns are subject to several contingencies, such as follows. Your corn may not be planted early enough. The hogs

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Communicating climate risk: Choices chances and chocolate wheels

Peter Hayman SARDI Climate Applications

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Farming is risky

  • Returns are subject to several contingencies, such as follows. Your

corn may not be planted early enough. The hogs may destroy one- fourth of it, the rains an eighth, and the thieves an eighth; and the drought a large portion of the remaining one half. Your cotton may not come up well, and you may not get a good stand to begin with. It may rain too little, and it may rain too much; and it may be overrun by the grass. Or the rust may take it, the army worm, and the grasshoppers may commence their ravages: or other worms may strip the stalk of its foliage, and then an early frost may nip it in the

  • bud. But if none of these things occur, you are quite likely to get

good crops; and then if none of it is stolen, and your gin house does not burn down, you may be fairly recompensed for your labour. But if any of these things happen, your profits of course will be less. (Charles Sterns 1872 cited in McGuire and Higgs 1977).

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There is a lot of risk in the world… can we help?

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Farming is risky business and mathematics can help…

  • 1. Tools eg weather forecast
  • 2. Risk assessment
  • 3. Risk management

Process – not just solutions

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  • Is our ability to have a conversation about

risk, hampered by a low level of functional numeracy.

  • Even for those of us for whom maths is a

second language – it is powerful

  • Innumeracy as a badge of honour, or at

least acceptable.

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Definitions of risk

  • Risk vs Uncertainty (Knight) Risk is

quantifying uncertainty

  • Risk is uncertainty with consequences
  • Risk is why the only thing accurate on a

farm budget is the date

  • Uncertainty vs confidence
  • Uncertainty vs range
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Definition of communication

  • Communication is “the reciprocal

construction and clarification of meaning by interacting people” not the one way flow

  • f a signal.
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Climate risk as a Rosetta stone between agricultural science, climate science and farmers

  • Attempt to understand and intervene in

farmers management of risk

  • Put down some of the climate science,

biological complexity along side the economic and social complexity

  • To develop simplicity on the far side of

complexity

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A long wait for SCFs

– As old as agriculture

– “when more records are available, an accurate forecast can probably be made for a considerable period in advance. Needless to say, when that time arrives, it will be possible to greatly reduce, or even entirely prevent, the now constantly recurring losses in stock and crops - John Barling, after the 1902 El Nino (Agricultural Gazette)

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Looking beyond cycles in rainfall

  • Barling based his forecasts on cycles
  • Forecasts of climate based on the

interactions between the oceans and the atmosphere is one of the premiere advances of the atmospheric science at the close of the 20th century. AAS (1999)

  • Science's gift to the 21st Century. Glantz
  • The New Green Revolution. Cited in Hansen 2002
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25 years La Nina type 25 years El Nino type

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El Nino = Drought ?

  • If we define drought as driest 10% of years
  • There have been about 25 El Nino events
  • So there have been more droughts than El

Nino events

  • El Nino means increased chance of

drought

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If_Then_Else

  • IF the season is going to be dry - THEN

plant wheat & chickpeas ELSE - canola

  • If the end point is better risk management,

misunderstanding forecasts as categorical will result in poorer risk management than if people never heard of the forecast

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Why we need probabilities

  • 1. It is honest to be clear about the
  • uncertainties. “Probability refers in part to
  • ur knowledge and in part to our

ignorance” Laplace

  • 2. Probabilities encourage risk

management- need to consider possible

  • utcomes
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We know communicating and using probability is hard

  • “Farmers have said they want to know

whether it is likely to be dry, wet or average, not whether there is a 60% chance of getting 40% of the average rainfall”

  • Mumbling so that we can never be wrong
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However …....

  • People deal with uncertainty all the time -

buy shares, get married, live on fault line, plant crops, buy cattle

  • Is it that people are not used to hearing

about uncertainty from scientists ?

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  • governments tend to look to the scientific community for clear and

simple answers and become frustrated with equivocation and the fact that, as fast as scientific research resolves key uncertainties, new uncertainties are identified;

  • those in the key stakeholder communities (especially the fossil fuel

industry and the environmental movement at the extremes) tend to

  • verstate either the uncertainties or the certainties to try to get

government and community acceptance of the message that they want the science to deliver; and

  • the community at large, who have learnt that science can predict the

exact time of eclipses centuries ahead and technology can land a man on the moon, do not understand what is preventing the scientific community from doing just as well with climate change.

  • Zillman JW (2005) 'Uncertainty in the science of climate change. Occasional Paper 2/2005. Policy Paper # 3.' (Academy of the Social Sciences in Australia: Canberra)
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Fooled by Randomness

  • Humans are not good intuitive statisticians
  • We are probability blind - we find it hard to

think of alternative futures much less alternative histories.

  • Consistent findings of cognitive biases
  • The way our brains work ?
  • Evolution?

Poor training in application of statistics ?

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Winter in Tamworth < 266 mm > 340 mm

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Average season 27% Poor season 53% Good season 20% Average season 29% Good season 52% Poor season 19%

June - Nov when April May SOI phase is negative or rapidly rising

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Risky but not gambling

“If everything is a matter of luck, risk management is a meaningless exercise. Invoking luck obscures the truth, because it separates an event from its cause”. Peter

Bernstein

Risk analysis puts numbers on uncertainty

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Decision making under uncertainty

  • The optimum stocking rate, fertiliser rate,

area planted can be strongly influenced by climate.

  • The decision is usually made prior to the

season so farmers are allocating resources (eg fertiliser) for an unknown demand.

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Seasonal climate forecasts are too good to ignore but not good enough to rely on

  • All that probability stuff is fine, but don’t

you realise that farmers have to make a decision.

  • Decision is an irrevocable allocation of

resources

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Risky Decisions

  • Choice Consequence
  • IF you use X rate of fertiliser you will get Y

yield

  • Choice chance consequences
  • If you use X rate of fertiliser, depending on

the season, you will get Y(1) Y(2) Y(3)

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Intuitive appeal of purposeful procrastination

  • The essence of real options is to use the

analogy of financial options to consider the value

  • f waiting for better but not complete

information.

  • The idea of making a decision and then waiting

to see what happens vs waiting to see what is starting to happen and then decide or adjust is central to the intuitive value of real options (Luehrman 1995). This concept is not new to farmers.

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How has a background in climate variability influenced my discussion

  • n climate change
  • The difference between change and variability matters.
  • Variability matters.. even if variability doesn’t increase.
  • Change and Variability are delivered through weather
  • Mismatch between what decision makers want and

climate science can deliver (spatial and temporal)

  • Close attention to decision context – emphasise risk

assessment rather than risk management

  • Communicating probability is difficult but worthwhile.
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Source: Bureau of Meteorology

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Roger Jones CSIRO What can we learn from managing our current variable and changing climate for the future variable and changing climate

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W hat destroyed the sand castle? In a variable and changing climate it will always be hard to distinguish between extreme events (wave) and trends (tide)

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David Cash & William Clarke – JFK school of Government, Harvard.

  • Salience – is the climate information

relevant to my decision context

  • Credibility – does the information have

credibility- would peer climate scientists agree.

  • Legitimacy – whose interests were at the

table when the climate information was developed and communicated

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Thinking about future climates

Climate change projections from GCMS Sensitivity analysis: 1,1.5, 2 degrees warming; 5%, 10%, 20% rainfall decline

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Thinking about future climates

Climate change projections from GCMS Sensitivity analysis: 1,1.5, 2 degrees warming; 5%, 10%, 20% rainfall decline Temporal analogues – eg drought Spatial analogues – study a warmer & drier site

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Thinking about future climates

Climate change projections from GCMS Sensitivity analysis: 1,1.5, 2 degrees warming; 5%, 10%, 20% rainfall decline Temporal analogues – eg drought Spatial analogues – study a warmer & drier site

Quantification is powerful in this discussion

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Is this drought or aridity; drought or drying; variability

  • r change; cycle or shift.
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April to October rainfall at Minnipa

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April to October rainfall at Minnipa

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April to October rainfall at Minnipa

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T emperature Projections

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Rainfall Projections

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Year

1850 1900 1950 2000 2050 2100

Temperature (oC)

  • 2.0
  • 1.5
  • 1.0
  • 0.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Year

1850 1900 1950 2000 2050 2100

Rainfall (mm)

  • 200
  • 100

100 200

BCCR GFDL 2.0 GFDL 2.1

MIUB CC50 MPI-ECHAM5 MRI NCAR-CCSM CC50 HADCM3 HADGEM1 Observation South Australia Annual temperature anomalies Annual rainfall anomalies

Projections from 13 Global Circulation Models (GCM) with SRES A2 CSIRO report for SA 2006 using 11 best models from 23 GCMs for current round of IPCC

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Vulnerability/resilience of wine grape growing

140 year old shiraz vines

Photo: Wine and Brandy Corporation

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Lyndoch Nurioopta Eden Valley

N

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Greg Jones

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With 2010 included

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Mildura

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Bicheno

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Bicheno

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Changes in mean change the odds

1960-90 0.5C 1C 1.5C

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Understanding decision context

  • False precision…Measure with a

micrometer, mark with a piece of chalk and cut with an axe

  • Purposeful procrastination – Real Options
  • Active users asking better questions. For

example seasonality rather than very high spatial resolution.

  • Working out which factors are uncertain
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Predictions

Predictions are not instructions that people simply follow to make better decisions. They are pieces of an intricate puzzle that may sometimes contribute to improved

  • decisions. Daniel Sarewitz. Nature 463:

2010 Learn from failure of predicting earthquakes (robust buildings) as well as success of predicting cyclones.

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