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ESTIMATING RISK PROBABILITIES

Roger JonesFebruary 26 2009

CENTRE FOR STRATEGIC ECONOMIC STUDIESBUSINESS AND LAW

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Structure of talk

• The problem with prediction• Estimating risk probabilities• Hedging adaptation and mitigation• How much climate change do we need to adapt to by

when?

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Scales of approach

Global

Local

Economic resources

Infrastructure

Institutions

Technology

Information & skills

Equity

Indicators based on:

Adaptive capacity

Vulnerability (social)

Bottom-up approach

Economic resources

Infrastructure

Institutions

Technology

Information & skills

Equity

Indicators based on:

Adaptive capacity

Vulnerability (social)

Bottom-up approach

Impacts

Regionalisation

Global climate models

Global greenhouse gases

Vulnerability (physical)

World development

Top-down approach

Impacts

Regionalisation

Global climate models

Global greenhouse gases

Vulnerability (physical)

World development

Top-down approach

Past Present Future

Climate adaptation

policy

Climate adaptation

policy

Dessai and Hulme 2005

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Characterising uncertainty in AR4

0

1

2

3

4

5

-1 0 1 2 3 4 5 6 7 8 9 10

Global Mean Temperature Change (°C)

Pro

babi

lity

(%)

Likely

Very Likely

Virtually Certain

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The likelihood of prediction

0

1

2

3

4

5

0 1 2 3 4 5 6 7 8 9 10

Global Mean Temperature Change (°C)

Pro

babi

lity

(%)

Virtually Certain

Highly Likely

Likely

As Likely As Not

Unlikely

Highly Unlikely

Exceptionally unlikely

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The likelihood of risk

0

1

2

3

4

5

6

1990 2010 2030 2050 2070 2090Year

Glo

bal m

ean

war

min

g (°

C)

.

Remote chance

Highly unlikely

Unlikely

As likely as not

Likely

Highly likely

Virtually certain

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Framing climate change risk

Almost certain

Highly likely

Least likely

Low probability, extreme outcomes

Damage to the most sensitive, many benefits

Increased damage to

many systems, fewer benefits

Considerable damage to most

systems

Moderately likely

Probability Consequence

Core benefits of adaptation and mitigation

Probability – the likelihood of reaching or exceeding a given level of global warmingConsequence – the effect of reaching or exceeding a given level of global warming

Risk = Probability × Consequence

Vulnerable to current climate

Happening now

Almost certain

Highly likely

Least likely

Low probability, extreme outcomes

Damage to the most sensitive, many benefits

Increased damage to

many systems, fewer benefits

Considerable damage to most

systems

Moderately likely

Probability Consequence

Core benefits of adaptation and mitigation

Probability – the likelihood of reaching or exceeding a given level of global warmingConsequence – the effect of reaching or exceeding a given level of global warming

Risk = Probability × Consequence

Vulnerable to current climate

Happening now

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0

20

40

60

80

100

0 100

Probability (%)

Sea

Lev

el R

ise

(cm

)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 100

Probability (%)

Sea

Lev

el R

ise

(cm

)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 100

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 5 10

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 5 10

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 100

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

25 cm

50 cm

75 cm

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The new global growth path

• Global growth has accelerated in the past decade, driven by the developing countries, especially China and India

• This growth is energy and coal intensive, and likely to continue• Realistic projections of energy use and CO2 emissions to 2030 are

above the SRES marker scenarios, including A1FI

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Implications for GHG emissions and atmospheric concentrations

• The implications this new growth path are explored by: • developing a reference case projections of CO2 emissions from fuel combustion to

2030• assuming that other emissions grow in a similar manner• developing policy emissions paths (minimum emissions paths)• explore CO2-e concentrations and temperatures in a simple climate model

• Minimum emissions paths (MEPs) from 2010 to 2030 were explored in Sheehan et al. GEC 2007

• The 2030 MEP resembles the SRES A1B “on steroids”• Current growth to 2100 under reference conditions resembles SRES

A1FI “on steroids”

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Minimum emissions paths 2010–2030

0

5

10

15

20

25

1990 2010 2030 2050 2070 2090

CO

2em

issi

ons

(Gt/y

r C)

Year

MEP2030 MEP 2025 MEP 2020

MEP 2015 MEP 2010

a)

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Likelihood of exceedance – range of reference and policy scenarios

0

1

2

3

4

5

6

7

1990 2010 2030 2050 2070 2090Year

Glo

bal m

ean

war

min

g (°

C)

.

RemotechanceHighlyunlikelyUnlikely

As likely asnotLikely

Highly likely

VirtuallycertainIPCC Low

IPCC High

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Framing adaptation

• Goal setting• Where do we want to go? (aspirational goals)• How do we want to get there?

• What are the risks? • What are the barriers? (e.g., lack of adaptive capacity)

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Planning horizons

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Operational pathways and aspirational goals

Operational pathways

Up-front responseStrong initial responseSome ongoing adjustment

Weak initial responseStrong late adjustment

Operational pathways

Up-front responseStrong initial responseSome ongoing adjustment

Weak initial responseStrong late adjustment

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How much climate change needs to be adapted to by when

Types of climate information required:• Climate variability (daily to decadal)• Ongoing rate of change• Past and near term commitments to climate change• Climate sensitivity• Regional climate change projections• Greenhouse gas emission policies (Mitigation)

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Reference and policy scenarios for hedging adaptation and mitigation

0

1

2

3

4

5

1990 2010 2030 2050 2070 2090

Year

Mea

n G

loba

l War

min

g (°

C)

Reference Policy Observations

Warming rate

Past and near-term

commitments

Climate sensitivity

Emission scenarios

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Hedging adaptation and mitigation – reference and policy scenarios

0

1

2

3

4

5

1990 2010 2030 2050 2070 2090

Year

Mea

n G

loba

l War

min

g (°

C)

Reference Policy Observations

Adaptation benefits

AD-MIT

Mitigation benefits

MIT-AD

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Hedging strategies between reference and policy scenarios with high policy uncertainty

0

1

2

3

4

5

6

7

1990 2010 2030 2050 2070 2090

Mea

n G

loba

l War

min

g (°

C)

Year

Adaptation Ad-Mit Mit-Ad Mitigation A1FI aug–MEP2030 MEP2010–2030

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Whole of climate approach

• Links current climate and adaptive responses with future possibilities • Ongoing variability and extremes are the main drivers of current adaptation to

climate, links between variability and longer-term change give these experiences a future dimension.

• Long-term fluctuations in natural climate variability may be affecting some regions• Not all change is anthropogenic

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Whole of climate approach

An understanding of the dynamics of climate variability is needed to:• Diagnose fluctuations, shifts or trends as temporary, persistent or

permanent. • If the dynamics of the change are not understood, statistical or other

methods can be used to explore “what if” questions based on understandings of climate model and historical behaviour.

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Regional example of climate changes – Melbourne, Australia

The Melbourne Region has experienced many step changes rather than trends

For a 1 by 1 degree area over greater Melbourne:Annual rainfall :statistically significant downward shift in 1996 in rainfall

from just over 900 mm to 750 mm, -17%. Max temp: Statistically significant upward step change 1998 of 0.6°C.

About half of this can be explained by the decrease in rainfall (due to a decrease in cloud cover). About half (0.3°C) is added warming

Analysis of annual frequency of days >35°C and >40°C not significant All days under 30°C have become significantly warmerDuring summer (DJF) almost 1°C warmer

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Regional example of impacts – south-eastern Australia

Streamflow: 60% up to 80% across western Victoria, 25–60% in eastern Victoria.Extreme fire weather index (temperature, lower humidity and higher winds):

100 on Black Friday in 1939, 115 on Ash Wednesday in 1983 150–200 on Black Saturday, February 2000

Viticulture: harvest 4–6 weeks earlier, crop losses, smoke damageHorticulture, dairy: under stress in irrigation regionsSnow: reduced snow coverHuman health (heat stress): hundreds(?) dead from heat stress, 220+ from fires,

event trauma, drought stress in rural regionsEnvironment: woodland birds decline, tree die-back accelerated, tree planting

failures, icon wetlands critical, frequent hot fires

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Exploring decision analysis

Post 1997 rain short term CV + climate

change

Post 1997 rain long term CV + climate

change

Post 1997 rain –climate change

Recovery expected soon, long-term

gradual reduction?

Partial recovery expected in decades

Serious long-term deficits

p1 p2 p3

Benefit if correctPenalty if incorrect

Benefit if correctPenalty if incorrect

Benefit if correctPenalty if incorrect

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Choosing climate information

• Understand risk and risk management options – how is climate information used in decision-making for specific risks?

• What is my planning horizon and operational pathway? E.g., up front, incremental, wait and see

• What’s my climate baseline?• Choose global scenarios based on sensitivity, risk tolerance and

hedging strategies – choose scenarios that are 50% likely to be exceeded to

• Determine local scaling and down-scaling needs for key climate variables

• Undertake assessment e.g., modelling, expert analysis

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Thresholds and key vulnerabilities

• Determine critical limits. E.g., sea level rise, storm severity or surge protection, flooding, public health limits, water quality and supply

• Diagnose specific climate conditions leading to critical limits• Establish plausible combinations of change in mean and variability,

natural and anthropogenic, leading to critical thresholds• Determine likelihood that such conditions may be exceeded within

planning horizons. For cities, many of these horizons will be long-term

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Caveats and working principles

• All probabilities are subjective – test different plausible assumptions to test whether outcomes (decisions on risk management) are sensitive to assumptions

• What information is required to make a specific decision? The less important climate is compared to other risk factors, the less precision will be required

• A 1°C warming in 2030 (from 1990) is as likely as not. From 2040+, considerable hedging between adaptation and mitigation is required. Without solid emissions policy, hedging for >3°C warming by 2100 needs to be contemplated.

• Sea level rise estimates need to consider outcomes not quantified in the AR4, including Greenland and perhaps West Antarctica

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ROGER N JONESBUSINESS AND LAWCENTRE FOR STRATEGIC ECONOMIC STUDIES

PHONE +61 3 9919 1992FAX +61 3 9919 1350EMAIL roger.jones@vu.edu.au

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