1WWW.VU.EDU.AU

A risk management approach to climate change adaptation

Roger N JonesMay 21 2009

CENTRE FOR STRATEGIC ECONOMIC STUDIESBUSINESS AND LAW

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Structure of talk

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

when?• Taking a whole of climate approach

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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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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

The probability of exceeding a given level of change

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0

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60

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100

0 100

Probability (%)

Sea

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el R

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(cm

)

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50 cm

75 cm

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75 cm

6

0

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0 100

Probability (%)

Sea

Lev

el R

ise

(cm

)

25 cm

50 cm

75 cm

Sea

Lev

el R

ise

(cm

)

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0 100

Probability (%)

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50 cm

75 cm

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0 5 10

Probability (%)S

ea

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vel R

ise

(cm

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50 cm

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Probability (%)

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75 cm

Most likely Most likely

Most likely

Robust

Uncertain

Problematic

Robust

Uncertain

Problematic

Robust

Uncertain

Problematic

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Climate change & impacts – what are the risks?

The post-2000 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

• Minimum emissions paths (MEPs) – strong policy measures from 2010 to 2030*

• The 2030 MEP resembles the SRES A1B “on steroids”• Current growth to 2100 under reference conditions resembles SRES A1FI

“on steroids”

• STOP PRESS: What about the GFC?

* Sheehan et al., Global Environmental Change 2007

Samples have been taken and referred to the stewards – still awaiting the results

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Minimum emissions paths 2010–2030

0

5

10

15

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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• Regional climate change projections• Climate sensitivity• 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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Business as usual world (reference)

0

1

2

3

4

5

6

7

1990 2010 2030 2050 2070 2090

Glo

bal m

ean

war

min

g (°

C)

.

Year

Remote chance

Highly unlikely

Unlikely

As likely as not

Likely

Highly likely

Virtually certain

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Business as usual world (reference)

0

1

2

3

4

5

6

7

1990 2010 2030 2050 2070 2090

Glo

bal m

ean

war

min

g (°

C)

.

Year

Remote chance

Highly unlikely

Unlikely

As likely as not

Likely

Highly likely

Virtually certain

SRES A2

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Climate policy world

0

1

2

3

4

5

6

7

1990 2010 2030 2050 2070 2090

Glo

bal m

ean

war

min

g (°

C)

.

Year

Remote chance

Highly unlikely

Unlikely

As likely as not

Likely

Highly likely

Virtually certain

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Climate policy world

0

1

2

3

4

5

6

7

1990 2010 2030 2050 2070 2090

Glo

bal m

ean

war

min

g (°

C)

.

Year

Remote chance

Highly unlikely

Unlikely

As likely as not

Likely

Highly likely

Virtually certain

SRES B1

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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

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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 current experiences a

future dimension.• Long-term fluctuations in natural climate variability may be affecting

some regions• Not all climate 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 2009

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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Annual Streamflows 1997-2007 c.f. Long Term Average

A

89

> high CC by 2055

8678 56

37

2369 64

3884

85

81

70

85

57 5583

35 31

553834

253871

64

4152

~ medium-high CC by 2055

Source. R. Moran DSE Vic.

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System InflowsRiver Murray Inflows - 1891 to 2008

(excluding Darling inflows and Snowy releases)

5000

10000

15000

20000

25000

30000

35000

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/200

0

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05/

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An

nu

al

Infl

ow

(G

L/y

ear

)

Average pre 1997 9,268 GL

Average post 1997 4,620 GL 50% less MURRAY

50% less

MELBOURNE 37% LESS

2006 LOWEST ON RECORD

Source. R. Moran DSE Vic.

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Supp

ly ch

ange

Demand change

Critical threshold

Future water supply system vulnerability

• Sensitivity to supply changes (climate, land-use, fire)• Level of utilisation• Demand projections

Current management

Marginal planned change

Substantial change

We thought we were

here

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Supp

ly ch

ange

Demand change

Critical threshold

Current water supply system vulnerability

• Large-scale climate change• Competing demands from systems under stress (urban,

industrial, agriculture, environment)• Pressure between managing short-term political risk and long-

term sustainability

Management as usual

Managing current vulnerability

Emergency management

We thought we were

here

But we are here

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Sensitivity of mean streamflow to climate change uncertainties

• Global warming signal ~25%• Rainfall change (as function of global warming) ~67%–75%• Potential evaporation change ~10%

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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 scenarios based on sensitivity, risk tolerance and hedging

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

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

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Explore consequences of decision-making – 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 IPCC AR4, including Greenland and perhaps West Antarctica

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Plan early!!

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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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