msu schneider2007

Stephen H. Schneider* Department of Biological Sciences

and Woods Institute for the Environment Stanford University, California, USA.

*[Website for more info: www.climatechange.net.]

“Key Vulnerabilities” and the Risks of Climate Change?

Michigan State University Ides of March 2007

The role of the scientific community #1: Provide climate change scenarios

The IPCC’s Special Report on Emissions Scenarios (SRES) ­ 2000

What will be our future emissions?

Higher

Lower

Source: Intergovernmental Panel on Climate Change

WG 1Approved AR 4 SPM: In this Summary for Policymakers, the following terms have been used to indicate the assessed likelihood, using expert judgment, of an outcome or a result: Virtually certain > 99% probability of occurrence, Extremely likely > 95%, Very likely > 90%, Likely > 66%, More likely than not > 50%, Unlikely < 33%, Very unlikely < 10%, Extremely unlikely < 5%.

Approved uncertainties language for 10­33% likelihood: “unlikely”

???

Pre­ Plenary proposed

“Low”

“High”

From the Sports Pages: Type 1 Error Aversion

Denver is allowing 7.3 points per game. Broncos defensive end Kenard Lang was asked to predict a point total for the Colts offense this Sunday:

"I‘m not predicting nothing. All I'm going to predict is a good game and I'm hoping the Broncos come out on top. If I make predictions and it goes the opposite way, then I'll look like a horse's fanny. And I ain't trying to look like a horse's fanny right now."

From the Sports Pages: Type 1 Error Aversion

Denver is allowing 7.3 points per game. Broncos defensive end Kenard Lang was asked to predict a point total for the Colts offense this Sunday:

"I'm not predicting nothing. All I'm going to predict is a good game and I'm hoping the Broncos come out on top. If I make predictions and it goes the opposite way, then I'll look like a horse's fanny. And I ain't trying to look like a horse's fanny right now."

From the Sports Pages: Type 1 Error Aversion

Denver is allowing 7.3 points per game. Broncos defensive end Kenard Lang was asked to predict a point total for the Colts offense this Sunday: "I'm not predicting nothing. All I'm going to predict is a good game and I'm hoping the Broncos come out on top. If I make predictions and it goes the opposite way, then I'll look like a horse's fanny. And I ain't trying to look like a horse's fanny right now."

“Type 1” versus “Type 2" errors and their consequences

Type 2 error

Correct Decision

Reject or ignore forecast (e.g., “too much” uncertainty)— no policy response

Correct decision

Type I error

[Squandered resources]

Accept forecast— policy response follows

Forecast proves true

Forecast proves false

Decision

“Type 1” versus “Type 2" errors and their consequences

Type 2 error [Unmitigated damages]

Correct Decision

Reject or ignore forecast (e.g., “too much” uncertainty)— no policy response

Correct decision

Type I error

[Squandered resources]

Accept forecast— policy response follows

Forecast proves true

Forecast proves false

Decision

“Type 1” versus “Type 2" errors and their consequences

************************************************* Role of Scientists: Assess Risk (= Consequence X Probability of Occurrence) as function of alternative policy choices ; confidence in the assessment of risks; distribution of risks and benefits; traceable account of aggregations. ­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­ Role of Decision­makers: Negotiate acceptability of risks and policies that alter risks; make policy choices; guide assessment process.

Type 2 error Correct Decision Reject or ignore forecast (e.g., “too much” uncertainty)—no policy response

Correct decision Type I error Accept forecast—policy response follows

Forecast proves true

Forecast proves false

Decision

“Type 1” versus “Type 2" errors and their consequences

************************************************* Role of Scientists: Assess Risk (= Consequence X Probability of Occurrence) as function of alternative policy choices ; confidence in the assessment of risks; distribution of risks and benefits; traceable account of aggregations. ­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­ Role of Decision­makers: Negotiate acceptability of risks and policies that alter risks; make policy choices; guide assessment process.

Type 2 error Correct Decision Reject or ignore forecast (e.g., “too much” uncertainty)—no policy response

Correct decision Type I error Accept forecast—policy response follows

Forecast proves true

Forecast proves false

Decision

Competing paradigms between science and policy communities.

It is common in policy analysis to refer to an incorrect forecast that was taken to be true as a “type 1 error” and a decision to ignore an uncertain forecast that turns out to be true as a “type 2 error”. The prime paradigm within the scientific community is to view the type 1 error as the more egregious mistake, whereas within the policy arena, the type 2 error is often more concerning. Decision­makers often prefer to hedge against a potentially damaging event rather than wait for it to possibly happen.

Chapter 19 (draft, do not quote) identifies seven criteria for assessing and defining key vulnerabilities:

• magnitude • distribution • timing • persistence and reversibility • likelihood and confidence • potential for adaptation • “importance” of the vulnerable system

No single metric can adequately aggregate the diversity of key vulnerabilities, nor determine their ranking.

What will be our future emissions?

Higher

Lower

Source: Intergovernmental Panel on Climate Change

Nearly stabilized at 2100

Not nearly stabilized at 2100

Emissions Scenarios Emissions Scenarios

0

1

2

3

4

5

6

2000 2050 2100 2150 2200 2250 Year

Radiative Forcing 600 ppm CO 2 e

500 ppm CO 2 e

(O’Neill and Oppenheimer, 2004)

Gradual increase to stabilization

Overshoot to stabilization

Source: Schneider and Mastrandrea, PNAS, Oct 2005

DT

Source: Schneider and Mastrandrea, PNAS, Oct 2005

Exceedence of DAI threshold: dependence on scenarios

The great “greenhouse gamble”…(for 2100)

Source: MIT Joint Program on the Science and Policy of Climate Change

<1°C (4.1%; 1 in 24 odds)

1 to 1.5°C (11.4%; 1 in 9 odds)

1.5 to 2°C (20.6%; 1 in 5 odds)

2 to 2.5°C (22.5%; 1 in 4 odds)

2.5 to 3°C (16.8%; 1 in 6 odds)

3 to 4°C (16.2%; 1 in 6 odds)

4 to 5°C (4.6%; 1 in 22 odds)

>5°C (3.8%; 1 in 26 odds)

A NEW WHEEL with lower odds

of EXTREMES

What would we buy with STABILIZATION

of CO 2 at 550 ppm?

Compared with NO POLICY

HOW CAN WE EXPRESS THE VALUE OF A CLIMATE POLICY UNDER UNCERTAINTY?

A NEW WHEEL with lower odds

of EXTREMES

What would we buy with STABILIZATION

of CO 2 at 550 ppm?

Compared with NO POLICY

HOW CAN WE EXPRESS THE VALUE OF A CLIMATE POLICY UNDER UNCERTAINTY?

Risk = Probability x Consequence

[What metrics of harm?] ­$/ton C avoided ­lives lost/ton C avoided ­species lost/ton C avoided ­increased inequity/ton C avoided* ­quality of life degraded/ton

*Perception that prime generators of the risks are not accepting responsibility for their emissions or helping victims to adapt (e.g., OECD countries refusing to join in Kyoto Protocol) itself creates risks.

[Source: “The Five Numeraires”, Schneider, Kuntz­Duriseti and Azar 2000]

Risk of catastrophic fires Risk of catastrophic fires (and other disturbances) (and other disturbances)

Agriculture: The Wine Industry

• ‘Potentially devastating’ effect on industry

•Water availability

•Temperature

•Storms

A u s t r a l i a n w i n e r e g i o n s A u s t r a l i a n w i n e r e g i o n s M e a n J a n u a r y T e m p e r a t u r e o f 2 3 C h i g h l i g h t e d M e a n J a n u a r y T e m p e r a t u r e o f 2 3 C h i g h l i g h t e d

( m a x i m u m v a l u e ( $ / h e c t a r e ) ) ( m a x i m u m v a l u e ( $ / h e c t a r e ) )

Climate change scenario A1B CSIRO Mk 3 model MJT23C2000

Leanne Webb CSIRO and Melbourne University

A u s t r a l i a n w i n e r e g i o n s A u s t r a l i a n w i n e r e g i o n s M e a n J a n u a r y T e m p e r a t u r e o f 2 3 C h i g h l i g h t e d M e a n J a n u a r y T e m p e r a t u r e o f 2 3 C h i g h l i g h t e d

( m a x i m u m v a l u e ( $ / h e c t a r e ) ) ( m a x i m u m v a l u e ( $ / h e c t a r e ) )

Climate change scenario A1B CSIRO Mk 3 model

MJT23C2000

MJT23C2030

Leanne Webb CSIRO and Melbourne University

A u s t r a l i a n w i n e r e g i o n s A u s t r a l i a n w i n e r e g i o n s M e a n J a n u a r y T e m p e r a t u r e o f 2 3 C h i g h l i g h t e d M e a n J a n u a r y T e m p e r a t u r e o f 2 3 C h i g h l i g h t e d

( m a x i m u m v a l u e ( $ / h e c t a r e ) ) ( m a x i m u m v a l u e ( $ / h e c t a r e ) )

Climate change scenario A1B CSIRO Mk 3 model

MJT23C2000

MJT23C2030

MJT23C2050

Leanne Webb CSIRO and Melbourne University

The cost to stabilize the atmosphere

Global GDP

0

50

100

150

200

250

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Year

Trillion USD/yr

Bau

350 ppm

450 ppm

550 ppm

Source: Azar & Schneider, 2002.

The cost to stabilise the atmosphere

Global GDP

0

50

100

150

200

250

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Year

Trillion USD/yr

Bau

350 ppm

450 ppm

550 ppm

Source: Azar & Schneider, 2002.

Delay time to 500% richer per capita with tough climate policy ~ 1­2 years

Questions?

Comments??

Risk = Probability x Consequence

[What metrics of harm?] ­$/ton C avoided ­lives lost/ton C avoided ­species lost/ton C avoided ­increased inequity/ton C avoided* ­quality of life degraded/ton

*Perception that prime generators of the risks are not accepting responsibility for their emissions or helping victims to adapt (e.g., two OECD countries refusing to join in Kyoto Protocol) itself creates risks.

[Source: “The Five Numeraires”, Schneider, Kuntz­Duriseti and Azar 2000]

Munich Re: “We need to stop this dangerous

experiment humankind is conducting on the Earth’s

atmosphere.”

What does “dangerous” climate change really mean?

Article 2 of the UN Framework Convention on Climate Change (UNFCCC) states that: “The ultimate objective of this Convention and any related legal instruments that the Conference of the Parties may adopt is to achieve, in accordance with the relevant provisions of the Convention, stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system”. The Framework Convention on Climate Change further suggests that “Such a level should be achieved within a time frame sufficient

• to allow ecosystems to adapt naturally to climate change, • to ensure that food production is not threatened and • to enable economic development to proceed in a sustainable manner.”

Climate Uncertainty Climate Uncertainty

• • Inherent uncertainty in projections of future Inherent uncertainty in projections of future climate climate

• • Best guess Best guess à à Range Range à à PDFs PDFs

Climate Uncertainty Climate Uncertainty

Climate Uncertainty Climate Uncertainty

0

0.01

0.02

0.03

0.04

0 1 2 3 4 5

Temperature Change above 2000 ( o C)

Den

sity

Climate Uncertainty Climate Uncertainty

0

0.01

0.02

0.03

0.04

0 1 2 3 4 5

Temperature Change above 2000 ( o C)

Den

sity

Temperature probability density function for 2100 based on PDF for climate sensitivity

The great “greenhouse gamble”…

Source: MIT Joint Program on the Science and Policy of Climate Change

<1°C (4.1%; 1 in 24 odds)

1 to 1.5°C (11.4%; 1 in 9 odds)

1.5 to 2°C (20.6%; 1 in 5 odds)

2 to 2.5°C (22.5%; 1 in 4 odds)

2.5 to 3°C (16.8%; 1 in 6 odds)

3 to 4°C (16.2%; 1 in 6 odds)

4 to 5°C (4.6%; 1 in 22 odds)

>5°C (3.8%; 1 in 26 odds)

Governor of California: 80% reduction in emissions by 2050

Strategic Plan for SA

‘Changes in the location of Goyder’s line’ 2070

Port Augusta

Quorn

Adelaide

2

Goyder’s Line Study Site

3

4

5

6

7

8

1

9

10

Mark Howden CSIRO sustainable ecosystems

Howden and Hayman – Greenhouse 2005

Premier of South Australia: 60% reduction in emissions by 2050

Governor of California: 80% reduction in emissions by 2050

Risk = Probability x Consequence

[What metrics of harm?] ­$/ton C avoided ­lives lost/ton C avoided ­species lost/ton C avoided ­increased inequity/ton C avoided* ­quality of life degraded/ton

*Perception that prime generators of the risks are not accepting responsibility for their emissions or helping victims to adapt (e.g., OECD countries refusing to join in Kyoto Protocol) itself creates risks.

[Source: “The Five Numeraires”, Schneider, Kuntz­Duriseti and Azar 2000]