Stephen H. Schneider*

Transcription

1 Stephen H. Schneider* Department of Biological Sciences and Woods Institute for the Environment Stanford University, California, USA. Key Vulnerabilities and the Risks of Climate Change? Michigan State University Ides of March 2007 *[Website for more info:

2 The role of the scientific community #1: Provide climate change scenarios The IPCC s Special Report on Emissions Scenarios (SRES) 2000

3 What will be our future emissions? Higher Lower Source: Intergovernmental Panel on Climate Change

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6 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%.

7 Approved uncertainties language for 10 33% likelihood: unlikely???

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9 Pre Plenary proposed

10 Low High

11 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

12 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

13 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."

14 Type 1 versus Type 2" errors and their consequences Decision Accept forecast policy response follows Reject or ignore forecast (e.g., too much uncertainty) no policy response Forecast proves false Type I error [Squandered resources] Correct Decision Forecast proves true Correct decision Type 2 error

15 Type 1 versus Type 2" errors and their consequences Decision Accept forecast policy response follows Reject or ignore forecast (e.g., too much uncertainty) no policy response Forecast proves false Type I error [Squandered resources] Correct Decision Forecast proves true Correct decision Type 2 error [Unmitigated damages]

16 Type 1 versus Type 2" errors and their consequences Decision Accept forecast policy response follows Forecast proves false Type I error Forecast proves true Correct decision Reject or ignore forecast (e.g., too much uncertainty) no policy response Correct Decision Type 2 error ************************************************* 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 Decision makers: Negotiate acceptability of risks and policies that alter risks; make policy choices; guide assessment process.

17 Type 1 versus Type 2" errors and their consequences Decision Accept forecast policy response follows Forecast proves false Type I error Forecast proves true Correct decision Reject or ignore forecast (e.g., too much uncertainty) no policy response Correct Decision Type 2 error ************************************************* 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 Decision makers: Negotiate acceptability of risks and policies that alter risks; make policy choices; guide assessment process.

18 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. Decision makers often prefer to hedge against a potentially damaging event rather than wait for it to possibly happen.

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25 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.

26 What will be our future emissions? Higher Lower Source: Intergovernmental Panel on Climate Change

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30 Not nearly stabilized at 2100 Nearly stabilized at 2100

31 Emissions Scenarios 6 5 Overshoot to stabilization Radiative Forcing Gradual increase to stabilization 600 ppm CO 2 e 500 ppm CO 2 e Year (O Neill and Oppenheimer, 2004)

32 DT Source: Schneider and Mastrandrea, PNAS, Oct 2005

33 Exceedence of DAI threshold: dependence on scenarios Source: Schneider and Mastrandrea, PNAS, Oct 2005

34 The great greenhouse gamble (for 2100) <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) Source: MIT Joint Program on the Science and Policy of Climate Change

35 HOW CAN WE EXPRESS THE VALUE OF A CLIMATE POLICY UNDER UNCERTAINTY? Compared with NO POLICY What would we buy with STABILIZATION of CO 2 at 550 ppm? A NEW WHEEL with lower odds of EXTREMES

36 HOW CAN WE EXPRESS THE VALUE OF A CLIMATE POLICY UNDER UNCERTAINTY? Compared with NO POLICY What would we buy with STABILIZATION of CO 2 at 550 ppm? A NEW WHEEL with lower odds of EXTREMES

37 [Source: The Five Numeraires, Schneider, Kuntz Duriseti and Azar 2000] 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.

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40 Risk of catastrophic fires (and other disturbances)

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42 Agriculture: The Wine Industry Potentially devastating effect on industry Water availability Temperature Storms

43 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 a x i m u m v a l u e ( $ / h e c t a r e ) ) MJT23C2000 Climate change scenario A1B CSIRO Mk 3 model Leanne Webb CSIRO and Melbourne University

44 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 a x i m u m v a l u e ( $ / h e c t a r e ) ) MJT23C2000 MJT23C2030 Climate change scenario A1B CSIRO Mk 3 model Leanne Webb CSIRO and Melbourne University

45 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 a x i m u m v a l u e ( $ / h e c t a r e ) ) MJT23C2000 MJT23C2030 MJT23C2050 Climate change scenario A1B CSIRO Mk 3 model Leanne Webb CSIRO and Melbourne University

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48 The cost to stabilize the atmosphere Global GDP Trillion USD/yr Bau 350 ppm 450 ppm 550 ppm Year Source: Azar & Schneider, 2002.

49 The cost to stabilise the atmosphere 250 Global GDP Delay time to 500% richer per capita with tough climate policy ~ 1 2 years 200 Trillion USD/yr Bau 350 ppm 450 ppm 550 ppm Year Source: Azar & Schneider, 2002.

50 Questions? Comments??

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52 [Source: The Five Numeraires, Schneider, Kuntz Duriseti and Azar 2000] 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.

53 Munich Re: We need to stop this dangerous experiment humankind is conducting on the Earth s atmosphere.

54 What does dangerous climate change really mean?

55 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.

56 Climate Uncertainty Inherent uncertainty in projections of future climate Best guess Range PDFs

57 Climate Uncertainty

58 Climate Uncertainty Density Temperature Change above 2000 ( o C)

59 Climate Uncertainty Density Temperature probability density function for 2100 based on PDF for climate sensitivity Temperature Change above 2000 ( o C)

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61 The great greenhouse gamble <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) Source: MIT Joint Program on the Science and Policy of Climate Change

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63 Governor of California: 80% reduction in emissions by 2050

64 Strategic Plan for SA

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66 Changes in the location of Goyder s line Port Augusta Quorn Adelaide Mark Howden CSIRO sustainable ecosystems Howden and Hayman Greenhouse 2005 Goyder s Study Site Line

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70 Governor of California: 80% reduction in emissions by 2050 Premier of South Australia: 60% reduction in emissions by 2050

71 [Source: The Five Numeraires, Schneider, Kuntz Duriseti and Azar 2000] 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.