Proposed NSF Center on Climate Decision Making Carnegie Mellon University 1 Climate and Related...

1Proposed NSF Center on Climate Decision Making

Carnegie Mellon University

Climate and RelatedDecision Making in the Face of

Irreducible Uncertainties

M. Granger Morgan, PIHead, Department of Engineering and Public PolicyCarnegie Mellon Universitytel: 412-268-2672e-mail: [email protected]



Proposed Agenda

8:00 Room open with coffee, etc.

8:15-8:30 Introductions

8:30-8:40 Welcome by Carnegie Mellon’s President - Jared Cohon

8:40-9:00 Questions and Discussion

9:00-9:15 Overview of Proposed Center - Granger Morgan




The Department of Engineering and Public Policy (EPP)

http://www.epp.cmu.edu

EPP is a department in the Engineering College that addresses policy problems in which the technical issues really matter.

Faculty: Total of 41. Include true 50:50 joint appointments with all five engineering departments as well as joint appointments with four different social science units in three other colleges (women: 3 tenured full Professors, 2 untenured).

Undergraduate double major degrees with traditional departments (570 BS graduates to date).

Graduate program is a research-oriented Ph.D. focused on problems in which the technical details really matter (current enrollment 48 (16 women), 119 Ph.D. graduates to date (30 women, 6 black).



Research Four major areas:

1. energy and environment.2. risk analysis and communication.3. telecommunication and information policy.4. technology policy.

In the context of these four areas, we also work on issues in technology and development (China and India) and on issues in dual-use technology, arms control, and defense policy.

EPP currently has several large collaborative group efforts:• Center for Integrated Study of the Human Dimensions of Global Change.• The Electricity Industry Center.• Green Design Initiative.• Center for Energy and Environmental Systems.• Center for the Study and Improvement of Regulation.• Brownfields Center.• IT and telecommunications policy.• Risk analysis, ranking, communication.



Our proposal……is predicated on the belief that irreducible uncertainties in the problem preclude the adoption of the traditional decision analytic model: estimate how climate will change

estimate the ecological and socio-economic impacts

estimate the relative costs and implications of alternative policies and strategies for adaptation and emissions control

estimate future GHG emissions

implementpolicies

Our center will characterize the (often irreducible) uncertainties and develop strategies to deal with them.



Research and Uncertainty

The conventional decision-analytic model assumes that research reduces uncertainty:

Research $



Research and Uncertainty …(Cont.)

Research $

or even grows…

Research $

Sometimes this is what happens, but often, for variables we really care about, the reality is that uncertainty either remains unchanged…

When the latter happens it is often because we find that the underlying physical model we had assumed is incomplete or wrong.



An example:David Keith and I conducted a set of expert elicitations among 16 leading climate scientists in 1994. The figure summarizes their estimates of climate sensitivity for a 2xCO2 climate change.

1

234

56789

10111213141516

Temperature response given 2x[CO ] (K)

0-5-10 5 10 15 20

2.9 2.9

2.3 .86

mean

2.7 2.3 3.1 2.0

0.3 0.2 2.7 2.0 3.1 1.5 2.9 1.4 2.9 1.8 2.6 .98

2.8 1.13.0 1.4

1.9 1.0 3.1 1.0 2.2 1.3 2.8 1.0

4 4.7 5.4 with "surprise"

expert

2

with state change

3.6 2.5

standarddeviation0-5-10 5 10 15 20

2

We asked the experts to design a $109/yr. 15yr. research program designed to reduce this and other key uncertainties.

Later we asked the experts to tell us how they thought their estimates of climate sensitivity might change.

Source: Morgan and Keith, 1995.



For example…

Mean value (μ) of the estimate of Δ 15 T after the .year research program has been completed

(Standard deviation σ) of the estimate of Δ T after the15 .year research program has been completed

<-0.25 μ

0.25 σ to

0

0.75 μ to

1.25 μ

1.25 μto

1.75 μ

1.75 μto

2.25 μ> 2.25 μ

0.25 μ to

0.75 μ

-0.25 μ to

0.25 μ

0.75 σ to

.25O σ

1.25 σ to

.75O σ

1.75 σ to

1.25 σ

2.25 σ to

1.75 σ

>2.25 σ

0° C1.5° C3° C4.7° C6.2° C4.8° C3.2° C1.6° C0.020.020.040.020.020.020.020.020.020.040.040.060.160.160.35Expert 9 12% chanceuncertainty mightgrow by >25%

…here is what Expert 9 told us:



Overall…

…the experts gave us estimates that their uncertainty about climate sensitivity might grow by > 25% that ranged from a low of 8% to a high of 40%!

Like all experienced scientists, they knew that research does not always reduce uncertainty.

ExpertNumber

Chance climatesensitivity

uncertainty grows>25% after a 15yr.$ 109/yr research

program

1 102 183 30 (Note 1)4 225 306 147 208 259 1210 2011 4012 1613 1214 1815 1416 8

Note 1: Expert 3 used a differentresponse mode for this question. He gavea 30% of an increase by a factor of ≥2.5.

Source: Morgan and Keith, 1995.



The work of our proposed Center……will start by acknowledging that, while some key uncertainties related to climate change, its impacts, and the options available for mitigation, will be reduced with additional research and experimentation, many will remain large, and some may even grow over the coming decades.

Our objective will be to:

• Develop better insight about what we are likely to be able to know, and not know, about the climate and energy systems over the course of the coming decades.

• Develop and demonstrate improved methods for decision making under this uncertainty, focusing on key problem domains both in climate impact and climate mitigation.



Irreducible uncertainty…(Cont.)In Part 3, we will consider a second important set of irreducible uncertainties involving the future structure of the energy system in which path dependency will be critical.

While future climate policy will be a key factor in shaping the future energy system, again, other social and economic factors will also play key roles.

These uncertainties will both shape the problems faced by decision makers in Part 3, and also will be shaped by decisions made by decision makers in domains such as insurance and electric power, two of the groups of decision makers we'll be working with in Part 2.



Why is our approach important?

• For the first time it will focus on what we are not likely to be able to know about future emissions (the energy system) and about climate change, as well as on what we do know and can expect to know.

• It will develop and demonstrate specific tools to support decision making in that environment.

Since some key uncertainties simply cannot be reduced, this means the tools we develop will include a focus on robust and adaptive strategies.



David Keith jokingly refers to this design as a sandwich

Characterization ofclimate scienceuncertainties.

Characterization ofenergy system uncertainties.

Focuseddecision-makingmeat.

But that description isactually too simple and too static…



The three parts interact

Part 1: Expert elicitationson climate science.

Part 2: Adaptive real-world decision strategies.

Climate changesof greatest

interest.

Possible climatepolicy responses.Part 3: Understanding futurestructure of the energy system.

3P/4PInsuranceetc.

Knowledge constraints.Likely climatechanges.

Identification ofadditional specific problem domains.

Identification ofadditional climatescience elicitation needs.



Methods developmentand

useful resultsThe Center will have a strong focus on developing and demonstrating new tools and methods. We'll give examples as we discuss the various parts of the research.

But, we also plan to produce results that will be directly useful in the ongoing work of the climate research and policy communities, and to decision makers in several key domains.

We see no conflict between these two objectives, since, in a complex problem domain such as this, progress on basic methods is often best made in the context of applied problem solving.



Examples of methods

Here are some of the methods we'll be using and developing across several different parts of the Center's work:

Method 1 2I 2F 2A 2P 3Expert Elicitation D L D DUse of influence diagrams to elicit beliefs and represent assumption L L D D L DEmergent properties of complex systems L L D L DAnalysis of mixed levels of uncertainty L L L D DScenario representation and use L D D D D DReal options/stochastic dynamic programming L L D LInnovative applications of decision analysis L L L D L1 = Part 1; 2I = Part 2 insurance; 2F = Part 2 forests and fisheries; 2A = Part 2 arctic;2P = Part 2 3P/4P; 3 = Part 3. D = definitely will use; L = likely to use.



Proposed Agenda8:00 Room open with coffee, etc.

8:15-8:30 Introductions

8:30-8:40 Welcome by Carnegie Mellon’s President - Jared Cohon


9:00-9:15 Overview of Proposed Center - Granger Morgan




Agenda - Part 1

9:25-9:50 Irreducible uncertainties in climate science - Steve Schneider and Granger Morgan


10:15-10:40 The thermohaline circulation Stefan Rahmstorf and Kirsten Zickfeld


11:00-11:25 Break/Executive Session

Proposed NSF Center on Climate Decision Making Carnegie Mellon University 1 Climate and Related...

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