1. Scenario Discovery Robert Lempert Director RAND Pardee Center on Longer-Range Global Policy and the Future Human Condition CEDM Project Meeting May 21, 2012

2. 2 Use of Scenarios Raises Important Questions Climate community uses scenarios for many purposes, including –Low confidence projections –“Story and simulation” approach, drawn from intuitive logics literature At their best, scenarios can –Provide simple means to communicate uncertainty –Expand range of (potentially inconvenient) futures considered by decision makers –Avoid leading with likelihoods, which may prove contentious But scenarios can fail because –Choice seems arbitrary –Key drivers create an illusion of communication –Their connection to decision making proves tenuous –Unlikely to incorporate potential for surprises Climate community uses scenarios for many purposes, including –Low confidence projections –“Story and simulation” approach, drawn from intuitive logics literature At their best, scenarios can –Provide simple means to communicate uncertainty –Expand range of (potentially inconvenient) futures considered by decision makers –Avoid leading with likelihoods, which may prove contentious But scenarios can fail because –Choice seems arbitrary –Key drivers create an illusion of communication –Their connection to decision making proves tenuous –Unlikely to incorporate potential for surprises

3. 3 Scenario Discovery Addresses These Problems Two key ideas: 1.Recognize that scenarios provide distinct and potentially competing contributions to decision structuring and to choice tasks 2.Consider scenarios as concise summaries of the future states of the world in which a proposed policy fails to meet its goals Two key ideas: 1.Recognize that scenarios provide distinct and potentially competing contributions to decision structuring and to choice tasks 2.Consider scenarios as concise summaries of the future states of the world in which a proposed policy fails to meet its goals Implemented as part of robust decision making analysis: 1)Generate large database of simulation model results where each entry projects performance of proposed policy in one future state of the world 2)Run cluster-finding algorithms to identify the combination of values for a small number of uncertain model inputs that best differentiates the cases where the policy meets its goals from those where it doesn’t 3)Use these clusters as scenarios to inform discussions regarding vulnerabilities, alternative policies, and tradeoffs among policies Implemented as part of robust decision making analysis: 1)Generate large database of simulation model results where each entry projects performance of proposed policy in one future state of the world 2)Run cluster-finding algorithms to identify the combination of values for a small number of uncertain model inputs that best differentiates the cases where the policy meets its goals from those where it doesn’t 3)Use these clusters as scenarios to inform discussions regarding vulnerabilities, alternative policies, and tradeoffs among policies

4. 4 Where Does “Safety First” Emissions Reduction Policy Have High Regret? Use modified DICE model to Compare alternative emission reduction strategies in face of potential abrupt change in Meridional Overturning Circulation (MOC) Over 2,662 cases exploring assumptions about climate sensitivity, rate of carbon intensity improvement, damages from MOC collapse, and MOC vulnerability Use modified DICE model to Compare alternative emission reduction strategies in face of potential abrupt change in Meridional Overturning Circulation (MOC) Over 2,662 cases exploring assumptions about climate sensitivity, rate of carbon intensity improvement, damages from MOC collapse, and MOC vulnerability Two scenarios describe high regret cases for “Safety First” strategy: Catastrophic Climate Sensitivity scenario needs to be about six times more likely than best-estimate before SF is less good than more aggressive strategies Over Reaction scenario suggests ways to improve learning algorithm so SF competes better with less aggressive strategies Two scenarios describe high regret cases for “Safety First” strategy: Catastrophic Climate Sensitivity scenario needs to be about six times more likely than best-estimate before SF is less good than more aggressive strategies Over Reaction scenario suggests ways to improve learning algorithm so SF competes better with less aggressive strategies Coverage and Density of 86% and 72%

5. 5 Where Does Metropolitan Water District Resource Plan Fail to Meet Its Reliability Goals? –Metropolitan’s 2010 Integrated Resources Plan Describes a 25 year investment and policy plan Calls for 10% buffer and adaptive management to address uncertainty –Use Metropolitan’s own planning models to identify vulnerabilities of IRP Ran 3,744 cases, exploring climate, demand, Delta fix, yields, and project delays Characterized cases where plan fails to meet goals –Metropolitan’s 2010 Integrated Resources Plan Describes a 25 year investment and policy plan Calls for 10% buffer and adaptive management to address uncertainty –Use Metropolitan’s own planning models to identify vulnerabilities of IRP Ran 3,744 cases, exploring climate, demand, Delta fix, yields, and project delays Characterized cases where plan fails to meet goals –The results suggest The IRP can meet its goals even if one big thing goes wrong, as along as everything else goes right Key indicators Metropolitan should track to determine whether it should adjust its IRP –The results suggest The IRP can meet its goals even if one big thing goes wrong, as along as everything else goes right Key indicators Metropolitan should track to determine whether it should adjust its IRP Without Delta Fix 1 of 12 climate projections at least this wet, Yield +12% can compensate 5 of 12 climate projections at least this wet Expected yield can compensate IRP Planning Case High Growth, Not Wetter Scenario Low Growth, Not Drier Scenario With Delta Fix x

6. 6 Summary and Next Steps Scenario discovery provides: –An intuitive-logics-like quantitative analysis within a well- structured decision support process –Scenarios that support both decision structuring and choice tasks Applications include water management, flood control, energy investments, terrorism insurance Hope to explore other potential applications with you Scenario discovery provides: –An intuitive-logics-like quantitative analysis within a well- structured decision support process –Scenarios that support both decision structuring and choice tasks Applications include water management, flood control, energy investments, terrorism insurance Hope to explore other potential applications with you