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Title: Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios

Abstract

An analytic scenario generation framework is developed based on the idea that the same climate outcome can result from very different socioeconomic and policy drivers. The framework builds on the Scenario Matrix Framework’s abstraction of “challenges to mitigation” and “challenges to adaptation” to facilitate the flexible discovery of diverse and consequential scenarios. We combine visual and statistical techniques for interrogating a large factorial data set of 33,750 scenarios generated using the Global Change Assessment Model. We demonstrate how the analytic framework can aid in identifying which scenario assumptions are most tied to user-specified measures for policy relevant outcomes of interest, specifically for our example high or low mitigation costs. We show that the current approach for selecting reference scenarios can miss policy relevant scenario narratives that often emerge as hybrids of optimistic and pessimistic scenario assumptions. We also show that the same scenario assumption can be associated with both high and low mitigation costs depending on the climate outcome of interest and the mitigation policy context. In the illustrative example, we show how agricultural productivity, population growth, and economic growth are most predictive of the level of mitigation costs. Formulating policy relevant scenarios of deeply and broadly uncertain futures benefitsmore » from large ensemble-based exploration of quantitative measures of consequences. To this end, we have contributed a large database of climate change futures that can support “bottom-up” scenario generation techniques that capture a broader array of consequences than those that emerge from limited sampling of a few reference scenarios.« less

Authors:
 [1];  [2]; ORCiD logo [3]; ORCiD logo [3];  [3]; ORCiD logo [3]
  1. Tufts Univ., Medford, MA (United States)
  2. Cornell Univ., Ithaca, NY (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1561111
Report Number(s):
PNNL-ACT-SA-10362
Journal ID: ISSN 2328-4277
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Earth's Future
Additional Journal Information:
Journal Volume: 6; Journal Issue: 3; Journal ID: ISSN 2328-4277
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Lamontagne, Jonathan R., Reed, Patrick, Link, Robert P., Calvin, Katherine V., Clarke, Leon E., and Edmonds, James A. Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios. United States: N. p., 2018. Web. doi:10.1002/2017EF000701.
Lamontagne, Jonathan R., Reed, Patrick, Link, Robert P., Calvin, Katherine V., Clarke, Leon E., & Edmonds, James A. Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios. United States. doi:10.1002/2017EF000701.
Lamontagne, Jonathan R., Reed, Patrick, Link, Robert P., Calvin, Katherine V., Clarke, Leon E., and Edmonds, James A. Fri . "Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios". United States. doi:10.1002/2017EF000701. https://www.osti.gov/servlets/purl/1561111.
@article{osti_1561111,
title = {Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios},
author = {Lamontagne, Jonathan R. and Reed, Patrick and Link, Robert P. and Calvin, Katherine V. and Clarke, Leon E. and Edmonds, James A.},
abstractNote = {An analytic scenario generation framework is developed based on the idea that the same climate outcome can result from very different socioeconomic and policy drivers. The framework builds on the Scenario Matrix Framework’s abstraction of “challenges to mitigation” and “challenges to adaptation” to facilitate the flexible discovery of diverse and consequential scenarios. We combine visual and statistical techniques for interrogating a large factorial data set of 33,750 scenarios generated using the Global Change Assessment Model. We demonstrate how the analytic framework can aid in identifying which scenario assumptions are most tied to user-specified measures for policy relevant outcomes of interest, specifically for our example high or low mitigation costs. We show that the current approach for selecting reference scenarios can miss policy relevant scenario narratives that often emerge as hybrids of optimistic and pessimistic scenario assumptions. We also show that the same scenario assumption can be associated with both high and low mitigation costs depending on the climate outcome of interest and the mitigation policy context. In the illustrative example, we show how agricultural productivity, population growth, and economic growth are most predictive of the level of mitigation costs. Formulating policy relevant scenarios of deeply and broadly uncertain futures benefits from large ensemble-based exploration of quantitative measures of consequences. To this end, we have contributed a large database of climate change futures that can support “bottom-up” scenario generation techniques that capture a broader array of consequences than those that emerge from limited sampling of a few reference scenarios.},
doi = {10.1002/2017EF000701},
journal = {Earth's Future},
number = 3,
volume = 6,
place = {United States},
year = {2018},
month = {2}
}

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