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Title: A probabilistic analysis of cumulative carbon emissions and long-term planetary warming

Abstract

Efforts to mitigate and adapt to long-term climate change could benefit greatly from probabilistic estimates of cumulative carbon emissions due to fossil fuel burning and resulting CO 2-induced planetary warming. Here we demonstrate the use of a reduced-form model to project these variables. We performed simulations using a large-ensemble framework with parametric uncertainty sampled to produce distributions of future cumulative emissions and consequent planetary warming. A hind-cast ensemble of simulations captured 1980–2012 historical CO 2 emissions trends and an ensemble of future projection simulations generated a distribution of emission scenarios that qualitatively resembled the suite of Representative and Extended Concentration Pathways. The resulting cumulative carbon emission and temperature change distributions are characterized by 5–95th percentile ranges of 0.96–4.9 teratonnes C (Tt C) and 1.4 °C–8.5 °C, respectively, with 50th percentiles at 3.1 Tt C and 4.7 °C. Within the wide range of policy-related parameter combinations that produced these distributions, we found that low-emission simulations were characterized by both high carbon prices and low costs of non-fossil fuel energy sources, suggesting the importance of these two policy levers in particular for avoiding dangerous levels of climate warming. With this analysis we demonstrate a probabilistic approach to the challenge of identifying strategiesmore » for limiting cumulative carbon emissions and assessing likelihoods of surpassing dangerous temperature thresholds.« less

Authors:
 [1];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Concordia Univ., Montreal, QC (Canada)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1335625
Report Number(s):
LA-UR-15-28788
Journal ID: ISSN 1748-9326
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Environmental Research Letters
Additional Journal Information:
Journal Volume: 10; Journal Issue: 11; Journal ID: ISSN 1748-9326
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Computer Science; Earth Sciences; Planetary Sciences

Citation Formats

Fyke, Jeremy Garmeson, and Matthews, H. Damon. A probabilistic analysis of cumulative carbon emissions and long-term planetary warming. United States: N. p., 2015. Web. doi:10.1088/1748-9326/10/11/115007.
Fyke, Jeremy Garmeson, & Matthews, H. Damon. A probabilistic analysis of cumulative carbon emissions and long-term planetary warming. United States. doi:10.1088/1748-9326/10/11/115007.
Fyke, Jeremy Garmeson, and Matthews, H. Damon. Mon . "A probabilistic analysis of cumulative carbon emissions and long-term planetary warming". United States. doi:10.1088/1748-9326/10/11/115007. https://www.osti.gov/servlets/purl/1335625.
@article{osti_1335625,
title = {A probabilistic analysis of cumulative carbon emissions and long-term planetary warming},
author = {Fyke, Jeremy Garmeson and Matthews, H. Damon},
abstractNote = {Efforts to mitigate and adapt to long-term climate change could benefit greatly from probabilistic estimates of cumulative carbon emissions due to fossil fuel burning and resulting CO2-induced planetary warming. Here we demonstrate the use of a reduced-form model to project these variables. We performed simulations using a large-ensemble framework with parametric uncertainty sampled to produce distributions of future cumulative emissions and consequent planetary warming. A hind-cast ensemble of simulations captured 1980–2012 historical CO2 emissions trends and an ensemble of future projection simulations generated a distribution of emission scenarios that qualitatively resembled the suite of Representative and Extended Concentration Pathways. The resulting cumulative carbon emission and temperature change distributions are characterized by 5–95th percentile ranges of 0.96–4.9 teratonnes C (Tt C) and 1.4 °C–8.5 °C, respectively, with 50th percentiles at 3.1 Tt C and 4.7 °C. Within the wide range of policy-related parameter combinations that produced these distributions, we found that low-emission simulations were characterized by both high carbon prices and low costs of non-fossil fuel energy sources, suggesting the importance of these two policy levers in particular for avoiding dangerous levels of climate warming. With this analysis we demonstrate a probabilistic approach to the challenge of identifying strategies for limiting cumulative carbon emissions and assessing likelihoods of surpassing dangerous temperature thresholds.},
doi = {10.1088/1748-9326/10/11/115007},
journal = {Environmental Research Letters},
issn = {1748-9326},
number = 11,
volume = 10,
place = {United States},
year = {2015},
month = {11}
}

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