Accounting for the climate–carbon feedback in emission metrics

Gasser, Thomas; Peters, Glen P.; Fuglestvedt, Jan S.; Collins, William; Shindell, D. T.; Ciais, Philippe

doi:10.5194/esd-8-235-2017

Cited by 88 publications

(78 citation statements)

References 38 publications

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“…While CO 2 is the most important climate forcer, individual models may also differ in their responses to non-CO 2 emissions. These emissions can also introduce uncertainty that is not captured in concentration-driven or CO 2 -only driven model experiments (Matthews and Zickfeld, 2012;Tachiiri et al, 2015;Gasser et al, 2017). As non-CO 2 forcing impacts temperature, which affects the efficiency of carbon sinks, non-CO 2 forcing agents themselves influence the carbon cycle (MacDougall et al, 2015;Tokarska et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

et al. 2018

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Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO 2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC) −1 (median and 5-95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS/TCR parameters but less sensitive to the ERF from a doubling of CO 2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081-2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS/TCR distributions.

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Section: Introductionmentioning

confidence: 99%

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

et al. 2018

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“…ALL-forcing carbon budgets may also be assessed using scenarios which avoid exceeding a given temperature threshold (threshold avoidance budgets [7]). While we examine carbon budgets for CO 2 alone under the assumption of a fixed relationship between non-CO 2 forcing and cumulative carbon emissions, recent studies [20,29,30] propose alternative approaches to a separate consideration of short-lived forcing agents in the cumulative emission framework, instead of their CO 2 equivalents.…”

Section: Discussionmentioning

confidence: 99%

The influence of non-CO ₂ forcings on cumulative carbon emissions budgets

Tokarska

Gillett

Arora

et al. 2018

Environ. Res. Lett.

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Carbon budgets provide a useful tool for policymakers to help meet the global climate targets, as they specify total allowable carbon emissions consistent with limiting warming to a given temperature threshold. Non-CO 2 forcings have a net warming effect in the Representative Concentration Pathways (RCP) scenarios, leading to reductions in remaining carbon budgets based on CO 2 forcing alone. Carbon budgets consistent with limiting warming to below 2.0 • C, with and without accounting for the effects of non-CO 2 forcings, were assessed in inconsistent ways by the Intergovernmental Panel on Climate Change (IPCC), making the effects of non-CO 2 forcings hard to identify. Here we use a consistent approach to compare 1.5 • C and 2.0 • C carbon budgets with and without accounting for the effects of non-CO 2 forcings, using CO 2 -only and RCP8.5 simulations. The median allowable carbon budgets for 1.5 • C and 2.0 • C warming are reduced by 257 PgC and 418 PgC, respectively, and the uncertainty ranges on the budgets are reduced by more than a factor of two when accounting for the net warming effects of non-CO 2 forcings. While our overall results are consistent with IPCC, we use a more robust methodology, and explain the narrower uncertainty ranges of carbon budgets when non-CO 2 forcings are included. We demonstrate that most of the reduction in carbon budgets is a result of the direct warming effect of the non-CO 2 forcings, with a secondary contribution from the influence of the non-CO 2 forcings on the carbon cycle. Such carbon budgets are expected to play an increasingly important role in climate change mitigation, thus understanding the influence of non-CO 2 forcings on these budgets and their uncertainties is critical.

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“…Turning now to composition, in addition to the response of CO 2 concentrations to CO 2 emissions, we include the very small "direct" oxidation of CH 4 and CO to CO 2 . We also include the "indirect" CO 2 response to all other climate drivers via their impact on the carbon cycle (Gasser et al, 2017; see dashed arrows in Figure 1). Hence, all emissions affect the carbon cycle, though no others as greatly as direct emissions of CO 2 .…”

Section: 1029/2018ef001030mentioning

confidence: 99%

Spatial Patterns of Crop Yield Change by Emitted Pollutant

et al. 2019

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Field measurements and modeling have examined how temperature, precipitation, and exposure to carbon dioxide (CO 2 ) and ozone affect major staple crops around the world. Most prior studies, however, have incorporated only a subset of these influences. Here we examine how emissions of each individual pollutant driving changes in these four factors affect present‐day yields of wheat, maize (corn), and rice worldwide. Our statistical modeling indicates that for the global mean, climate and composition changes have decreased wheat and maize yields substantially whereas rice yields have increased. Well‐mixed greenhouse gasses drive most of the impacts, though aerosol‐induced cooling can be important, particularly for more polluted area including India and China. Maize yield losses are most strongly attributable to methane emissions (via both temperature and ozone). In tropical areas, wheat yield losses are primarily driven by CO 2 (via temperature), whereas in temperate zones other well‐mixed greenhouse gases dominate. Rice yields increase in tropical countries due to a larger impact from CO 2 fertilization plus aerosol‐induced cooling than losses due to CO 2 ‐induced warming and impacts of non‐CO 2 gasses, whereas there are net losses in temperate zones driven largely by methane and other non‐CO 2 gasses. Though further work is needed, particularly on the effects of aerosol changes and on nutritional impacts, these results suggest that crop yields over coming decades will be strongly influenced by changes in non‐CO 2 greenhouse gasses, ozone precursors, and aerosols and that these should be taking into account in plant‐level models and when examining linkages between climate change mitigation and sustainable development.

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Accounting for the climate–carbon feedback in emission metrics

Cited by 88 publications

References 38 publications

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

The influence of non-CO ₂ forcings on cumulative carbon emissions budgets

Spatial Patterns of Crop Yield Change by Emitted Pollutant

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Accounting for the climate–carbon feedback in emission metrics

Cited by 88 publications

References 38 publications

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

The influence of non-CO 2 forcings on cumulative carbon emissions budgets

Spatial Patterns of Crop Yield Change by Emitted Pollutant

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Product

Resources

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The influence of non-CO ₂ forcings on cumulative carbon emissions budgets