Global carbon budgets: determining limits on fossil fuel emissions

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Differences between carbon budget estimates unravelled

ArticleSeveral methods exist to estimate the cumulative carbon emissions that would keep global warming to below a given temperature limit. Here we review estimates reported by the IPCC and the recent literature, and discuss the reasons underlying their differences. The most scientifically robust number — the carbon budget for CO2-induced warming only — is also the least relevant for real-world policy. Including all greenhouse gases and using methods based on scenarios that avoid instead of exceed a given temperature limit results in lower carbon budgets. For a >66% chance of limiting warming below the internationally agreed temperature limit of 2 °C relative to pre-industrial levels, the most appropriate carbon budget estimate is 590–1,240 GtCO2 from 2015 onwards. Variations within this range depend on the probability of staying below 2 °C and on end-of-century non-CO2 warming. Current CO2 emissions are about 40 GtCO2 yr–1, and global CO2 emissions thus have to be reduced urgently to keep within a 2 °C-compatible budge

The utility of the historical record for assessing the transient climate response to cumulative emissions

This is the final version of the article. Available from the publisher via the DOI in this record.The historical observational record offers a way to constrain the relationship between cumulative carbon dioxide emissions and global mean warming. We use a standard detection and attribution technique, along with observational uncertainties to estimate the all-forcing or 'effective' transient climate response to cumulative emissions (TCRE) from the observational record. Accounting for observational uncertainty and uncertainty in historical non-CO2radiative forcing gives a best-estimate from the historical record of 1.84°C/TtC (1.43-2.37°C/TtC 5-95% uncertainty) for the effective TCRE and 1.31°C/TtC (0.88-2.60°C/TtC 5-95% uncertainty) for the CO2-only TCRE. While the best-estimate TCRE lies in the lower half of the IPCC likely range, the high upper bound is associated with the not-ruled-out possibility of a strongly negative aerosol forcing. Earth System Models have a higher effective TCRE range when compared like-for-like with the observations over the historical period, associated in part with a slight underestimate of diagnosed cumulative emissions relative to the observational best-estimate, a larger ensemble mean-simulated CO2-induced warming, and rapid post-2000 non-CO2warming in some ensemble members.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.R.J.M. an

The Origin and Limits of the Near Proportionality between Climate Warming and Cumulative CO2 Emissions

PublishedThe transient climate response to cumulative CO2 emissions (TCRE) is a useful metric of climate warming that directly relates the cause of climate change (cumulative carbon emissions) to the most used index of climate change (global mean near-surface temperature change). In this paper, analytical reasoning is used to investigate why TCRE is near constant over a range of cumulative emissions up to 2000 Pg of carbon. In addition, a climate model of intermediate complexity, forced with a constant flux of CO2 emissions, is used to explore the effect of terrestrial carbon cycle feedback strength on TCRE. The analysis reveals that TCRE emerges from the diminishing radiative forcing from CO2 per unit mass being compensated for by the diminishing ability of the ocean to take up heat and carbon. The relationship is maintained as long as the ocean uptake of carbon, which is simulated to be a function of the CO2 emissions rate, dominates changes in the airborne fraction of carbon. Strong terrestrial carbon cycle feedbacks have a dependence on the rate of carbon emission and, when present, lead to TRCE becoming rate dependent. Despite these feedbacks, TCRE remains roughly constant over the range of the representative concentration pathways and therefore maintains its primary utility as a metric of climate change.AHMD is grateful for support from the University of Victoria, NSERC CGS, and subsequently NSERC CREATE. The Michael Smith foreign study supplement provided funding that allowed AHMD to travel to the United Kingdom to work with PF. AHMD is grateful to A. J. Weaver for support and supervision. The authors acknowledge the financial support by the European Union FP7-ENVIRONMENT project PAGE21 under Contract GA282700. We are thankful for mathematical assistance provided by A. H. Monahan. We thank M. Raupach, R. Knutti, and an anonymous reviewer for their useful comments

A steep road to climate stabilization: The only way to stabilize Earth’s climate is to stabilize the concentration of greenhouse gases in the atmosphere, but future changes in the carbon cycle might make this more difficult than has been thought.

SupplementInternational audienceThe only way to stabilize Earth’s climate is to stabilize the concentration of greenhouse gases in the atmosphere, but future changes in the carbon cycle might make this more difficult than has been thought

Quantifying process-level uncertainty contributions to TCRE and carbon budgets for meeting Paris Agreement climate targets

This is the final version. Available on open access from IOP Publishing via the DOI in this recordTo achieve the goals of the Paris Agreement requires deep and rapid reductions in anthropogenic CO2 emissions, but uncertainty surrounds the magnitude and depth of reductions. Earth system models provide a means to quantify the link from emissions to global climate change. Using the concept of TCRE - the transient climate response to cumulative carbon emissions - we can estimate the remaining carbon budget to achieve 1.5 or 2 °C. But the uncertainty is large, and this hinders the usefulness of the concept. Uncertainty in carbon budgets associated with a given global temperature rise is determined by the physical Earth system, and therefore Earth system modelling has a clear and high priority remit to address and reduce this uncertainty. Here we explore multi-model carbon cycle simulations across three generations of Earth system models to quantitatively assess the sources of uncertainty which propagate through to TCRE. Our analysis brings new insights which will allow us to determine how we can better direct our research priorities in order to reduce this uncertainty. We emphasise that uses of carbon budget estimates must bear in mind the uncertainty stemming from the biogeophysical Earth system, and we recommend specific areas where the carbon cycle research community needs to re-focus activity in order to try to reduce this uncertainty. We conclude that we should revise focus from the climate feedback on the carbon cycle to place more emphasis on CO2 as the main driver of carbon sinks and their long-term behaviour. Our proposed framework will enable multiple constraints on components of the carbon cycle to propagate to constraints on remaining carbon budgets.Joint UK BEIS/Defra Met Office Hadley Centre Climate ProgrammeEuropean Union Horizon 202

The cumulative carbon budget and its implications

The cumulative impact of carbon dioxide (CO2) emissions on climate has potentially profound economic and policy implications. It implies that the long-term climate change mitigation challenge should be reframed as a stock problem, while the overwhelming majority of climate policies continue to focus on the flow of CO2 into the atmosphere in 2030 or 2050. An obstacle, however, to the use of a cumulative carbon budget in policy is uncertainty in the size of this budget consistent with any specific temperature-based goal such as limiting warming to 2°C. This arises from uncertainty in the climate response to CO2 emissions, which is relatively tractable, and uncertainty in future warming due to non-CO2 drivers, which is less so. We argue these uncertainties are best addressed through policies that recognize the need to reduce net global CO2 emissions to zero to stabilize global temperatures but adapt automatically to evolving climate change. Adaptive policies would fit well within the Paris Agreement under the UN Framework Convention on Climate Change

Emergent constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system models

Journal ArticleAn emergent linear relationship between the long-term sensitivity of tropical land carbon storage to climate warming (γLT) and the short-term sensitivity of atmospheric carbon dioxide (CO2) to interannual temperature variability (γIAV) has previously been identified by Cox et al. (2013) across an ensemble of Earth system models (ESMs) participating in the Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP). Here we examine whether such a constraint also holds for a new set of eight ESMs participating in Phase 5 of the Coupled Model Intercomparison Project. A wide spread in tropical land carbon storage is found for the quadrupling of atmospheric CO2, which is of the order of 252 ± 112 GtC when carbon-climate feedbacks are enabled. Correspondingly, the spread in γLT is wide (-49 ± 40 GtC/K) and thus remains one of the key uncertainties in climate projections. A tight correlation is found between the long-term sensitivity of tropical land carbon and the short-term sensitivity of atmospheric CO2 (γLT versus γIAV), which enables the projections to be constrained with observations. The observed short-term sensitivity of CO2 (-4.4 ± 0.9 GtC/yr/K) sharpens the range of γLT to -44 ± 14 GtC/K, which overlaps with the probability density function derived from the C4MIP models (-53 ± 17 GtC/K) by Cox et al. (2013), even though the lines relating γLT and γIAV differ in the two cases. Emergent constraints of this type provide a means to focus ESM evaluation against observations on the metrics most relevant to projections of future climate change. Key Points Tropical land carbon loss is a key uncertainty in climate change projections CO2 interannual variability is linearly related to tropical carbon loss in CMIP5 Observed variability in CO2 constrains projections of future carbon losses ©2014. American Geophysical Union. All Rights Reserved.European Commission's Seventh Framework Programme, EMBRACE and ESMVa

Process-based analysis of terrestrial carbon flux predictability

Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question to what extent the terrestrial carbon cycle is predictable, and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max-Planck-Institute Earth System Model. In order to asses the role of local carbon flux predictability (CFpred) on the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature and radiation for NPP and soil organic carbon, air temperature and precipitation for Rh). NPPpred is driven to 62 and 30 % by the predictability of soil moisture and temperature, respectively. Rhpred is driven to 52 and 27 % by the predictability of soil organic carbon temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system