Regional estimates of the transient climate response to cumulative CO2 emissions

Abstract The 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.

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Estimating remaining carbon budgets using temperature responses informed by CMIP6

10.21203/rs.3.rs-104000/v3 ◽

2021 ◽

Author(s):

Martin Rypdal ◽

Niklas Boers ◽

Hege-Beate Fredriksen ◽

Kai-Uwe Eiselt ◽

Andreas Johansen ◽

...

Keyword(s):

Co2 Emissions ◽

Climate Sensitivity ◽

Carbon Budget ◽

Climate Models ◽

Climate Response ◽

Earth System ◽

Transient Climate Response ◽

Carbon Cycle Model ◽

Uncertainty Range ◽

Temperature Responses

Abstract A remaining carbon budget (RCB) estimates how much CO2 we can emit and still reach a specific temperature target. The RCB concept is attractive since it easily communicates to the public and policymakers, but RCBs are also subject to uncertainties. The expected warming levels for a given carbon budget has a wide uncertainty range, which increases with less ambitious targets, i.e., with higher CO2 emissions and temperatures. Leading causes of RCB uncertainty are the future non-CO2 emissions, Earth system feedbacks, and the spread in the climate sensitivity among climate models. The latter is investigated in this paper, using a simple carbon cycle model and emulators of the temperature responses of the Earth System Models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. It is shown that the transient climate response to cumulative emissions of carbon (TCRE) is approximately proportional to the transient climate response (TCR), suggesting that observational constraints imposed on climate sensitivity in the model ensemble can reduce uncertainty in RCB estimates.

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Effect of ocean mixing on the transient climate response to a CO2increase: Analysis of recent model results

Journal of Geophysical Research Atmospheres ◽

10.1029/jd091id02p02709 ◽

1986 ◽

Vol 91 (D2) ◽

pp. 2709 ◽

Cited By ~ 19

Author(s):

L. D. Danny Harvey

Keyword(s):

Recent Model ◽

Ocean Mixing ◽

Climate Response ◽

Transient Climate Response

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Relationship between physical and biogeochemical parameters and the scenario dependence of the transient climate response to cumulative carbon emissions

Progress in Earth and Planetary Science ◽

10.1186/s40645-020-00392-6 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Kaoru Tachiiri

Keyword(s):

Carbon Emissions ◽

Carbon Budget ◽

Extreme Case ◽

Initial Increase ◽

Climate Response ◽

Earth System ◽

Transient Climate Response ◽

Large Correlation ◽

Before And After ◽

Biogeochemical Parameters

AbstractThe transient climate response to cumulative carbon emissions (TCRE) is a key metric in estimating the remaining carbon budget for given temperature targets. However, the TCRE has a small scenario dependence that can be non-negligible for stringent temperature targets. To investigate the parametric correlations and scenario dependence of the TCRE, the present study uses a 512-member ensemble of an Earth system model of intermediate complexity (EMIC) perturbing 11 physical and biogeochemical parameters under scenarios with steady increases of 0.25%, 0.5%, 1%, 2%, or 4% per annum (ppa) in the atmospheric CO2 concentration (pCO2), or an initial increase of 1% followed by an annual decrease of 1% thereafter. Although a small difference of 5% (on average) in the TCRE is observed between the 1-ppa and 0.5-ppa scenarios, a significant scenario dependence is found for the other scenarios, with a tendency toward large values in gradual or decline-after-a-peak scenarios and small values in rapidly increasing scenarios. For all scenarios, correlation analysis indicates a remarkably large correlation between the equilibrium climate sensitivity (ECS) and the relative change in the TCRE, which is attributed to the longer response time of the high ECS model. However, the correlations of the ECS with the TCRE and its scenario dependence for scenarios with large pCO2 increase rates are slightly smaller, and those of biogeochemical parameters such as plant respiration and the overall pCO2–carbon cycle feedback are larger, than in scenarios with gradual increases. The ratio of the TCREs under the overshooting (i.e., 1-ppa decrease after a 1-ppa increase) and 1-ppa increase only scenarios had a clear positive relation with zero-emission commitments. Considering the scenario dependence of the TCRE, the remaining carbon budget for the 1.5 °C target could be reduced by 17 or 22% (before and after considering the unrepresented Earth system feedback) for the most extreme case (i.e., the 67th percentile when using the 0.25-ppa scenario as compared to the 1-ppa increase scenario). A single ensemble EMIC is also used to indicate that, at least for high ECS (high percentile) cases, the scenario dependence of the TCRE should be considered when estimating the remaining carbon budget.

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Equilibrium Climate Sensitivity and Transient Climate Response biased low in historical simulations of CMIP6 models

10.1002/essoar.10507840.1 ◽

2021 ◽

Author(s):

Yue Dong ◽

Kyle C. Armour ◽

Cristian Proistosescu ◽

Timothy Andrews ◽

David S. Battisti ◽

...

Keyword(s):

Climate Sensitivity ◽

Climate Response ◽

Transient Climate Response ◽

Equilibrium Climate Sensitivity

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Large ensemble assessment of how the global surface warming response to cumulative carbon differs for negative and positive carbon emissions

10.5194/egusphere-egu21-12718 ◽

2021 ◽

Author(s):

Negar Vakilifard ◽

Katherine Turner ◽

Ric Williams ◽

Philip Holden ◽

Neil Edwards ◽

...

Keyword(s):

Carbon Emissions ◽

Radiative Forcing ◽

Climate Model ◽

Climate Feedback ◽

Climate Response ◽

Ocean Heat Uptake ◽

Transient Climate Response ◽

Surface Warming ◽

Radiative Processes ◽

Negative Emission

The controls of the effective transient climate response (TCRE), defined in terms of the dependence of surface warming since the pre-industrial to the cumulative carbon emission, is explained in terms of climate model experiments for a scenario including positive emissions and then negative emission over a period of 400 years. We employ a pre-calibrated ensemble of GENIE, grid-enabled integrated Earth system model, consisting of 86 members to determine the process of controlling TCRE in both CO2 emissions and drawdown phases. Our results are based on the GENIE simulations with historical forcing from AD 850 including land use change, and the future forcing defined by CO2 emissions and a non-CO2 radiative forcing timeseries. We present the results for the point-source carbon capture and storage (CCS) scenario as a negative emission scenario, following the medium representative concentration pathway (RCP4.5), assuming that the rate of emission drawdown is 2 PgC/yr CO2 for the duration of 100 years. The climate response differs between the periods of positive and negative carbon emissions with a greater ensemble spread during the negative carbon emissions. The controls of the spread in ensemble responses are explained in terms of a combination of thermal processes (involving ocean heat uptake and physical climate feedback), radiative processes (saturation in radiative forcing from CO2 and non-CO2 contributions) and carbon dependences (involving terrestrial and ocean carbon uptake).&#160;&#160;

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Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

Earth System Dynamics ◽

10.5194/esd-12-709-2021 ◽

2021 ◽

Vol 12 (2) ◽

pp. 709-723

Author(s):

Philip Goodwin ◽

B. B. Cael

Keyword(s):

Climate Sensitivity ◽

Future Climate ◽

Climate Feedback ◽

Future Climate Change ◽

Climate Projections ◽

Climate Response ◽

Ocean Heat Uptake ◽

Transient Climate Response ◽

Posterior Probability Distribution ◽

The Impact

Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.

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Quantifying Carbon Cycle Feedbacks

Journal of Climate ◽

10.1175/2009jcli2949.1 ◽

2009 ◽

Vol 22 (19) ◽

pp. 5232-5250 ◽

Cited By ~ 160

Author(s):

J. M. Gregory ◽

C. D. Jones ◽

P. Cadule ◽

P. Friedlingstein

Keyword(s):

Carbon Cycle ◽

Co2 Emissions ◽

Co2 Concentration ◽

Atmospheric Co2 ◽

Climate Feedback ◽

Climate Response ◽

Carbon Cycle Model ◽

Atmospheric Co2 Concentration ◽

Intercomparison Project ◽

Earth System Models

Abstract Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration–carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate–carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), climate–carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration–carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate–carbon feedback and more uncertain. The concentration–carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon–climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration–carbon and climate–carbon feedbacks do not combine linearly, and the concentration–carbon feedback is dependent on scenario and time.

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