Equilibrium Climate Sensitivity Estimated by Equilibrating Climate Models

Rugenstein, Maria; Bloch‐Johnson, Jonah; Gregory, Jonathan M.; Andrews, Timothy; Mauritsen, Thorsten; Li, Chao; Frölicher, Thomas L.; Paynter, David; Danabasoglu, Gökhan; Yang, Shuting; Dufresne, Jean‐Louis; Cao, Long; Schmidt, Gavin A.; Abe‐Ouchi, Ayako; Geoffroy, Olivier; Knutti, Reto

doi:10.1029/2019gl083898

Cited by 121 publications

(151 citation statements)

References 78 publications

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“…likely bounds of our TCR constraint. We find no good evidence to support a particular non-linear relationship between ECS and TCR (Rugenstein et al, 2019), and therefore little evidence of a direct emergent constraint on ECS from recent warming alone (Jiménez-de-la Cuesta and Mauritsen, 2019). In the future, we hope that our TCR constraint will become the basis for constraints also on ECS and TCRE (Transient Climate Response to Emissions), but this will require the inclusion of additional constraints on ocean heat uptake, and land and ocean carbon uptake, respectively.…”

Section: Discussioncontrasting

confidence: 63%

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

Nijsse

Cox

Williamson

2020

Preprint

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Abstract. The transient climate response (TCR) is the metric of temperature sensitivity that is most relevant to warming in the next few decades, and contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets (Arora et al., 2019). In the IPCC 5th Assessment Report (AR5), the stated likely range of TCR was given as 1.0 to 2.5 K, with a central estimate which was broadly consistent with the ensemble mean of the CMIP5 Earth System Models (ESMs) available at the time (1.8 ± 0.4 K). Many of the latest CMIP6 ESMs have larger climate sensitivities, with 6 of 23 models having TCR values above 2.5 K, and an ensemble mean TCR of 2.1 ± 0.4 K. On the face of it, these latest ESM results suggest that the IPCC likely range of TCR may need revising upwards, which would cast further doubt on the feasibility of the Paris targets. Here we show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to further constrain the likely range of TCR to 1.5–2.2 K, with a central estimate of 1.82 K. We reach this conclusion through an emergent constraint approach which relates the value of TCR to the global warming from 1970 onwards. We confirm a consistent emergent constraint on TCR when we apply the same method to CMIP5 models (Jiménez-de-la Cuesta and Mauritsen, 2019). Our emergent constraint on TCR benefits from both the large range of TCR values across the CMIP6 models, and also from the extension of the historical simulations into a period when the uncertain changes in aerosol forcing have had a far less significant impact on the trend in global warming.

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Section: Discussioncontrasting

confidence: 63%

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

Nijsse

Cox

Williamson

2020

Preprint

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“…b GHG to scale the ocean heat content was derived for the period 1955-2012 in both cases, and b GHG to scale temperature derived for the period 1862-2012 in both cases (see section 3.2). often lower than the true ECS when using equilibrated simulations (Rugenstein et al 2019).…”

Section: Energy Budget Approach To the Greenhouse Gas Attributable Efmentioning

confidence: 99%

“…The global ocean stores more than 90% of the Earth's energy imbalance, taking centuries to reach equilibrium (Hansen et al 2011, Church et al 2013. We refer to the climate sensitivity inferred from the historical and present-day conditions as the effective climate sensitivity (S hist ), which may be different from climate sensitivity at equilibrium (Knutti et al 2017, Rugenstein et al 2019, due to slow response additional climate feedbacks that come into play on longer timescales and when the pattern of global warming is fully developed (including, for example, sea ice retreat). (Note that the Earth system sensitivity that includes the response to ice sheet melt and release of CO 2 from permafrost may be even higher; Knutti et al 2017.…”

Section: Motivationmentioning

confidence: 99%

Observational constraints on the effective climate sensitivity from the historical period

Tokarska

Hegerl

Schurer

et al. 2020

Environ. Res. Lett.

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The observed warming in the atmosphere and ocean can be used to estimate the climate sensitivity linked to present-day feedbacks, which is referred to as the effective climate sensitivity (S hist ). However, such an estimate is affected by uncertainty in the radiative forcing, particularly aerosols, over the historical period. Here, we make use of detection and attribution techniques to derive the surface air temperature and ocean warming that can be attributed directly to greenhouse gas increases. These serve as inputs to a simple energy budget to infer the likelihood of S hist in response to observed greenhouse gases increases over two time periods (1862-2012 and 1955-2012). The benefit of using greenhouse gas attributable quantities is that they are not subject to uncertainties in the aerosol forcing (other than uncertainty in the attribution to greenhouse gas versus aerosol forcing not captured by the multi-model aerosol response pattern). The resulting effective climate sensitivity estimate, S hist , ranges from 1.3°C to 3.1°C (5%-95% range) over the full instrumental period (1862-2012) for our best estimate, and gets slightly wider when considering further uncertainties. This estimate increases to 1.7°C-4.6°C if using the shorter period . We also evaluate the climate model simulated surface air temperature and ocean heat content increase in response to greenhouse gas forcing over the same periods, and compare them with the observationally-constrained values. We find that that the ocean warming simulated in greenhouse gas only simulations in models considered here is consistent with that attributed to greenhouse gas increases from observations, while one model simulates more greenhouse gas-induced surface air warming than observed. However, other models with sensitivity outside our range show greenhouse gas warming that is consistent with that attributed in observations, emphasising that feedbacks during the historical period may differ from the feedbacks at CO 2 doubling and from those at true equilibrium.

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“…Journal of Geophysical Research: Atmospheres underestimate the true ECS obtained from equilibrating the climate models (Rugenstein et al, 2020). However, since only a small subset of the CMIP models provides the long-running simulations necessary for the calculation of the true ECS, we use the Gregory method for an approximate, yet consistent ECS assessment for all climate models.…”

Section: 1029/2019jd032321mentioning

confidence: 99%

Quantifying Progress Across Different CMIP Phases With the ESMValTool

et al. 2020

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More than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change. Here, we use the Earth System Model Evaluation Tool (ESMValTool) to assess the performance of the CMIP6 ensemble compared to the previous generations CMIP3 and CMIP5. While CMIP5 models did not capture the observed pause in the increase in global mean surface temperature between 1998 and 2013, the historical CMIP6 simulations agree well with the observed recent temperature increase, but some models have difficulties in reproducing the observed global mean surface temperature record of the second half of the twentieth century. While systematic biases in annual mean surface temperature and precipitation remain in the CMIP6 multimodel mean, individual models and high-resolution versions of the models show significant reductions in many long-standing biases. Some improvements are also found in the vertical temperature, water vapor, and zonal wind speed distributions, and root-mean-square errors for selected fields are generally smaller with reduced intermodel spread and higher average skill in the correlation patterns relative to observations. An emerging property of the CMIP6 ensemble is a higher effective climate sensitivity with an increased range between 2.3 and 5.6 K. A possible reason for this increase in some models is improvements in cloud representation resulting in stronger shortwave cloud feedbacks than in their predecessor versions.

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Equilibrium Climate Sensitivity Estimated by Equilibrating Climate Models

Cited by 121 publications

References 78 publications

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

Observational constraints on the effective climate sensitivity from the historical period

Quantifying Progress Across Different CMIP Phases With the ESMValTool

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