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An assessment of Earth's climate sensitivity using multiple lines of evidence

TLDR
Evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S, is assessed, using a Bayesian approach to produce a probability density function for S given all the evidence, and promising avenues for further narrowing the range are identified.
Abstract
We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.

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Citations
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Why is climate sensitivity so unpredictable

TL;DR: It is shown that the breadth of the distribution and, in particular, the probability of large temperature increases are relatively insensitive to decreases in uncertainties associated with the underlying climate processes.
Journal ArticleDOI

GISS-E2.1: Configurations and Climatology.

TL;DR: There have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice.

Time-varying climate sensitivity from regional feedbacks

TL;DR: In this article, the global climate sensitivity with respect to forcing is defined in terms of the global radiative response per degree of global annual mean surface temperature change, and a reformulation of global climate feedback is proposed, providing a clear physical insight into this behavior.
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Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Claudia Tebaldi, +61 more
TL;DR: In this paper, the authors present a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations, focusing mainly on the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation.
References
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Journal ArticleDOI

An Overview of CMIP5 and the Experiment Design

TL;DR: The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance the authors' knowledge of climate variability and climate change.
Journal ArticleDOI

Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

TL;DR: In this article, the authors present the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21-CMIP6-Endorsed MIPs.
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