2016
DOI: 10.1038/nclimate3066
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Reconciled climate response estimates from climate models and the energy budget of Earth

Abstract: Correcing for these biases and accouning for wider uncertainies in radiaive forcing based on recent evidence, we infer an observaion-based best esimate for TCR of 1.66 °C with a 5-95 % range of 1.0-3.3 °C, consistent with the climate models considered in the IPCC 5 th Assessment Report.TCR for the Climate Model Intercomparison Project, phase 5 (CMIP5) models is deined using simulaions in which atmospheric CO 2 increases at 1 % per year and the muli-model mean is 1.8 °C (1.2-2.4 °C, henceforth bracketed values … Show more

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Cited by 125 publications
(147 citation statements)
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References 27 publications
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“…Some observational targets have important (and sometimes unrecognized) structural uncertainties and therefore any tuning to those targets risks overfitting the model to imperfect data, potentially reducing skill in "out-of-sample" predictions (those for which the evaluation data either did not exist at the time of the prediction or were not used in model development or tuning). This is a particular problem for transient observations such as estimates of early 20th-century temperature changes (Thompson et al, 2008;Richardson et al, 2016), pre-1979 sea-ice extent (Meier et al, 2012;Walsh et al, 2017), pre-1990 ocean heat content change (Levitus et al, 2000;Church et al, 2011), or water vapor trends (Dessler and Davis, 2010), which have all been corrected in recent years, as nonclimate artifacts in the raw observations have been found and adjusted for. In contrast, many climatologies over the satellite era are robust metrics whose estimates over any fixed period have not changed appreciably as understanding of the observations evolved.…”
Section: Why Is Climate Model Tuning Necessary?mentioning
confidence: 99%
“…Some observational targets have important (and sometimes unrecognized) structural uncertainties and therefore any tuning to those targets risks overfitting the model to imperfect data, potentially reducing skill in "out-of-sample" predictions (those for which the evaluation data either did not exist at the time of the prediction or were not used in model development or tuning). This is a particular problem for transient observations such as estimates of early 20th-century temperature changes (Thompson et al, 2008;Richardson et al, 2016), pre-1979 sea-ice extent (Meier et al, 2012;Walsh et al, 2017), pre-1990 ocean heat content change (Levitus et al, 2000;Church et al, 2011), or water vapor trends (Dessler and Davis, 2010), which have all been corrected in recent years, as nonclimate artifacts in the raw observations have been found and adjusted for. In contrast, many climatologies over the satellite era are robust metrics whose estimates over any fixed period have not changed appreciably as understanding of the observations evolved.…”
Section: Why Is Climate Model Tuning Necessary?mentioning
confidence: 99%
“…This supports previous work that also 72 emphasized the impact of internal variability on estimates of l and ECS (Huber et Thus, there are a number of issues that need to be considered when interpreting estimates of l 85 and ECS derived from the historical period. In addition to the precision and accuracy issues 86 discussed above, it also includes the large and evolving uncertainty in forcing over the 20 th 87 century (Forster, 2016), different forcing efficacies of greenhouse gases and aerosols (Shindell,88 2014; Kummer and Dessler, 2014), and geographically incomplete or inhomogeneous 89 observations (Richardson et al, 2016). 90…”
Section: The Problem 23mentioning
confidence: 99%
“…4 focuses on this dataset. We rebase all of the temperatures to the 1861-1880 mean following Richardson et al (2016), to represent a preindustrial state that is relatively free of volcanic eruptions but with a reasonable global coverage of temperature observations. An ordinary least-squares regression of temperature change versus time from 1880 to 2016 is used to calculate the linear warming trend in each ensemble member.…”
Section: Constraint To Historical Temperature Observationsmentioning
confidence: 99%