Constraining human contributions to observed warming since the pre-industrial period

Gillett, Nathan P.; Kirchmeier‐Young, Megan C.; Ribes, Aurélien; Shiogama, Hideo; Hegerl, Gabriele C.; Knutti, Reto; Gastineau, Guillaume; John, Jasmin G.; Li, Lijuan; Nazarenko, Larissa; Rosenbloom, Nan; Seland, Øyvind; Wu, Tongwen; Yukimoto, Seiji; Ziehn, Tilo

doi:10.1038/s41558-020-00965-9

Cited by 159 publications

(185 citation statements)

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“…The choice of stratospheric aerosol level specified in the CMIP6 preindustrial control simulations also has a substantial effect on comparisons between global warming relative to 1850 to 1900 in CMIP5 models and observations presented in the IPCC Special Report on

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C ( 22 ). Additionally, this choice would influence attribution studies, which calculate scaling factors based on a comparison of simulated and observed temperature evolution since 1850 ( 23 – 25 ). If we are interested in making like-for-like comparisons of models and observations, then the most appropriate stratospheric aerosol background level would be what most closely approximates conditions over the previous several decades prior to the 1850 start of the historical simulation ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

“…Since the CMIP6 models as a group tend to be cooler than observations and CMIP5 models in the second half of the 20th century ( 24 , 29 ), it has been suggested that their average anthropogenic aerosol forcing in this period may be too strong ( 24 , 29 – 31 ). In CanESM5, however, which was used to compare the responses to CMIP5 and CMIP6 aerosol forcings, the CMIP6 aerosol and aerosol precursor emissions result in a warmer climate from 1850 to 2000.…”

Section: Discussionmentioning

confidence: 99%

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Significant impact of forcing uncertainty in a large ensemble of climate model simulations

Fyfe

Kharin

Santer

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Significant impact of forcing uncertainty in a large ensemble of climate model simulations

Fyfe

Kharin

Santer

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…Whether this reduction in spread improves the reliability of projections is still uncertain, although some recent analysis supports these approaches: Gillett et al (2021) applied an (im-) perfect model approach to estimate the attributable warming to CMIP6 models, and Schurer et al (2018) an approach to estimate the transient climate sensitivity from individual simulations with withheld climate models for CMIP5. Gillett et al (2021) found high reliability of the estimate of attributable warming, which increases confidence in its use for projections. Schurer et al (2018) found that the method was somewhat overconfident for future warming if using the multi-model mean fingerprint,…”

Section: Trend and Attribution Based Methods (Ask Method)mentioning

confidence: 99%

“…When doing so, it would be useful to consider forecast evaluation terminology used in predictions and to assess reliability (i.e., if model simulations that are synthetically predicted are within the uncertainty range of the prediction, given the statistical expectation; Schurer et al, 2018;Gillett et al, 2021), and if they show improved sharpness, i.e., their RMS error is smaller in order to avoid penalizing more confident methods unnecessarily). Another avenue is to draw perfect models from a different generation as explored, e.g., by Brunner et al (2020b) where the skill of weighting CMIP6 was explored based on models from CMIP5 in order to provide an out-of-sample test to the extent that CMIP6 can be considered independent of CMIP5.…”

Section: Contrasting and Combining Constraints From Different Methodologiesmentioning

confidence: 99%

Toward Consistent Observational Constraints in Climate Predictions and Projections

et al. 2021

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Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution.

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“…Understanding 7 , but as aerosol emissions decrease in the future, their counteracting influence will likely weaken.…”

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confidence: 99%

Uncertain with a chance of showers

2021

Nat. Clim. Chang.

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Constraining human contributions to observed warming since the pre-industrial period

Cited by 159 publications

References 42 publications

Significant impact of forcing uncertainty in a large ensemble of climate model simulations

Significant impact of forcing uncertainty in a large ensemble of climate model simulations

Toward Consistent Observational Constraints in Climate Predictions and Projections

Uncertain with a chance of showers

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