Making climate projections conditional on historical observations

Ribes, Aurélien; Qasmi, Saïd; Gillett, Nathan P.

doi:10.1126/sciadv.abc0671

Cited by 135 publications

(237 citation statements)

References 48 publications

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“…These were averaged globally or separately over land and oceans for time series analysis (no correction for drift was performed) and regridded to a common 1 • grid by linear interpolation for pattern analysis. All figures of this paper are produced with the Earth System Model Evaluation Tool (ESMValTool) version 2.0 (v2.0) (Righi et al, 2020;Eyring et al, 2020;Lauer et al, 2020), a tool specifically designed to improve and facilitate the complex evaluation and analysis of CMIP models and ensembles.…”

Section: Resultsmentioning

confidence: 99%

“…A2) and CMIP6 (see Table A1) model output is available through the Earth System Grid Foundation (ESGF) and can be directly used within the ESM-ValTool (e.g., https://esgf-data.dkrz.de/projects/esgf-dkrz/, last access: 7 January 2021) (ESGF, 2021). The corresponding recipe that can be used to reproduce the figures of this paper will be included in ESMValTool v2.0 (Righi et al, 2020;Eyring et al, 2020;Lauer et al, 2020;Weigel et al, 2020) as soon as the paper is published. ESMValTool (v2.1) is released under the Apache License, VER-SION 2.0.…”

Section: Figure A8mentioning

confidence: 99%

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Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

et al. 2021

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Abstract. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 ∘C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 ∘C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 ∘C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 ∘C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s.

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Section: Resultsmentioning

confidence: 99%

Section: Figure A8mentioning

confidence: 99%

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

et al. 2021

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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

1.5

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%

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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“…Historical trends in the UKCP18 methodology (Figure 1, labeled SAT) tend to reduce the upper tails of projected changes. Similarly, the HistC methodology (Brunner et al, 2020a section) is largely based on trend information, which also consistently downweights high end projected changes (Ribes et al, 2021), in response to too large change in such models over parts of the historical period.…”

Section: Contrasting and Combining Constraints From Different Methodologiesmentioning

confidence: 99%

“…Multiple techniques are available to constrain future projections drawing on different lines of evidence and considering different sources of uncertainty (e.g., Giorgi and Mearns, 2002;Knutti, 2010;Knutti et al, 2017;Sanderson et al, 2017;Lorenz et al, 2018;Brunner et al, 2020a,b;Ribes et al, 2021). Models that explore the full uncertainty in parameter space provide very wide uncertainty ranges (Stainforth, 2005), motivating the need to use observational constraints.…”

Section: Introductionmentioning

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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Making climate projections conditional on historical observations

Cited by 135 publications

References 48 publications

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

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

Toward Consistent Observational Constraints in Climate Predictions and Projections

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