Emergent constraints on climate sensitivities

Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble that the emergent constraint was originally derived from. The emergently- constrained best estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average. This can be at least partly explained by the increased number of high-ECS (above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes rather than changes in strength of those previously dominant. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as constraints for the process or feedback they are actually targeting.

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A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP

10.5194/egusphere-egu2020-3046 ◽

2020 ◽

Author(s):

Martin Renoult ◽

James Annan ◽

Julia Hargreaves ◽

Navjit Sagoo ◽

Clare Flynn ◽

...

Keyword(s):

Climate Sensitivity ◽

Bayesian Method ◽

Ordinary Least Squares ◽

Bayesian Framework ◽

Future Climate Change ◽

Filter Method ◽

Data Sets ◽

Uncertainty Estimates ◽

Emergent Constraints ◽

Model Ensembles

<p>In this study we introduce a Bayesian framework, which is flexible and explicit about the prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on Ordinary Least Squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (1.1 - 4.8, 5 - 95 percentiles) using the PMIP2, PMIP3 and PMIP4 data sets for the LGM, and 2.4 K (0.4 - 5.0) with the PlioMIP1 and PlioMIP2 data sets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (1.1 - 4.3) using the LGM and &#160;2.4 K (0.4 - 5.1) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a slightly tighter constraint of 2.6 K (1.1 - 3.9). We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95% probability of climate sensitivity mostly below 5 and never exceeding 6 K. The approach is compared with other approaches based on OLS, a Kalman filter method and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, suggesting a higher bound by construction in case of weaker correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation of their potential use in future probabilistic estimation of climate sensitivity.</p>

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Limitations of Emergent Constraints on Multi-Model Projections: Case Study of Constraining Vegetation Productivity With Observed Greening Sensitivity

10.5194/esd-2018-71 ◽

2018 ◽

Author(s):

Alexander J. Winkler ◽

Ranga B. Myneni ◽

Victor Brovkin

Keyword(s):

Co2 Concentration ◽

Accurate Estimation ◽

Spatial And Temporal Variability ◽

Large Area ◽

Area Index ◽

Emergent Constraints ◽

Data Source ◽

Fertilization Effects ◽

Historical Past ◽

Co2 Forcing

Abstract. Recent research on Emergent Constraints (EC) has delivered promising results. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in a difficult-to-measure variable (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure critically depends on, first, accurate estimation of the predictor from observations and models, and second, on a robust relationship between inter-model variations in the predictor-predictand space. We investigate issues related to these two themes in this article, using vegetation greening sensitivity to CO2 forcing during the satellite era as a predictor of change in Gross Primary Productivity (GPP) of the Northern High Latitudes region (60° N–90° N, NHL) for a doubling of pre-industrial CO2 concentration in the atmosphere. Greening sensitivity is defined as changes in annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the greening sensitivity of a large area, the NHL, from pixel-level LAImax data. This requires an investigation into uncertainties in LAImax data source and an evaluation of the spatial and temporal variability in greening sensitivity to forcing in both the data and model simulations. Second, the relationship between greening sensitivity and ΔGPP across the model ensemble depends on a strong coupling among simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time-rate of application (scenarios) and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can impair the EC estimate of the predictand (ΔGPP). Accounting for uncertainties in greening sensitivity and stability of the relation between inter-model variations results in a quantitative estimate of the uncertainty (±0.2 Pg C yr−1) on constrained GPP enhancement (ΔGPP = +3.4 Pg C yr−1) for a doubling of pre-industrial atmospheric CO2 concentration in NHL. This ΔGPP is 60 % larger than the conventionally used average of model projections. The illustrated sources of uncertainty and limitations of the EC method go beyond carbon cycle research and are generally relevant for Earth system sciences.

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Emergent constraints on Earth’s transient and equilibrium response to doubled CO2 from post-1970s global warming

Nature Geoscience ◽

10.1038/s41561-019-0463-y ◽

2019 ◽

Vol 12 (11) ◽

pp. 902-905 ◽

Cited By ~ 22

Author(s):

Diego Jiménez-de-la-Cuesta ◽

Thorsten Mauritsen

Keyword(s):

Global Warming ◽

Equilibrium Response ◽

Emergent Constraints

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CMIP model evaluation with the ESMValTool v2.0

10.5194/egusphere-egu2020-13306 ◽

2020 ◽

Author(s):

Axel Lauer ◽

Fernando Iglesias-Suarez ◽

Veronika Eyring ◽

the ESMValTool development team

Keyword(s):

Model Evaluation ◽

Large Scale ◽

Performance Metrics ◽

Comprehensive Evaluation ◽

Coupled Model ◽

Data Sets ◽

Evaluation Tool ◽

Emergent Constraints ◽

Data Volume ◽

And Performance

<p>The Earth System Model Evaluation Tool (ESMValTool) has been developed with the aim of taking model evaluation to the next level by facilitating analysis of many different ESM components, providing well-documented source code and scientific background of implemented diagnostics and metrics and allowing for traceability and reproducibility of results (provenance). This has been made possible by a lively and growing development community continuously improving the tool supported by multiple national and European projects. The latest version (2.0) of the ESMValTool has been developed as a large community effort to specifically target the increased data volume of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the related challenges posed by analysis and evaluation of output from multiple high-resolution and complex ESMs. For this, the core functionalities have been completely rewritten in order to take advantage of state-of-the-art computational libraries and methods to allow for efficient and user-friendly data processing. Common operations on the input data such as regridding or computation of multi-model statistics are now centralized in a highly optimized preprocessor written in Python. The diagnostic part of the ESMValTool includes a large collection of standard recipes for reproducing peer-reviewed analyses of many variables across atmosphere, ocean, and land domains, with diagnostics and performance metrics focusing on the mean-state, trends, variability and important processes, phenomena, as well as emergent constraints. While most of the diagnostics use observational data sets (in particular satellite and ground-based observations) or reanalysis products for model evaluation some are also based on model-to-model comparisons. This presentation introduces the diagnostics newly implemented into ESMValTool v2.0 including an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of ESMs, new diagnostics for extreme events, regional model and impact evaluation and analysis of ESMs, as well as diagnostics for emergent constraints and analysis of future projections from ESMs. The new diagnostics are illustrated with examples using results from the well-established CMIP5 and the newly available CMIP6 data sets.</p>

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Review of « A mathematical approach to understanding emergent constraints » by F. Nijsse and H. Dijkstra

10.5194/esd-2018-15-rc1 ◽

2018 ◽

Author(s):

Anonymous

Keyword(s):

Mathematical Approach ◽

Emergent Constraints

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Increasing confidence in projecting the Arctic Ice-free year with emergent constraints

Environmental Research Letters ◽

10.1088/1748-9326/ac0b17 ◽

2021 ◽

Author(s):

Bin Wang ◽

Xiao Zhou ◽

Qinghua Ding ◽

Jiping Liu

Keyword(s):

The Arctic ◽

Emergent Constraints ◽

Arctic Ice

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On structural errors in emergent constraints

10.5194/esd-2020-85 ◽

2021 ◽

Author(s):

Benjamin M. Sanderson ◽

Angeline Pendergrass ◽

Charles D. Koven ◽

Florent Brient ◽

Ben B. B. Booth ◽

...

Keyword(s):

Degrees Of Freedom ◽

Climate Models ◽

Future Climate ◽

Anthropogenic Emissions ◽

Probability Bounds ◽

The Earth ◽

Climate Simulations ◽

Emergent Constraints ◽

Out Of Sample ◽

Climate Responses

Abstract. Studies of emergent constraints have frequently proposed that a single metric alone can constrain future responses of the Earth system to anthropogenic emissions. The prevalence of this thinking has led to literature and messaging which is sometimes confusing to policymakers, with a series of studies over the last decade making confident, yet contradictory, claims on the probability bounds of key climate variables. Here, we illustrate that emergent constraints are more likely to occur where the variance across an ensemble of climate models of both observable and future climate arises from common structural assumptions and few degrees of freedom. Such cases are likely to occur when processes are represented in a common, oversimplified fashion throughout the ensemble, about which we have the least confidence in performance out of sample. We consider these issues in the context of a number of published constraints, and argue that the application of emergent constraints alone to estimate uncertainties in unknown climate responses can potentially lead to bias and overconfidence in constrained projections. Together with statistical robustness and plausibility of mechanism, assessments of climate responses must include multiple lines of evidence to identify biases that arise from common oversimplified modeling assumptions which impact both present and future climate simulations in order to mitigate against the influence of common structural biases.

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A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP

Climate of the Past ◽

10.5194/cp-16-1715-2020 ◽

2020 ◽

Vol 16 (5) ◽

pp. 1715-1735 ◽

Cited By ~ 1

Author(s):

Martin Renoult ◽

James Douglas Annan ◽

Julia Catherine Hargreaves ◽

Navjit Sagoo ◽

Clare Flynn ◽

...

Keyword(s):

Climate Sensitivity ◽

Bayesian Method ◽

Regression Line ◽

Ordinary Least Squares ◽

Bayesian Framework ◽

Future Climate Change ◽

Filter Method ◽

Structural Uncertainty ◽

Uncertainty Estimates ◽

Emergent Constraints

Abstract. In this paper we introduce a Bayesian framework, which is explicit about prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on ordinary least squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (0.6–5.2, 5th–95th percentiles) using the PMIP2, PMIP3, and PMIP4 datasets for the LGM and 2.3 K (0.5–4.4) with the PlioMIP1 and PlioMIP2 datasets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (0.7–5.2) using the LGM and 2.3 K (0.4–4.5) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a tighter constraint of 2.5 K (0.8–4.0) using the restricted ensemble. We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95 % probability of climate sensitivity mostly below 5 K and only exceeding 6 K in a single and most uncertain case assuming a large structural uncertainty. The approach is compared with other approaches based on OLS, a Kalman filter method, and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, an artefact due to a flatter regression line in the case of lack of correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation for their potential use in future probabilistic estimations of climate sensitivity.

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