scholarly journals Evaluating Emergent Constraints on Equilibrium Climate Sensitivity

2018 ◽  
Vol 31 (10) ◽  
pp. 3921-3942 ◽  
Author(s):  
Peter M. Caldwell ◽  
Mark D. Zelinka ◽  
Stephen A. Klein
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Temperature Response ◽  
Complex Nature ◽  
Physical Processes ◽  
Physical Explanation ◽  
Equilibrium Climate Sensitivity ◽  
Geographical Regions ◽  
Emergent Constraints ◽  
Emergent Constraint

Emergent constraints are quantities that are observable from current measurements and have skill predicting future climate. This study explores 19 previously proposed emergent constraints related to equilibrium climate sensitivity (ECS; the global-average equilibrium surface temperature response to CO2 doubling). Several constraints are shown to be closely related, emphasizing the importance for careful understanding of proposed constraints. A new method is presented for decomposing correlation between an emergent constraint and ECS into terms related to physical processes and geographical regions. Using this decomposition, one can determine whether the processes and regions explaining correlation with ECS correspond to the physical explanation offered for the constraint. Shortwave cloud feedback is generally found to be the dominant contributor to correlations with ECS because it is the largest source of intermodel spread in ECS. In all cases, correlation results from interaction between a variety of terms, reflecting the complex nature of ECS and the fact that feedback terms and forcing are themselves correlated with each other. For 4 of the 19 constraints, the originally proposed explanation for correlation is borne out by our analysis. These four constraints all predict relatively high climate sensitivity. The credibility of six other constraints is called into question owing to correlation with ECS coming mainly from unexpected sources and/or lack of robustness to changes in ensembles. Another six constraints lack a testable explanation and hence cannot be confirmed. The fact that this study casts doubt upon more constraints than it confirms highlights the need for caution when identifying emergent constraints from small ensembles.

scholarly journals Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?

2020 ◽  
Vol 11 (4) ◽  
pp. 1233-1258
Author(s):  
Manuel Schlund ◽  
Axel Lauer ◽  
Pierre Gentine ◽  
Steven C. Sherwood ◽  
Veronika Eyring
Keyword(s):  
Climate Sensitivity ◽  
Statistical Significance ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Co2 Concentration ◽  
Intergovernmental Panel ◽  
Equilibrium Climate Sensitivity ◽  
Emergent Constraints ◽  
Statistical Relationships ◽  
Emergent Constraint

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.

scholarly journals On the Emergent Constraints of Climate Sensitivity

2018 ◽  
Vol 31 (2) ◽  
pp. 863-875 ◽  
Author(s):  
Xin Qu ◽  
Alex Hall ◽  
Anthony M. DeAngelis ◽  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Selection Process ◽  
Temperature Response ◽  
Cloud Feedback ◽  
Emergent Constraints ◽  
Low Cloud ◽  
Model Ensembles ◽  
Statistical Connection ◽  
Emergent Constraint

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.

scholarly journals Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models

2020 ◽  
Vol 11 (3) ◽  
pp. 737-750 ◽  
Author(s):  
Femke J. M. M. Nijsse ◽  
Peter M. Cox ◽  
Mark S. Williamson
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Sensitivity ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Climate Response ◽  
Transient Climate Response ◽  
Equilibrium Climate Sensitivity ◽  
Emergent Constraint ◽  
Constraint Approach

Abstract. Climate sensitivity to CO2 remains the key uncertainty in projections of future climate change. Transient climate response (TCR) is the metric of temperature sensitivity that is most relevant to warming in the next few decades and contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets. Equilibrium climate sensitivity (ECS) is vital for understanding longer-term climate change and stabilisation targets. In the IPCC 5th Assessment Report (AR5), the stated “likely” ranges (16 %–84 % confidence) of TCR (1.0–2.5 K) and ECS (1.5–4.5 K) were broadly consistent with the ensemble of CMIP5 Earth system models (ESMs) available at the time. However, many of the latest CMIP6 ESMs have larger climate sensitivities, with 5 of 34 models having TCR values above 2.5 K and an ensemble mean TCR of 2.0±0.4 K. Even starker, 12 of 34 models have an ECS value above 4.5 K. On the face of it, these latest ESM results suggest that the IPCC likely ranges may need revising upwards, which would cast further doubt on the feasibility of the Paris targets. Here we show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to constrain the likely range of TCR to 1.3–2.1 K, with a central estimate of 1.68 K. We reach this conclusion through an emergent constraint approach which relates the value of TCR linearly to the global warming from 1975 onwards. This is a period when the signal-to-noise ratio of the net radiative forcing increases strongly, so that uncertainties in aerosol forcing become progressively less problematic. We find a consistent emergent constraint on TCR when we apply the same method to CMIP5 models. Our constraints on TCR are in good agreement with other recent studies which analysed CMIP ensembles. The relationship between ECS and the post-1975 warming trend is less direct and also non-linear. However, we are able to derive a likely range of ECS of 1.9–3.4 K from the CMIP6 models by assuming an underlying emergent relationship based on a two-box energy balance model. Despite some methodological differences; this is consistent with a previously published ECS constraint derived from warming trends in CMIP5 models to 2005. Our results seem to be part of a growing consensus amongst studies that have applied the emergent constraint approach to different model ensembles and to different aspects of the record of global warming.

scholarly journals Equilibrium Climate Sensitivity: Is It Accurate to Use a Slab Ocean Model?

2009 ◽  
Vol 22 (9) ◽  
pp. 2494-2499 ◽  
Author(s):  
Gokhan Danabasoglu ◽  
Peter R. Gent
Keyword(s):  
Carbon Dioxide ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Temperature Response ◽  
Accurate Estimate ◽  
Ocean Model ◽  
Climate System Model ◽  
Equilibrium Climate Sensitivity ◽  
Model Version ◽  
Slab Ocean

Abstract The equilibrium climate sensitivity of a climate model is usually defined as the globally averaged equilibrium surface temperature response to a doubling of carbon dioxide. This is virtually always estimated in a version with a slab model for the upper ocean. The question is whether this estimate is accurate for the full climate model version, which includes a full-depth ocean component. This question has been answered for the low-resolution version of the Community Climate System Model, version 3 (CCSM3). The answer is that the equilibrium climate sensitivity using the full-depth ocean model is 0.14°C higher than that using the slab ocean model, which is a small increase. In addition, these sensitivity estimates have a standard deviation of nearly 0.1°C because of interannual variability. These results indicate that the standard practice of using a slab ocean model does give a good estimate of the equilibrium climate sensitivity of the full CCSM3. Another question addressed is whether the effective climate sensitivity is an accurate estimate of the equilibrium climate sensitivity. Again the answer is yes, provided that at least 150 yr of data from the doubled carbon dioxide run are used.

scholarly journals Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

2020 ◽  
Author(s):  
Manuel Schlund ◽  
Axel Lauer ◽  
Pierre Gentine ◽  
Steven C. Sherwood ◽  
Veronika Eyring
Keyword(s):  
Climate Sensitivity ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Correlation Coefficients ◽  
Co2 Concentration ◽  
Significance Test ◽  
Intergovernmental Panel ◽  
Equilibrium Climate Sensitivity ◽  
Best Estimate ◽  
Emergent Constraints

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 stem the major source of uncertainty in ECS. 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 has 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 large decrease of the correlation coefficients for most emergent relationships, indicating that nearly all of the constraints are less skillful in predicting ECS than they were in CMIP5. Many do not appear sufficiently skillful to be useful in constraining ECS, and several of them do not pass a significance test. 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 the emergent constraint was originally derived from. The emergent-constrained best estimate of ECS increased from CMIP5 to CMIP6, with a best estimate range of 2.97–3.88 K for CMIP5 and 3.41–4.36 K for CMIP6. This can be at least partly explained by the increased number of high-ECS models in CMIP6 with an ECS above 4.5 K without a corresponding change in the constraint predictors. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that emergent constraints focusing on specific processes contributing to ECS are more promising than emergent constraints based on statistical model analysis.

scholarly journals Emergent constraint on equilibrium climate sensitivity from global temperature variability

Nature ◽  
2018 ◽  
Vol 553 (7688) ◽  
pp. 319-322 ◽  
Author(s):  
Peter M. Cox ◽  
Chris Huntingford ◽  
Mark S. Williamson
Keyword(s):  
Climate Sensitivity ◽  
Temperature Variability ◽  
Global Temperature ◽  
Equilibrium Climate Sensitivity ◽  
Emergent Constraint

scholarly journals Reconstruction of the Interannual to Millennial Scale Patterns of the Global Surface Temperature

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 147
Author(s):  
Nicola Scafetta
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Empirical Models ◽  
Natural Variability ◽  
Global Temperature ◽  
Temperature Record ◽  
Equilibrium Climate Sensitivity ◽  
Global Surface Temperature ◽  
Semi Empirical ◽  
Global Surface

Climate changes are due to anthropogenic factors, volcano eruptions and the natural variability of the Earth’s system. Herein the natural variability of the global surface temperature is modeled using a set of harmonics spanning from the inter-annual to the millennial scales. The model is supported by the following considerations: (1) power spectrum evaluations show 11 spectral peaks (from the sub-decadal to the multi-decadal scales) above the 99% confidence level of the known temperature uncertainty; (2) spectral coherence analysis between the independent global surface temperature periods 1861–1937 and 1937–2013 highlights at least eight common frequencies between 2- and 20-year periods; (3) paleoclimatic temperature reconstructions during the Holocene present secular to millennial oscillations. The millennial oscillation was responsible for the cooling observed from the Medieval Warm Period (900–1400) to the Little Ice Age (1400–1800) and, on average, could have caused about 50% of the warming observed since 1850. The finding implies an equilibrium climate sensitivity of 1.0–2.3 °C for CO2 doubling likely centered around 1.5 °C. This low sensitivity to radiative forcing agrees with the conclusions of recent studies. Semi-empirical models since 1000 A.D. are developed using 13 identified harmonics (representing the natural variability of the climate system) and a climatic function derived from the Coupled Model Intercomparison Project 5 (CMIP5) model ensemble mean simulation (representing the mean greenhouse gas—GHG, aerosol, and volcano temperature contributions) scaled under the assumption of an equilibrium climate sensitivity of 1.5 °C. The harmonic model is evaluated using temperature data from 1850 to 2013 to test its ability to predict the major temperature patterns observed in the record from 2014 to 2020. In the short, medium, and long time scales the semi-empirical models predict: (1) temperature maxima in 2015–2016 and 2020, which is confirmed by the 2014–2020 global temperature record; (2) a relatively steady global temperature from 2000 to 2030–2040; (3) a 2000–2100 mean projected global warming of about 1 °C. The semi-empirical model reconstructs accurately the historical surface temperature record since 1850 and hindcasts mean surface temperature proxy reconstructions since the medieval period better than the model simulation that is unable to simulate the Medieval Warm Period.

scholarly journals A Positive Iris Feedback: Insights from Climate Simulations with Temperature-Sensitive Cloud–Rain Conversion

2019 ◽  
Vol 32 (16) ◽  
pp. 5305-5324 ◽  
Author(s):  
R. L. Li ◽  
T. Storelvmo ◽  
A. V. Fedorov ◽  
Y.-S. Choi
Keyword(s):  
Carbon Dioxide ◽  
Surface Temperature ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Cirrus Clouds ◽  
Climate Feedback ◽  
Convective Precipitation ◽  
Temperature Sensitive ◽  
Equilibrium Climate Sensitivity ◽  
Climate Simulations

AbstractEstimates for equilibrium climate sensitivity from current climate models continue to exhibit a large spread, from 2.1 to 4.7 K per carbon dioxide doubling. Recent studies have found that the treatment of precipitation efficiency in deep convective clouds—specifically the conversion rate from cloud condensate to rain Cp—may contribute to the large intermodel spread. It is common for convective parameterization in climate models to carry a constant Cp, although its values are model and resolution dependent. In this study, we investigate how introducing a potential iris feedback, the cloud–climate feedback introduced by parameterizing Cp to increase with surface temperature, affects future climate simulations within a slab ocean configuration of the Community Earth System Model. Progressively stronger dependencies of Cp on temperature unexpectedly increase the equilibrium climate sensitivity monotonically from 3.8 to up to 4.6 K. This positive iris feedback puzzle, in which a reduction in cirrus clouds increases surface temperature, is attributed to changes in the opacity of convectively detrained cirrus. Cirrus clouds reduced largely in ice content and marginally in horizontal coverage, and thus the positive shortwave cloud radiative feedback dominates. The sign of the iris feedback is robust across different cloud macrophysics schemes, which control horizontal cloud cover associated with detrained ice. These results suggest a potentially strong but highly uncertain connection among convective precipitation, detrained anvil cirrus, and the high cloud feedback in a climate forced by increased atmospheric carbon dioxide concentrations.

scholarly journals Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity

2016 ◽  
Vol 29 (2) ◽  
pp. 513-524 ◽  
Author(s):  
Peter M. Caldwell ◽  
Mark D. Zelinka ◽  
Karl E. Taylor ◽  
Kate Marvel
Keyword(s):  
Climate Sensitivity ◽  
Temperature Response ◽  
Cloud Feedback ◽  
Lapse Rate ◽  
Specific Humidity ◽  
New Approach ◽  
Cloud Feedbacks ◽  
Equilibrium Climate Sensitivity ◽  
Dominant Source ◽  
Average Temperature

Abstract This study clarifies the causes of intermodel differences in the global-average temperature response to doubled CO2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covariances between cloud feedback and forcing, between lapse rate and longwave cloud feedbacks, and between albedo and shortwave cloud feedbacks play an important and previously underappreciated role in determining model differences in ECS. Defining feedbacks based on fixed relative rather than specific humidity (as suggested by Held and Shell) reduces the covariances between processes and leads to more straightforward interpretations of results.

scholarly journals How Are Emergent Constraints Quantifying Uncertainty and What Do They Leave Behind?

2019 ◽  
Vol 100 (12) ◽  
pp. 2571-2588 ◽  
Author(s):  
Daniel B. Williamson ◽  
Philip G. Sansom
Keyword(s):  
Climate Sensitivity ◽  
Software Tool ◽  
Linear Constraints ◽  
Prediction Intervals ◽  
Equilibrium Climate Sensitivity ◽  
Sources Of Uncertainty ◽  
Linear Relationships ◽  
Statistical Assumptions ◽  
Emergent Constraints ◽  
Quantifying Uncertainty

Abstract The use of emergent constraints to quantify uncertainty for policy-relevant quantities such as equilibrium climate sensitivity (ECS) has become increasingly widespread in recent years. Many researchers, however, claim that emergent constraints are inappropriate or even underreport uncertainty. In this paper we contribute to this discussion by examining the emergent constraints methodology in terms of its underpinning statistical assumptions. We argue that the statistical assumptions required to underpin existing frameworks are strong, hard to defend, and lead to an underreporting of uncertainty. We show how weakening them leads to a more transparent Bayesian framework wherein hitherto-ignored sources of uncertainty, such as how reality might differ from models, can be quantified. We present a guided framework for the quantification of additional uncertainties that is linked to the confidence we can have in the underpinning physical arguments for using linear constraints. We provide a software tool for implementing our framework for emergent constraints and use it to illustrate the methods on a number of recent emergent constraints for ECS. We find that the robustness of any constraint to additional uncertainties depends strongly on the confidence we have in the underpinning physics, allowing a future framing of the debate over the validity of a particular constraint around underlying physical arguments, rather than statistical assumptions. We also find that when physical arguments lead to confidence in the linear relationships underpinning emergent constraints, prediction intervals are only slightly widened by including additional uncertainties, and they show this across a range of emergent constraints for ECS.

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