The mechanisms of cloudiness evolution responsible for equilibrium climate sensitivity in climate model INM-CM4-8

2021 ◽  
Author(s):  
Evgeny M. Volodin
Keyword(s):  
Climate Sensitivity ◽  
Climate Model ◽  
Equilibrium Climate Sensitivity

scholarly journals Could the Pliocene constrain the Equilibrium Climate Sensitivity?

2016 ◽  
Author(s):  
J. C. Hargreaves ◽  
J. D. Annan
Keyword(s):  
Carbon Dioxide ◽  
Boundary Conditions ◽  
Climate Sensitivity ◽  
Atmospheric Carbon Dioxide ◽  
Climate Models ◽  
Climate Model ◽  
Equilibrium Climate Sensitivity ◽  
Model Experiments ◽  
First Time ◽  
Industrial State

Abstract. The mid-PlioceneWarm Period (mPWP) is the most recent interval in which atmospheric carbon dioxide was substantially higher than in modern pre-industrial times. It is, therefore, a potentially valuable target for testing the ability of climate models to simulate climates warmer than the pre-industrial state. The recent Pliocene model inter-comparison Project (PlioMIP) presented boundary conditions for the mPWP, and a protocol for climate model experiments. Here we analyse results from the PlioMIP and, for the first time, discuss the potential for this interval to usefully constrain the equilibrium climate sensitivity. We present an estimate of 1.8–3.6 °C, but there are considerable uncertainties surrounding the analysis. We consider the extent to which these uncertainties may be lessened in the next few years.

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 Could the Pliocene constrain the equilibrium climate sensitivity?

2016 ◽  
Vol 12 (8) ◽  
pp. 1591-1599 ◽  
Author(s):  
J. C. Hargreaves ◽  
J. D. Annan
Keyword(s):  
Climate Sensitivity ◽  
Atmospheric Carbon Dioxide ◽  
Climate Models ◽  
Climate Model ◽  
Temperature Anomaly ◽  
Model Intercomparison ◽  
Equilibrium Climate Sensitivity ◽  
Intercomparison Project ◽  
First Time ◽  
Industrial State

Abstract. The mid-Pliocene Warm Period (mPWP) is the most recent interval in which atmospheric carbon dioxide was substantially higher than in modern pre-industrial times. It is, therefore, a potentially valuable target for testing the ability of climate models to simulate climates warmer than the pre-industrial state. The recent Pliocene Model Intercomparison Project (PlioMIP) presented boundary conditions for the mPWP and a protocol for climate model experiments. Here we analyse results from the PlioMIP and, for the first time, discuss the potential for this interval to usefully constrain the equilibrium climate sensitivity. We observe a correlation in the ensemble between their tropical temperature anomalies at the mPWP and their equilibrium sensitivities. If the real world is assumed to also obey this relationship, then the reconstructed tropical temperature anomaly at the mPWP can in principle generate a constraint on the true sensitivity. Directly applying this methodology using available data yields a range for the equilibrium sensitivity of 1.9–3.7 °C, but there are considerable additional uncertainties surrounding the analysis which are not included in this estimate. We consider the extent to which these uncertainties may be better quantified and perhaps lessened in the next few years.

Estimate of equilibrium climate sensitivity and uncertainties by using observations and CMIP6 global coupled climate models

2021 ◽  
Author(s):  
Jonathan Chenal ◽  
Benoit Meyssignac
Keyword(s):  
Temperature Dependence ◽  
Climate Sensitivity ◽  
Energy Budget ◽  
Climate Model ◽  
Regression Method ◽  
Historical Period ◽  
Ocean Heat Uptake ◽  
Equilibrium Climate Sensitivity ◽  
Pattern Effect ◽  
Uncertainty Range

<p>Energy budget estimates of the effective climate sensitivity (effCS) are derived based on estimates of the historical forcing and of observations of the sea surface temperature variations and the ocean heat uptake. Recent revisions to Greenhouse gas forcing and aerosol forcing estimates are included and the data is extended to 2018. We consider two different approaches to derive the effCS from the energy budget: 1) a difference of the energy budget between the recent period 2005-2018 and a base period 1861-1880 (following Sherwood 2020) and 2)  a regression of the differential form of energy budget over the period 1955-2017 (following Gregory et al. 2020). These estimates of the effCS over the historical period are representative of the climate feedback experienced by the climate during the historical period. When accounting for the uncertainty in the forcing, the surface temperature and the ocean heat uptake estimates plus the uncertainty due to the internal variability we find a range of effCS of [1.0;9.7] (at the 95%CL) with a median of 2.0 K with approach (1) and [1.2;2.7] with a median of 1.7 K with approch (2). We find that the lower and the upper tail of the distribution in effCS arise dominantly from the uncertainty in the historical forcing, particularly for the regression method, and at a lower extent for the difference method. This is consistent with previous studies (e.g. Lewis and Curry 2018 and Sherwood et al. 2020).</p><p>Using the same approach based on historical observations but accounting for the pattern effect and the temperature dependence of the feedback estimated with climate model simulations, we derive new estimates of the effECS that should encompass the equilibrium climate sensitivity (assuming that climate model simulate properly the pattern effect and the temperature dependence of feedback). We find that adding the pattern effect and the temperature dependence of the feedbacks shifts upwards the median of the effECS and increases significantly the uncertainty range. For the difference method, the median is now 2.5 K and the uncertainty range [1.1;17.2]. For the regression method the median is now 2.0 K and the uncertainty range is [1.2;4.7] K ((5-95%). On the overall, we find that the regression method performs better to constraint the equilibrium climate sensitivity and that the major source of uncertainties comes from the differences in the simulation of the pattern effect among climate models rather than the uncertainties on the historical forcing.</p>

Equilibrium Climate Sensitivity in AWI-ESM: Mechanisms and Effects

2020 ◽  
Author(s):  
Christopher Danek ◽  
Paul Gierz ◽  
Christian Stepanek ◽  
Gerrit Lohmann
Keyword(s):  
Climate Sensitivity ◽  
Climate Model ◽  
Climate Modeling ◽  
Carbon Dioxide Concentration ◽  
Surface Air Temperature ◽  
Marine Research ◽  
Equilibrium Climate Sensitivity ◽  
Low Cloud ◽  
Intercomparison Project ◽  
The Impact

<p><span>The global-mean surface air temperature change due to a doubled carbon dioxide concentration in the atmosphere (equilibrium climate sensitivity, ECS) is an important measure to quantify the impact of predicted anthropogenic climate change. The latest climate modeling intercomparison project (CMIP6) exhibits a higher ECS compared to the previous climate model generation (1.8 to 5.6 K for CMIP6 versus 1.5 to 4.5 K for CMIP5). The increase in ECS is likely due to decreases in extratropical low cloud coverage and albedo, caused by improvements in the numerical aerosol schemes. Our state-of-the-art Earth system model AWI-ESM, developed at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, yields an ECS of 3.59-3.62 K, which is close to the CMIP5 mean. Using a set of varying model configurations, we identify dynamic vegetation and model resolution as the primary driving factors which influence the modeled global response to an increased greenhouse gas forcing.</span></p>

Equilibrium Climate Sensitivity and Transient Climate Response biased low in historical simulations of CMIP6 models

2021 ◽  
Author(s):  
Yue Dong ◽  
Kyle C. Armour ◽  
Cristian Proistosescu ◽  
Timothy Andrews ◽  
David S. Battisti ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Climate Response ◽  
Transient Climate Response ◽  
Equilibrium Climate Sensitivity

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.

Sign in / Sign up

Export Citation Format

Share Document