Values Of Equilibrium Climate Sensitivity Research Articles

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

Simulating the Hydrologic Response to Climate Change in Three New Zealand Headwater Basins Using CMIP6 Datasets

Abstract Air temperature and precipitation outputs from 10 CMIP6 GCMs were input to the Precipitation-Runoff Modeling System hydrologic model to evaluate water and energy responses in three headwater basins to projected climate change over the twenty-first century. The headwater basins (398–801 km2) are located within the Mataura River basin in the South Island of New Zealand. CMIP6 datasets included two emission scenarios [shared socioeconomic pathways (SSPs) SSP2-4.5 and SSP5-8.5]. Half of the 10 GCMs selected in the study have equilibrium climate sensitivity (ECS) values above 4.5°C, which has been considered the upper end of equilibrium climate sensitivity. Modeling results included increased annual air temperature, evapotranspiration, and precipitation by the end of the twenty-first century for both SSP emissions scenarios, both high- and low-ECS GCMs, and all three headwater basins. Monthly precipitation and evapotranspiration totals also increased for all or most months. Monthly streamflow changes generally corresponded with monthly precipitation changes. Snowpack decreased significantly in depth and seasonal duration in all basins. However, streamflow increased for all SSP and ECS groups and basins because increased precipitation was consistently greater than increased evapotranspiration losses. Sources of uncertainty include the GCMs, climate sensitivity, downscaling, bias adjustment, emission scenarios, and the hydrologic model. Simulated hydrologic responses based on climate data from GCMs with ECS values of greater than 4.5°C could be less plausible since previous studies have suggested true ECS ranges from 1.5° to 4.5°C.

Correlation Between Cloud Adjustments and Cloud Feedbacks Responsible for Larger Range of Climate Sensitivities in CMIP6

AbstractWhile the higher mean Equilibrium Climate Sensitivity (ECS) in CMIP6 has been attributed to more positive cloud feedbacks, it is unclear what causes the greater range of ECS values across CMIP6 models compared to CMIP5. Here, we investigate the relationship between radiative forcing and cloud feedbacks across the two model generations to explain the very high ECS values in some CMIP6 models. The relationship is sensitive to the definition of the forcing, particularly in CMIP6, but fixed‐sea surface temperature simulations suggest the shortwave cloud feedback (λSW,CL) is anticorrelated with the forcing in CMIP5 and either uncorrelated or weakly positively correlated with the forcing in CMIP6. These relationships reflect the cloud adjustment to the forcing, which is anticorrelated with λSW,CL in CMIP5 and positively correlated in CMIP6. Although we are unable to identify a systematic change across the model generations, we do show that the difference is not due to land effects, and that cloud adjustments are generally driven by low and, especially, midlevel clouds. Furthermore, models derived from a small number modeling centers seem to be responsible for much of the difference between the CMIP5 and CMIP6 ensembles. Our analysis is severely limited by the available simulations, highlighting the need for targeted experiments to better understand cloud adjustments and to probe the sources of intermodel differences.

Does Disabling Cloud Radiative Feedbacks Change Spatial Patterns of Surface Greenhouse Warming and Cooling?

AbstractThe processes controlling idealized warming and cooling patterns are examined in 150-yr-long fully coupled Community Earth System Model, version 1 (CESM1), experiments under abrupt CO2forcing. By simulation end, 2 × CO2global warming was 20% larger than 0.5 × CO2global cooling. Not only was the absolute global effective radiative forcing ∼10% larger for 2 × CO2than for 0.5 × CO2, global feedbacks were also less negative for 2 × CO2than for 0.5 × CO2. Specifically, more positive shortwave cloud feedbacks led to more 2 × CO2global warming than 0.5 × CO2global cooling. Over high-latitude oceans, differences between 2 × CO2warming and 0.5 × CO2cooling were amplified by familiar linked positive surface albedo and lapse rate feedbacks associated with sea ice change. At low latitudes, 2 × CO2warming exceeded 0.5 × CO2cooling almost everywhere. Tropical Pacific cloud feedbacks amplified the following: 1) more fast warming than fast cooling in the west, and 2) slow pattern differences between 2 × CO2warming and 0.5 × CO2cooling in the east. Motivated to quantify cloud influence, a companion suite of experiments was run without cloud radiative feedbacks. Disabling cloud radiative feedbacks reduced the effective radiative forcing and surface temperature responses for both 2 × CO2and 0.5 × CO2. Notably, 20% more global warming than global cooling occurred regardless of whether cloud feedbacks were enabled or disabled. This surprising consistency resulted from the cloud influence on non-cloud feedbacks and circulation. With the exception of the tropical Pacific, disabling cloud feedbacks did little to change surface temperature response patterns including the large high-latitude responses driven by non-cloud feedbacks. The findings provide new insights into the regional processes controlling the response to greenhouse gas forcing, especially for clouds.Significance StatementWe analyze the processing controlling idealized warming and cooling under abrupt CO2forcing using a modern and highly vetted fully coupled climate model. We were especially interested to compare simulations with and without cloud radiative feedbacks. Notably, 20% more global warming than global cooling occurred regardless of whether cloud feedbacks were enabled or disabled. This surprising consistency resulted from the cloud influence on forcing, non-cloud feedbacks, and circulation. With the exception of the tropical Pacific, disabling cloud feedbacks did little to change surface temperature response patterns including the large high-latitude responses driven by non-cloud feedbacks. The findings provide new insights into the regional processes controlling the response to greenhouse gas forcing, especially for clouds. When combined with estimates of cooling at the Last Glacial Maximum, the findings also help rule out large (4+ K) values of equilibrium climate sensitivity.

Testing the CMIP6 GCM Simulations versus Surface Temperature Records from 1980–1990 to 2011–2021: High ECS Is Not Supported

The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC AR6, 2021). Even assuming that some of them are sufficiently reliable for scenario forecasts, such a large ECS uncertainty requires a pre-selection of the most reliable models. Herein the performance of 38 CMIP6 models are tested in reproducing the surface temperature changes observed from 1980–1990 to 2011–2021 in three temperature records: ERA5-T2m, ERA5-850mb, and UAH MSU v6.0 Tlt. Alternative temperature records are briefly discussed but found to be not appropriate for the present analysis because they miss data over large regions. Significant issues emerge: (1) most GCMs overestimate the warming observed during the last 40 years; (2) there is great variability among the models in reconstructing the climatic changes observed in the Arctic; (3) the ocean temperature is usually overestimated more than the land one; (4) in the latitude bands 40° N–70° N and 50° S–70° S (which lay at the intersection between the Ferrel and the polar atmospheric cells) the CMIP6 GCMs overestimate the warming; (5) similar discrepancies are present in the east-equatorial pacific region (which regulates the ENSO) and in other regions where cooling trends are observed. Finally, the percentage of the world surface where the (positive or negative) model-data discrepancy exceeds 0.2, 0.5 and 1.0 °C is evaluated. The results indicate that the models with low ECS values (for example, 3 °C or less) perform significantly better than those with larger ECS. Therefore, the low ECS models should be preferred for climate change scenario forecasts while the other models should be dismissed and not used by policymakers. In any case, significant model-data discrepancies are still observed over extended world regions for all models: on average, the GCM predictions disagree from the data by more than 0.2 °C (on a total mean warming of about 0.5 °C from 1980–1990 to 2011–2021) over more than 50% of the global surface. This result suggests that climate change and its natural variability remain poorly modeled by the CMIP6 GCMs. Finally, the ECS uncertainty problem is discussed, and it is argued (also using semi-empirical climate models that implement natural oscillations not predicted by the GCMs) that the real ECS could be between 1 and 2 °C, which implies moderate warming for the next decades.

Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Climate modelling output that was provided under the latest Coupled Model Intercomparison Project (CMIP6) shows significant changes in model-specific Equilibrium Climate Sensitivity (ECS) as compared to CMIP5. The newer versions of many Global Climate Models (GCMs) report higher ECS values that result in stronger global warming than previously estimated. At the same time, the multi-GCM spread of ECS is significantly larger than under CMIP5. Here, we analyse how the differences between CMIP5 and CMIP6 affect climate projections for Germany. We use the statistical-empirical downscaling method EPISODES in order to downscale GCM data for the scenario pairs RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. We use data sets of the GCMs CanESM, EC-Earth, MPI-ESM, and NorESM. The results show that the GCM-specific changes in the ECS also have an impact at the regional scale. While the temperature signal under regional climate change remains comparable for both CMIP generations in the MPI-ESM chain, the temperature signal increases by up to 3 °C for the RCP8.5/SSP5-8.5 scenario pair in the EC-Earth chain. Changes in precipitation are less pronounced and they only show notable differences at the seasonal scale. The reported changes in the climate signal will have direct consequences for society. Climate change impacts previously projected for the high-emission RCP8.5 scenario might occur equally under the new SSP2-4.5 scenario.

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

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.

Characteristics of Future Warmer Base States in CESM2

AbstractSimulations of 21st century climate with Community Earth System Model version 2 (CESM2) using the standard atmosphere (CAM6), denoted CESM2(CAM6), and the latest generation of the Whole Atmosphere Community Climate Model (WACCM6), denoted CESM2(WACCM6), are presented, and a survey of general results is described. The equilibrium climate sensitivity (ECS) of CESM2(CAM6) is 5.3°C, and CESM2(WACCM6) is 4.8°C, while the transient climate response (TCR) is 2.1°C in CESM2(CAM6) and 2.0°C in CESM2(WACCM6). Thus, these two CESM2 model versions have higher values of ECS than the previous generation of model, the CESM (CAM5) (hereafter CESM1), that had an ECS of 4.1°C, though the CESM2 versions have lower values of TCR compared to the CESM1 with a somewhat higher value of 2.3°C. All model versions produce credible simulations of the time evolution of historical global surface temperature. The higher ECS values for the CESM2 versions are reflected in higher values of global surface temperature increase by 2,100 in CESM2(CAM6) and CESM2(WACCM6) compared to CESM1 between comparable emission scenarios for the high forcing scenario. Future warming among CESM2 model versions and scenarios diverges around 2050. The larger values of TCR and ECS in CESM2(CAM6) compared to CESM1 are manifested by greater warming in the tropics. Associated with a higher climate sensitivity, for CESM2(CAM6) the first instance of an ice‐free Arctic in September occurs for all scenarios and ensemble members in the 2030–2050 time frame, but about a decade later in CESM2(WACCM6), occurring around 2040–2060.

On the climate sensitivity and historical warming evolution in recent coupled model ensembles

Abstract. The Earth's equilibrium climate sensitivity (ECS) to a doubling of atmospheric CO2, along with the transient climate response (TCR) and greenhouse gas emissions pathways, determines the amount of future warming. Coupled climate models have in the past been important tools to estimate and understand ECS. ECS estimated from Coupled Model Intercomparison Project Phase 5 (CMIP5) models lies between 2.0 and 4.7 K (mean of 3.2 K), whereas in the latest CMIP6 the spread has increased to 1.8–5.5 K (mean of 3.7 K), with 5 out of 25 models exceeding 5 K. It is thus pertinent to understand the causes underlying this shift. Here we compare the CMIP5 and CMIP6 model ensembles and find a systematic shift between CMIP eras to be unexplained as a process of random sampling from modeled forcing and feedback distributions. Instead, shortwave feedbacks shift towards more positive values, in particular over the Southern Ocean, driving the shift towards larger ECS values in many of the models. These results suggest that changes in model treatment of mixed-phase cloud processes and changes to Antarctic sea ice representation are likely causes of the shift towards larger ECS. Somewhat surprisingly, CMIP6 models exhibit less historical warming than CMIP5 models, despite an increase in TCR between CMIP eras (mean TCR increased from 1.7 to 1.9 K). The evolution of the warming suggests, however, that several of the CMIP6 models apply too strong aerosol cooling, resulting in too weak mid-20th century warming compared to the instrumental record.

The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity

AbstractEnergy budget estimates of equilibrium climate sensitivity (ECS) and transient climate response (TCR) are derived based on the best estimates and uncertainty ranges for forcing provided in the IPCC Fifth Assessment Report (AR5). Recent revisions to greenhouse gas forcing and post-1990 ozone and aerosol forcing estimates are incorporated and the forcing data extended from 2011 to 2016. Reflecting recent evidence against strong aerosol forcing, its AR5 uncertainty lower bound is increased slightly. Using an 1869–82 base period and a 2007–16 final period, which are well matched for volcanic activity and influence from internal variability, medians are derived for ECS of 1.50 K (5%–95% range: 1.05–2.45 K) and for TCR of 1.20 K (5%–95% range: 0.9–1.7 K). These estimates both have much lower upper bounds than those from a predecessor study using AR5 data ending in 2011. Using infilled, globally complete temperature data give slightly higher estimates: a median of 1.66 K for ECS (5%–95% range: 1.15–2.7 K) and 1.33 K for TCR (5%–95% range: 1.0–1.9 K). These ECS estimates reflect climate feedbacks over the historical period, assumed to be time invariant. Allowing for possible time-varying climate feedbacks increases the median ECS estimate to 1.76 K (5%–95% range: 1.2–3.1 K), using infilled temperature data. Possible biases from non–unit forcing efficacy, temperature estimation issues, and variability in sea surface temperature change patterns are examined and found to be minor when using globally complete temperature data. These results imply that high ECS and TCR values derived from a majority of CMIP5 climate models are inconsistent with observed warming during the historical period.

Emergent constraint on equilibrium climate sensitivity from global temperature variability.

Equilibrium climate sensitivity (ECS) remains one of the most important unknowns in climate change science. ECS is defined as the global mean warming that would occur if the atmospheric carbon dioxide (CO2) concentration were instantly doubled and the climate were then brought to equilibrium with that new level of CO2. Despite its rather idealized definition, ECS has continuing relevance for international climate change agreements, which are often framed in terms of stabilization of global warming relative to the pre-industrial climate. However, the 'likely' range of ECS as stated by the Intergovernmental Panel on Climate Change (IPCC) has remained at 1.5-4.5 degrees Celsius for more than 25 years. The possibility of a value of ECS towards the upper end of this range reduces the feasibility of avoiding 2 degrees Celsius of global warming, as required by the Paris Agreement. Here we present a new emergent constraint on ECS that yields a central estimate of 2.8 degrees Celsius with 66 per cent confidence limits (equivalent to the IPCC 'likely' range) of 2.2-3.4 degrees Celsius. Our approach is to focus on the variability of temperature about long-term historical warming, rather than on the warming trend itself. We use an ensemble of climate models to define an emergent relationship between ECS and a theoretically informed metric of global temperature variability. This metric of variability can also be calculated from observational records of global warming, which enables tighter constraints to be placed on ECS, reducing the probability of ECS being less than 1.5 degrees Celsius to less than 3 per cent, and the probability of ECS exceeding 4.5 degrees Celsius to less than 1 per cent.

Is climate sensitivity related to dynamical sensitivity?

AbstractThe atmospheric response to increasing CO2 concentrations is often described in terms of the equilibrium climate sensitivity (ECS). Yet the response to CO2 forcing in global climate models is not limited to an increase in global‐mean surface temperature: for example, the midlatitude jets shift poleward, the Hadley circulation expands, and the subtropical dry zones are altered. These changes, which are referred to here as “dynamical sensitivity,” may be more important in practice than the global‐mean surface temperature. This study examines to what degree the intermodel spread in the dynamical sensitivity of 23 Coupled Model Intercomparison Project phase 5 (CMIP5) models is captured by ECS. In the Southern Hemisphere, intermodel differences in the value of ECS explain ~60% of the intermodel variance in the annual‐mean Hadley cell expansion but just ~20% of the variance in the annual‐mean midlatitude jet response. In the Northern Hemisphere, models with larger values of ECS significantly expand the Hadley circulation more during winter months but contract the Hadley circulation more during summer months. Intermodel differences in ECS provide little significant information about the behavior of the Northern Hemisphere subtropical dry zones or midlatitude jets. The components of dynamical sensitivity correlated with ECS appear to be driven largely by increasing sea surface temperatures, whereas the components of dynamical sensitivity independent of ECS are related in part to changes in surface temperature gradients. These results suggest that efforts to narrow the spread in dynamical sensitivity across global climate models must also consider factors that are independent of global‐mean surface temperature.

Reexamining the Relationship between Climate Sensitivity and the Southern Hemisphere Radiation Budget in CMIP Models

Abstract Recent efforts to narrow the spread in equilibrium climate sensitivity (ECS) across global climate models have focused on identifying observationally based constraints, which are rooted in empirical correlations between ECS and biases in the models’ present-day climate. This study reexamines one such constraint identified from CMIP3 models: the linkage between ECS and net top-of-the-atmosphere radiation biases in the Southern Hemisphere (SH). As previously documented, the intermodel spread in the ECS of CMIP3 models is linked to present-day cloud and net radiation biases over the midlatitude Southern Ocean, where higher cloud fraction in the present-day climate is associated with larger values of ECS. However, in this study, no physical explanation is found to support this relationship. Furthermore, it is shown here that this relationship disappears in CMIP5 models and is unique to a subset of CMIP models characterized by unrealistically bright present-day clouds in the SH subtropics. In view of this evidence, Southern Ocean cloud and net radiation biases appear inappropriate for providing observationally based constraints on ECS. Instead of the Southern Ocean, this study points to the stratocumulus-to-cumulus transition regions of the SH subtropical oceans as key to explaining the intermodel spread in the ECS of both CMIP3 and CMIP5 models. In these regions, ECS is linked to present-day cloud and net radiation biases with a plausible physical mechanism: models with brighter subtropical clouds in the present-day climate show greater ECS because 1) subtropical clouds dissipate with increasing CO2 concentrations in many models and 2) the dissipation of brighter clouds contributes to greater solar warming of the surface.

Spread of model climate sensitivity linked to double‐Intertropical Convergence Zone bias

AbstractDespite decades of climate research and model development, two outstanding problems still plague the latest global climate models (GCMs): the double‐Intertropical Convergence Zone (ITCZ) bias and the 2−5°C spread of equilibrium climate sensitivity (ECS). Here we show that the double‐ITCZ bias and ECS in 44 GCMs from Coupled Model Intercomparison Project Phases 3/5 are negatively correlated. The models with weak (strong) double‐ITCZ biases have high (low)‐ECS values of ~4.1(2.2)°C. This indicates that the double‐ITCZ bias is a new emergent constraint for ECS based on which ECS might be in the higher end of its range (~4.0°C) and most models might have underestimated ECS. In addition, we argue that the double‐ITCZ bias can physically affect both cloud and water vapor feedbacks (thus ECS) and is a more easily measured emergent constraint for ECS than previous ones. It can be used as a performance metric for evaluating and comparing different GCMs.

Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is usually defined as the global mean equilibrium temperature response to a doubling CO2 concentration of its preindustrial level in the atmosphere [Hegerl et al., 2007; Meehl et al., 2007]. The pre-industrial concentration of CO2 is generally accepted to be 280×10 −6 and the doubled concentration was considered to be 560×10 accordingly in early studies. New studies tend to use 600×10 instead to stand for the doubled concentration of CO2 around 1900 AD. The initial values of ECS estimated by professionals [Charney et al., 1979], including those reported in IPCC FAR [Mitchell et al., 1990], SAR [Kattenberg et al., 1996] and TAR [Cubasch et al., 2001], were in the range of (3±1.5)◦C or 1.5–4.5◦C. Since IPCC TAR, great many efforts have been made to re-examine the ranges of ECS. As a result, IPCC AR4 [Meehl et al., 2007] offered a comprehensive discussions on this issue and proposed the range of ECS to be 2.0–4.5◦C, which was slightly narrower than previous estimations. Figure 1, derived from IPCC AR4, shows the probability density function of ECS from 10 studies, among which the first 7 results [Forster and Gregory, 2006; Frame et al., 2005; Knutti et al., 2002; Andronova and Schlesinger, 2001; Forest et al., 2002; 2006; Gregory et al., 2002] were from observation-constrained simulations and the other 3 results [Hegerl et al., 2006; von Schneider et al., 2006; Annan et al., 2005] came from Last Glacial Maximum(LGM) and millennia-scale proxy data constrained simulations. It is evident in Figure 1 that the spread of the probability density functions differs widely with the peaks of the curves ranging from around 0.2 to above 0.6 and the broadness of the curves also varies greatly while the mean values of ECS are at 2.0–3.5◦C, which are often greater than the most likely values of ECS (corresponding to the peaks of the probability curves). If should be noted that the uncertainty in estimated ECS mainly comes from the different sources of data that used to constrain the models. The observations with centennial length and LGM proxys were

Comment on “Climate Sensitivity Estimated from Temperature Reconstructions of the Last Glacial Maximum”

Schmittner et al. (Reports, 9 December 2011, p. 1385) report a new, low estimate of equilibrium climate sensitivity based on a comparison of Last Glacial Maximum climate model simulations and paleoproxy data. Here, we show that exclusion of questionable comparison points and constructive changes to model design are both likely capable of altering the most probable value of equilibrium climate sensitivity suggested in Schmittner et al.

Bayesian Learning of Climate Sensitivity I: Synthetic Observations

The instrumental temperature records are affected by both external climate forcings—in particular, the increase of long-lived greenhouse gas emissions—and natural, internal variability. Estimates of the value of equilibrium climate sensitivity—the change in global-mean equilibrium near-surface temperature due to a doubling of the pre-industrial CO2 concentration—and other climate parameters using these observational records are affected by the presence of the internal variability. A different realization of the natural variability will result in different estimates of the values of these climate parameters. In this study we apply Bayesian estimation to simulated temperature and ocean heat-uptake records generated by our Climate Research Group’s Simple Climate Model for known values of equilibrium climate sensitivity, T2x direct sulfate aerosol forcing in reference year 2000, FASA, and oceanic heat diffusivity, ΔT2x. We choose the simulated records for one choice of values of the climate parameters to serve as the synthetic observations. To each of the simulated temperature records we add a number of draws of the quasi-periodic oscillations and stochastic noise, determined from the observed temperature record. For cases considering only values of ΔT2x and/or κ, the Bayesian estimation converges to the value(s) of ΔT2x and/or κ used to generate the synthetic observations. However, for cases studying FASA, the Bayesian analysis does not converge to the “true” value used to generate the synthetic observations. We show that this is a problem of low signal-to-noise ratio: by substituting an artificial, continuously increasing sulfate record, we greatly improve the value obtained through Bayesian estimation. Our results indicate Bayesian learning techniques will be useful tools in constraining the values of ΔT2x and κ but not FASA In our Group’s future work we will extend the methods used here to the observed, instrumental records of global-mean temperature increase and ocean heat uptake.