Estimates Of Equilibrium Climate Sensitivity Research Articles

Comparison of Proxy-Shortwave Cloud Albedo from SBUV Observations with CMIP6 Models

Abstract We construct a long-term record of top of atmosphere (TOA) shortwave (SW) albedo of clouds and aerosols from 340-nm radiances observed by NASA and NOAA satellite instruments from 1980 to 2013. We compare our SW cloud+aerosol albedo with simulated cloud albedo from both AMIP and historical CMIP6 simulations from 47 climate models. While most historical runs did not simulate our observed spatial pattern of the trends in albedo over the Pacific Ocean, four models qualitatively simulate our observed patterns. Those historical models and the AMIP models collectively estimate an equilibrium climate sensitivity (ECS) of ∼3.5°C, with an uncertainty from 2.7° to 5.1°C. Our ECS estimates are sensitive to the instrument calibration, which drives the wide range in ECS uncertainty. We use instrument calibrations that assume a neutral change in reflectivity over the Antarctic ice sheet. Our observations show increasing cloudiness over the eastern equatorial Pacific and off the coast of Peru as well as neutral cloud trends off the coast of Namibia and California. To produce our SW cloud+aerosol albedo, we first retrieve a black-sky cloud albedo (BCA) and empirically correct the sampling bias from diurnal variations. Then, we estimate the broadband proxy albedo using multiple nonlinear regression along with several years of CERES cloud albedo to obtain the regression coefficients. We validate our product against CERES data from the years not used in the regression. Zonal mean trends of our SW cloud+aerosol albedo show reasonable agreement with CERES as well as the Pathfinder Atmospheres–Extended (PATMOS-x) observational dataset. Significance Statement Equilibrium climate sensitivity is a measure of the rise in global temperature over hundreds of years after a doubling of atmospheric CO2 concentration. Current state-of-the-art climate models forecast a wide range of equilibrium climate sensitivities (1.5°–6°C), due mainly to how clouds, aerosols, and sea surface temperatures are simulated within these models. Using data from NASA and NOAA satellite instruments from 1980 to 2013, we first construct a dataset that describes how much sunlight has been reflected by clouds over the 34 years and then we compare this data record to output from 47 climate models. Based on these comparisons, we conclude the best estimate of equilibrium climate sensitivity is about 3.5°C, with an uncertainty range of 2.7°–5.1°C.

CMIP6 GCM Validation Based on ECS and TCR Ranking for 21st Century Temperature Projections and Risk Assessment

Global climate models (GCMs) from the sixth Coupled Model Intercomparison Project Phases (CMIP6) have been employed to simulate the twenty-first-century temperatures for the risk assessment of future climate change. However, their transient climate response (TCR) ranges from 1.2 to 2.8 °C, whereas their equilibrium climate sensitivity (ECS) ranges from 1.8 to 5.7 °C, leading to large variations in the climatic impact of an anthropogenic increase in atmospheric CO2 levels. Moreover, there is growing evidence that many GCMs are running “too hot” and are hence unreliable for directing policies for future climate changes. Here, I rank 41 CMIP6 GCMs according to how successfully they hindcast the global surface warming between 1980 and 2021 using both their published ECS and TCR estimates. The sub-ensemble of GCMs with the best performance appears to be composed of the models with ECS ranging between 1.8 and 3.0 °C (which confirms previous studies) and TCR ranging between 1.2 and 1.8 °C. This GCM sub-ensemble is made up of a total of 17 models. Depending on the emission scenarios, these GCMs predict a 2045–2055 warming of 1.5–2.5 °C compared to the pre-industrial era (1850–1900). As a result, the global aggregated impact and risk estimates seem to be moderate, which implies that any negative effects of future climate change may be adequately addressed by adaptation programs. However, there are also doubts regarding the actual magnitude of global warming, which might be exaggerated because of urban heat contamination and other local non-climatic biases. A final section is dedicated to highlighting the divergences observed between the global surface temperature records and a number of alternative temperature reconstructions from lower troposphere satellite measurements, tree-ring-width chronologies, and surface temperature records based on rural stations alone. If the global warming reported by the climate records is overestimated, the real ECS and TCR may be significantly lower than what is produced by the CMIP6 GCMs, as some independent studies have already suggested, which would invalidate all of the CMIP6 GCMs.

Climate Sensitivity and Ecoclimate Sensitivity: Theory, Usage, and Past Implications for Future Biospheric Responses

Two usages of ‘climate sensitivity’ co-exist: one climatological and one ecological. The earlier climatological usage quantifies the sensitivity of global mean surface temperature to atmospheric CO2, with formal variants differing by timescale and processes. The ecological usage, renamed here as ecoclimate sensitivity, is defined as a change in an ecological response variable per unit climate change. The two concepts are treated very differently: climatologists have focused on reducing uncertainty of global climate sensitivity estimates, while ecologists have focused on the multivariate processes governing variations in ecoclimate sensitivity across drivers, response variables, and scales. Because radiative forcing scales logarithmically to [CO2]atm, ecological impacts per ppm [CO2]atm often also scale logarithmically, although non-linear ecoclimate sensitivities can alter this expectation. Critically, past estimates of climate and ecoclimate sensitivity carry an implicit tradeoff, in which smaller estimates of climate sensitivity indicate higher ecoclimate sensitivities. For the LGM, estimates of equilibrium climate sensitivity have narrowed to 2.4 to 4.5 °C, while high ecoclimate sensitivity is indicated by post-glacial biome conversions, continental-scale species range shifts, and high community turnover. We introduce a new term, ecocarbon sensitivity, defined as the product of global climate sensitivity, local ecoclimate sensitivity, and a global-to-local climate scaling factor. Given past biospheric transformations, we can expect high sensitivity of the terrestrial biosphere to current rises in [CO2]atm, a conclusion that is insensitive to estimates of climate sensitivity. The next frontier is better quantification of the processes governing the form and variations of ecoclimate and ecocarbon sensitivity across systems and scales.

Biased Estimates of Equilibrium Climate Sensitivity and Transient Climate Response Derived From Historical CMIP6 Simulations

AbstractThis study assesses the effective climate sensitivity (EffCS) and transient climate response (TCR) derived from global energy budget constraints within historical simulations of eight CMIP6 global climate models (GCMs). These calculations are enabled by use of the Radiative Forcing Model Intercomparison Project (RFMIP) simulations, which permit accurate quantification of the radiative forcing. Long‐term historical energy budget constraints generally underestimate EffCS from CO2 quadrupling and TCR from CO2 ramping, owing to changes in radiative feedbacks and changes in ocean heat uptake efficiency. Atmospheric GCMs forced by observed warming patterns produce lower values of EffCS that are more in line with those inferred from observed historical energy budget changes. The differences in the EffCS estimates from historical energy budget constraints of models and observations are traced to discrepancies between modeled and observed historical surface warming patterns.

A re-evaluation of the Earth’s surface temperature response to radiative forcing

There is much current debate about the way in which the earth’s climate and temperature are responding to anthropogenic and natural forcing. In this paper we re-assess the current evidence at the globally averaged level by adopting a generic ‘data-based mechanistic’ modelling strategy that incorporates statistically efficient parameter estimation. This identifies a low order, differential equation model that explains how the global average surface temperature variation responds to the influences of total radiative forcing (TRF). The model response includes a novel, stochastic oscillatory component with a period of about 55 years (range 51.6–60 years) that appears to be associated with heat energy interchange between the atmosphere and the ocean. These ‘quasi-cycle’ oscillations, which account for the observed pauses in global temperature increase around 1880, 1940 and 2001, appear to be related to ocean dynamic responses, particularly the Atlantic multidecadal oscillation. The model explains 90% of the variance in the global average surface temperature anomaly and yields estimates of the equilibrium climate sensitivity (ECS) (2.29 ∘C with 5%–95% range 2.11 ∘C to 2.49 ∘C) and the transient climate response (TCR) (1.56 ∘C with 5%–95% range 1.43 ∘C to 1.68 ∘C), both of which are smaller than most previous estimates. When a high level of uncertainty in the TRF is taken into account, the ECS and TCR estimates are unchanged but the ranges are increased to 1.43 ∘C to 3.14 ∘C and 0.99 ∘C to 2.16 ∘C, respectively.

CO2 Increase Experiments Using the CESM: Relationship to Climate Sensitivity and Comparison of CESM1 to CESM2

AbstractWe examine the response of the Community Earth System Model Versions 1 and 2 (CESM1 and CESM2) to abrupt quadrupling of atmospheric CO2 concentrations (4xCO2) and to 1% annually increasing CO2 concentrations (1%CO2). Different estimates of equilibrium climate sensitivity (ECS) for CESM1 and CESM2 are presented. All estimates show that the sensitivity of CESM2 has increased by 1.5 K or more over that of CESM1. At the same time the transient climate response (TCR) of CESM1 and CESM2 derived from 1%CO2 experiments has not changed significantly—2.1 K in CESM1 and 2.0 K in CESM2. Increased initial forcing as well as stronger shortwave radiation feedbacks are responsible for the increase in ECS seen in CESM2. A decomposition of regional radiation feedbacks and their contribution to global feedbacks shows that the Southern Ocean plays a key role in the overall behavior of 4xCO2 experiments, accounting for about 50% of the total shortwave feedback in both CESM1 and CESM2. The Southern Ocean is also responsible for around half of the increase in shortwave feedback between CESM1 and CESM2, with a comparable contribution arising over tropical ocean. Experiments using a thermodynamic slab‐ocean model (SOM) yield estimates of ECS that are in remarkable agreement with those from fully coupled Earth system model (ESM) experiments for the same level of CO2 increase. Finally, we show that the similarity of TCR in CESM1 and CESM2 masks significant regional differences in warming that occur in the 1%CO2 experiments for each model.

Comparison of Equilibrium Climate Sensitivity Estimates From Slab Ocean, 150‐Year, and Longer Simulations

AbstractWe compare equilibrium climate sensitivity (ECS) estimates from pairs of long (≥800‐year) control and abruptly quadrupled CO2 simulations with shorter (150‐ and 300‐year) coupled atmosphere‐ocean simulations and slab ocean models (SOMs). Consistent with previous work, ECS estimates from shorter coupled simulations based on annual averages for years 1–150 underestimate those from SOM (−8% ± 13%) and long (−14% ± 8%) simulations. Analysis of only years 21–150 improved agreement with SOM (−2% ± 14%) and long (−8% ± 10%) estimates. Use of pentadal averages for years 51–150 results in improved agreement with long simulations (−4% ± 11%). While ECS estimates from current generation U.S. models based on SOM and coupled annual averages of years 1–150 range from 2.6°C to 5.3°C, estimates based longer simulations of the same models range from 3.2°C to 7.0°C. Such variations between methods argues for caution in comparison and interpretation of ECS estimates across models.

Improved methods for estimating equilibrium climate sensitivity from transient warming simulations

Equilibrium climate sensitivity (ECS) refers to the total global warming caused by an instantaneous doubling of atmospheric CO2 from the pre-industrial level in a climate system. ECS is commonly used to measure how sensitive a climate system is to CO2 forcing; but it is difficult to estimate for the real world and for fully coupled climate models because of the long response time in such a system. Earlier studies used a slab ocean coupled to an atmospheric general circulation model to estimate ECS, but such a setup is not the same as the fully coupled system. More recent studies used a linear fit between changes in global-mean surface air temperature (ΔT) and top-of-atmosphere net radiation (ΔN) to estimate ECS from relatively short simulations. Here we analyze 1000 years of simulation with abrupt quadrupling (4 × CO2) and another 500-year simulation with doubling (2 × CO2) of pre-industrial CO2 using the CESM1 model, and three other multi-millennium (~5000 year) abrupt 4 × CO2 simulations to show that the linear-fit method considerably underestimates ECS due to the flattening of the −dN/dT slope, as noticed previously. We develop and evaluate three other methods, and propose a new method that makes use of the realized warming near the end of the simulations and applies the −dN/dT slope calculated from a best fit of the ΔT and ΔN data series to a simple two-layer model to estimate the unrealized warming. Using synthetic data and the long model simulations, we show that the new method consistently outperforms the linear-fit method with small biases in the estimated ECS using 4 × CO2 simulations with at least 180 years of simulation. The new method was applied to 4 × CO2 experiments from 20 CMIP5 and 19 CMIP6 models, and the resulting ECS estimates are about 10% higher on average and up to 25% higher for models with medium–high ECS (> 3 K) than those reported in the IPCC AR5. Our new estimates suggest an ECS range of about 1.78–5.45 K with a mean of 3.61 K among the CMIP5 models and about 1.85–6.25 K with a mean of 3.60 K for the CMIP6 models. Furthermore, stable ECS estimates require at least 240 (180) years of simulation for using 2 × CO2 (4 × CO2) experiments, and using shorter simulations may underestimate the ECS substantially. Our results also suggest that it is the forced −dN/dT slope after year 40, not the internally-generated −dN/dT slope, that is crucial for an accurate estimate of the ECS, and this forced slope may be fairly stable.

Climate sensitivity revisited

The commonly used energy balance model from Gregory et al. (2002) that underlies many published estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) to anthropogenic forcing requires only four parameters for calculation of ECS and three for TCR. Both estimates require a value for the increase in global mean surface air temperature (ΔT) over a period of time, the increment in forcing driving the temperature change over that period (ΔF), and knowledge of the radiative forcing resulting from a doubling in CO2 concentration (F2×CO2). For ECS a value for the associated global heating rate (ΔQ) is also required. Each of these parameters has a best estimate available from the IPCC’s Fifth Assessment Report, but the authors did not provide best estimates for ECS and TCR within the broad uncertainty ranges quoted, 1.5–4.5 K for ECS and 1.0–2.5 K for TCR. Best estimates for ECS and TCR consistent with AR5 best estimates for ΔF and F2×CO2 are provided here. A well-known heuristic model was modified and applied to seven observation-based global temperature datasets to isolate temperature trend due to anthropogenic forcing from confounding effects of variability due to volcanism, cycles in solar irradiance and internal climate variability. The seven estimates of ECS and TCR were remarkably similar despite very large differences in time-base of the datasets analysed, yielding best estimates of 2.36 ± 0.13 K and 1.58 ± 0.09 K respectively at 95% confidence based on the AR5 best estimates for ΔF, F2×CO2 and ΔQ from Wijffels et al. (2016). The ECS and TCR best estimates here are tied to those AR5 and ΔQ best estimates, but can be simply scaled were those best estimate values to be refined in the future.

The Strength of Low-Cloud Feedbacks and Tropical Climate: A CESM Sensitivity Study

Abstract Variability in the strength of low-cloud feedbacks across climate models is the primary contributor to the spread in their estimates of equilibrium climate sensitivity (ECS). This raises the question: What are the regional implications for key features of tropical climate of globally weak versus strong low-cloud feedbacks in response to greenhouse gas–induced warming? To address this question and formalize our understanding of cloud controls on tropical climate, we perform a suite of idealized fully coupled and slab-ocean climate simulations across which we systematically scale the strength of the low-cloud-cover feedback under abrupt 2 × CO2 forcing within a single model, thereby isolating the impact of low-cloud feedback strength. The feedback strength is varied by modifying the stratus cloud fraction so that it is a function of not only local conditions but also global temperature in a series of abrupt 2 × CO2 sensitivity experiments. The unperturbed decrease in low cloud cover (LCC) under 2 × CO2 is greatest in the mid- and high-latitude oceans, and the subtropical eastern Pacific and Atlantic, a pattern that is magnified as the feedback strength is scaled. Consequently, sea surface temperature (SST) increases more in these regions as well as the Pacific cold tongue. As the strength of the low-cloud feedback increases this results in not only increased ECS, but also an enhanced reduction of the large-scale zonal and meridional SST gradients (structural climate sensitivity), with implications for the atmospheric Hadley and Walker circulations, as well as the hydrological cycle. The relevance of our results to simulating past warm climate is also discussed.

Emergent Scale Invariance and Climate Sensitivity

Earth’s global surface temperature shows variability on an extended range of temporal scales and satisfies an emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature of the observed temperature fluctuations, but an inherent property of the temperature response to radiative forcing, and a principle that links the fast and slow climate responses. It provides a bridge between the decadal- and centennial-scale fluctuations in the instrumental temperature record, and the millennial-scale equilibration following perturbations in the radiative balance. In particular, the emergent scale invariance makes it possible to infer equilibrium climate sensitivity (ECS) from the observed relation between radiative forcing and global temperature in the instrumental era. This is verified in ensembles of Earth system models (ESMs), where the inferred values of ECS correlate strongly to estimates from idealized model runs. For the range of forcing data explored in this paper, the method gives best estimates of ECS between 1.8 and 3.7 K, but statistical uncertainties in the best estimates themselves will provide a wider likely range of the ECS.

A Test of Emergent Constraints on Cloud Feedback and Climate Sensitivity Using a Calibrated Single-Model Ensemble

AbstractA calibrated single-model ensemble (SME) derived from the NCAR Community Atmosphere Model, version 3.1, is used to test two hypothesized emergent constraints on cloud feedback and equilibrium climate sensitivity (ECS). The Fasullo and Trenberth relative humidity (RH) metric and the Sherwood et al. lower-tropospheric mixing (LTMI) metric are computed for the present-day climate of the SME, and the relationships between the metrics, ECS, and cloud and other climate feedbacks are examined. The tropical convergence zone relative humidity (RHM) and the parameterized lower-tropospheric mixing (LTMIS) are positively correlated to ECS, and each is associated with a different spatial pattern of tropical shortwave cloud feedback in the SME. However, neither of those metrics is linked to the type of cloud response hypothesized by its authors. The resolved lower-tropospheric mixing (LTMID) is positively correlated to ECS for a subset of the SME having LTMID over a threshold value. LTMI and the RH for the dry, descending branch of the Hadley cell (RHD) narrow and shift upward the posterior estimates of ECS in the SME, but the SME bias in RHD and concerns over poorly understood physical mechanisms suggest the narrowing could be spurious for both constraints. While calibrated SME results may not generalize to multimodel ensembles, they can enhance the process understanding of emergent constraints and serve as out-of-sample tests of robustness.

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

Energy 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.

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC)−1 (median and 5–95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS∕TCR parameters but less sensitive to the ERF from a doubling of CO2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081–2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS∕TCR distributions.

Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations

AbstractAn emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979–2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2 radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long‐term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long‐term sensitivity.

Emerging Technologies and Synergies for Airborne and Space-Based Measurements of Water Vapor Profiles

A deeper understanding of how clouds will respond to a warming climate is one of the outstanding challenges in climate science. Uncertainties in the response of clouds, and particularly shallow clouds, have been identified as the dominant source of the discrepancy in model estimates of equilibrium climate sensitivity. As the community gains a deeper understanding of the many processes involved, there is a growing appreciation of the critical role played by fluctuations in water vapor and the coupling of water vapor and atmospheric circulations. Reduction of uncertainties in cloud-climate feedbacks and convection initiation as well as improved understanding of processes governing these effects will result from profiling of water vapor in the lower troposphere with improved accuracy and vertical resolution compared to existing airborne and space-based measurements. This paper highlights new technologies and improved measurement approaches for measuring lower tropospheric water vapor and their expected added value to current observations. Those include differential absorption lidar and radar, microwave occultation between low-Earth orbiters, and hyperspectral microwave remote sensing. Each methodology is briefly explained, and measurement capabilities as well as the current technological readiness for aircraft and satellite implementation are specified. Potential synergies between the technologies are discussed, actual examples hereof are given, and future perspectives are explored. Based on technical maturity and the foreseen near-mid-term development path of the various discussed measurement approaches, we find that improved measurements of water vapor throughout the troposphere would greatly benefit from the combination of differential absorption lidar focusing on the lower troposphere with passive remote sensors constraining the upper-tropospheric humidity.

Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks

Estimates of equilibrium climate sensitivity differ depending on the method of calculation. This study shows estimates based on the historical energy budget are low as climate feedbacks vary with time and the bias depends on the sensitivity of the system. Global energy budget constraints1,2,3 suggest an equilibrium climate sensitivity around 2 °C, which is lower than estimates from palaeoclimate reconstructions4, process-based observational analyses5,6,7, and global climate model simulations8,9. A key assumption is that the climate sensitivity inferred today also applies to the distant future. Yet, global climate models robustly show that feedbacks vary over time, with a strong tendency for climate sensitivity to increase as equilibrium is approached9,10,11,12,13,14,15,16,17,18. Here I consider the implications of inconstant climate feedbacks for energy budget constraints on climate sensitivity. I find that the long-term value of climate sensitivity is, on average, 26% above that inferred during transient warming within global climate models, with a larger discrepancy when climate sensitivity is high. Moreover, model values of climate sensitivity inferred during transient warming are found to be consistent with energy budget observations1,2,3, indicating that the models are not overly sensitive. Using model-based estimates of how climate feedbacks will change in the future, in conjunction with recent energy budget constraints1,19, produces a current best estimate of equilibrium climate sensitivity of 2.9 °C (1.7–7.1 °C, 90% confidence). These findings suggest that climate sensitivity estimated from global energy budget constraints is in agreement with values derived from other methods and simulated by global climate models.

EMPIRICALLY CONSTRAINED CLIMATE SENSITIVITY AND THE SOCIAL COST OF CARBON

Integrated Assessment Models (IAMs) require parameterization of both economic and climatic processes. The latter includes Equilibrium Climate Sensitivity (ECS), or the temperature response to doubling CO2 levels, and Ocean Heat Uptake (OHU) efficiency. ECS distributions in IAMs have been drawn from climate model runs that lack an empirical basis, and in Monte Carlo experiments may not be constrained to consistent OHU values. Empirical ECS estimates are now available, but have not yet been applied in IAMs. We incorporate a new estimate of the ECS distribution conditioned on observed OHU efficiency into two widely used IAMs. The resulting Social Cost of Carbon (SCC) estimates are much lower than those from models based on simulated ECS parameters. In the DICE model, the average SCC falls by approximately 40–50% depending on the discount rate, while in the FUND model the average SCC falls by over 80%. The span of estimates across discount rates also shrinks substantially.

Quantification of temperature response to CO2 forcing in atmosphere–ocean general circulation models

The present study establishes a general formulation to represent the behavior and variation of an ensemble of complex climate models in terms of the global mean surface temperature response to atmospheric CO2 increase. The response parameters of this formulation provide a set of metrics that extends the conventional concept of climate sensitivity and quantifies transient temperature changes with sufficient simplicity and transparency to serve studies on climate change mitigation. Two commonly used metrics for transient and equilibrium climate sensitivity are analytically derived from the formulation, such that conventional estimates of equilibrium climate sensitivity based on standard numerical experiments for quadrupling CO2 increase are properly scaled down to the reference level of doubling CO2. The characteristics and variations of a specific ensemble of complex climate models can be simulated with a statistical model built using the principal components (PCs) of the response parameters. This approach is applied to the probabilistic assessment of temperature changes as well as to the diagnosis of the base ensemble. In current complex climate models, the ratio of transient-to-equilibrium sensitivity decreases with an increase of equilibrium sensitivity, as identified in variations associated with two specific PCs that characterize coherence between transient temperature response and properties of heat uptake by the ocean.

Constraints on Climate Sensitivity from Space-Based Measurements of Low-Cloud Reflection

Abstract Physical uncertainties in global-warming projections are dominated by uncertainties about how the fraction of incoming shortwave radiation that clouds reflect will change as greenhouse gas concentrations rise. Differences in the shortwave reflection by low clouds over tropical oceans alone account for more than half of the variance of the equilibrium climate sensitivity (ECS) among climate models, which ranges from 2.1 to 4.7 K. Space-based measurements now provide an opportunity to assess how well models reproduce temporal variations of this shortwave reflection on seasonal to interannual time scales. Here such space-based measurements are used to show that shortwave reflection by low clouds over tropical oceans decreases robustly when the underlying surface warms, for example, by −(0.96 ± 0.22)% K−1 (90% confidence level) for deseasonalized variations. Additionally, the temporal covariance of low-cloud reflection with temperature in historical simulations with current climate models correlates strongly (r = −0.67) with the models’ ECS. Therefore, measurements of temporal low-cloud variations can be used to constrain ECS estimates based on climate models. An information-theoretic weighting of climate models by how well they reproduce the measured deseasonalized covariance of shortwave cloud reflection with temperature yields a most likely ECS estimate around 4.0 K; an ECS below 2.3 K becomes very unlikely (90% confidence).