Climate Feedback Parameter Research Articles

Ice Sheet‐Albedo Feedback Estimated From Most Recent Deglaciation

AbstractIce sheet feedbacks are underrepresented in model assessments of climate sensitivity and their magnitudes are still poorly constrained. We combine a recently published record of Earth's Energy Imbalance (EEI) with existing reconstructions of temperature, atmospheric composition, and sea level to estimate both the magnitude and timescale of the ice sheet‐albedo feedback since the Last Glacial Maximum. This facilitates the first opportunity to quantify this feedback over the most recent deglaciation using a proxy data‐driven approach. We find the ice sheet‐albedo feedback to be amplifying, increasing the total climate feedback parameter by 42% and reaching an equilibrium magnitude of 0.55 Wm−2K−1, with a 66% confidence interval of 0.45–0.63 Wm−2K−1. The timescale to equilibrium is estimated as 3.6 ka (66% confidence: 1.9–5.5 ka). These results provide new evidence for the timescale and magnitude of the amplifying ice sheet‐albedo feedback that will drive anthropogenic warming for millennia to come.

Multi-centennial evolution of the climate response and deep-ocean heat uptake in a set of abrupt stabilization scenarios with EC-Earth3

Abstract. Understanding long-term committed climate change due to anthropogenic forcing is key to informing climate policies, yet these timescales are still underexplored. We present here a set of 1000-year-long abrupt stabilization simulations performed with EC-Earth3. Each simulation follows a sudden stabilization of the external forcing at the level specified by CMIP6 for historical (1990) or SSP5-8.5 scenario (2025, 2050, 2065, 2080, 2100) conditions, with a final temperature increase ranging between 1.4 and 9.6 K with respect to the pre-industrial baseline. Remarkably, the simulation stabilized at a greenhouse gas (GHG) level close to the present day (2025) exceeds the Paris Agreement goals of 1.5 and 2° warming above pre-industrial in the long term, and only the 1990 simulation leads to a stabilized climate below 1.5° warming. We first focus on the evolution of the climate response at multi-centennial timescales and its dependence on the level of forcing. We note a decrease in the magnitude of the climate feedback parameter at longer timescales. Conversely, simulations with higher forcing exhibit a larger feedback parameter (in magnitude). Subsequently, the evolution of surface warming patterns over multi-centennial timescales is studied. While the response is generally consistent across simulations, some variations, particularly in the South Pacific and at high latitudes, suggest a certain level of state or forcing dependence. The patterns of precipitation change also evolve during the stabilization runs: the drying trends found in the subtropical oceans and in Mediterranean-like hotspots in the SSP5-8.5 scenario tend to be reduced or even reversed. We finally focus on the rate of heat storage in the global ocean, which is the main driver of the climate response at multi-centennial timescales. We find that the rate of warming of the deep ocean is almost independent from the amplitude of the forcing so that most of the additional heat remains in the upper layers at high forcing. This might be due – at least partly – to a decreased ventilation of the deep ocean, caused by changes in the Meridional Overturning Circulation (MOC). These results highlight the importance of studying multi-centennial timescales of climate change to better understand the response of the deep ocean, which will play a crucial role in determining the final state of the climate system once GHG concentrations are stabilized.

Inferring Northern Hemisphere Continental Warming Patterns from the Amplitude and Phase of the Seasonal Cycle in Surface Temperature

Abstract The seasonal cycle in temperature is a large and well-observed response to radiative forcing, suggesting its potential as a natural analog to human-caused climate change. Although there have been advances constraining some climate feedback parameters using seasonal observations, the seasonal cycle has not been used to inform about the local temperature sensitivity to greenhouse gas forcing. In this study, we uncover a nonlinear relationship between the amplitude and phase of the seasonal cycle and forced temperature trends in seven CMIP5-era large ensembles across the Northern Hemisphere extratropical continents. We develop a mixture energy balance model that reproduces this relationship and reveals the unexpected finding that the phasing of the seasonal cycle—in addition to the amplitude—contains information about local temperature sensitivity to seasonal forcing over land. Using this energy balance model framework, we compare the pattern and magnitude of the seasonally inferred sensitivity of the surface temperature response to anthropogenic radiative forcing. The seasonally constrained model largely reproduces the pattern of human-caused temperature trends seen in climate models (r = 0.81, p value < 0.01), including polar amplification, but the magnitude of the response is smaller by about a factor of 3. Our results show the relevance of both phasing and amplitude for constraining patterns of local feedbacks and suggest the utility of additional research to better understand the differences in sensitivity between seasonal and greenhouse gas forcing. Significance Statement Warming in response to increased greenhouse gases is not spatially uniform across land. We wanted to understand whether the familiar seasonal cycle in temperature could provide information about climate change. We found that climate models show a strong link between the seasonal cycle and future warming: places with a larger and more delayed temperature response to the seasonal cycle in solar forcing tend to warm more across the Northern Hemisphere midlatitudes. A very simple model for the climate system, whose parameters are based on the seasonal cycle, captures the pattern but not the magnitude of warming. Our findings suggest that there are some similarities between the processes that control temperature change on seasonal and climate change time scales, but that we must understand the difference between seasonal and longer-term sensitivity to warming before the seasonal cycle can be used to reduce uncertainty about climate change.

Revisiting the Global Energy Budget Dynamics with a Multivariate Earth Energy Balance Model to Account for the Warming Pattern Effect

Abstract Climate feedbacks are sensitive to the geographical distribution of sea surface temperature (SST). This sensitivity, called the pattern effect, affects the amplitude of the Earth radiative response to anomalies in global mean surface temperature (GMST) and thus is essential in shaping the global energy budget dynamics. Zero-dimensional energy balance models (EBMs) are the simplest representation of the global energy budget dynamics. Many only depend on GMST anomalies and cannot account for the pattern effect explicitly. In EBMs, the pattern effect leads to apparent variations of the global climate feedback parameter λ. Assuming a variable λ in EBMs enables them to more accurately reproduce AOGCM simulations of the GMST anomalies but it leads to variations in λ of >+15%. These large variations mean λ is not a constant and the Taylor expansion underpinning EBMs’ formulation does not hold, casting doubts on the physical grounding of such EBMs. Here we propose a new EBM based on a multivariate linear Earth radiative response, which depends on both the GMST and the surface warming pattern. The resulting multilinear EBM accurately reproduces AOGCM simulations of anomalies in Earth radiative response and GMST under abrupt 4xCO2 forcing. When interpreted in terms of variable λ, the multivariate EBM leads to small variations in λ that are physically consistent with the underpinning Taylor expansion. We analyze with the multivariate framework the variations of the planetary heat uptake N as a function of the GMST and the pattern of warming through a 3D generalization of the Gregory plot. We show that the apparent nonlinear behavior of the radiative response of the Earth against GMST seen in classical monovariate EBMs (and in classical Gregory plots) can actually be explained by a bilinear dependance of the radiative response of the Earth on the GMST and the pattern of warming. The multivariate EBM further provides an explicit dependence of the global energy budget on the pattern of warming and on the climate state. It has important consequences on the expression of the climate sensitivity.

Time-variations of the climate feedback parameter λ are associated with the Pacific Decadal Oscillation

Climate models suggest that the climate feedback parameter λ, which denotes the magnitude of the Earth radiative response to a change in global surface temperature, varies with time. This is because λ depends on the pattern of sea-surface temperature. However, the time-variability of λ and its relation to the sea-surface temperature pattern has not been evaluated in multi-decadal observations. Here, using up-to-date observations, we evaluate the global energy budget over successive 25-year windows and derive a time-series of λ over 1970–2005. We find λ varied within the range [−3.2, −1.0]W ⋅ m−2 ⋅ K−1 since 1970. These variations are linked to the sea-surface temperature pattern changes associated with the Pacific Decadal Oscillation. Climate model simulations forced with observations of historical sea-surface temperature show a 1970–2005 mean λ that is consistent with observations. However, they fail in reproducing observed λ time-variations since 1970 which are associated to the Pacific Decadal Oscillation, meaning that climate models underestimate the pattern effect at decadal time scales.

Why Is Climate Sensitivity for Solar Forcing Smaller than for an Equivalent CO2 Forcing?

Abstract Previous studies have shown that climate sensitivity, defined as the global mean surface temperature change per unit radiative forcing, is smaller for solar radiative forcing compared to an equivalent CO2 radiative forcing. We investigate the causes for this difference using the NCAR CAM4 model. The contributions to the climate feedback parameter, which is inversely related to climate sensitivity, are estimated for water vapor, lapse rate, Planck, albedo, and cloud feedbacks using the radiative kernel technique. The total feedback estimated for CO2 and solar radiative forcing from our model simulations is −1.23 and −1.45 W m−2 K−1, respectively. We find that the difference in feedback between the two cases is primarily due to differences in lapse rate, water vapor, and cloud feedbacks, which together explain 65% of the difference in total feedback. The rest comes from Planck and albedo feedbacks. The differences in feedbacks arise mainly from differences in the horizontal (meridional) structure of forcing and the consequent warming. Our study provides important insights into the effects of the meridional structure of forcing on climate feedback, which is important for evaluating global climate change from different forcing agents. Significance Statement An increase in atmospheric CO2 concentration or an increase in incoming solar radiation leads to a rise in the radiative budget and consequent climate warming, which is amplified by the presence of multiple climate feedbacks. These feedbacks, from changes in surface albedo, combined effect of water vapor and the vertical lapse rate of temperature, and changes in clouds, differ between solar and CO2 forcing. Using radiative kernels, this study quantifies these individual feedbacks for an equivalent radiative change caused by an increase in CO2 or incoming solar radiation, showing how the differences arise from differences in the meridional patterns of warming. In agreement with prior studies, these differences can explain the smaller efficacy of solar forcing compared to CO2 forcing.

A Computational Approach to a Mathematical Model of Climate Change Using Heat Sources and Diffusion

The present work aims to extend the climate change energy balance models using a heat source. An ordinary differential equations (ODEs) model is extended to a partial differential equations (PDEs) model using the effects of diffusion over the spatial variable. In addition, numerical schemes are presented using the Taylor series expansions. For the climate change model in the form of ODEs, a comparison of the presented scheme is made with the existing Trapezoidal method. It is found that the presented scheme converges faster than the existing scheme. Also, the proposed scheme provides fewer errors than the existing scheme. The PDEs model is also solved with the presented scheme, and the results are displayed in the form of different graphs. The impact of the climate feedback parameter, the heat uptake parameter of the deep ocean, and the heat source parameter on global mean surface temperature and deep ocean temperature is also portrayed. In addition, these recently developed techniques exhibit a high level of predictability. Doi: 10.28991/CEJ-2022-08-07-04 Full Text: PDF

Interpreting Differences in Radiative Feedbacks From Aerosols Versus Greenhouse Gases

AbstractExperiments with seven Coupled Model Intercomparison Project phase 6 models were used to assess the climate feedback parameter for net historical, historical greenhouse gas (GHG) and anthropogenic aerosol forcings. The net radiative feedback is found to be more amplifying (higher effective climate sensitivity) for aerosol than GHG forcing, and hence also less amplifying for net historical (GHG + aerosol) than GHG only. We demonstrate that this difference is consistent with their different latitudinal distributions. Historical aerosol forcing is most pronounced in northern extratropics, where the boundary layer is decoupled from the free troposphere, so the consequent temperature change is confined to low altitude and causes low‐level cloud changes. This is caused by change in stability, which also affects upper‐tropospheric clear‐sky emission, affecting both shortwave and longwave radiative feedbacks. This response is a feature of extratropical forcing generally, regardless of its sign or hemisphere.

Episodic deluges in simulated hothouse climates.

Earth's distant past and potentially its future include extremely warm 'hothouse'1 climate states, but little is known about how the atmosphere behaves in such states. One distinguishing characteristic of hothouse climates is that they feature lower-tropospheric radiative heating, rather than cooling, due to the closing of the water vapour infrared window regions2. Previous work has suggested that this could lead to temperature inversions and substantial changes in cloud cover3-6, but no previous modelling of the hothouse regime has resolved convective-scale turbulent air motions and cloud cover directly, thus leaving many questions about hothouse radiative heating unanswered. Here we conduct simulations that explicitly resolve convection and find that lower-tropospheric radiative heating in hothouse climates causes the hydrologic cycle to shift from a quasi-steady regime to a 'relaxation oscillator' regime, in which precipitation occurs in short and intense outbursts separated by multi-day dry spells. The transition to the oscillatory regime is accompanied by strongly enhanced local precipitation fluxes, a substantial increase in cloud cover, and a transiently positive (unstable) climate feedback parameter. Our results indicate that hothouse climates may feature a novel form of 'temporal' convective self-organization, with implications for both cloud coverage and erosion processes.

Negligible Unforced Historical Pattern Effect on Climate Feedback Strength Found in HadISST-Based AMIP Simulations

AbstractRecently it has been suggested that natural variability in sea surface temperature (SST) patterns over the historical period causes a low bias in estimates of climate sensitivity based on instrumental records, in addition to that suggested by time variation of the climate feedback parameter in atmospheric general circulation models (GCMs) coupled to dynamic oceans. This excess, unforced, historical “pattern effect” (the effect of evolving surface temperature patterns on climate feedback strength) has been found in simulations performed using GCMs driven by AMIPII SST and sea ice changes (amipPiForcing). Here we show, in both amipPiForcing experiments with one GCM and by using Green’s functions derived from another GCM, that whether such an unforced historical pattern effect is found depends on the underlying SST dataset used. When replacing the usual AMIPII SSTs with those from the HadISST1 dataset in amipPiForcing experiments, with sea ice changes unaltered, the first GCM indicates pattern effects that are indistinguishable from the forced pattern effect of the corresponding coupled GCM. Diagnosis of pattern effects using Green’s functions derived from the second GCM supports this result for five out of six non-AMIPII SST reconstruction datasets. Moreover, internal variability in coupled GCMs is rarely sufficient to account for an unforced historical pattern effect of even one-quarter the strength previously reported. The presented evidence indicates that, if unforced pattern effects have been as small over the historical record as our findings suggest, they are unlikely to significantly bias climate sensitivity estimates that are based on long-term instrumental observations and account for forced pattern effects obtained from GCMs.

Understanding the Extreme Spread in Climate Sensitivity within the Radiative‐Convective Equilibrium Model Intercomparison Project

AbstractThe Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) consists of simulations at three fixed sea‐surface temperatures (SSTs: 295, 300, and 305 K) and thus allows for a calculation of the climate feedback parameter based on the change of the top‐of‐atmosphere radiation imbalance. Climate feedback parameters range widely across RCEMIP, roughly from −6 to 3 W m−2 K−1, particularly across general‐circulation models (GCMs) as well as global and large‐domain cloud‐resolving models (CRMs). Small‐domain CRMs and large‐eddy simulations have a smaller range of climate feedback parameters due to the absence of convective self‐aggregation. More than 70–80% of the intermodel spread in the climate feedback parameter can be explained by the combined temperature dependencies of convective aggregation and shallow cloud fraction. Low climate sensitivities are associated with an increase of shallow cloud fraction (increasing the planetary albedo) and/or an increase in convective aggregation with warming. An increase in aggregation is associated with an increase in outgoing longwave radiation, caused primarily by mid‐tropospheric drying, and secondarily by an expansion of subsidence regions. Climate sensitivity is neither dependent on the average amount of aggregation nor on changes in deep/anvil cloud fraction. GCMs have a lower overall climate sensitivity than CRMs because in most GCMs convective aggregation increases with warming, whereas in CRMs, convective aggregation shows no consistent temperature trend.

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

Abstract. Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1 % yr−1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm−1) and ocean (0.79 ± 0.07 PgC ppm−1), while the carbon–climate feedback over land (−45.1 ± 50.6 PgC ∘C−1) is about 3 times larger than over ocean (−17.2 ± 5.0 PgC ∘C−1). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 ∘C EgC−1 and is similar to that found in CMIP5 models (1.63 ± 0.48 ∘C EgC−1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

Combining Emergent Constraints for Climate Sensitivity

AbstractA method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.

Stratospheric water vapor feedback and its climate impacts in the coupled atmosphere–ocean Goddard Earth Observing System Chemistry-Climate Model

The stratospheric water vapor feedbacks on climate for abrupt CO2 quadrupling are investigated with the coupled atmosphere–ocean Goddard Earth Observing System Chemistry-Climate model. A feedback suppression method is used to quantify the stratospheric water vapor climate feedback parameter and the impacts of stratospheric water vapor increases on temperature and circulation. It is found that increases in stratospheric water vapor change the model’s net climate feedback parameter by 0.11 W m−2 K−1, contributing to 0.5 K, or 10%, of the global-mean surface warming under abrupt CO2 quadrupling. Stratospheric water vapor increases lead to significant impacts on stratospheric temperature and dynamics. The increases induce stratospheric dynamical changes that strongly modify stratospheric cooling patterns. About 30% of the acceleration of the stratospheric Brewer-Dobson circulation under 4 × CO2 is attributed to the stratospheric water vapor increases. In the troposphere, the stratospheric water vapor feedback plays a role in Arctic amplification and is responsible for 14% of the Arctic warming. It also affects tropospheric circulation, causing greater poleward shift of the northern hemisphere tropospheric midlatitude jet.

Quantifying the probability distribution function of the transient climate response to cumulative CO2 emissions

The Transient Climate Response to Cumulative CO2 Emissions (TCRE) is the proportionality between global temperature change and cumulative CO2 emissions. The TCRE implies a finite quantity of CO2 emissions, or carbon budget, consistent with a given temperature change limit. The uncertainty of the TCRE is often assumed be normally distributed, but this assumption has yet to be validated. We calculated the TCRE using a zero-dimensional ocean diffusive model and a Monte-Carlo error propagation (n = 10 000 000) randomly drawing from probability density functions of the climate feedback parameter, the land-borne fraction of carbon, radiative forcing from an e-fold increase in CO2 concentration, effective ocean diffusivity, and the ratio of sea to global surface temperature change. The calculated TCRE has a positively skewed distribution, ranging from 1.1 to 2.9 K EgC−1 (5%–95% confidence), with a mean and median value of 1.9 and 1.8 K EgC−1. The calculated distribution of the TCRE is well described by a log-normal distribution. The CO2-only carbon budget compatible with 2 °C warming is 1100 PgC, ranging from 700 to 1800 PgC (5%–95% confidence) estimated using a simplified model of ocean dynamics. Climate sensitivity is the most influential Earth System parameter on the TCRE, followed by the land-borne fraction of carbon, radiative forcing from an e-fold increase in CO2, effective ocean diffusivity, and the ratio of sea to global surface temperature change. While the uncertainty of the TCRE is considerable, the use of a log-normal distribution may improve estimations of the TCRE and associated carbon budgets.

Uncertainty in the Evolution of Climate Feedback Traced to the Strength of the Atlantic Meridional Overturning Circulation

AbstractIn most coupled climate models, effective climate sensitivity increases for a few decades following an abrupt CO2 increase. The change in the climate feedback parameter between the first 20 years and the subsequent 130 years is highly model dependent. In this study, we suggest that the intermodel spread of changes in climate feedback can be partially traced to the evolution of the Atlantic Meridional Overturning Circulation. Models with stronger Atlantic Meridional Overturning Circulation recovery tend to project more amplified warming in the Northern Hemisphere a few decades after a quadrupling of CO2. Tropospheric stability then decreases as the Northern Hemisphere gets warmer, which leads to an increase in both the lapse‐rate and shortwave cloud feedbacks. Our results suggest that constraining future ocean circulation changes will be necessary for accurate climate sensitivity projections.

Weak dependence of future global mean warming on the background climate state

It is debated whether the global mean transient temperature response to forcing is largely independent of biases in the simulated base state climate. To test this, we run the Community Earth System Model (CESM1) into new quasi-equilibria by altering the solar constant by ± 25 Wm−2 and initialize idealized CO2 climate change experiments from these climate states. The simulations branched off from the warm and control states, with increased and unaltered solar constant, respectively, show similar global mean feedback parameters, effective CO2 radiative forcings (ERF) and ocean heat uptake efficiencies and therefore simulate indiscernibly different amounts of global warming. The experiments starting from the cold climate state behave differently: The CO2 ERF is lower mostly because of rapid adjustments in the clouds, and the climate sensitivity is enhanced, mainly due to a large surface albedo feedback caused by the increased sea ice and snow coverage that extends to lower latitudes. The global heat uptake efficiency is reduced in the cold state. While taking up more heat in the Southern Ocean than the experiments from the warmer states much less heat is taken up by the North Atlantic. The less stabilizing net climate feedback parameter and the decreased ocean heat uptake efficiency dominate over the reduced CO2 ERF, and the simulations initialized from the cold state show ~ 10% more global warming compared to those from the control state. This enhanced warming mainly occurs at higher-latitudes over sea ice and snow-covered areas and the North Atlantic. While the local climate response may depend strongly on the base state, the future global mean temperature increase simulated by CESM1 is remarkably independent on the initial climate state.

Estimating Contributions of Sea Ice and Land Snow to Climate Feedback

AbstractIn this study, we use the National Center for Atmospheric Research Community Earth System Model to investigate the contribution of sea ice and land snow to the climate sensitivity in response to increased atmospheric carbon dioxide content. We focus on the overall effect arising from the presence or absence of sea ice and/or land snow. We analyze our results in terms of the radiative forcing and climate feedback parameter. We find that the presence of sea ice and land snow decreases the climate feedback parameter (and thus increases climate sensitivity). Adjusted radiative forcing from added carbon dioxide is comparatively less sensitive to the presence of sea ice or land snow. The effect of sea ice on the climate feedback parameter decreases as sea ice cover diminishes at higher CO2 concentration. However, the influence of both sea ice and land snow on the climate feedback parameter remains substantial under the CO2 concentration range considered here (to eight times preindustrial CO2 content). Approximately, one quarter of the effect of sea ice and land snow on the climate feedback parameter is a consequence of the effect of these components on longwave feedback that is mainly associated with cloud change. Polar warming in response to added CO2 is approximately doubled by the presence of sea ice and land snow. Relative to the case in which sea ice and land snow are absent in the model, in response to increased CO2 concentrations, the presence of sea ice and land snow results in an increase in global mean warming by over 40%.

Equilibrium Climate Sensitivity Obtained From Multimillennial Runs of Two GFDL Climate Models

AbstractEquilibrium climate sensitivity (ECS), defined as the long‐term change in global mean surface air temperature in response to doubling atmospheric CO2, is usually computed from short atmospheric simulations over a mixed layer ocean, or inferred using a linear regression over a short‐time period of adjustment. We report the actual ECS from multimillenial simulations of two Geophysical Fluid Dynamics Laboratory (GFDL) general circulation models (GCMs), ESM2M, and CM3 of 3.3 K and 4.8 K, respectively. Both values are ~1 K higher than estimates for the same models reported in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change obtained by regressing the Earth's energy imbalance against temperature. This underestimate is mainly due to changes in the climate feedback parameter (−α) within the first century after atmospheric CO2 has stabilized. For both GCMs it is possible to estimate ECS with linear regression to within 0.3 K by increasing CO2 at 1% per year to doubling and using years 51–350 after CO2 is constant. We show that changes in −α differ between the two GCMs and are strongly tied to the changes in both vertical velocity at 500 hPa (ω500) and estimated inversion strength that the GCMs experience during the progression toward the equilibrium. This suggests that while cloud physics parametrizations are important for determining the strength of −α, the substantially different atmospheric state resulting from a changed sea surface temperature pattern may be of equal importance.

The Diversity of Cloud Responses to Twentieth Century Sea Surface Temperatures

AbstractLow‐level clouds are shown to be the conduit between the observed sea surface temperatures (SST) and large decadal fluctuations of the top of the atmosphere radiative imbalance. The influence of low‐level clouds on the climate feedback is shown for global mean time series as well as particular geographic regions. The changes of clouds are found to be important for a midcentury period of high sensitivity and a late century period of low sensitivity. These conclusions are drawn from analysis of amip‐piForcing simulations using three atmospheric general circulation models (AM2.1, AM3, and AM4.0). All three models confirm the importance of the relationship between the global climate sensitivity and the eastern Pacific trends of SST and low‐level clouds. However, this work argues that the variability of the climate feedback parameter is not driven by stratocumulus‐dominated regions in the eastern ocean basins, but rather by the cloudy response in the rest of the tropics.