Papers on climate feedback

This is a list of papers on climate feedback. Basically, this is second part of papers on climate sensitivity, but here papers that concentrate on determining climate feedback parameter are listed. Note that list contains both model and observational analyses. The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative.

UPDATE (November 21, 2019): Dessler (2013) added.
UPDATE (March 23, 2011): Coakley (1977), Hansen et al. (1984), and Cacuci & Hall (1984) added.
UPDATE (October 23, 2010): Kellogg (1983) added.
UPDATE (September 9, 2010): Cess (1976) added.

Shortwave and longwave radiative contributions to global warming under increasing CO2 – Donohoe et al. (2014) [Full text]
Abstract: In response to increasing concentrations of atmospheric CO₂, high-end general circulation models (GCMs) simulate an accumulation of energy at the top of the atmosphere not through a reduction in outgoing longwave radiation (OLR)—as one might expect from greenhouse gas forcing—but through an enhancement of net absorbed solar radiation (ASR). A simple linear radiative feedback framework is used to explain this counterintuitive behavior. It is found that the timescale over which OLR returns to its initial value after a CO₂ perturbation depends sensitively on the magnitude of shortwave (SW) feedbacks. If SW feedbacks are sufficiently positive, OLR recovers within merely several decades, and any subsequent global energy accumulation is because of enhanced ASR only. In the GCM mean, this OLR recovery timescale is only 20 y because of robust SW water vapor and surface albedo feedbacks. However, a large spread in the net SW feedback across models (because of clouds) produces a range of OLR responses; in those few models with a weak SW feedback, OLR takes centuries to recover, and energy accumulation is dominated by reduced OLR. Observational constraints of radiative feedbacks—from satellite radiation and surface temperature data—suggest an OLR recovery timescale of decades or less, consistent with the majority of GCMs. Altogether, these results suggest that, although greenhouse gas forcing predominantly acts to reduce OLR, the resulting global warming is likely caused by enhanced ASR.
Citation: Aaron Donohoe, Kyle C. Armour, Angeline G. Pendergrass, David S. Battisti (2014). Proceedings of the National Academy of Sciences Nov 2014, 111 (47) 16700-16705; DOI: 10.1073/pnas.1412190111

Observations of Climate Feedbacks over 2000–10 and Comparisons to Climate Models – Dessler (2013) [Full text]
Abstract: Feedbacks in response to climate variations during the period 2000–10 have been calculated using reanalysis meteorological fields and top-of-atmosphere flux measurements. Over this period, the climate was stabilized by a strongly negative temperature feedback (~−3 W m⁻² K⁻¹); climate variations were also amplified by a strong positive water vapor feedback (~+1.2 W m⁻² K⁻¹) and smaller positive albedo and cloud feedbacks (~+0.3 and +0.5 W m⁻² K⁻¹, respectively). These observations are compared to two climate model ensembles, one dominated by internal variability (the control ensemble) and the other dominated by long-term global warming (the A1B ensemble). The control ensemble produces global average feedbacks that agree within uncertainties with the observations, as well as producing similar spatial patterns. The most significant discrepancy was in the spatial pattern for the total (shortwave + longwave) cloud feedback. Feedbacks calculated from the A1B ensemble show a stronger negative temperature feedback (due to a stronger lapse-rate feedback), but that is cancelled by a stronger positive water vapor feedback. The feedbacks in the A1B ensemble tend to be more smoothly distributed in space, which is consistent with the differences between El Niño–Southern Oscillation (ENSO) climate variations and long-term global warming. The sum of all of the feedbacks, sometimes referred to as the thermal damping rate, is −1.15 ± 0.88 W m⁻² K⁻¹ in the observations and −0.60 ± 0.37 W m⁻² K⁻¹ in the control ensemble. Within the control ensemble, models that more accurately simulate ENSO tend to produce thermal damping rates closer to the observations. The A1B ensemble average thermal damping rate is −1.26 ± 0.45 W m⁻² K⁻¹.
Citation: Dessler, A.E., 2013: Observations of Climate Feedbacks over 2000–10 and Comparisons to Climate Models. J. Climate, 26, 333–342, https://doi.org/10.1175/JCLI-D-11-00640.1

Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs – Sanderson et al. (2009) “We apply the radiative kernel technique to transient simulations from a multi-thousand member perturbed physics ensemble of coupled atmosphere-ocean general circulation models, comparing distributions of model feedbacks with those taken from the CMIP-3 multi GCM ensemble. Although the range of clear sky longwave feedbacks in the perturbed physics ensemble is similar to that seen in the multi-GCM ensemble, the kernel technique underestimates the net clear-sky feedbacks (or the radiative forcing) in some perturbed models with significantly altered humidity distributions.” [Full text]

Atmospheric radiative feedbacks associated with transient climate change and climate variability – Colman & Power (2009) “This study examines in detail the ‘atmospheric’ radiative feedbacks operating in a coupled General Circulation Model (GCM). These feedbacks (defined as the change in top of atmosphere radiation per degree of global surface temperature change) are due to responses in water vapour, lapse rate, clouds and surface albedo. Two types of radiative feedback in particular are considered: those arising from century scale ‘transient’ warming (from a 1% per annum compounded CO2 increase), and those operating under the model’s own unforced ‘natural’ variability.”

Tropospheric Adjustment Induces a Cloud Component in CO₂ Forcing – Gregory & Webb (2008) “The radiative forcing of CO₂ and the climate feedback parameter are evaluated in several climate models with slab oceans by regressing the annual-mean global-mean top-of-atmosphere radiative flux against the annual-mean global-mean surface air temperature change ΔT following a doubling of atmospheric CO₂ concentration. The method indicates that in many models there is a significant rapid tropospheric adjustment to CO₂ leading to changes in cloud, and reducing the effective radiative forcing, in a way analogous to the indirect and semidirect effects of aerosol. … Tropospheric adjustment to CO₂ may be responsible for some of the model spread in equilibrium climate sensitivity and could affect time-dependent climate projections.” [Full text]

CO₂ forcing induces semi-direct effects with consequences for climate feedback interpretations – Andrews & Forster (2008) “All models show a cloud semi-direct effect, indicating a rapid cloud response to CO2; cloud typically decreases, enhancing the warming. Similarly there is evidence of semi-direct effects from water-vapour, lapse-rate, ice and snow. Previous estimates of climate feedbacks are unlikely to have taken these semi-direct effects into account and so misinterpret processes as feedbacks that depend only on the forcing, but not the global surface temperature.” [Full text]

An assessment of the primary sources of spread of global warming estimates from coupled atmosphere-ocean models – Dufresne & Bony (2008) “Here these contributions from the classical feedback analysis framework are defined and quantified for an ensemble of 12 third phase of the Coupled Model Intercomparison Project (CMIP3)/Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) coupled atmosphere–ocean GCMs. In transient simulations, the multimodel mean contributions to global warming associated with the combined water vapor–lapse-rate feedback, cloud feedback, and ocean heat uptake are comparable.” [Full text]

Potential Biases in Feedback Diagnosis from Observational Data: A Simple Model Demonstration – Spencer & Braswell (2008) “Here a simple model is used to demonstrate that any nonfeedback source of top-of-atmosphere radiative flux variations can cause temperature variability, which then results in a positive bias in diagnosed feedbacks. This effect is demonstrated with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover.” [Full text]

The Climate Sensitivity and Its Components Diagnosed from Earth Radiation Budget Data – Forster & Gregory (2006) “Here, data are combined from the 1985–96 Earth Radiation Budget Experiment (ERBE) with surface temperature change information and estimates of radiative forcing to diagnose the climate sensitivity. Importantly, the estimate is completely independent of climate model results. A climate feedback parameter of 2.3 ± 1.4 W m⁻² K⁻¹ is found. This corresponds to a 1.0–4.1-K range for the equilibrium warming due to a doubling of carbon dioxide (assuming Gaussian errors in observable parameters, which is approximately equivalent to a uniform “prior” in feedback parameter).” [Full text]

How Well Do We Understand and Evaluate Climate Change Feedback Processes? – Bony et al. (2006) A review article. “By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations.” [Full text]

An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models – Soden & Held (2006) “Water vapor is found to provide the largest positive feedback in all models and its strength is consistent with that expected from constant relative humidity changes in the water vapor mixing ratio. The feedbacks from clouds and surface albedo are also found to be positive in all models, while the only stabilizing (negative) feedback comes from the temperature response. Large intermodel differences in the lapse rate feedback are observed and shown to be associated with differing regional patterns of surface warming.” [Full text]

A new method for diagnosing radiative forcing and climate sensitivity – Gregory et al. (2004) “We describe a new method for evaluating the radiative forcing, the climate feedback parameter (W m−2 K−1) and hence the effective climate sensitivity from any GCM experiment in which the climate is responding to a constant forcing. The method is simply to regress the top of atmosphere radiative flux against the global average surface air temperature change. … We show that for CO2 and solar forcing in a slab model and an AOGCM the method gives results consistent with those obtained by conventional methods.” [Full text]

Interactions among Cloud, Water Vapor, Radiation, and Large-Scale Circulation in the Tropical Climate. Part I: Sensitivity to Uniform Sea Surface Temperature Changes – Larson & Hartmann (2003) “The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model is used with doubly periodic boundary conditions and a uniform constant sea surface temperature (SST). The SST is varied and the equilibrium statistics of cloud properties, water vapor, and circulation at different temperatures are compared. … The clear-sky mean temperature and water vapor feedbacks have similar magnitudes to each other and opposite signs. The net clear-sky feedback is thus about equal to the lapse rate feedback, which is about −2 W m−2 K−1. … The clear-sky outgoing longwave radiation (OLR) thus increases with SST, but the high cloud-top temperature is almost constant with SST, and the high cloud amount increases with SST. The result of these three effects is an increase of cloud longwave forcing with SST and a mean OLR that is almost independent of SST. The high cloud albedo remains almost constant with increasing SST, but the increase in high cloud area causes a negative shortwave cloud radiative forcing feedback, which partly cancels the longwave cloud feedback.” [Full text]

Intercomparison and Interpretation of Climate Feedback Processes in 19 Atmospheric General Circulation Models – Cess et al. (1990) “The need to understand differences among general circulation model projections of CO₂-induced climatic change has motivated the present study, which provides an intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. … A roughly threefold variation in one measure of global climate sensitivity is found among the 19 models. The important conclusion is that most of this variation is attributable to differences in the models’ depiction of cloud feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as reliable climate predictors.” [Full text]

Climate sensitivity: Analysis of feedback mechanisms – Hansen et al. (1984) “We study climate sensitivity and feedback processes in three independent ways: (1) by using three dimensional (3-D) global climate model for experiments in which solar irradiance S_o is increased 2 percent or CO₂ is doubled, (2) by using CLIMAP climate boundary conditions to analyze the contributions of different physical processes to the cooling of the last ice age (18K years ago), and (3) by using estimated changes in global temperature and the abundance of atmospheric greenhouse gases to deduce an empirical climate sensitivity for the period 1850-1980. Our 3-D global climate model yields a warming of ~4˚C for either a 2 percent increase of S_o or doubled CO₂. This indicates a net feedback factor of f = 3-4, because either of these forcings would cause the earth’s surface temperature to warm 1.2-1.3˚C to restore radiative balance with space, if other factors remained unchanged. Principal positive feedback processes in the model are changes in atmospheric water vapor, clouds and snow/ice cover. Feedback factors calculated for these processes, with atmospheric dynamical feedbacks implicitly incorporated, are respectively f_{water vapor} ~ 1.6, f_clouds ~ 1.3 and f_snow/ice ~ 1.1, with the latter mainly caused by sea ice changes. A number of potential feedbacks, such as land ice cover, vegetation cover and ocean heat transport were held fixed in these experiments. We calculate land ice, sea ice and vegetation feedbacks for the 18K climate to be f_{land ice} ~ 1.2-1.3, f_{sea ice} ~ 1.2, and f_vegetation ~ 1.05-1.1 from their effect on the radiation budget at the top of the atmosphere. This sea ice feedback at 18K is consistent with smaller f_snow/ice ~ 1.1 in the S_o and CO₂ experiments, which applied to a warmer earth with less sea ice. We also obtain an empirical estimate of f = 2-4 for the fast feedback processes (water vapor, clouds, sea ice) operating on 10-100 year time scales by comparing the cooling due to slow or specified changes (land ice, CO₂, vegetation) to the total cooling at 18K. The temperature increase believed to have occurred in the past 130 years (approximately 0.5˚C) is also found to imply a climate sensitivity of 2.5-5˚C for doubled CO₂ (f = 2-4), if (1) the temperature increase is due to added greenhouse gases, (2) the 1850 CO₂ abundance was 270±10 ppm, and (3) the heat perturbation is mixed like a passive tracer in the ocean with vertical mixing coefficient k ~ 1 cm² s^-1. These analyses indicate that f is substantially greater than unity on all time scales. Our best estimate for the current climate due to processes operating on the 10-100 year time scale is f = 2-4, corresponding to a climate sensitivity of 2.5-5˚C for doubled CO₂. The physical process contributing the greatest uncertainty to f on this time scale appears to be cloud feedback. We show that the ocean’s thermal relaxation time depends strongly on f. The e-folding time constant for response of the isolated ocean mixed layer is about 15 years, for the estimated value of f. This time is sufficiently long to allow substantial heat exchange between the mixed layer and deeper layers. For f = 3-4 the response time of the surface temperature to a heating perturbation is of order 100 years, if the perturbation is sufficiently small that it does not alter the rate of heat exchange with the deeper ocean. The climate sensitivity we have inferred is larger than that stated in the Carbon Dioxide Assessment Committee report (CDAC, 1983). Their result is based on the empirical temperature increase in the past 130 years, but their analysis did not account for the dependence of the ocean response time on climate sensitivity. Their choice of a fixed 15 year response time biased their result to low sensitivities. We infer that, because of recent increases in atmospheric CO₂ and trace gases, there is a large, rapidly growing gap between current climate and the equilibrium climate for current atmospheric composition. Based on the climate sensitivity we have estimated, the amount of greenhhouse gases presently in the atmosphere will cause an eventual global mean warming of about 1˚C, making the global temperature at least comparable to that of the Altithermal, the warmest period in the past 100,000 years. Projection of future climate trends on the 10-100 year time scale depends crucially upon improved understanding of ocean dynamics, particularly upon how ocean mixing will respond to climate change at the ocean surface.” [Full text]

Efficient Estimation of Feedback Effects with Application to Climate Models – Cacuci & Hall (1984) “This work presents an efficient method for calculating the sensitivity of a mathematical model’s result to feedback. Feedback is defined in terms of an operator acting on the model’s dependent variables. The sensitivity to feedback is defined as a functional derivative, and a method is presented to evaluate this derivative using adjoint functions. Typically, this method allows the individual effect of many different feedbacks to be estimated with a total additional computing time comparable to only one recalculation. The effects on a C02-doubling experiment of actually incorporating surface albedo and water vapor feedbacks in a radiative-convective model are compared with sensitivities calculated using adjoint functions. These sensitivities predict the actual effects of feedback with at least the correct sign and order of magnitude. It is anticipated that this method of estimating the effect of feedback will be useful for more complex models where extensive recalculations for each of a variety of different feedbacks is impractical.” Cacuci, Dan G., Matthew C. G. Hall, 1984, J. Atmos. Sci., 41, 2063–2068. [Full text]

Feedback Mechanisms in the Climate System Affecting Future Levels of Carbon Dioxide – Kellogg (1983) “The rate of increase of concentration of atmospheric carbon dioxide depends on the consumption of fossil fuels (the major source of ‘new’ carbon dioxide) and the natural sinks for this trace constituent, primarily the oceans and the biosphere. (It is now fairly well established that the biosphere cannot be a major source, as has been claimed.) The rate of operation of these sinks depends on several factors determined by the state of the climate system, and they will therefore presumably change as the greenhouse effect of increasing carbon dioxide warms the earth. Five specific feedback loops are discussed, two of which are positive (amplifying the rate of increase), two are weakly negative (damping the rate of increase), and one is indeterminate but probably positive. It is concluded that it would be well to be prepared for the possibility that carbon dioxide may increase faster than predicted by models based on the current or past state of the climate system.” Kellogg, W. W. (1983), J. Geophys. Res., 88(C2), 1263–1269, doi:10.1029/JC088iC02p01263.

Feedbacks in Vertical-Column Energy Balance Models – Coakley (1977) “Order-of-magnitude estimates are made for the feedbacks between the global mean surface temperature and the mean tropospheric lapse rate, the mean surface relative humidity and the mean atmospheric relative humidity profile. It is found that with the possible exception of the surface temperature-surface relative humidity feedback the magnitudes of these feedback are not sufficient to significantly alter the sensitivity of the global mean surface temperature to changes in the solar constant as calculated using a vertical-column energy balance model of the earth’s atmosphere.” Coakley, James A., 1977, J. Atmos. Sci., 34, 465–470. [Full text]

Climate Change: An Appraisal of Atmospheric Feedback Mechanisms Employing Zonal Climatology – Cess (1976) “The sensitivity of the earth’s surface temperature to factors which can induce long-term climate change, such as a variation in solar constant, is estimated by employing two readily observable climate changes. One is the latitudinal change in annual mean climate, for which an interpretation of climatological data suggests that cloud amount is not a significant climate feedback mechanism, irrespective of how cloud amount might depend upon surface temperature, since there are compensating changes in both the solar and infrared optical properties of the atmosphere. It is further indicated that all other atmospheric feedback mechanisms, resulting, for example, from temperature-induced changes in water vapor amount, cloud altitude and lapse rate, collectively double the sensitivity of global surface temperature to a change in solar constant. The same conclusion is reached by considering a second type of climate change, that associated with seasonal variations for a given latitude zone. The seasonal interpretation further suggests that cloud amount feedback is unimportant zonally as well as globally. Application of the seasonal data required a correction for what appears to be an important seasonal feedback mechanism. This is attributed to a variability in cloud albedo due to seasonal changes in solar zenith angle. No attempt was made to individually interpret the collective feedback mechanisms which contribute to the doubling in surface temperature sensitivity. It is suggested, however, that the conventional assumption of fixed relative humidity for describing feedback due to water vapor amount might not be as applicable as is generally believed. Climate models which additionally include ice-albedo feedback are discussed within the framework of the present results.” Cess, Robert D., 1976, J. Atmos. Sci., 33, 1831–1843. [Full text]