Hourly estimation of soil heat flux density at the soil surface with three models and two field methods

Heat flux density at the soil surface (G ₀) was evaluated hourly on a vegetal cover 0.08 m high, with a leaf area index of 1.07 m² m^?2, during daylight hours, using Choudhury et al. (Agric For Meteorol 39:283–297, 1987) ( $ G_0^{\text{rn}} $ ), Santanello and Friedl (J Appl Meteorol 42:851–862, 2003) ( $ G_0^{\text{s}} $ ), and force-restore ( $ G_0^{\text{fr}} $ ) models and the plate calorimetry methodology ( $ G_0^{\text{pco}} $ ), where the gradient calorimetry methodology (G _0R ) served as a reference for determining G ₀. It was found that the peak of G _0R was at 1 p.m., with values that ranged between 60 and 100 W m^?2 and that the G ₀/Rn relation varied during the day with values close to zero in the early hours of the morning and close to 0.25 in the last hours of daylight. The $ G_0^{\text{s}} $ model presented the best performance, followed by the $ G_0^{\text{rn}} $ and $ G_0^{\text{fr}} $ models. The plate calorimetry methodology showed a similar behavior to that of the gradient calorimetry referential methodology.     

“Reasons for concern” (about climate change) in the United States

Article 2 of the United Nations Framework Convention on Climate Change commits its parties to stabilizing greenhouse gas concentrations in the atmosphere at a level that “would prevent dangerous anthropogenic interference with the climate system.” Authors of the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC 2001a, b) offered some insight into what negotiators might consider dangerous by highlighting five “reasons for concern” (RFC’s) and tracking concern against changes in global mean temperature; they illustrated their assessments in the now iconic “burning embers” diagram. The Fourth Assessment Report reaffirmed the value of plotting RFC’s against temperature change (IPCC 2007a, b), and Smith et al. (2009) produced an unpated embers visualization for the globe. This paper applies the same assessment and communication strategies to calibrate the comparable RFC’s for the United States. It adds “National Security Concern” as a sixth RFC because many now see changes in the intensity and/or frequency of extreme events around the world as “risk enhancers” that deserve attention at the highest levels of the US policy and research communities. The US embers portrayed here suggest that: (1) US policy-makers will not discover anything really “dangerous” over the near to medium term if they consider only economic impacts that are aggregated across the entire country but that (2) they could easily uncover “dangerous anthropogenic interference with the climate system” by focusing their attention on changes in the intensities, frequencies, and regional distributions of extreme weather events driven by climate change.     

Do probabilistic expert elicitations capture scientists’ uncertainty about climate change?

Expert elicitation studies have become important barometers of scientific knowledge about future climate change (Morgan and Keith, Environ Sci Technol 29(10), 1995; Reilly et al., Science 293(5529):430–433, 2001; Morgan et al., Climate Change 75(1–2):195–214, 2006; Zickfeld et al., Climatic Change 82(3–4):235–265, 2007, Proc Natl Acad Sci 2010; Kriegler et al., Proc Natl Acad Sci 106(13):5041–5046, 2009). Elicitations incorporate experts’ understanding of known flaws in climate models, thus potentially providing a more comprehensive picture of uncertainty than model-driven methods. The goal of standard elicitation procedures is to determine experts’ subjective probabilities for the values of key climate variables. These methods assume that experts’ knowledge can be captured by subjective probabilities—however, foundational work in decision theory has demonstrated this need not be the case when their information is ambiguous (Ellsberg, Q J Econ 75(4):643–669, 1961). We show that existing elicitation studies may qualitatively understate the extent of experts’ uncertainty about climate change. We designed a choice experiment that allows us to empirically determine whether experts’ knowledge about climate sensitivity (the equilibrium surface warming that results from a doubling of atmospheric CO₂ concentration) can be captured by subjective probabilities. Our results show that, even for this much studied and well understood quantity, a non-negligible proportion of climate scientists violate the choice axioms that must be satisfied for subjective probabilities to adequately describe their beliefs. Moreover, the cause of their violation of the axioms is the ambiguity in their knowledge. We expect these results to hold to a greater extent for less understood climate variables, calling into question the veracity of previous elicitations for these quantities. Our experimental design provides an instrument for detecting ambiguity, a valuable new source of information when linking climate science and climate policy which can help policy makers select decision tools appropriate to our true state of knowledge.     

Time-scale and state dependence of the carbon-cycle feedback to climate

Climate and atmospheric CO₂ concentration are intimately coupled in the Earth system: CO₂ influences climate through the greenhouse effect, but climate also affects CO₂ through its impact on the amount of carbon stored on land and in the ocean. The change in atmospheric CO₂ as a response to a change in temperature ( $\varDelta CO_{2}/\varDelta T$ ) is a useful measure to quantify the feedback between the carbon cycle and climate. Using an ensemble of experiments with an Earth system model of intermediate complexity we show a pronounced time-scale dependence of $\varDelta CO_{2}/\varDelta T$ . A maximum is found on centennial scales with $\varDelta CO_{2}/\varDelta T$ values for the model ensemble in the range 5–12 ppm °C^?1, while lower values are found on shorter and longer time scales. These results are consistent with estimates derived from past observations. Up to centennial scales, the land carbon response to climate dominates the CO₂ signal in the atmosphere, while on longer time scales the ocean becomes important and eventually dominates on multi-millennial scales. In addition to the time-scale dependence, modeled $\varDelta CO_{2}/\varDelta T$ show a distinct dependence on the initial state of the system. In particular, on centennial time-scales, high $\varDelta CO_{2}/\varDelta T$ values are correlated with high initial land carbon content. A similar relation holds also for the CMIP5 models, although for $\varDelta CO_{2}/\varDelta T$ computed from a very different experimental setup. The emergence of common patterns like this could prove to usefully constrain the climate–carbon cycle feedback.     

Consumption tradeoff vs. catastrophes avoidance: implications of some recent results in happiness studies on the economics of climate change

Recent discussion of climate change focuses on the trade-off between present and future consumption and hence correctly emphasizes the discount rate. Stern (2007) favours immediate and strong actions of environmental protection, but this has been questioned as the discount rate used is much lower than the market or commonly used rates. Focussed only on consumption trade-off, the use of these higher discount rates completely reverses the need for strong actions. However, an even more important problem has been largely neglected. This is the avoidance of catastrophes that may threaten the extinction of the human species. But ??we lack a usable economic framework for dealing with these kinds of ... extreme disasters?? (Weitzman, J Econ Lit 45(3):703?C724, 2007, p. 723). To analyse this, the comparison of marginal utility with total utility is needed. As happiness studies suggest a low ratio of marginal to total utility and as scientific and technological advances (especially in brain stimulation and genetic engineering) may dramatically increase future welfare, immediate and actions stronger than proposed by Stern may be justified despite high discount rates on future consumption, as discount rates on future utility/welfare should be much lower.     

Natural reduction of CO2 observed in the pre-monsoon period at the coastal station, Goa

Measurements of carbon dioxide (CO₂) concentration were made at a coastal land station, Goa, on the west coast of India from March to June 2003 as part of the ARMEX (ARabian sea Monsoon Experiment) campaign. The observations show a systematic reduction (~120?mg?m^?3) of CO₂ concentration during the pre-monsoon months, March–May, during which no significant change in anthropogenic emissions takes place. CO₂ shoots up from 520 to 635?mg?m^?3 in June with the onset of the South West monsoon. Back trajectories show that the source of air mass gradually shifts from the coastal land mass to the open southern Arabian Sea during the pre-monsoon period. The observed reduction in CO₂ is explained in terms of earlier measurements in the Arabian Sea indicating maximum chlorophyll a (Sarupria and Bhargava in J Mar Sci 27:292–297, 1998) and minimum partial pressure of CO₂ (Sarma in J Geophys Res 108:3225, 2003) in the sea waters off the west coast of India during the pre-monsoon period, cleaner marine air mass advection from the open sea, and negligible local vertical CO₂ flux.     

Compliance and emission trading rules for asymmetric emission uncertainty estimates

Greenhouse gases emission inventories are computed with rather low precision. Moreover, their uncertainty distributions may be asymmetric. This should be accounted for in the compliance and trading rules. In this paper we model the uncertainty of inventories as intervals or using fuzzy numbers. The latter allows us to better shape the uncertainty distributions. The compliance and emission trading rules obtained generalize the results for the symmetric uncertainty distributions that were considered in the earlier papers by the present authors (Nahorski et al., Water Air & Soil Pollution. Focus 7(4–5):539–558, 2007; Nahorski and Horabik, 2007, J Energy Eng 134(2):47–52, 2008). However, unlike in the symmetric distribution, in the asymmetric fuzzy case it is necessary to apply approximations because of nonlinearities in the formulas. The final conclusion is that the interval uncertainty rules can be applied, but with a much higher substitutional noncompliance risk, which is a parameter of the rules.     

Estimating a Lagrangian Length Scale Using Measurements of CO2 in a Plant Canopy

Analytical Lagrangian equations capable of predicting concentration profiles from known source distributions offer the opportunity to calculate source/sink distributions through inverted forms of these equations. Inverse analytical Lagrangian equations provide a practical means of estimating source profiles using concentration and turbulence measurements. Uncertainty concerning estimates of the essentially immeasurable Lagrangian length scale ( ${\mathcal{L}}$ ), a key input, impedes the operational practicality of this method. The present study evaluates ${\mathcal{L}}$ within a corn canopy by using field measurements to constrain an analytical Lagrangian equation. Measurements of net CO₂ flux, soil-to-atmosphere CO₂ flux, and in-canopy profiles of CO₂ concentration provided the information required to solve for ${\mathcal{L}}$ in a global optimization algorithm for 30-min time intervals. For days when the canopy was a strong CO₂ sink, the optimization frequently located ${\mathcal{L}}$ profiles that follow a convex shape. A constrained optimization then fit the profile shape to a smooth sigmoidal equation. Inputting the optimized ${\mathcal{L}}$ profiles in the forward and inverse Lagrangian equations leads to strong correlations between measured and calculated concentrations and fluxes. Coefficients of the sigmoidal equation were specific to each 30-min period and did not scale with any measured variable. Plausible looking ${\mathcal{L}}$ profiles were associated with negative bulk Richardson number values. Once the canopy senesced, a simple eddy diffusivity profile sufficed to relate concentrations and sources in the analytical Lagrangian equations.     

An Approach to Minimizing Artifacts Caused by Cross-Sensitivity in the Determination of Air–Sea \text{ CO }_{2} Flux Using the Eddy-Covariance Technique

The air–sea $\text{ CO }_{2}$ flux was measured from a research vessel in the North Yellow Sea in October 2007 using an open-path eddy-covariance technique. In 11 out of 64 samples, the normalized spectra of scalars ( $\text{ CO }_{2}$ , water vapour, and temperature) showed similarities. However, in the remaining samples, the normalized $\text{ CO }_{2}$ spectra were observed to be greater than those of water vapour and temperature at low frequencies. In this paper, the noise due to cross-sensitivity was identified through a combination of intercomparisons among the normalized spectra of three scalars and additional analyses. Upon examination, the cross-sensitivity noise appeared to be mainly present at frequencies ${<}0.8\,\text{ Hz }$ . Our analysis also suggested that the high-frequency fluctuations of $\text{ CO }_{2}$ concentration (frequency ${>}0.8\,\text{ Hz }$ ) was probably less affected by the cross-sensitivity. To circumvent the cross-sensitivity issue, the cospectrum in the high-frequency range 0.8–1.5 Hz, instead of the whole range, was used to estimate the $\text{ CO }_{2}$ flux by taking the contribution of the high frequency to the $\text{ CO }_{2}$ flux to be the same as the contribution to the water vapour flux. The estimated air–sea $\text{ CO }_{2}$ flux in the North Yellow Sea was $-0.039\,\pm \,0.048\,\text{ mg } \text{ m }^{-2}\,\text{ s }^{-1},$ a value comparable to the estimates using the inertial dissipation method and Edson’s method (Edson et al., J Geophys Res 116:C00F10, 2011).     

Reply to the Comment by Mauder on “How Well Can We Measure the Vertical Wind Speed? Implications for Fluxes of Energy and Mass”

In Kochendorfer et al. (Boundary-Layer Meteorol 145:383–398, 2012, hereafter K2012) the vertical wind speed $(w)$ measured by a non-orthogonal three-dimensional sonic anemometer was shown to be underestimated by $\approx $ 12 %. Turbulent statistics and eddy-covariance fluxes estimated using $w$ were also affected by this underestimate in $w$ . Methodologies used in K2012 are clarified here in response to Mauder’s comment. In addition, further analysis of the K2012 study is presented to help address questions raised in the comment. Specific responses are accompanied with examples of time series, calculated correlation coefficients, and additional explanation of the K2012 methods and assumptions. The discussion and analysis included in the comment and in this response do not affect the validity of the methods or conclusions presented in K2012.     

Can developed economies combat dangerous anthropogenic climate change without near-term reductions from developing economies?

Developing economy greenhouse gas emissions are growing rapidly relative to developed economy emissions (Boden et al. 2010) and developing economies as a group have greater emissions than developed economies. These developments are expected to continue (U.S. Energy Information Administration 2010), which has led some to question the effectiveness of emissions mitigation in developed economies without a commitment to extensive mitigation action from developing economies. One often heard argument against proposed U.S. legislation to limit carbon emissions to mitigate climate change is that, without participation from large developing economies like China and India, stabilizing temperature at 2 degrees Celsius above preindustrial (United Nations 2009), or even reducing global emissions levels, would be impossible (Driessen 2009; RPC Energy Facts 2009) or prohibitively expensive (Clarke et al. 2009). Here we show that significantly delayed action by rapidly developing countries is not a reason to forgo mitigation efforts in developed economies. This letter examines the effect of a scenario with no explicit international climate policy and two policy scenarios, full global action and a developing economy delay, on the probability of exceeding various global average temperature changes by 2100. This letter demonstrates that even when developing economies delay any mitigation efforts until 2050 the effect of action by developed economies will appreciably reduce the probability of more extreme levels of temperature change. This paper concludes that early carbon mitigation efforts by developed economies will considerably affect the distribution over future climate change, whether or not developing countries begin mitigation efforts in the near term.     

Accuracy and sensitivity analysis for 54 models of computing hourly diffuse solar irradiation on clear sky

Fifty-four broadband models for computation of solar diffuse irradiation on horizontal surface were tested in Romania (South-Eastern Europe). The input data consist of surface meteorological data, column integrated data, and data derived from satellite measurements. The testing procedure is performed in 21 stages intended to provide information about the sensitivity of the models to various sets of input data. There is no model to be ranked “the best” for all sets of input data. However, some of the models performed better than others, in the sense that they were ranked among the best for most of the testing stages. The best models for solar diffuse radiation computation are, on equal footing, ASHRAE 2005 model (ASHRAE 2005) and King model (King and Buckius, Solar Energy 22:297–301, 1979). The second best model is MAC model (Davies, Bound Layer Meteor 9:33–52, 1975). Details about the performance of each model in the 21 testing stages are found in the Electronic Supplementary Material.     

The Volumetric Particle Approach for Concentration Fluctuations and Chemical Reactions in Lagrangian Particle and Particle-grid Models

A new approach is proposed to predict concentration fluctuations in the framework of one-particle Lagrangian stochastic models. The approach is innovative since it allows the computation of concentration fluctuations in dispersing plumes using a Lagrangian one-particle model with micromixing but with no need for the simulating of background particles. The extension of the model for the treatment of chemically reactive plumes is also accomplished and allows the computation of plume-related chemical reactions in a Lagrangian one-particle framework separately from the background chemical reactions, accounting for the effect of concentration fluctuations on chemical reactions in a general, albeit approximate, manner. These characteristics should make the proposed approach an ideal tool for plume-in-grid calculations in chemistry transport models. The results are compared to the wind-tunnel experiments of Fackrell and Robins (J Fluid Mech, 117:1–26, 1982) for plume dispersion in a neutral boundary layer and to the measurements of Legg et al. (Boundary-Layer Meteorol, 35:277–302, 1986) for line source dispersion in and above a model canopy. Preliminary reacting plume simulations are also shown comparing the model with the experimental results of Brown and Bilger (J Fluid Mech, 312:373–407, 1996; Atmos Environ, 32:611–628, 1998) to demonstrate the feasibility of computing chemical reactions in the proposed framework.     

Characterization of Oscillatory Motions in the Stable Atmosphere of a Deep Valley

In a valley sheltered from strong synoptic effects, the dynamics of the valley atmosphere at night is dominated by katabatic winds. In a stably stratified atmosphere, these winds undergo temporal oscillations, whose frequency is given by $N \sin {\alpha }$ N sin α for an infinitely long slope of constant slope angle $\alpha $ α , $N$ N being the buoyancy frequency. Such an unsteady flow in a stably stratified atmosphere may also generate internal gravity waves (IGWs). The numerical study by Chemel et al. (Meteorol Atmos Phys 203:187–194, 2009) showed that, in the stable atmosphere of a deep valley, the oscillatory motions associated with the IGWs generated by katabatic winds are distinct from those of the katabatic winds. The IGW frequency was found to be independent of $\alpha $ α and about $0.8N$ 0.8 N . Their study did not consider the effects of the background stratification and valley geometry on these results. The present work extends this study by investigating those effects for a wide range of stratifications and slope angles, through numerical simulations for a deep valley. The two oscillatory systems are reproduced in the simulations. The frequency of the oscillations of the katabatic winds is found to be equal to $N$ N times the sine of the maximum slope angle. Remarkably, the IGW frequency is found to also vary as $C_\mathrm{w}N$ C w N , with $C_\mathrm{w}$ C w in the range $0.7$ 0.7 – $0.95$ 0.95 . These values for $C_\mathrm{w}$ C w are similar to those reported for IGWs radiated by any turbulent field with no dominant frequency component. Results suggest that the IGW wavelength is controlled by the valley depth.     

Testing the robustness of semi-empirical sea level projections

We determine the parameters of the semi-empirical link between global temperature and global sea level in a wide variety of ways, using different equations, different data sets for temperature and sea level as well as different statistical techniques. We then compare projections of all these different model versions (over 30) for a moderate global warming scenario for the period 2000–2100. We find the projections are robust and are mostly within ±20% of that obtained with the method of Vermeer and Rahmstorf (Proc Natl Acad Sci USA 106:21527–21532, 2009), namely ~1 m for the given warming of 1.8°C. Lower projections are obtained only if the correction for reservoir storage is ignored and/or the sea level data set of Church and White (Surv Geophys, 2011) is used. However, the latter provides an estimate of the base temperature T ₀ that conflicts with the constraints from three other data sets, in particular with proxy data showing stable sea level over the period 1400–1800. Our new best-estimate model, accounting also for groundwater pumping, is very close to the model of Vermeer and Rahmstorf (Proc Natl Acad Sci USA 106:21527–21532, 2009).     

Environmental policy under model uncertainty: a robust optimal control approach

The design of optimal environmental policy inherits model uncertainty. We investigate the consequences in a simple linear model, where the aim of the policymaker is to stabilise the atmospheric content of carbon. We study how decision-makers’ concerns about robustness alters policy using the Hansen and Sargent (2003, 2008) approach. The analysis shows that a policymaker, who fears about model misspecification should react more aggressively to changes in the stock of atmospheric carbon and reduce emissions stronger.     

Are there basic physical constraints on future anthropogenic emissions of carbon dioxide?

Global Circulation Models (GCMs) provide projections for future climate warming using a wide variety of highly sophisticated anthropogenic CO₂ emissions scenarios as input, each based on the evolution of four emissions ??drivers??: population p, standard of living g, energy productivity (or efficiency) f and energy carbonization c (IPCC WG III 2007). The range of scenarios considered is extremely broad, however, and this is a primary source of forecast uncertainty (Stott and Kettleborough, Nature 416:723?C725, 2002). Here, it is shown both theoretically and observationally how the evolution of the human system can be considered from a surprisingly simple thermodynamic perspective in which it is unnecessary to explicitly model two of the emissions drivers: population and standard of living. Specifically, the human system grows through a self-perpetuating feedback loop in which the consumption rate of primary energy resources stays tied to the historical accumulation of global economic production??or p×g??through a time-independent factor of 9.7±0.3 mW per inflation-adjusted 1990 US dollar. This important constraint, and the fact that f and c have historically varied rather slowly, points towards substantially narrowed visions of future emissions scenarios for implementation in GCMs.     

Climate model simulation of anthropogenic influence on greenhouse-induced climate change (early agriculture to modern): the role of ocean feedbacks

We present further steps in our analysis of the early anthropogenic hypothesis (Ruddiman, Clim Change 61:261–293, 2003) that increased levels of greenhouse gases in the current interglacial, compared to lower levels in previous interglacials, were initiated by early agricultural activities, and that these increases caused a warming of climate long before the industrial era (～1750). These steps include updating observations of greenhouse gas and climate trends from earlier interglacials, reviewing recent estimates of greenhouse gas emissions from early agriculture, and describing a simulation by a climate model with a dynamic ocean forced by the low levels of greenhouse gases typical of previous interglacials in order to gauge the magnitude of the climate change for an inferred (natural) low greenhouse gas level relative to a high present day level. We conduct two time slice (equilibrium) simulations using present day orbital forcing and two levels of greenhouse gas forcing: the estimated low (natural) levels of previous interglacials, and the high levels of the present (control). By comparing the former to the latter, we estimate how much colder the climate would be without the combined greenhouse gas forcing of the early agriculture era (inferred from differences between this interglacial and previous interglacials) and the industrial era (the period since ～1750). With the low greenhouse gas levels, the global average surface temperature is 2.7 K lower than present day—ranging from ～2 K lower in the tropics to 4–8 K lower in polar regions. These changes are large, and larger than those reported in a pre-industrial (～1750) simulation with this model, because the imposed low greenhouse gas levels (CH₄ = 450 ppb, CO₂ = 240 ppm) are lower than both pre-industrial (CH₄ = 760 ppb, CO₂ = 280 ppm) and modern control (CH₄ = 1,714 ppb, CO₂ = 355 ppm) values. The area of year-round snowcover is larger, as found in our previous simulations and some other modeling studies, indicating that a state of incipient glaciation would exist given the current configuration of earth’s orbit (reduced insolation in northern hemisphere summer) and the imposed low levels of greenhouse gases. We include comparisons of these snowcover maps with known locations of earlier glacial inception and with locations of twentieth century glaciers and ice caps. In two earlier studies, we used climate models consisting of atmosphere, land surface, and a shallow mixed-layer ocean (Ruddiman et al., Quat Sci Rev 25:1–10, 2005; Vavrus et al., Quat Sci Rev 27:1410–1425, 2008). Here, we replaced the mixed-layer ocean with a complete dynamic ocean. While the simulated climate of the atmosphere and the surface with this improved model configuration is similar to our earlier results (Vavrus et al., Quat Sci Rev 27:1410–1425, 2008), the added information from the full dynamical ocean is of particular interest. The global and vertically-averaged ocean temperature is 1.25 K lower, the area of sea ice is larger, and there is less upwelling in the Southern Ocean. From these results, we infer that natural ocean feedbacks could have amplified the greenhouse gas changes initiated by early agriculture and possibly account for an additional increment of CO₂ increase beyond that attributed directly to early agricultural, as proposed by Ruddiman (Rev Geophys 45:RG4001, 2007). However, a full test of the early anthropogenic hypothesis will require additional observations and simulations with models that include ocean and land carbon cycles and other refinements elaborated herein.     

No consensus on consensus: the challenge of finding a universal approach to measuring and mapping ensemble consistency in GCM projections

Communicating information about consistency in projections is crucial to the successful understanding, interpretation and appropriate application of information from climate models about future climate and its uncertainties. However, mapping the consistency of model projections in such a way that this information is communicated clearly remains a challenge that several recently published papers have sought to address in the run up to the IPCC AR5. We highlight that three remaining issues have not been fully addressed by the literature to date. Allen and Ingram (Nature 419:224, 2002) While additional information about regions where projected changes in rainfall are not ‘statistically significant’ can provide useful information for policy, the spatial scale at which changes are assessed has a substantial impact on the signal-to-noise ratio, and thus the detectability of changes. We demonstrate that by spatially smoothing the model projections we can provide more information about the nature of the signal for larger regions of the world. Christensen et al. (2007) Combining information about magnitude, consistency and statistical significance of projected changes in a single map can cause reduced legibility. We demonstrate the difficulty in finding a ‘universal’ method suitable for a wide range of audiences DEFRA (2012) We highlight that regions where projected changes in average rainfall are not statistically significant, changes in variability may still cause significant impacts. We stress the need to communicate this effectively in order to avoid mis-leading users. Finally, we comment on regions of the world where messages for users of climate information about ensemble consistency have changed since AR4, noting that these changes are due largely to changes in the methods of measuring consistency rather than any discernable differences between the CMIP3 and CMIP5 ensembles.     

Correction to “the vertical distribution of climate forcings and feedbacks from the surface to top of atmosphere”

We discuss here a mistake in the analysis of Previdi and Liepert (Clim Dyn, 2011). In that article, the surface albedo radiative kernels were calculated incorrectly. We present in this brief comment the corrected albedo kernels. We then use these kernels to compute the surface albedo radiative feedback in climate model simulations driven by increasing carbon dioxide, as in Previdi and Liepert (Clim Dyn, 2011). We find that the use of the corrected albedo kernels does not change the conclusions of our earlier work.