1. Introduction
Warming of the earth’s climate system over the past century has been manifested by an increase in global mean (near) surface (air) temperature (GMST) of about 0.8 K, and by changes in the altitudinal and geographical distributions of air temperature, widespread melting of snow and of land and sea ice, and rising sea level (Solomon et al. 2007). This warming has been ascribed by the Intergovernmental Panel on Climate Change (IPCC) as “very likely” due mainly to increased concentrations of long-lived greenhouse gases (GHGs), carbon dioxide, methane, chlorofluorocarbons, and nitrous oxide. Confidence in the quantitative attribution of these changes to the products of human activity is essential to planning and decision making regarding global energy resources. However, the observed increase of GMST over the industrial period is less than 40% of what would be expected from present best estimates of the earth’s climate sensitivity and the forcing (imposed change in energy balance, W m−2) by the observed increases in GHGs. Here we examine the discrepancy between the observed increase in GMST over the industrial period and that expected from the increased GHG concentrations and four major factors that might contribute to this discrepancy: (i) natural variation in global temperature over the industrial period, (ii) lack of attainment of equilibrium of the climate system to applied forcings over the industrial period, (iii) current estimates of climate sensitivity being too high, and (iv) countervailing forcings over the industrial period offsetting the warming forcings by incremental greenhouse gases. We show that relatively little of this warming discrepancy can be attributed to a countervailing natural cooling over this time period or to thermal lag of the climate system response to forcing. We argue that this discrepancy is therefore due mainly to offsetting forcing by increased concentrations of atmospheric aerosols and/or to climate sensitivity being lower than current estimates; the discrepancy cannot be apportioned between these two causes primarily because of present uncertainty in aerosol forcing.
2. Discrepancy between observed and expected temperature increase
a. Natural variation in global temperature
Some or all of the warming expected over the industrial period might have been offset by cooling over this time period due to natural variability of the climate system. We use variation in preindustrial global temperature as inferred from proxy records, mainly tree rings, ice cores, corals, and varved sediments to estimate the likely magnitude of any natural cooling over the 150-yr interval of the instrumental record. The standard deviation of the difference in temperature over 150-yr intervals for the period (1000–1850) based on the synthesis reconstruction of Juckes et al. (2007) yields 0.2 K, which is 25% of the observed increase in GMST (Fig. 2). Somewhat smaller changes in GMST were found in simulations of the twentieth century with coupled ocean–atmosphere global climate models using estimated natural forcings only (as reported by Solomon et al. 2007, see their Fig. 9.5), which for 19 runs with 5 models yielded a temperature increase of 0.09 K (standard deviation is 0.19 K, maximum is 0.49 K). An offset of expected warming by a natural cooling of 0.2 K would account only for about 15% of the discrepancy between observed increase in GMST and that expected for the IPCC’s best-estimate sensitivity, ΔT2× = 3 K (8%, 32%, for ΔT2× = 4.5 K, 2 K, respectively). If, on the other hand, natural variability has contributed to increase GMST over the industrial period, the discrepancy would be even greater, by similar amounts.
b. Lack of attainment of equilibrium
Values given by other investigators (Gouretski and Koltermann 2007; Domingues et al. 2008; Wijffels et al. 2008; Ishii and Kimoto 2009) of the planetary heating rate determined from heat flux into the ocean are for the most part comparable to or less than the value presented here. From the short-term time derivative of the ocean heat content given by Domingues et al., Douglass and Knox (2009) infer a time-dependent planetary heating rate that ranges from −0.15 W m−2 (i.e., cooling) to +0.15 W m−2; by a similar analysis Murphy et al. (2009) infer a time-dependent planetary heating rate that varies from −0.6 W m−2 to +0.8 W m−2 (1954–2001 average, 0.24 ± 0.32 W m−2, 1σ). A considerably greater planetary heating rate was given by Willis et al. (2004), based on an indicated heating rate of 0.86 ± 0.12 W m−2 of the ocean from 1993 to 2003 for the upper 750 m of the water column (0.61 W m−2 globally). This heat flux had served as the basis for the conclusion by Hansen et al. (2005) of a substantial planetary radiative imbalance, which in turn served as the basis for the imbalance given in the review of the global energy budget by Trenberth et al. (2009). However, much of the apparent increase in heat content reported by Willis et al. is now attributed to problems in the measurements (Gouretski and Koltermann 2007; Wijffels et al. 2008), so such a large heating rate and planetary radiative imbalance seems unlikely; for these reasons we retain the value of 0.37 ± 0.12 W m−2.
Because a planetary heating rate of 0.37 ± 0.12 W m−2 is only a small fraction, 14% ± 5%, of the forcing by long-lived GHGs (Fig. 1), the heat flux into the planet can account for only a modest reduction in the expected increase in GMST due to forcing by increases in long-lived GHGs, relative to the equilibrium increase in GMST (Fig. 2). For sensitivity ΔT2× = 3 K, the corresponding fraction of the warming discrepancy attributable to thermal disequilibrium is 22% ± 7% (19% ± 6% and 32% ± 10% for ΔT2× = 4.5 K and 2 K, respectively). Thus, the thermal lag of the climate system accounts for a small, albeit nonnegligible, fraction of the warming discrepancy. A similar result, 10% ± 7%, was obtained by Murphy et al. (2009) by comparing the increase in ocean heat content between 1950 and the present with the integral of the greenhouse gas plus solar forcing over that period.
c. Overestimate of climate sensitivity
The sensitivity of the earth’s climate system is thought to substantially exceed that of a blackbody radiator at the global mean surface temperature 288 K, ΔT2× = 1.1 K, because of feedbacks in the climate system. These feedbacks are not well understood, with the consequence that the climate sensitivity is quite uncertain. The IPCC Assessment Report (Solomon et al. 2007) specified the “likely” uncertainty range associated with its estimate of climate sensitivity by the central 66% of the probability distribution function characterizing this quantity (2.0–4.5 K; Fig. 2). The IPCC report further stated that it was “very unlikely” (less than 5% probability) that the climate sensitivity is less than 1.5 K, but was unable to recommend a corresponding very unlikely upper bound to the estimate, stating rather that on the basis of present understanding values greater than 4.5 K could not be excluded. The expected increase in GMST that would result from forcing by only the long-lived GHGs is well above the observed increase (Fig. 2) for the entire likely range in climate sensitivity given by the IPCC. Consequently the actual sensitivity must be even lower than the IPCC very unlikely limit if other causes are negligible; alternatively, the low observed increase in GMST must be attributed at least in part to other causes.
d. Countervailing forcings over the industrial period
Tropospheric aerosols from human activity scatter light (direct effect) and increase cloud reflectivity (the cloud albedo effect) and possibly also cloud persistence; all these effects are thought to exert a negative forcing (cooling influence) on climate. Some aerosols absorb light and can exert a positive forcing, and can also decrease the persistence of clouds by enhancing evaporation of droplets and suppressing convection (Ramanathan and Feng 2008). The combined positive and negative aerosol forcings yield the IPCC best estimate of −1.2 W m−2 (5%–95% range −0.6 to −2.4 W m−2) that is substantial compared to the forcing by long-lived GHGs but quite uncertain (Fig. 1). Under the assumption of additivity of forcings (a premise of the linear forcing-response paradigm), the best estimate of the total aerosol forcing, in global and annual average, would be offsetting 45% of the forcing by the long-lived GHGs. An aerosol forcing of −1.2 W m−2 would reduce the effective forcing over the industrial period to 1.5 W m−2. This would result in a much lower expected increase in GMST than would forcing by GHGs alone (Fig. 2) and would actually be compatible with the lower end of the IPCC likely range of climate sensitivity. However, the large uncertainty associated with present estimates of aerosol forcing and the resultant uncertainty in the total effective forcing over the industrial period (Fig. 1) imply a range of expected increase in GMST that is compatible with, and extends well beyond, the entire range of the 2007 IPCC estimated climate sensitivity. Thus, countervailing aerosol forcing could account for much or all of the discrepancy between the expected and observed increase in GMST over the industrial period.
3. Determining climate sensitivity
In view of the central role of climate sensitivity in understanding and projecting the response of the earth’s climate to future changes in atmospheric composition, much effort has been directed toward determining this quantity, or at least bounding it. Fundamentally there are only two generally accepted approaches to determining climate sensitivity: empirical and modeling.
a. Empirical approaches
Empirical approaches consist of attributing and quantitatively relating an observed change in GMST over some time period to a known or estimated forcing. The underlying assumptions are that the magnitude of the observed temperature change is greater than unforced variability, that there is a cause and effect relationship between the net assigned forcing and the observed temperature change, and that the observed change reflects the full impact of the forcing or that disequilibrium can be accounted for. Knowledge of both the responsible forcing and the resulting temperature change is required. Prehistoric periods of change in GMST to which this approach has been applied include the warming between the last glacial maximum (LGM) and the present temperate era and cooling between the Cretaceous period and the present; changes in global temperature are inferred from changes in the distribution of stable isotopes in ice cores and in sediments, and changes in atmospheric composition are inferred from ice cores or from mass balance considerations. Although this paleological approach is thought by many to give reliable estimates of the earth’s climate sensitivity, the uncertainties are substantial. The IPCC report (Solomon et al. 2007, chapter 6) gives the total forcing between the present and the LGM as “approximately −8 W m−2” with no uncertainty range specified but with the level of scientific understanding ascribed to the main non-GHG forcings, which arise from changes in continental ice and sea level, mineral dust, and vegetation (−3.2, −1.4, and −1.2 W m−2, respectively), indicated as low, very low, and very low, respectively. The very likely range of change in GMST is given as −4 to −7 K. The range of climate sensitivity (ΔT2×) due to the uncertainty in temperature change alone is 1.9–3.2 K, but this uncertainty range is surely an underestimate because of uncertainty in the forcing. Based on estimated uncertainties in the magnitude of temperature change in the Antarctic between the present and the LGM (−7 to −11 K), the ratio of the Antarctic temperature change to global mean change (1–2), and the forcing between the two climate states (−6 to −10 W m−2); Ganopolski and Schneider von Deimling (2008) conclude that the range of ΔT2× that can be inferred from this transition cannot be narrowed below 1.3–6.8 K. A further concern would be the applicability of the climate sensitivity inferred from such a large forcing and temperature change to the smaller anthropogenic perturbations associated with response to forcing by incremental GHGs.
Another situation of known forcing and temperature response that might in principle be used to infer equilibrium climate sensitivity is cooling following an explosive volcanic eruption. However, because of the short duration of the forcing, this sensitivity cannot be inferred from the temperature record unless the rate of change of global heat content is known explicitly (Boer et al. 2007).
A potentially very valuable empirical approach is to determine climate sensitivity from the known forcing and the increase in GMST over the industrial period, accounting for disequilibrium by the global heating rate (Gregory et al. 2002). For the increase in GMST taken as 0.8 K and the effective forcing evaluated by Eq. (4) with the IPCC Fourth Assessment Report (AR4) best estimate of forcing over the industrial period, Ftot = 1.72 W m−2, and planetary heating rate N = 0.37 W m−2, the resulting sensitivity, evaluated as S = ΔT/Feff is 0.59 K (W m−2)−1, corresponding to a CO2 doubling temperature ΔT2× = 2.2 K, near the low end of the likely range of sensitivity given by the IPCC Assessment Report (Solomon et al. 2007). This calculation is illustrated in Fig. 3, a graph of effective forcing versus the inverse of climate sensitivity, for which the known slope (temperature increase over the industrial period) permits sensitivity to be determined for a specified effective forcing. However, as shown in the figure, the present uncertainty in effective forcing, arising mainly from the uncertainty in aerosol forcing, introduces a very large uncertainty into the calculated sensitivity and CO2 doubling temperature, with bounds that exceed the Solomon et al. (2007) likely range at both ends. Further uncertainties are introduced by uncertainty in the forced temperature change over the industrial period, shown by blue shading in the figure, and by uncertainty in forcings other than by aerosols and in the heating rate, estimated to be about ±0.4 W m−2 in the aggregate. However, the dominant source of uncertainty is in the aerosol forcing, and hence any empirical determination of the earth’s climate sensitivity from temperature change over the instrumental record is forestalled by the present uncertainty in aerosol forcing.
Such an approach has been applied by Forster and Gregory (2006) and by Murphy et al. (2009) using data from the Earth Radiation Budget Experiment (ERBE) satellite and/or the Clouds and the Earth’s Radiant Energy System (CERES) satellites, who obtained much narrower uncertainty bounds than those obtained elsewhere. [Murphy et al. (2009) distinguish the quantity they obtain from the equilibrium climate response.] However, several concerns might be noted. Both studies assumed anthropogenic aerosol forcing to be unchanged during the relatively short durations of the satellite measurements examined [ERBE data 1985–97, Forster and Gregory (2006); ERBE data 1985–99 and CERES data 2000–05, Murphy et al. (2009)], without examining the consequences of this assumption. A second concern is that the changes in annual mean temperature and radiative fluxes over these rather short periods are so small that any unforced variation in temperature or irradiance or unrecognized forcing would exert a large influence on the inferred climate sensitivity; to overcome this limitation Murphy et al. (2009) determined sensitivity using monthly-mean as well as annual-mean temperatures and fluxes. However, as temperature change over the annual cycle does not account for seasonal variations in large-scale circulations, use of monthly mean data cannot yield an accurate climate sensitivity (e.g., Lindzen et al. 1995). Also, the approach using monthly data would require confident estimate of the seasonal variation of all forcings, including aerosol forcing. The best estimate for S−1 and the rather narrow associated 1σ uncertainty range presented by Murphy et al. (2009) from analysis of the seasonal data (1.25 ± 0.5) W m−2 K−1 differ markedly from the values they obtained for the three annual datasets examined (0.04 ± 1.03, 1.03 ± 0.58, 0.69 ± 0.78) W m−2 K−1 and from the value obtained by a similar analysis of a subset of the same data by Forster and Gregory, 2.3 ± 0.7 W m−2 K−1 (1σ). A further concern would be the suitability of the satellite data to obtaining the necessary global average fluxes: the ERBE satellite does not sample poleward of 60°; the CERES satellite, although capturing the full planet from polar orbit, does not sample over the diurnal cycle. All of these considerations would seem to limit the confidence that can be placed in determination of climate sensitivity by this approach, at least at present.
b. Climate modeling approach
Global climate models, numerical representations of the processes that comprise the earth’s climate system, permit a highly differentiated examination both of prior climate change and of future climate change that would result from prospective changes in atmospheric composition. As such, they are the most powerful tools available for examining the consequences of perturbations of the climate system. Determining climate sensitivity by this approach requires development and evaluation of models that represent the key processes of the climate system with sufficient accuracy that the consequences of the rather small GHG perturbations can be determined accurately and with confidence. Current climate models exhibit considerable diversity in their representations of key atmospheric and hydrologic processes, mainly because of insufficient understanding and computational limitations; changing representations of individual physical processes, even within a given climate model, can result in large changes in the model’s climate sensitivity (Sanderson et al. 2008), mainly because of the amplifying effects of feedbacks (e.g., Hansen et al. 1984; Schlesinger 1988; Roe and Baker 2007). For reasons such as these, current climate models exhibit a range of sensitivity of almost a factor of 2, roughly equal to the IPCC likely uncertainty range for this quantity. As evaluation of climate models requires comparison with observations, accurate knowledge of all forcings, including aerosol forcings, is required.
The limitations on determination of climate sensitivity imposed by the present uncertainty in forcing by anthropogenic aerosols are increasingly becoming recognized for both the empirical and modeling approaches. Hansen (2008) has argued that “estimates of climate sensitivity based on the last 100 years of climate change are practically worthless, because we do not know the net climate forcing.” Uncertainty in aerosol forcing allows the observed change in GMST over the twentieth century, to be reproduced with climate models exhibiting a wide range of sensitivities (Schwartz et al. 2007); this has been ascribed in large part to an inverse correlation of the forcings chosen and employed by the several modeling groups with the sensitivities of their models (Kiehl 2007). This situation led Knutti (2008) to conclude recently that “constraining the aerosol effects from data, models and from the observed warming trends is therefore a critical step in order to decide whether our understanding of human influence on climate and our climate models are consistent with observed trends.” Clearly, progress in reducing uncertainty in climate sensitivity by either the empirical or modeling approach requires reducing the uncertainty in aerosol forcing. Even if the modeling approach converged upon a narrow range for climate sensitivity, it would still be essential to reduce the uncertainty in forcing to test the models’ ability to reproduce the twentieth-century temperature record, in order to place confidence in the models. For these reasons, reducing uncertainty in aerosol forcing is of singular importance for reducing uncertainty in climate sensitivity.
4. Strategies for reducing uncertainty in aerosol forcing
Over the three plus decades since the initial attempts to identify and quantify the direct and cloud albedo climate forcings by anthropogenic aerosols (Junge 1975), major advances have been made in characterizing aerosol properties and distributions and in understanding the controlling processes (Climate Change Science Program 2009). Recognition of the need for a global system for observing anthropogenic aerosols has led to the development and deployment of substantial capabilities for remote sensing of aerosols from satellites and the surface (Kahn et al. 2004; Yu et al. 2006). Key current satellite capabilities from instruments orbiting in formation in low earth orbit yield global near-simultaneous observations of radiance from aerosols at multiple wavelengths and scattering angles and of vertical profiles by lidar (Anderson et al. 2005). Advanced satellite instruments promising additional, urgently needed capabilities are being readied for launch in the near future (e.g., the Glory Mission; Mishchenko et al. 2007), or, more importantly, could be readied for early deployment as in the Deep Space Climate Observatory (DSCOVR), which would be stationed at the Lagrange point between the sun and earth to provide continuous calibrated measurements of clouds and aerosols (Yeager 2008). Surface-based networks yield multiwavelength aerosol optical depth and angular scattering distribution by sun and sky photometry and vertical distributions by lidar at multiple aerosol-impacted locations around the globe. Near-surface in situ observations can provide very detailed long-term but spatially limited coverage of aerosol optical, microphysical, and chemical properties (Delene and Ogren 2002; Malm et al. 2004; Zhang et al. 2007). Together these measurements provide essential information about the abundance and properties of natural and anthropogenic aerosols as well as ground truth information needed to evaluate the satellite aerosol retrievals. A dozen or so major short-term field studies in a variety of locations globally have provided detailed coincident measurements of multiple environmental attributes related to key aerosol processes (e.g., new particle formation and gas-to-particle conversion rates) and properties (e.g., size distributed chemical composition for identifying the anthropogenic fraction; Bates et al. 2006). Substantial advances are being made as well in modeling the global distribution of aerosol amounts and properties, the geographical distribution of aerosol influences, and the attribution to sources and in intercomparing the model results (Kinne et al. 2006). Such modeling, which requires input and constraints from process studies, in situ measurements, and satellite observations, is receiving increased attention by the research community and is making much progress (Climate Change Science Program 2009).
Despite these many advances, the ability to model the amount, properties, and geographical distribution of anthropogenic aerosols with the accuracy required to confidently calculate their radiative forcing either in stand-alone mode or in climate models has thus far been elusive (Ghan and Schwartz 2007). Also, and importantly, essential observational data are not available to directly yield forcing or to adequately constrain global aerosol models. Satellite measurements alone are unable to reduce the uncertainty in aerosol forcing because of sampling limitations and biases (Kahn et al. 2009) and currently cannot unambiguously attribute satellite-derived quantities to natural versus anthropogenic aerosols (Anderson et al. 2005); also, satellite measurements are limited in their ability to detect the smallest-sized aerosol particles that influence clouds (Heintzenberg and Charlson 2009). These considerations suggest that a combination of approaches is required to make progress in quantifying aerosol forcing. This quantification and its relationship to anthropogenic emissions would be greatly advanced by the ability to integrate global measurements of aerosol and cloud properties into radiative transfer models (e.g., in a reanalysis mode). Such a linking of satellite remote sensing, in situ observations, and models, which has been called for repeatedly (Charlson et al. 1992; National Research Council 1996, 2005; Diner et al. 2004; Climate Change Science Program 2009) could be expected to make rapid progress. Although the key elements of the needed research program are present, progress has been slow because of the lack of a coherent international or even national program to bring these elements together. A focused, integrated approach could lead to the understanding of aerosol processes needed to represent them in chemical transport models and climate models with the required accuracy in forcing. What seems to be absent is the integration of the necessary observational and modeling capabilities.
5. Implications
The preceding analysis establishes that the warming discrepancy is due to some combination of low climate sensitivity and/or offset of the expected increase in GMST due to incremental GHG concentrations by other forcing influences, of which the most likely candidate is anthropogenic aerosols. As shown below, the actual situation and the extent of aerosol offset are of enormous importance in understanding climate change over the industrial period and in developing policy to deal with future climate change. Importantly, the present large uncertainty in climate sensitivity results in an even greater uncertainty in the amount of additional carbon dioxide that can be added to the atmosphere for a given allowable increase in global mean temperature.
The implications of the present uncertainty in climate sensitivity on allowable future CO2 emission can be readily quantified by a simple calculation. For a given climate sensitivity S and target maximum increase in GMST above preindustrial not to be exceeded, ΔTmax, it is possible to calculate the total further amount of equivalent CO2, in addition to the incremental amounts of CO2 and other long-lived GHGs in the present atmosphere above their preindustrial values, that can be introduced into the atmosphere; here the term “equivalent CO2” includes other long-lived GHGs with emissions scaled to that of CO2 by their specific forcings. An example of such a calculation is given in Table 1 for the target maximum increase in GMST ΔTmax taken as 2 K, a widely adopted value proposed by the European Union as early as 1996 based on consideration of risk (European Union Council 1996). The choice of ΔTmax is ultimately a societal decision that must take into consideration the consequences of a given increase in GMST, the costs of achieving such a temperature stabilization, and the uncertainties in the calculation; results for other values of ΔTmax are shown in Fig. 4. The calculation is made here for the best-estimate value for ΔT2× as given by the 2007 IPCC Assessment Report (3 K) and for the values corresponding to the limits of the very likely and likely uncertainty range–cumulative values of the probability distribution function for this quantity of 5% (1.5 K, roughly 1.6σ below the best estimate), 17% (2 K, 1σ below), and 83% (4.5 K, 1σ above); as noted above the IPCC report did not provide an estimate of ΔT2× for 95% cumulative probability. In this calculation it is necessary to account for the forcing by incremental amounts of CO2 and other long-lived GHGs already in the atmosphere. The calculation does not take into account any disequilibrium contribution, which would result only in a time lag in the temperature increase. The calculation excludes the cooling influence of anthropogenic aerosols because of their short atmospheric residence times, about a week. Similarly, the warming forcing due to increases of tropospheric ozone is excluded as this short-lived GHG would likely decrease rapidly as emissions of precursors, mainly nitrogen oxides associated with fossil fuel combustion, were decreased in response to future pollution controls or as future CO2 emissions were decreased. Additionally, the table presents the time in years allowed for total CO2 emission to continue at the present rate of CO2 emission from fossil fuel combustion and cement manufacture that would correspond to the allowable cumulative emission.
This simple calculation (for more detailed calculations see Caldeira et al. 2003; Edmonds and Smith 2006; Allen et al. 2009) illustrates the dramatic differences in future fuel use scenarios between low and high climate sensitivity. For climate sensitivity at the low end of the very likely range of the 2007 IPCC estimate (ΔT2× = 1.5 K), the cumulative allowable future emission of equivalent CO2 compatible with a maximum increase in GMST above preindustrial ΔTmax = 2 K is about 700 Pg C; at the present rate of emission of CO2 from fossil fuel combustion and cement manufacture ∼9 Pg C yr−1 (Raupach et al. 2007), this allowable increase in CO2 would be reached in about 70 yr. For a somewhat greater sensitivity ΔT2× = 2 K, corresponding to the low end of the IPCC likely range, the cumulative future allowable equivalent emission of CO2 is a factor of 2 lower, and the allowable cumulative emission would be reached in about 40 yr. For the IPCC best estimate of climate sensitivity (ΔT2× = 3 K) the present committed increase in GMST due to incremental GHGs already present in the atmosphere (2.1 K) essentially already equals the target maximum 2-K increase in GMST above preindustrial. On the basis of present understanding of climate sensitivity this possibility cannot be precluded. Thus, if the CO2 doubling temperature of the earth’s climate is 3 K, an immediate cessation of emission of CO2 and other GHGs would be required for the equilibrium temperature increase above preindustrial not to exceed 2 K. If climate sensitivity is 4.5 K, at the high end of the IPCC estimated range, the present committed temperature increase above preindustrial is 3.2 K, which substantially exceeds the target maximum temperature increase of 2 K, and attaining the target maximum temperature increase would require substantial reduction in atmospheric GHG concentrations from their present values. The possibility of this situation also cannot be precluded on the basis of present understanding.
In principle, a greater amount of incremental CO2 might be allowed if the cooling influence by aerosols were accounted for in the calculation. However, reliance on continued aerosol offset of warming by GHGs would ultimately fail, as the short residence time of these aerosols would entail a sustained commitment to replenish aerosol concentrations, even as CO2 emissions were reduced. Climate model calculations (e.g., Brasseur and Roeckner 2005; Matthews and Caldeira 2007) find that abruptly decreasing aerosol forcing would result in a rapid jump in temperature as the climate system reacts to the increased net forcing.
The strong dependence of allowable future CO2 emissions on climate sensitivity, which holds for other choices of maximum allowable increase in GMST (Fig. 4) underscores the need to improve knowledge of the earth’s climate sensitivity for understanding climate change that has already occurred and for developing strategies to limit future increase in GMST.
6. Conclusions
The current best estimate and uncertainty range of the earth’s climate sensitivity suggest an equilibrium increase in the earth’s global mean surface temperature for forcing by anthropogenic long-lived greenhouse gases of 2.1 K (range 1.5–3.2 K, roughly 1 standard deviation), well in excess of the observed increase relative to preindustrial times, about 0.8 K. The discrepancy is attributed mainly to uncertainty in climate sensitivity and/or cooling forcing by anthropogenic aerosols, also highly uncertain; countervailing natural cooling and thermal lag in climate response seem to be relatively small. Because of the great difference in atmospheric residence times of greenhouse gases and aerosols, the effect of the greenhouse gases will dominate long-term forcing and climate response. Even if the earth’s climate sensitivity is at the low end of the IPCC estimated “likely” range, continued emission of CO2 at the present rate would exhaust in just a few decades the shared global resource of the incremental amount of CO2 that can be added to the atmosphere without exceeding proposed maximum increases in GMST. If the sensitivity is greater, the allowable incremental emission decreases sharply, essentially to zero at the present best estimate of climate sensitivity, and is actually negative for greater values of this sensitivity. As has been widely discussed elsewhere, redirecting the world’s energy economy from its present reliance on fossil fuels or developing effective means of sequestering CO2 emissions would require immense and rapid changes in how the world meets its energy needs. Advance knowledge of the earth’s climate sensitivity would be of enormous monetary value, estimated in the tens of trillions of dollars (Edmonds and Smith 2006) in terms of efficient planning and averting costs associated with abandoning fossil fuel plants or retrofitting CO2 sequestration systems to existing facilities. Consequently much improved knowledge of the earth’s climate sensitivity is urgently and rapidly required for determining the extent and timing of reductions in CO2 emissions needed to limit the increase in GMST to a given value. The principal limitation to empirical determination of climate sensitivity or to the evaluation of the performance of climate models over the period of instrumental measurements is the present uncertainty in forcing by anthropogenic aerosols. This situation calls for greatly enhanced efforts to reduce this uncertainty.
Acknowledgments
Work by SES was supported by the U.S. Department of Energy (Office of Science, OBER) under Contract DE-AC02-98CH10886. Work by RJC was supported in part by NASA’s CALIPSO Mission (Contract NAS1-99105) and NSF Grant ATM-0601177.
REFERENCES
Allen, M. R., and Coauthors, 2009: Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature, 458 , 1163–1166.
Anderson, T. L., and Coauthors, 2005: An “A-train” strategy for quantifying direct climate forcing by anthropogenic aerosols. Bull. Amer. Meteor. Soc., 86 , 1795–1809.
Bates, T. S., and Coauthors, 2006: Aerosol direct radiative effects over the northwest Atlantic, northwest Pacific, and North Indian Oceans: Estimates based on in-situ chemical and optical measurements and chemical transport modeling. Atmos. Chem. Phys., 6 , 1657–1732.
Boer, G. J., M. Stowasser, and K. Hamilton, 2007: Inferring climate sensitivity from volcanic events. Climate Dyn., 28 , 481–502.
Brasseur, G. P., and E. Roeckner, 2005: Impact of improved air quality on the future evolution of climate. Geophys. Res. Lett., 32 , L23704. doi:10.1029/2005GL023902.
Caldeira, K., A. K. Jain, and M. I. Hoffert, 2003: Climate sensitivity uncertainty and the need for energy without CO2 emission. Science, 239 , 2052–2054.
Charlson, R. J., S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley Jr., J. E. Hansen, and D. J. Hofmann, 1992: Climate forcing by anthropogenic aerosols. Science, 255 , 423–430.
Climate Change Science Program, 2009: Atmospheric aerosol properties and climate impacts. Synthesis and Assessment Product 2.3, 128 pp. [Available online at http://downloads.climatescience.gov/sap/sap2-3/sap2-3-final-report-all.pdf].
Cubasch, U., and Coauthors, 2001: Projections of future climate change. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 526–582.
Delene, D. J., and J. A. Ogren, 2002: Variability of aerosol optical properties at four North American surface monitoring sites. J. Atmos. Sci., 59 , 1135–1150.
Diner, D. J., and Coauthors, 2004: PARAGON: An integrated approach for characterizing aerosol climate impacts and environmental interactions. Bull. Amer. Meteor. Soc., 85 , 1495–1501.
Domingues, C. M., J. A. Church, N. J. White, P. J. Gleckler, S. E. Wijffels, P. M. Barker, and J. R. Dunn, 2008: Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature, 453 , 1090–1094.
Douglass, D. H., and R. S. Knox, 2009: Ocean heat content and Earth’s radiation imbalance. Phys. Lett. A, 373 , 3296–3300.
Edmonds, J., and S. Smith, 2006: The technology of two degrees. Avoiding Dangerous Climate Change, H. J. Schellnhuber et al., Eds., Cambridge University Press, 385–392.
European Union Council, cited. 1996: Community strategy on climate change—Council conclusions. 1939th Council Meeting Environment, Document 8518/96. [Available online at http://ue.eu.int/ueDocs/cms_Data/docs/pressData/en/envir/011a0006.htm].
Forster, P. Mde F., and J. M. Gregory, 2006: The climate sensitivity and its components diagnosed from Earth Radiation Budget data. J. Climate, 19 , 39–52.
Ganopolski, A., and T. Schneider von Deimling, 2008: Comment on “Aerosol radiative forcing and climate sensitivity deduced from the Last Glacial Maximum to Holocene transition” by Petr Chylek and Ulrike Lohmann. Geophys. Res. Lett., 35 , L23703. doi:10.1029/2008GL033888.
Ghan, S. J., and S. E. Schwartz, 2007: Aerosol properties and processes: A path from field and laboratory measurements to global climate models. Bull. Amer. Meteor. Soc., 88 , 1059–1083.
Gouretski, V., and K. P. Koltermann, 2007: How much is the ocean really warming? Geophys. Res. Lett., 34 , L01610. doi:10.1029/2006GL027834.
Gregory, J. M., R. J. Stouffer, S. C. B. Raper, P. A. Stott, and N. A. Rayner, 2002: An observationally based estimate of the climate sensitivity. J. Climate, 15 , 3117–3121.
Hansen, J. E., 2008: Threat to the planet: Dark and bright sides of global warming. Eos, Trans. Amer. Geophys. Union, 89 .(Fall Meeting Suppl.). Abstract A33D-01.
Hansen, J. E., A. Lacis, D. Rind, G. Russell, P. Stone, and I. Fung, 1984: Climate sensitivity: Analysis of feedback mechanisms. Climate Processes and Climate Sensitivity, Geophys. Monogr., Vol. 29, Amer. Geophys. Union, 130–163.
Hansen, J. E., and Coauthors, 2005: Earth’s energy imbalance: Confirmation and implications. Science, 308 , 1431–1435.
Heintzenberg, J., and R. J. Charlson, Eds. 2009: Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation. MIT Press, 576 pp.
Huang, S., 2006: Land warming as part of global warming. Eos, Trans. Amer. Geophys. Union,87, pp. 477, 480.
Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., 65 , 287–299.
Juckes, M. N., M. R. Allen, K. R. Briffa, J. Esper, G. C. Hegerl, A. Moberg, T. J. Osborn, and S. L. Weber, 2007: Millennial temperature reconstruction intercomparison and evaluation. Climate Past, 3 , 591–609.
Junge, C. E., 1975: The possible influence of aerosols on the general circulation and climate and possible approaches for modelling. The Physical Basis of Climate and Climate Modelling, Global Atmospheric Research Program (GARP), Publication 16, World Meteorological Organization, International Council of Scientific Unions Joint Organizing Committee, 244–251.
Kahn, R. A., and Coauthors, 2004: Aerosol data sources and their roles within PARAGON. Bull. Amer. Meteor. Soc., 85 , 1511–1522.
Kahn, R. A., and Coauthors, 2009: MISR aerosol product attributes and statistical comparison with MODIS. IEEE Trans. Geosci. Remote Sens., 47 , 4095–4114.
Kiehl, J. T., 2007: Twentieth century climate model response and climate sensitivity. Geophys. Res. Lett., 34 , L22710. doi:10.1029/2007GL031383.
Kinne, S., and Coauthors, 2006: An AeroCom initial assessment optical properties in aerosol component modules of global models. Atmos. Chem. Phys., 6 , 1815–1834.
Knutti, R., 2008: Why are climate models reproducing the observed global surface warming so well? Geophys. Res. Lett., 35 , L18704. doi:10.1029/2008GL034932.
Levitus, S., J. I. Antonov, and T. P. Boyer, 2005: Warming of the world ocean, 1955–2003. Geophys. Res. Lett., 32 , L02604. doi:10.1029/2004GL021592.
Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov, 2009: Global ocean heat content 1955-2008 in light of recently revealed instrumentation problems. Geophys. Res. Lett., 36 , L07608. doi:10.1029/2008GL037155.
Lindzen, R. S., B. Kirtman, D. Kirk-Davidoff, and E. K. Schneider, 1995: Seasonal surrogate for climate. J. Climate, 8 , 1681–1684.
Malm, W. C., B. A. Schichtel, M. L. Pitchford, L. L. Ashbaugh, and R. A. Eldred, 2004: Spatial and monthly trends in speciated fine particle concentration in the United States. J. Geophys. Res., 109 , D03306. doi:10.1029/2003JD003739.
Matthews, H. D., and K. Caldeira, 2007: Transient climate-carbon simulations of planetary geoengineering. Proc. Natl. Acad. Sci. USA, 104 , 9949–9954.
Mishchenko, M. I., and Coauthors, 2007: Accurate monitoring of terrestrial aerosols and total solar irradiance: Introducing the Glory Mission. Bull. Amer. Meteor. Soc., 88 , 677–691.
Murphy, D. M., S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster, and T. Wong, 2009: An observationally based energy balance for the Earth since 1950. J. Geophys. Res., 114 , D17107. doi:10.1029/2009JD012105.
National Research Council, 1996: A Plan for a Research Program on Aerosol Radiative Forcing and Climate Change. National Academies Press, 180 pp.
National Research Council, 2005: Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties. National Academies Press, 224 pp.
Ramanathan, V., and Y. Feng, 2008: On avoiding dangerous anthropogenic interference with the climate system: Formidable challenges ahead. Proc. Natl. Acad. Sci. USA, 105 , 14245–14250.
Raupach, M., G. Marland, P. Ciais, C. L. Quéré, J. G. Canadell, G. Klepper, and C. Field, 2007: Global and regional drivers of accelerating CO2 emissions. Proc. Natl. Acad. Sci. USA, 104 , 10288–10293.
Roe, G. H., and M. B. Baker, 2007: Why is climate sensitivity so unpredictable? Science, 318 , 629–632.
Sanderson, B., C. Piani, W. Ingram, D. Stone, and M. R. Allen, 2008: Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Climate Dyn., 30 , 175–190.
Schlesinger, M. E., 1988: Quantitative analysis of feedbacks in climate model simulations of CO2 induced warming. Physically Based Modelling and Simulation of Climate and Climate Change, M. E. Schlesinger, Ed., NATO ASI Series C, Vol. 243, Kluwer Academic, 653–735.
Schwartz, S. E., R. J. Charlson, and H. Rodhe, 2007: Quantifying climate change—Too rosy a picture? Nature Rep. Climate Change, 1 , 23–24. doi:10.1038/climate.2007.22.
Solomon, S., D. Qin, M. Manning, M. Marquis, K. Averyt, M. M. B. Tignor, H. L. Miller Jr., and Z. Chen, Eds. 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.
Trenberth, K., J. T. Fasullo, and J. Kiehl, 2009: Earth’s global energy budget. Bull. Amer. Meteor. Soc., 90 , 311–324.
Wijffels, S. E., J. Willis, C. M. Domingues, P. Barker, N. J. White, A. Gronell, K. Ridgway, and J. A. Church, 2008: Changing eXpendable BathyThermograph fall-rates and their impact on estimates of thermosteric sea level rise. J. Climate, 21 , 5657–5672.
Willis, J. K., D. Roemmich, and B. Cornuelle, 2004: Interannual variability in upper ocean heat content, temperature, and thermosteric expansion on global scales. J. Geophys. Res., 109 , C12036. doi:10.1029/2003JC002260.
Yeager, A., 2008: Satellite risks losing sight of Earth. Nature, 456 , 292.
Yu, H., and Coauthors, 2006: A review of measurement-based assessment of aerosol direct radiative effect and forcing. Atmos. Chem. Phys., 6 , 613–666.
Zhang, Q., and Coauthors, 2007: Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes. Geophys. Res. Lett., 34 , L13801. doi:10.1029/2007GL029979.
Global average radiative forcing estimates and associated uncertainty ranges (5%–95% confidence intervals, roughly 1.6 standard deviation) in 2005, relative to the preindustrial climate, for anthropogenic aerosols and long-lived greenhouse gases as given by the IPCC Assessment Report (Solomon et al. 2007). Total forcing includes other anthropogenic and natural (solar) forcings not shown, of which forcing by tropospheric ozone (0.35 W m−2) is the greatest. Effective forcing is equal to total forcing minus average increase in global heat content, which is expressed as a flux and shown as a negative quantity.
Citation: Journal of Climate 23, 10; 10.1175/2009JCLI3461.1
Equilibrium increase (yellow) and expected present increase (black) in GMST above preindustrial temperature from the forcing by present (2005) incremental concentrations of long-lived greenhouse gases as a function of CO2 doubling temperature ΔT2×, (bottom axis) or equilibrium climate sensitivity S (top axis); expected increase accounts for global heating rate. Vertical purple solid and dashed lines denote the IPCC best estimate and central 66% uncertainty range for equilibrium climate sensitivity. Red lines show expected increase in GMST, accounting for all forcings over the industrial period (Solomon et al. 2007, their Fig. SPM-2), for indicated values of forcings by anthropogenic aerosols. The blue line denotes observed increase in GMST of present climate relative to preindustrial (0.8 K); the blue band denotes estimated portion of increase in GMST that can be attributed to natural variability.
Citation: Journal of Climate 23, 10; 10.1175/2009JCLI3461.1
Empirical determination of the earth’s climate sensitivity. The blue line denotes the relation between climate sensitivity S top axis or CO2 doubling temperature ΔT2×, auxiliary top axis, both on inverse scale, and effective forcing over the industrial period, left axis; total forcing, right auxiliary axis; or aerosol forcing, right axis, for increase in GMST over industrial period equal to 0.8 K; the blue band notes associated uncertainty due to natural variability. The solid horizontal magenta line denotes the IPCC best estimate (Solomon et al. 2007) of aerosol forcing and total forcing over the industrial period Ftot, and effective forcing in Fig. 1. Vertical solid line denotes inferred climate sensitivity corresponding to this forcing. Dashed lines and fill denote “likely” uncertainty range in forcing (Fig. 1) and resultant uncertainty range in inferred sensitivity.
Citation: Journal of Climate 23, 10; 10.1175/2009JCLI3461.1
Allowable cumulative future equivalent CO2 emission ECO2, Pg C (green), compatible with a given maximum acceptable increase in GMST above preindustrial ΔTmax (ordinate) as a function of equilibrium climate sensitivity expressed as CO2 doubling temperature ΔT2×, (abscissa). Line ECO2 = 0 (yellow) denotes expected equilibrium increase in GMST above preindustrial for present incremental greenhouse gas concentrations as a function of ΔT2× as in Fig. 2. Negative values of ECO2 (red) indicate amount of equivalent CO2 that would need to be removed from present atmosphere to achieve a given ΔTmax as a function of ΔT2×. Time (years) corresponding to cumulative emission ECO2 is evaluated as tCO2 = ECO2/q, where the current emission rate q is taken for simplicity as 10 Pg C yr−1. Vertical purple line indicates the best estimate and the shaded area denotes range (66% likelihood) for CO2 doubling temperature given by the 2007 IPCC Assessment Report, as in Fig. 2. Horizontal brown line indicates widely accepted value of maximum allowable increase in GMST. Calculations do not account for the cooling influence of enhanced short-lived atmospheric aerosols or the warming influence of enhanced tropospheric ozone.
Citation: Journal of Climate 23, 10; 10.1175/2009JCLI3461.1
Allowable future equivalent CO2 emission* for increase in GMST above its preindustrial value not to exceed ΔTmax = 2 K.
