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AVOIDING DANGEROUS ANTHROPOGENIC INTERFERENCE WITH THE CLIMATE SYSTEM KLAUS KELLER1 , MATT HALL2 , SEUNG-RAE KIM3 , DAVID F. BRADFORD3,4,5,6,7 and M. OPPENHEIMER3,8 1

Department of Geosciences, Penn State, PA, U.S.A. E-mail: [email protected] 2 Brookings Institution, Washington, DC, U.S.A. 3 Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, U.S.A. 4 Department of Economics, Princeton University, Princeton, NJ, U.S.A. 5 School of Law, New York University, NY, U.S.A. 6 National Bureau of Economic Research, Cambridge, MA, U.S.A. 7 CESifo, Munich, Germany 8 Department of Geosciences, Princeton University, Princeton, NJ, U.S.A.

Abstract. The UN Framework Convention on Climate Change calls for the avoidance of “dangerous anthropogenic interference with the climate system”. Among the many plausible choices, dangerous interference with the climate system may be interpreted as anthropogenic radiative forcing causing distinct and widespread climate change impacts such as a widespread demise of coral reefs or a disintegration of the West Antarctic ice sheet. The geological record and numerical models suggest that limiting global warming below critical temperature thresholds significantly reduces the likelihood of these eventualities. Here we analyze economically optimal policies that may ensure this risk-reduction. Reducing the risk of a widespread coral reef demise implies drastic reductions in greenhouse gas emissions within decades. Virtually unchecked greenhouse gas emissions to date (combined with the inertia of the coupled natural and human systems) may have already committed future societies to a widespread demise of coral reefs. Policies to reduce the risk of a West Antarctic ice sheet disintegration allow for a smoother decarbonization of the economy within a century and may well increase consumption in the long run.

1. Introduction We explore climate policies to reduce the risk of two environmental outcomes that may be interpreted as “dangerous anthropogenic interference” with the climate system (UNFCCC, 1992). Specifically, we derive climate policies that significantly reduce the risk of sustained, widespread bleaching of coral reefs and the disintegration of the West Antarctic Ice Sheet (WAIS) (Oppenheimer, 1998; Hoegh-Guldberg, 1999; O’Neill and Oppenheimer, 2002). These two climate limits represent two distinct interpretations of dangerous interference, involving hazards of differing scope, probability, and timing of occurrence. Other interpretations of “dangerous interference” could be explored, including a slowdown of the ocean’s thermohaline circulation (Cubasch and Meehl, 2001), low-latitude vegetation transition (Claussen et al., 1999), changing ENSO properties (Fedorov and Philander, 2000), or thresholds in Climatic Change (2005) 73: 227–238 DOI: 10.1007/s10584-005-0426-8

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social vulnerability, such as climate change impacts exceeding a certain threshold level (Dessai et al., 2004). Coral reefs constitute a “unique” ecosystem (Smith et al., 2001) that requires warm waters at low latitudes. Their destruction would affect marine biodiversity, tourism, and fisheries. A global mean warming by 1.5 ◦ C relative to preindustrial (≈the year 1700) temperatures has been postulated to result in severe, widespread damage due to coral bleaching (Hoegh-Guldberg, 1999). The use of a single and globally averaged temperature value to describe the bleaching limit is a simplification, as it neglects, for example, the spatial pattern of global temperature changes (Hoegh-Guldberg, 1999; Sheppard, 2003), or the potentially detrimentally effects of increasing CO2 concentrations on reef growth (Kleypas et al., 1999). It neglects, furthermore, potential future acclimation and evolution of corals, which may raise the temperature limit. The extent of future acclimation and evolution is, however, uncertain (Hoegh-Guldberg, 1999; Hughes et al., 2003). The impacts could be abrupt and the time scale for recovery, while controversial, could be lengthy compared to that for bleaching. The disintegration of the WAIS is a possible consequence of unabated anthropogenic greenhouse gas emissions (Mercer, 1978; Oppenheimer, 1998). The probability of a complete WAIS disintegration is believed to be rather low during this century and to increase gradually thereafter (Oppenheimer, 1998; Vaughan and Spouge, 2002). The limitations of current ice-sheet models only permit scenarios relating to the WAIS’ future behavior, rather than reliable estimates of the probability or potential rate of disintegration (Bindschadler, 1997). Here, we adopt a warming of 2.5 ◦ C compared to preindustrial temperatures as a climate limit that would reduce the probability of triggering WAIS disintegration to low levels (Oppenheimer, 1998; O’Neill and Oppenheimer, 2002). This precautionary view is based on the interpretation of proxy data. The aim is to stay below global mean temperature that characterized earlier eras when the WAIS may have been absent (e.g., during one of the Pleistocene interglacials (Mercer, 1968; Scherer et al., 1998)). It is important to distinguish between the triggering of a threshold response (i.e., the forcing that could move the system to a different basin of attraction (Keller et al., 2004b)) and the completion of the threshold response. This distinction is important for the WAIS case because crossing the precautionary climate limit of 2.5 ◦ C is a distinct possibility within this century absent stringent reductions in greenhouse gas emissions (Cubasch and Meehl, 2001). In contrast, the completion of the potentially triggered disintegration would be rather unlikely in this century (Oppenheimer, 1998; Vaughan and Spouge, 2002). The impacts of WAIS disintegration (sometimes characterized as “a disaster” (Mercer, 1978) or “a catastrophe” (Canals et al., 2003)) on social systems are uncertain and would depend on how fast disintegration occurred. Disintegration could raise the global mean sea level by 4–6 m within as little as centuries (Oppenheimer, 1998). Extensive loss of land would result along the coastlines with considerable impacts especially for low-lying and heavily populated areas such as Bangladesh or Louisiana.

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2. The Model We analyze policies to avoid these climate limits using a modified version of the Regional Integrated model of Climate and the Economy (RICE) (Nordhaus and Yang, 1996; Nordhaus and Boyer, 2000; Keller et al., 2003; Yang, 2003). The RICE model couples natural and human systems in a relatively simple and transparent framework. Policy selection is treated as an intertemporal optimization. The RICE model links an optimal economic growth model to a description of anthropogenic climate change with the implied economic impacts. Economic output in each region is described by a constant-returns-to-scale Cobb–Douglas production function with labor, capital, and level of technology parameters. Economic activity results in CO2 production with an exogenously given and time varying ratio between gross world product (GWP) and CO2 production. Atmospheric CO2 in excess of the preindustrial level is removed, over time, from the atmosphere by natural carbon sinks. The carbon cycle model is a linear three-box model. Greenhouse gases other than CO2 are prescribed exogenously. Greenhouse gas concentrations above preindustrial levels cause an anthropogenic greenhouse effect and act to increase globally averaged temperatures. The global mean temperature change is used only as an indicator for global climate change. A simple model consisting of an atmosphere, a surface ocean, and a deep ocean represents the climate system. One key parameter determining the link between atmospheric CO2 concentrations and global warming is the climate sensitivity, the equilibrium temperature change for a doubling of CO2 . Here we adopt a recent estimate of the climate sensitivity with an expected (mean) value of 3.4 ◦ C (Andronova and Schlesinger, 2001). Alternative studies yield slightly different estimates (Forest et al., 2002; Gregory et al., 2002), due to methodological differences (e.g., the use of “expert knowledge” in a Bayesian framework). Climate change damage estimates involve the willingness-to-pay method and are expressed as a function of global mean temperature increase from its preindustrial level (Nordhaus and Yang, 1996). The costs of climate control as well as the climate damages are subtracted from the economic output. The costs of CO2 reductions are derived from engineering studies and expressed as a function of the reduction of CO2 emissions relative to a no-control policy. In the original RICE model, CO2 emissions can be reduced by cutting fossil fuel use (abatement). Here we update and expand the structure of the RICE model by adding an additional policy instrument to reduce CO2 emissions: sequestration of CO2 into geological reservoirs (Keller et al., 2003). The carbon sequestered into the reservoirs is assumed to leak back to the atmosphere following a first-order kinetics and with a half-life time of 200 years. This relatively high leakage rate is arguably a conservative assumption, as some carbon sequestration methods have the potential to result in substantially lower leakage rates (Lackner, 2003). The marginal costs for carbon sequestration are declining in the model as a function of the cumulative installed capacity to account for the observed “learning-by-doing” effect

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(Argote and Epple, 1990). Note that the availability of a carbon backstop technology with learning-by-doing reduces the optimal CO2 concentrations (Keller et al., 2003). The detailed effects of the additional policy instrument of carbon sequestration on optimal policies without climate thresholds are discussed elsewhere (Keller et al., 2003). In a nutshell, the availability of carbon sequestration at the estimated current costs reduces the optimal carbon emissions and, as a result, the optimal levels of atmospheric CO2 and warming. Our forthcoming results are hence not directly comparable to the results discussed in Nordhaus and Boyer (2000). Designing a climate policy is reduced in the model to choosing three schedules of “investment”: (i) accumulation of the stock of capital, (ii) abatement of CO2 emissions into the atmosphere, and (iii) sequestration of CO2 emissions into nonatmospheric reservoirs. The three schedules are chosen to maximize an objective function of present and future consumption subject to the relevant constraints. The objective function is the weighted sum of the utility of consumption over time. In particular, per-capita consumption is translated into utility of consumption (using a logarithmic transformation) to account for the decreasing marginal utility of consumption with increasing consumption levels. The utility of consumption at each time is weighted by three factors: (i) the population size to represent the effects of population growth, (ii) a discount factor to account for the positive rate of social time preference, and (iii) regional weights to equalize the value of an additional unit of output to each region. The numerical model starts in the year 1995 with historic values and approximates the underlying infinite horizon problem using a finite time horizon of 260 years. We represent the virtually unchecked CO2 emissions in the past by imposing zero abatement and sequestration fluxes for the historic time periods. Socioeconomic inertia for the introduction of new technologies is approximated by imposing an upper limit of 0.1 Gt C per year on the CO2 sequestration flux in 2005 and a maximum sequestration growth rate of 5% per year thereafter (Grübler et al., 1999). This approximation follows the assumption in other models (Manne et al., 1995). More complex representations are, of course, possible (Grubb et al., 1995; HaDuong et al., 1997), but the parameterization of these more complex relationships is an area of active research. We represent the climate limits by constraining the temperature change in the optimal growth model to stay below the threshold values. The analysis framework relies, as does every model, on simplified representations of the underlying systems. For example, it does not resolve the stochastic nature of the problem. The model is described in more detail in Keller et al. (2003). The model code is available from the authors upon request.

3. Results and Discussion We use the modified RICE model (Keller et al., 2003) to analyze four policy choices: (i) no control, (ii) optimal policy neglecting climate limits (but considering other


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climate change impacts as represented in RICE), (iii) optimal policy to stay below the WAIS temperature limit of 2.5 ◦ C, and (iv) optimal policy to stay below a 1.5 ◦ C limit (to reduce the risk of a widespread demise of coral reefs). For all four policies, temperatures continue to increase well into this century (Figure 1A). The optimal policy neglecting climate limits leads to a warming of approximately

Figure 1. Constrained optimal temperature (A) and carbon dioxide (CO2 , (B)) trajectories to avoid the climate limits imposed by possible coral bleaching (squares) and a disintegration of the West Antarctic Ice Sheet (WAIS) (stars). Temperature changes refer to the preindustrial condition (≈1700). The crosses represent the no-control policy. The dashed line illustrates the unconstrained optimal policy (i.e., neglecting the climate limits).

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3 ◦ C in the next century and breaches the coral and WAIS limit. For the no-control scenario, the average temperature is projected to increase beyond 4 ◦ C within the next century. In the no-control scenario, atmospheric CO2 rise to approximately 800 ppm, accompanied by moderate increase in radiative forcing by other greenhouse gases (Figure 1B). The WAIS and the coral limits imply peak CO2 concentrations around 460 and 370 ppm, respectively. These lower CO2 concentrations in the future translate into considerable reductions in allowable CO2 emissions (Figure 2). Without climate control, the CO2 emissions rise to 11 Gt C a−1 in 2050. The allowable CO2 emissions for the WAIS and coral limit in 2050 are 8 and 0.6 Gt C a−1 , respectively. Avoiding the coral limit requires a precipitous drop in CO2 emissions on a decadal timescale. The virtually unchecked CO2 emission to date, in combination with the inertia of the coupled natural and human systems, may have already committed future societies to cross the coral temperature limit (Wetherald, 2001). The costs of reducing the CO2 emissions have to be compared with the benefits of avoiding (i) the climate limit and (ii) economic damages besides the climate limit. We approximate the net costs of choosing a specific climate constraint by the net present value of the changes in consumption (Figure 3), taking into account the economic damage embodied in the RICE model (Keller et al., 2003). Crossing the climate limits is treated as having no effect on the measured economic output. This assumption very likely underestimates the economic damages of crossing the climate limit but simplifies the forthcoming economic analysis.

Figure 2. Industrial CO2 emissions to the atmosphere corresponding to the climate management strategies shown in Figure 1. One Gt C equals 109 tonnes of carbon or 3.67 Gt of CO2 . Other symbols and abbreviations are defined in Figure 1.


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Figure 3. Effects of adopting the climate policies shown in Figure 1 on the projected average per capita consumption (A). The changes in per-capita consumption (B) are relative to the no-control policy. The calculations assume that the passing of climate limits would cause zero economic damage. The consumption values are in constant 1995 U.S.$. Symbols and abbreviations are defined in Figure 1.

Perhaps surprisingly, the consumption paths are quite similar for all policies (Figure 3). To paraphrase Schelling (1992) choosing to stay below the WAIS-limit (rather than following the no-control policy) postpones the consumption in 2100 by less than a year in the model. In the long term, choosing the WAIS policy actually improves consumption in this simulation. The costs of choosing a policy to avoid the WAIS limit over (i) the no-control policy or (ii) optimal policy neglecting the

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WAIS constraint are equivalent to a one-time investment of ≈0.4 and ≈2% of present annual GWP, respectively. One might interpret the previous assessment of a WAIS disintegration as “a disaster” (Mercer, 1978) to imply expected economic damages exceeding these estimated costs to avoid it. In this case, choosing the WAIS policy would be a superior choice in the optimal growth framework compared to the no-control or the optimal policy neglecting the WAIS constraint. Adopting the WAIS policy implies a cost in net present value terms, even though the per-capita consumption increases in the long run. The key to this result lies in the application of a discount rate to future consumption changes. Applying a lower discount rate would decrease the present value of the net costs because future benefits of climate control (Figure 3) are discounted less.

4. Caveats and Open Questions Our results are contingent on many features of the adopted analysis framework, such as (i) the limited treatment of uncertainty, (ii) the value judgments regarding intergenerational tradeoffs, or (iii) the distinct possibility that climate thresholds besides the analyzed coral bleaching and WAIS disintegration may be crossed as a result of anthropogenic climate change. Here we briefly discuss the implications of these key issues for our forthcoming conclusions. More general discussion can be found elsewhere (Grubb et al., 1995; Bradford, 1999; Janssen and De Vries, 2000; Tol, 2003; Keller et al., 2004a). First, our simple analysis is mostly silent on uncertainty. Uncertainty implies that one cannot avoid a specific climate limit with absolute certainty; rather, climate policies can only reduce a specific risk, often at higher net costs (Dowlatabadi, 2000; Keller et al., 2000). One can illustrate the risk-cost tradeoff by varying the climate sensitivity in the model. Reducing the likelihood of crossing a temperature limit to ≤5% by commitment to a policy now would involve basing it on a climate sensitivity ≥ the 95th percentile. Adopting the 95 percentile estimate for the climate sensitivity of 9.4 ◦ C (Andronova and Schlesinger, 2001) compared to the expected value of 3.4 ◦ C implies more stringent (and costly) CO2 control policies. As a result, the net costs of choosing the WAIS policy over the no-control policy increases from ≈0.4 to ≈110% of current GWP. Reducing the likelihood of crossing the temperature threshold even further, say to a de minimis level – say 10−5 (Colmer, 1979) – would imply even higher and arguably politically infeasible costs. Note that simple analysis considers only the parametric uncertainty about a single model parameter, neglects structural uncertainty, and is silent on the distinct possibility that we may reduce uncertainty in the future through learning about the climate system (Kelly and Kolstad, 1999; Keller et al., 2004a). Second, the value judgments regarding intergenerational tradeoffs affect the timing and extent of the optimal policies (Schelling, 1992; Nordhaus, 1997; Bradford, 1999). For example, the model approximates the observed pattern of


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time preference, resulting in a relatively high discount rate of roughly 6% a−1 in the near term (assumed to decline over time). As a correlative of the broader question of wealth distribution between present and future generations, climate policy involves the controversial issue of discounting over long time horizons (Schelling, 1992). Using lower discount rates for the far future – as often urged (Weitzman, 1998) – would give more weight to future benefits of climate control and reduce the net-present value of the costs. Third, the considered climate thresholds of coral bleaching and a WAIS disintegration are just two examples of possible interpretations of dangerous anthropogenic interference with the climate system (Parry et al., 1996; Smith et al., 2001; Dessai et al., 2004; Mastrandrea and Schneider, 2004). For example, anthropogenic changes in ENSO properties may be triggered before a WAIS disintegration (Timmermann et al., 1999; Fedorov and Philander, 2000). Triggering one threshold may well affect the vulnerability of other thresholds. For example, a melting of the Greenland ice sheet would increase the meltwater flux into the North Atlantic, thus potentially affecting the stability of the ocean’s thermohaline circulation (Huybrechts et al., 2002; Fichefet et al., 2003). Finally, our analysis is silent on issues of adaptation. This is important because past CO2 emissions may have already rendered infeasible the avoidance of “dangerous anthropogenic interference with the climate system” under some interpretations. For example, coral ecosystems may adapt and partially return if climate change is stabilized at higher levels. This poses the long-term policy question of how to manage climate change after the peak of anthropogenic fossil fuel use: should the global climate system be restored to preindustrial conditions or some new, potentially warmer, climate state to minimize impacts? 5. Conclusions Given these caveats, we draw from our analysis two main conclusions. First, anthropogenic carbon emissions have to be reduced considerably compared to the business as usual to reduce the risk of dangerous anthropogenic interference with the climate system. To significantly reduce the risk of a widespread coral bleaching would require a decarbonization of the economy within a few decades. Whether such fast decarbonization rates are politically feasible is an open question. A precautionary policy to reduce the risk of a disintegrating West Antarctic ice sheet would imply to decarbonize the global energy system within this century. Second, depending on choice of discount rate, adopting these risk reduction policies could be costly, but arguably not prohibitively so, involving expected sacrifices of consumption with net present values of a few percent of current gross world product. A quarter century ago, Mercer (1978) concluded with respect to a WAIS disintegration that “this deglaciation may be part of the price that must be paid in order to buy enough time for industrial civilization to make the changeover from

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fossil fuels to other sources of energy”. Our analysis suggests that it may well be an economically sound policy to pay the price of avoiding a WAIS disintegration. Acknowledgments We thank R. Alley, J. Edmonds, J. Kasting, D.-H. Min, and Rob Socolow for helpful discussions. Reviews and comments from S. Schneider and M. D. Mastrandrea helped to clarify the manuscript. Funding from NSF (SES #0345925), the Carbon Mitigation Initiative (CMI) at Princeton University, and the Penn State Institutes for the Environment is gratefully acknowledged. Any opinions, findings, and conclusions expressed in this publication are those of the authors and do not necessarily reflect the views of NSF. References Andronova, N. G. and Schlesinger, M. E.: 2001, ‘Objective estimation of the probability density function for climate sensitivity’, J. Geophys. Res. Atmos. 106, 22605–22611. Argote, L. and Epple, D.: 1990, ‘Learning curves in manufacturing’, Science 247, 920–924. Bindschadler, R.: 1997, ‘West Antarctic ice sheet collapse?’, Science 276, 662–663. Bradford, D. F.: 1999, ‘On the uses of benefit-cost reasoning in choosing policy toward global climate change’, in Portney, P. R. and Weyant, J. P. (eds.), Discounting and Intergenerational Equity. Resources for the Future, Washington, DC, pp. 37–44. Canals, A. C., Calafat, A., Camerlenghi, A., De Batist, M., Urgeles, R., Farran, M., Geletti, R., Versteeg, W., Amblas, D., Willmott, V., Lastras, Ga, and Imbo, Y.: 2003, ‘Uncovering the footprint of former ice streams in Antarctica’, EOS 84, 97–108. Claussen, M., Kubatzki, C., Brovkin, V., Ganopolski, A., Hoelzmann, P., and Pachur, H. J.: 1999, ‘Simulation of an abrupt change in Saharan vegetation in the mid-Holocene’, Geophys. Res. Lett. 26, 2037–2040. Colmer, C. L.: 1979, ‘Risk: A pragmatic De Minimis Approach’, Science 203, 319. Cubasch, U. and Meehl, G. A.: 2001, ‘Projections of future climate change’, in Climate Change 2001 – The Scientific Basis. Contribution of Working Group I of the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, pp. 526–582. Dessai, S., Adger, W. N., Hulme, M., Turnpenny, J., Kohler, J., and Warren, R.: 2004, ‘Defining and experiencing dangerous climate change – an editorial essay’, Clim. Change 64, 11–25. Dowlatabadi, H.: 2000, ‘Bumping against a gas ceiling’, Clim. Change 46, 391–407. Fedorov, A. V. and Philander, S. G.: 2000, ‘Is El Niño Changing?’, Science 288, 1997–2002. Fichefet, T., Poncin, C., Goosse, H., Huybrechts, P., Janssens, I., and Le Treut, H.: 2003, ‘Implications of changes in freshwater flux from the Greenland ice sheet for the climate of the 21st century’, Geophys. Res. Lett. 30:doi:10.1029/2003GL017826. Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., and Webster, M. D.: 2002, ‘Quantifying uncertainties in climate system properties with the use of recent climate observations’, Science 295, 113–117. Gregory, J. M., Stouffer, R. J., Raper, S. C. B., Stott, P. A., and Rayner, N. A.: 2002, ‘An observationally based estimate of the climate sensitivity’, J. Clim. 15, 3117–3121. Grubb, M., Chapuis, T., and Duong, M. H.: 1995, ‘The economics of changing course – implications of adaptability and inertia for optimal climate policy’, Energy Policy 23, 417–431.


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