Value of information for climate observing systems
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The Interagency Working Group Memo on the social cost of carbon is used to compute the value of information (VOI) of climate observing systems. A generic decision context is posited in which society switches from a business as usual (BAU) emissions path to a reduced emissions path upon achieving sufficient confidence that a trigger variable exceeds a stipulated critical value. Using assessments of natural variability and uncertainty of measuring instruments, it is possible to compute the time at which the required confidence would be reached under the current and under a new observing system, if indeed the critical value is reached. Economic damages (worldwide) from carbon emissions are computed with an integrated assessment model. The more accurate observing system acquires the required confidence earlier and switches sooner to the reduced emissions path, thereby avoiding more damages which would otherwise be incurred by BAU emissions. The difference in expected net present value of averted damages under the two observing systems is the VOI of the new observing system relative to the existing system. As illustration, the VOI for the proposed space-borne CLARREO system relative to current space-borne systems is computed. Depending on details of the decision context, the VOI ranges from 2 to 30 trillion US dollars.
KeywordsValue of information Climate observing system Social cost of carbon DICE CLARREO
In early 2010, the United States government published estimates of the social cost of carbon for use in regulatory cost–benefit analysis (Interagency Working Group on Social Cost of Carbon; IWG SCC 2010, hereafter SCC). The estimates concern the monetized climatic benefits of regulations for the transportation, electricity, and other economic sectors that reduce carbon emissions. Since formal publication of the SCC, it has been employed in more than 20 regulations ranging from fuel economy standards for vehicles to air pollution regulations for power plants to energy efficiency standards for appliances and equipment (Kopp and Mignone 2012).
The value of learning about climate change has been emphasized in a host of papers including Kelly and Kolstad (1999), O’Neill et al. (2006), Webster et al. (2008), and McInerney et al. (2011). The related notion of value of design has been used in appraising aeronautical systems (Brathwaite and Saleh 2013). This paper uses the SCC to compute the value of information (VOI) provided by projected climate observing system (COS) improvements designed to learn about key climate parameters faster than existing observations. For background on VOI, see Laxminarayan and Macauley (2012). The key to computing this VOI is to place this new COS in a decision context where its information can be used. Indeed, if the new information is not used, then the COS can be valued only in terms of pure knowledge accretion, and its potential social value is lost. The VOI formalism is the essential tool in structuring the decision problem in which the social value of a new COS can be computed. Although based on the US baseline for computing the SCC, the climate damages are worldwide. Switching from a business as usual (BAU) to a reduced emissions path (see Sect. 4) upon achieving a given level of confidence that a climate parameter has been exceeded avoids damages worldwide whose net present value (NPV) runs into tens of trillions of US dollars.
To illustrate VOI calculations of COSs, this paper considers the proposed CLARREO space-borne observing system. Driving this choice is the fact that key accuracy parameters of this system as compared to the current space-based systems have been computed (Wielicki et al. 2013). This initial VOI estimate uses several simplifying assumptions. Besides the assumptions underlying the SCC, we use the integrated assessment model DICE coupling climate change to economic damages (Nordhaus 2008), and we simplify the decision context in many ways. Sensitivity tests of selected parameters suggest that, while total societal costs vary greatly, the VOI of accelerated climate change information is relatively robust against the selection of a reduced emissions scenario, a climate trigger for switching to the reduced emissions scenario, and the level of confidence required for the switch. The main message of this paper, however, is that VOI calculations of new COSs are possible, and should be used to assess their potential contribution, beyond a current baseline, of new observing systems.
Section 1 describes the SCC. Section 2 discusses the COS improvements treated in this paper. Section 3 describes the decision context for evaluating the VOI for the improved COS. Section 4 presents results, and a final section summarizes and concludes. Supplementary Online Material gives the mathematical basis for learning a trend from noisy signals, based on Leroy et al. (2008). The SSC explicitly introduced quantitative uncertainty analysis into the social cost of carbon. This is certainly not the last word on the subject; see Cooke (2012) for background on uncertainty analysis and climate change.
2 Interagency memo on the social cost of carbon
The SCC is intended to be a comprehensive estimate of the economic impacts of climate change, including impacts ranging from (but not limited to) changes in crop productivity, loss of land to sea level rise, health effects, and potential economic catastrophes associated with Earth system tipping points. The SCC does not currently include costs due to changing international political stability, ocean acidification, species and wildlife loss, or ecosystem services.
Calculating the SCC involves specifying a baseline emissions scenario, computing the NPV of the resulting climate damage, and subtracting this from the NPV of damages resulting from adding one extra unit of carbon emissions in the current time period. Three discount rates are stipulated for the calculation 2.5, 3, and 5 %. The range of discount rates is chosen to acknowledge uncertainties in the appropriate rates for long-term global climate change (Arrow et al. 1996; Stern 2008). Three integrated assessment models (IAMs) are used to couple emissions to temperature rise, and to climate damages: DICE (Nordhaus 2008), FUND (Anthoff and Tol 2010; Tol 2002) and PAGE (Hope 2006). These IAMs combine simple climate, carbon cycle, and economic models with assumptions about population and income growth, technological change, and public policies.
There is a very wide range of uncertainty in estimates of the SCC due to the difficulty of assessing future policies, economic developments, the climate response to CO2 forcing, and other assumptions used in the analysis (Tol 2005; Kopp and Mignone 2012). The SCC acknowledges the many uncertainties involved and the need to update SCC estimates over time to reflect advances in the science and economics of climate impacts (IWG SCC 2010, p. 32).1
In spite of these acknowledged uncertainties, the US-SCC establishes a common decision context and a common baseline for rigorous VOI computations. This, for the first time, enables quantitative, monetary valuation of the social benefits of climate system observations.
Social cost of CO2, 2010–2050 (2007 dollars per ton)
5 % avg
3 % avg
2.5 % avg
3 % 95th
The SCC increases over time reflecting larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. The SCC estimates for 2010 range from $5 to $35 per metric ton of CO2 equivalent depending on the discount rate.2 The SCC estimate also provides a 95th percentile value for low probability but high economic impacts as might be found for high CS and/or climate tipping points such as destabilization of major ice sheets.
3 Climate observing system
Earth is observed more completely today than at any other time in its history (GCOS 2011; GEO 2005, 2010). Yet major challenges remain, especially for observations of climate change, where unprecedented accuracy and rigor are required to observe subtle but critical climate changes on decade and longer timescales (Trenberth et al. 2012; GCOS 2011; NRC 2007). Major challenges remain in achieving traceability to international physical standards for space-based global observations (NRC 2007; Ohring et al. 2005; Ohring 2007), in providing independent observations and analysis to allow verification of surprising results (CCSP 2003), and in achieving sufficient sampling to document climate extremes (Trenberth et al. 2012).
Specific areas of uncertain climate change science with large impacts on societal costs include uncertainty in the rate and magnitude of sea level change from the major ice sheets in Greenland and Antarctica, uncertainty in CS including cloud and carbon cycle feedbacks, uncertainty in anthropogenic aerosol radiative forcing, and uncertainty in future ocean acidification (IPCC 2007). The list is not exhaustive, but serves to demonstrate the diversity of climate science challenges. Solving these challenges requires both improved observations as well as improved climate system predictive models. More accurate climate predictions, validated by improved observations, can then provide the basis for more cost-effective and lower risk climate policies.
Currently, there are no VOI estimates for climate observations or climate modeling science. In contrast, we do have more rigorously traceable estimates of the economic value of weather predictions (Morss et al. 2008; Katz and Murphy 2005; Teisberg et al. 2005; Freebairn and Zillman 2002). Such estimates can be based on an extensive past history of weather events and their economic impacts. Climate change, meanwhile, has its primary impacts well into the future, and is a very different prediction challenge than weather (Hurrell et al. 2009; IPCC 2007). Weather prediction is primarily a dynamical prediction based on initial conditions and predicting a specific place (your city) and a specific instant of time (to within hours) within a few days into the future. Climate prediction on decadal up to century scales is primarily an energetics prediction based on changing boundary conditions and predicting the climate system response over long time scales (decades to century) with spatial averages from local (city) to regional (continent) to global. As a result, weather prediction VOI metrics are not directly applicable to climate prediction. The problem of decade to century time scale climate science VOI is sufficiently daunting and complex that it has remained largely unexplored. Most climate-related VOI studies have focused on short-term seasonal prediction as opposed to long-term climate change (Katz and Murphy 2005).
Consideration of all of the above climate science uncertainties and their potential observational improvements is beyond the scope of the present paper. Instead, we focus on the crucial climate uncertainty identified in the US-SCC, the uncertainty in CS. This selection is made because of its large impact on potential future climate change societal impacts. As mentioned in Sect. 1, an uncertainty of a factor of 4 in CS leads to a factor of 16 uncertainty in future economic impacts (IWG SCC 2010). The selection of CS is also motivated by recent advances in more clearly defining the relationship between decadal change climate observation accuracy and uncertainty in CS (Wielicki et al. 2013; Soden et al. 2008). Finally, we focus on the space-borne component of climate observations because of its unique global perspective. This focus allows us to take an initial step toward more rigorous climate science VOI that can provide a basis for later expansion to a more complete range of climate science uncertainties.
Climate sensitivity is the result of a wide range of both negative (stabilizing) and positive (destabilizing) feedbacks. The Stefan–Boltzmann law provides the strongest negative feedback. As the Earth’s surface warms, it emits greater infrared energy. Water vapor feedback is a strong positive feedback driven by the Clausius Clapeyron relationship (Soden and Held 2006; IPCC 2007). Ice albedo feedback is a moderately strong positive feedback (Soden et al. 2008). The major uncertainty in CS, however, is cloud feedback (IPCC 2007; Roe and Baker 2007; Soden and Held 2006; Soden et al. 2008) which produces most of the uncertainty in the probability distributions shown in Fig. 1 (Roe and Baker 2007).
There are multiple methods that have attempted to determine CS, all of which have different uncertainties (IPCC 2007). Use of glacial/interglacial paleo data has the advantage of long climate records, but also has concerns about observation accuracy, spatial sampling, and variations of CS from the peak of glacial epochs to the interglacial of today (Hansen et al. 2011; IPCC 2007). Ensemble distributions of climate model simulations (including perturbed physics ensembles) struggle to relate climate model prediction errors in climate base state or seasonal cycles to decade to century-scale CS uncertainties (IPCC 2007; Roe and Baker 2007; Murphy et al. 2004; Klocke et al. 2011). Efforts to relate climate change to CO2 concentrations over the last several decades struggle with both surface and air temperature accuracy (IPCC 2007; Karl et al. 2006; Hansen et al. 2010) and even more so with uncertainties in anthropogenic aerosol radiative forcing. Uncertainty in anthropogenic aerosol radiative forcing causes a factor of 3 uncertainty in the current total anthropogenic radiative forcing of the climate system (IPCC 2007; Hansen et al. 2005). Fortunately, recent advances in separating climate feedbacks in climate model simulations (Soden et al. 2008; Soden and Vecchi 2011) have helped clarify the observations needed on long time scales, including estimates of decadal changes in cloud radiative forcing for cloud feedbacks.
Obtaining a full set of observations of the feedbacks, along with the basic anthropogenic radiative forcing and global temperature response, would provide fully independent verification of CS. In the present paper, we do not consider all of these variables, but focus on global average temperature, which is key to observing climate system response. Measures of surface temperature and tropospheric air temperature are considered here. Future work can extend this to consider uncertainty in aerosol and cloud radiative forcing, but direct and indirect aerosol forcing are more complex issues than low cloud feedback (IPCC 2007; Hansen et al. 2005).
All estimates of anthropogenic climate change must be observed against the noise produced by natural variability of the climate system. This natural variability is driven primarily by the internal nonlinear dynamics of ocean and atmosphere in the climate system. Examples include El-Nino Southern Oscillation (ENSO), Arctic Oscillation, and Pacific Oscillation, with ENSO typically providing the largest noise source for global means (Foster and Rahmstorf 2011; Lean and Rind 2009). Sources of external natural variability include solar variability and large volcanic eruptions such as Pinatubo (IPCC 2007; Lean and Rind 2009). In order to quantify uncertainty in decadal trends, we use the simplifying concept of linear decadal trends as a metric. While decadal change is not strictly linear, this assumption provides a very useful metric for understanding the effect of natural variability on uncertainty in observing anthropogenic trends (Weatherhead et al. 1998; Von Storch and Zwiers 1999; Leroy et al. 2008).
In addition to the noise of natural variability, climate trend uncertainty can also be increased by uncertainties in the COS. One of the largest sources of observing system uncertainty is changing calibration of satellite instruments over time (Leroy et al. 2008; Karl et al. 2006; Trenberth et al. 2012). This can be caused either by slow drifts of instrument calibration over years in orbit, or by differences in absolute calibration between successive instruments that either cannot be fully removed during overlap time periods, or cannot be removed because there is a time gap between the end of one observation and the start of its replacement. A second major source of observing system uncertainty is sampling error which can be caused either by limited space/time sampling or by systematic drifts in local time of day sampling for satellite instruments (Karl et al. 2006; IPCC 2007).
Global temperature trend uncertainty
CLARREO improved COS
I/A/C current system
Orbit sampling uncertainty
Figure 2 suggests a framework for evaluating the economic impact of higher accuracy climate change observations by studying the ability to reach given levels of confidence earlier than for a less capable COS. While the examples given here are for one of the future CLARREO advances relative to current satellite sensors, the concept is general and can in principle be extended to a wide range of climate observations with economic impacts such as sea level rise, anthropogenic aerosol radiative forcing, carbon cycle, or ocean acidification. The next section provides an example of how to link the social cost of carbon discussed in Sect. 2, with the climate observation trend accuracy in Sect. 3.
4 Decision context for VOI calculations
Total carbon emissions per year through 2115 for each of the 4 scenarios used in the VOI calculations
Total carbon emissions (GTC per year)
Damages in trillion 2008 US international dollars per year and global surface air temperature warming above pre-industrial levels
For CS = 3C
lim 2.5 dam
lim d.5 temp
Global temperature change/decade
Trigger value Δs
Confidence level Zs
After 2020 in 5-year steps
Altered emissions policy: switch from BAU to:
Raising the trigger value to 0.3C (upper right panel) shifts all curves up and to the right, and makes the difference between CLAR and I/A/C a little larger. Requiring higher confidence (97.5 %) increases the separation between CLARREO and I/A/C (lower right panel). Delaying the launch to 2030 (lower left panel) decreases the difference between CLAR and I/A/C, as the latter system has a longer head start. We start both observing systems in 2020 for the base case because of the current large uncertainties in total anthropogenic forcing of the climate system. But these uncertainties will reduce as aerosol forcing climate science improves and as greenhouse gas emissions increase their fraction of total climate forcing with time (IPCC 2007).
In the base case (upper left panel of Fig. 5), when the switch from the BAU to a reduced emissions scenario is triggered by 95 % certainty of at least 0.2 °C temperature rise per decade, then the averted damages, given CS = 4, will be lower between 2040 and 2050 if we have the CLARREO system. To compute the VOI of CLARREO in this base case, we compute the NPV (under various discount rates) for each value of CS of the difference in averted damages with and without CLARREO and take their expected value over the frequency distribution in Fig. 1.
VOI for CLARREO in base case
VOI: BAU → DICE optimum emissions; Launch = 2020, Conf = 95 %, Trigger = 0.2 °C
BAU and altered emissions path
Mean NPV damages trillion USD 2008
Delta mean averted damages: increase in VOI with CLARREO advanced COS over I/A/C current observations
BAU 2.5 %
BAU 3 %
BAU 5 %
Discovered by CLARREO
VOI-CLARREO 2.5 %
VOI-CLARREO 3 %
VOI-CLARREO 5 %
Discovered by A/C/I
VOI-I/A/C 2.5 %
VOI-I/A/C 3 %
VOI-I/A/C 5 %
CLARREO VOI results for decadal temperature rise
Delta mean averted damages trillion USD (2008)
Raising the trigger value or the required confidence increases the difference in time between discovery of exceedence with CLARREO and the existing system. Hence, the NPV of mean averted damages increases relative to the base case. Switching to a more aggressive emissions reduction scenario also increases the difference in damages between the two observing systems. On the other hand, delaying the launch time gives the existing system a greater head start and reduces the mean averted damages of CLARREO. Comparing the 2020 CLARREO launch VOI with the 2030 launch VOI allows an estimate of the cost of delaying an advanced COS at roughly 250 billion USD in NPV per year of delay. Given the fact that the STERN emissions scenario is much more aggressive than the DICE optimal scenario, one might have expected that switching from BAU to STERN instead of DICE OPT would have a greater impact on CLARREO’s VOI. The results are explained by noting that mean averted damages are the differences in the NPV of damages when the switch is triggered by the two observing systems.
In all cases shown in Table 7, the VOI of an advanced COS using the CLARREO example appears to be large relative to their cost. Current climate observations costs in the US are roughly 2.5 billion USD/year (USGCRP 2012), with international efforts of roughly similar magnitude for a total of 5 billion USD/year on climate observations. A complete advanced COS might easily reach 3 times these costs, or roughly an additional 10 billion USD/year globally. These additional costs would include advances in climate monitoring, climate process studies, as well as advanced climate modeling. Such an advanced COS might then cost 200–250 billion USD in total NPV for 30 years of observations from 2020 to 2050. But relative to the VOI estimates in this paper at 2–30 trillion USD in NPV, such an investment would pay back between 8 and 120 USD per dollar invested.
Following the SCC, only damages are considered in computing the social cost of carbon. Switching to a reduced emissions scenario undoubtedly entails costs which themselves depend on many uncertain parameters on both the climate and the economic side. It is important to appreciate that the SCC is not solving a social choice problem, it is computing a price that should be added to the price of carbon to account for environmental damages. As analogy, the amount we should be willing to pay for a low emissions car depends on the damages averted by reduced emissions. In the same way, the amount we should be willing to pay for an improved COS depends on the value of averted damages. This is what the VOI computes. Mitigation costs are not included in the analysis as they have no traceability equivalent to that for damages in the SCC. For example, mitigation cost estimates in the IPCC report (2007) vary by a factor of 12 for achieving stabilization of CO2 at 535–590 ppm. Future VOI developments should examine inclusion of these costs.
Again following the SCC, only CS is considered uncertain. There are many other uncertain parameters in these calculations, including the carbon cycle, ice sheet dynamics, economic damages, and abatement costs. Agreement on uncertainty distributions for these other uncertain parameters would enable improvements in the present calculations.
Observing the decadal temperature rise is not the only way to learn about CS, nor is it the best way. Observing cloud radiative forcing and temperature change together provide more direct information about cloud feedbacks and therefore CS (Dessler 2010; Soden et al. 2008). While not shown here, a similar advance in the knowledge of cloud radiative forcing and cloud feedback using CLARREO higher accuracy reflected solar radiation observations has been shown in Wielicki et al. (2013).
Any real decision context is more complex than that modeled here. For example, these calculations assume that a switch to a reduced emissions scenario would happen instantaneously, on a time scale discretized into 5-year steps. A policy ramp would be more realistic, involving additional decision parameters. Since this policy ramp would apply to switches under both the new and current observing systems, its effect might be relatively small on VOI values.
Despite these caveats, the results show that a uniform yardstick, however imperfect, can enable calculations supporting complex social decisions. The same method could be used with improved climate and economic models and with a broader range of uncertain inputs. This in itself will hopefully motivate improvements in second generation tools for computing the social cost of carbon, as well as a better understanding of the economic value of future advances in climate observations.
The interagency report (p. 32) states: “It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling.”.
CO2 equivalent is a metric measure to compare emissions from different greenhouse gases based on their global warming potential (GWP), the cumulative radiative forcing effects of a gas over a specified time horizon relative to a reference gas. For the procedure used by the US EPA in inventorying US greenhouse gases, the reference gas is carbon dioxide (CO2). The CO2 equivalent for a gas is derived by multiplying the tons of the gas by its associated GWP.
The units in Eq. (1) are [C/year]2, where C is degrees Celsius.
Specifically, a trend of 0.3 K/decade is outside the [2.5%, 97.5%] confidence band.
This is based on (Nordhaus 2008) where Stern industrial emissions per decade are given out to 2105. Industrial emissions for Stern are zero beyond 2095. Total Stern emissions are determined by adding emissions due to land use changes, which are the same for all scenarios.
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