Skip to main content

Advertisement

Log in

Monetizing the Value of Measurements of Equilibrium Climate Sensitivity Using the Social Cost of Carbon

  • Original Paper
  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

There has been much work on the value of learning about climate and the value of information regarding the climatic system. The present research moves beyond an abstract hypothesis about future learning and considers concrete Earth Observing Systems that could enhance knowledge of the climatic system and better inform decision makers. This study shows how real options theory in combination with the social cost of carbon may help to calculate the value of information regarding equilibrium climate sensitivity and estimate the relative advantages of two different Earth Observing Systems (EOSs) for learning about ECS. One system aims to improve measurements of decadal global temperature increase and another targets measurements of decadal change of global cloud radiative effect. The paper concludes that a new EOS that substantially reduces the uncertainty in cloud radiative effect would be expected to have more value than improving estimations of the global surface temperature alone.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Cloud radiative effect or CRE has also often been called cloud radiative forcing (CRF). The quantities are identically defined. We use CRE in this paper as it has become the more common usage

  2. There are some differences in our assumptions: we assumed 10-year instrument lifetime (tau cal) for the CRE obs and only 5 years for the GST. This means that calibration errors decrease in time more slowly for CRE than for GST. We did this to be consistent with the previous papers and because the weather system tends to update its instruments more often on orbit than research climate data. If the world were to implement a designed climate observing system, then this would likely change to 5 years for CRS as well.

References

  1. Cooke, R. M., Golub, A., Wielicki, B. A., Young, D. F., Mlynczak, M. G., & Baize, R. R. (2015). Integrated assessment modeling of value of information in Earth Observing Systems. Climate Policy ISSN: 1469-3062 (Print) 1752-7457 (Online) Journal homepage: http://www.tandfonline.com/loi/tcpo20.

  2. Cooke, R. M., Wielicki, B. A., Young, D. F., & Mlynczak, M. G. (2013). Value of information for climate observing systems. Environment, Systems and Decisions., 34, 98–109. https://doi.org/10.1007/s10669-013-9451-8.

    Article  Google Scholar 

  3. Cooke, R. M., Golub, A., Wielicki, B., Mlynczak, M., Young, D., & Baize, R. R. (2016). Real option value for new measurements of cloud radiative forcing. RFF DP, 16–19 http://www.rff.org/research/publications/real-option-value-new-measurements-cloud-radiative-forcing.

  4. Dessler, A. E. (2013). Observations of climate feedbacks over 2000–2010 and comparisons to climate models. Journal of Climate, 26, 333–342. https://doi.org/10.1175/JCLI-D-11-00640.1.

    Article  Google Scholar 

  5. Dowell et al., (2013). Strategy towards an architecture for climate monitoring from space. WMO/CGMS/CEOS. 38pp.

  6. Dufresne, J.-L., & Bony, S. (2008). An assessment of the primary sources of spread of global warming estimates from coupled ocean-atmosphere models. Journal of Climate, 21, 5135–5144. https://doi.org/10.1175/2008JCLI2239.1.

    Article  Google Scholar 

  7. Fox, N. Kaiser-Weiss, A. Schmutz, W. Thome, K. Young, D. Wielicki, B. Winkler, R. & Woolliams, E. Accurate radiometry from space: an essential tool for climate studies. https://doi.org/10.1098/rsta.2011.0246.

    Article  CAS  Google Scholar 

  8. GCOS. (2016). Implementation plan, 2016. WMO, GCOS-200 315pp.

  9. Goldberg, M., et al. (2011). The global space-based inter-calibration system (GSICS). Bulletin of the American Meteorological Society, 92, 467–475.

    Article  Google Scholar 

  10. Hanea, et al. (2017). Hanea, A. M., G. F. Nane, R. M. Cooke, B. A. Wielicki, 2018: Climate trend uncertainty quantification with non-parametric Bayesian networks. Journal of Risk Research, accepted. https://doi.org/10.1080/13669877.2018.1437059.

    Article  Google Scholar 

  11. Hope, C. (2015). The $10 trillion value of better information about the transient climate response. Philosophical Transactions of the Royal Society A, 373, 20140429. https://doi.org/10.1098/rsta.2014.0429.

    Article  Google Scholar 

  12. IPCC. (2013). In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA, 1535 pp: Cambridge University Press. https://doi.org/10.1017/CBO9781107415324.

    Chapter  Google Scholar 

  13. IWGSCC (Interagency Working Group on Social Cost of Carbon). (2010). Social cost of carbon for regulatory impact analysis under Executive Order 12866, Appendix 15a (p. 53). Washington, DC: US Government.

    Google Scholar 

  14. IWGSCC (Interagency Working Group on Social Cost of Carbon). (2013). Technical support document: technical update of the social cost of carbon for regulatory impact analysis under Executive Order 12866. Washington, DC: US Government. May 2013, revised Nov. 2013.

  15. Karl, T. R., S. J. Hassol, C. D. Miller, & W. L. Murray (2006). Synthesis and assessment product 1.1 report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. U.S. Climate Change Science Program, April, 2006, 164 pp.

  16. Kelly, D. L., & Kolstad, C. D. (1999). Bayesian learning, growth, and pollution. Journal of Economic Dynamics and Control, 23(4), 491–518.

    Article  Google Scholar 

  17. Kopp, G., Smith, P., Belting, C., Castleman, Z., Drake, G., Espejo, J., Heuerman, K., Lanzi, J., & Stuchlik, D. (2017). Radiometric flight results from the HyperSpectral Imager for Climate Science (HySICS). Geosci. Instrum. Method. Data Syst., 6, 169–191. https://doi.org/10.5194/gi-6-169-2017.

    Article  CAS  Google Scholar 

  18. Leroy, S. S., Anderson, J. G., & Ohring, G. (2008). Climate signal detection times and constraints on climate benchmark accuracy requirements. Journal of Climate, 21, 184–846.

  19. Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., & Kato, S. (2018). Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) Edition-4.0 Data Product. Journal of Climate, 31, 24. https://doi.org/10.1175/JCLI-D-17-0208.1.

    Article  Google Scholar 

  20. McInerney, D., Lempert, R., & Keller, K. (2011). What are robust strategies in the face of uncertain climate threshold responses? Climatic Change, 112, 547–568. https://doi.org/10.1007/s10584-011-0377-1.

    Article  Google Scholar 

  21. NRC. (2007). Earth science and applications from space: national imperatives for the next decade and beyond. Washington, D.C: National Academy Press 428 pp.

    Google Scholar 

  22. National Academies of Sciences, Engineering, and Medicine. (2018). Thriving on our changing planet: a decadal strategy for earth observation from space. Washington, DC: The National Academies Press. https://doi.org/10.17226/24938.

    Book  Google Scholar 

  23. National Academies of Sciences, Engineering, and Medicine. (2017). Valuing climate damages: updating estimation of the social cost of carbon dioxide. Washington, DC: The National Academies Press. https://doi.org/10.17226/24651.

    Book  Google Scholar 

  24. Nordhaus, W. D. (2008). A question of balance: weighing the options on global warming policies. New Haven, CT: Yale University Press.

    Google Scholar 

  25. National Academies of Sciences, Engineering, and Medicine. (2015). Continuity of NASA Earth observations from space: a value framework. Washington, DC: The National Academies Press.

    Google Scholar 

  26. Nordhaus, W., & Sztorc, P. (2013). DICE 2013R: introduction and user’s manual. 2nd ed. http://www.econ.yale.edu/~nordhaus/homepage/documents/DICE_Manual_103113r2.pdf

  27. O’Neill, B. C., Crutzen, P., Gruebler, A., Duong, M. H., Keller, K., Kolstad, C., Koomey, J., Lange, A., Obersteiner, M., Oppenheimer, M., Pepper, W., Sanderson, W., Schlesinger, M., Treich, N., Ulph, A., Webster, M., & Wilson, C. (2006). Learning and climate change. Climate Policy, 6, 585–589.

    Article  Google Scholar 

  28. Soden, B. J., & Vecchi, G. A. (2011). The vertical distribution of cloud feedback in coupled ocean–atmosphere models. Geophysical Research Letters, 38, L12704. https://doi.org/10.1029/2011GL047632.

    Article  Google Scholar 

  29. Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., & Shields, C. A. (2008). Quantifying climate feedbacks using radiative kernels. Journal of Climate, 21, 3504–3520.

    Article  Google Scholar 

  30. Webster, M. D., Jakobovits, L., & Norton, J. (2008). Learning about climate change and implications for near-term policy. Climatic Change, 89, 67–85. https://doi.org/10.1007/s10584-008-9406-0.

    Article  Google Scholar 

  31. Wielicki, B. A., Young, D. F., Mlynczak, M. G., Thome, K. J., Leroy, S., Corliss, J., Anderson, J. G., Ao, C. O., Bantges, R., Best, F., Bowman, K., Brindley, H., Butler, J. J., Collins, W., Dykema, J. A., Doelling, D. R., Feldman, D. R., Fox, N., Huang, X., Holz, R., Huang, Y., Jin, Z., Jennings, D., Johnson, D. G., Jucks, K., Kato, S., Kirk-Davidoff, D. B., Knuteson, R., Kopp, G., Kratz, D. P., Liu, X., Lukashin, C., Mannucci, A. J., Phojanamongkolkij, N., Pilewskie, P., Ramaswamy, V., Revercomb, H., Rice, J., Roberts, Y., Roithmayr, C. M., Rose, F., Sandford, S., Shirley, E. L., Smith, W. L., Soden, B., Speth, P. W., Sun, W., Taylor, P. C., Tobin, D., & Xiong, X. (2013). Achieving climate change absolute accuracy in orbit. Bulletin of the American Meteorological Society, 94, 1519–1539. https://doi.org/10.1175/BAMS-D-12-00149.1.

    Article  Google Scholar 

  32. Trenberth, K. E., Belward, A., Brown, O., Haberman, E., Karl, T. R., Running, S., Ryan, B., Tanner, M., & Wielicki, B. A. (2012). In G. R. Asrar & J. W. Hurrell (Eds.), Challenges of a sustained climate observing system, in the monograph: Climate science for serving society: research, modelling and prediction priorities. Berlin: Springer.

    Google Scholar 

  33. Weatherhead, B., B. A. Wielicki, V. Ramaswamy, M. Abbott, T. Ackerman, B. Atlas, G. Brasseur, L. Bruhwiler, T. Busalacchi, J. Butler, C. T. M. Clack, R. Cooke, L. Cucurull, S. Davis, J. M. English, D. Fahey, S. S. Fine, J. K. Lazo, S. L., N. Loeb, E. Rignot, B. Soden, D. Stanitski, G. Stephens, B. Tapley, A. M. Thompson, K. Trenberth, D. Wuebbles, 2017: Designing the climate observing system of the future. Earth’s Future, 23pp, https://doi.org/10.1002/2017EF000627

    Article  Google Scholar 

  34. Zhou, C., Zelinka, M. D., Dessler, A. E., & Klein, S. A. (2015). The relationship between interannual and long-term cloud feedbacks. Geophysical Research Letters, 42, 10,463–10,469. https://doi.org/10.1002/2015GL066698.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander Golub.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This reports on work done under contract NNX13AQ72A with support from the NASA Applications Program.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cooke, R.M., Golub, A., Wielicki, B. et al. Monetizing the Value of Measurements of Equilibrium Climate Sensitivity Using the Social Cost of Carbon. Environ Model Assess 25, 59–72 (2020). https://doi.org/10.1007/s10666-019-09662-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-019-09662-0

Keywords

Navigation