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