TOPSHOT – COP24 president Michal Kurtyka jumps at the end of the final session of the COP24 summit on climate change in Katowice, southern Poland, on December 15, 2018. (Photo by Janek SKARZYNSKI / AFP) (Photo credit should read JANEK SKARZYNSKI/AFP/Getty Images)

FROM SCHNEIDER 1975

FIGURE 1: CONSTRAINED & UNCONSTRAINED ECS ESTIMATES IN THE LITERATURE 

FIGURE 2: ECS ESTIMATES OUTSIDE CHARNEY INTERVAL

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1. Figure 1 is a graphical representation of a large number of climate sensitivity values from the literature 1970-2018, both purely empirical (unconstrained) and constrained by climate models. The Charney 1979 estimate of ECS=3 with 90% confidence interval of ECS=[1.5, 4.5] is sanctioned by the IPCC and widely accepted in climate science. It is used here to compare all values in Figure 1 against this interval.
2. Figure 2 is a comparison of all the reported ECS values in Figure 1 against the Charney/IPCC interval ECS=[1.5, 4.5]. In the GT section of Figure 2 we find that in the full sample 1963-2018, only 20% of the ECS values shown in Figure 1 were greater than the the Charney interval ECS=[1.5, 4.5]. This rate is somewhat lower in the early period 1963-2001 at just 13% but much higher in the later period 2001-2018 at 30%. It appears that there has been a gradual inflation of ECS estimates in the literature over the period 1963-2018.
3. In the LT section of Figure 2 we find that in the full sample 1963-2018, 18% of the ECS values shown in Figure 1 were less than the the Charney interval ECS=[1.5, 4.5]. This rate is somewhat lower in the early period 1963-2001 at just 13% but somewhat higher in the later period 2001-2018 at 23%.
4. The EITHER section of the chart in Figure 2 is test of whether the reported ECS value lies within the Charney interval. The first column displays the sum of the GT and LT values and the second column, computed as 100% minus the sum, is the percent of reported ECS values that were within the Charney interval. Here we find good agreement of reported values with the Charney interval with 62% of the reported values within the interval in the full sample period 1963-2018. However, the agreement appears to be driven primarily by early values 1963-2001 with 75% within the interval. The agreement is less impressive in the later period 2001-2018 with less than half or 48% of the reported values within the Charney/IPCC interval of ECS=[1.5,4.5].
5. A possible reason for the gradual departure from the Charney interval over time is that both the Charney and Manabe estimates of old were derived from computer models with little if any constraints of observational data. This approach to climate sensitivity has gradually changed over time with both paleo and observational data used directly for ECS estimates. Many of these estimates are of course “constrained” by climate models but lately the trend has been mostly to empirical estimates. This evolution of ECS estimation methodology is consistent with the observed divergence of ECS estimates from Charney’s climate model derived interval.
6. It should also be considered that the high rate of agreement with the Charney interval (particularly in older estimates) derives in large part from the great width of this interval from ECS=1.5 to ECS=4.5. The carbon budget and climate action implications of the two ends are so different that the interval loses all value as a tool for formulating climate action plans. The Charney interval is not very useful in that context because of its large span which in turn also serves to show good agreement with a large and varied set of climate sensitivity estimates.
7. In fact the large span of the Charney climate sensitivity interval of ECS=[1.5, 4.5] traverses significant differences in carbon budget and climate action options and possibilities. This interval is not useful information but rather an admission of the absence of information. It is not possible for climate science to propose climate action options without sensitivity information and the IPCC climate sensitivity range is a useless range in that regard and perhaps an inadvertent admission by the IPCC that though we urge and promote climate action, we do not have the information we need to formulate climate action plans.
8. A related issue in constructing climate action plans is a statistical weakness in the TCRE parameter that forms the basis of computing carbon budgets in terms of cumulative emissions. This issue is presented in a related post [LINK] .

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REFERENCES

1. Box, G. (1994). Time series analysis: forecasting and control. . Englewood Cliffs, NJ: : Prentice Hall.
2. Caldeira, K. (2003). Climate sensitivity uncertainty and the need for energy without CO2 emission. Science, 299.5615 (2003): 2052-2054.
3. Callendar, G. (1938). The artificial production of carbon dioxide and its influence on temperature.” . Quarterly Journal of the Royal Meteorological Society, 64.275 (1938): 223-240.
4. Callendar, G. (1958). On the amount of carbon dioxide in the atmosphere. Tellus, 10.2 (1958): 243-248.
5. Cess, R. (1974). Radiative transfer due to atmospheric water vapor: Global considerations of the earth’s energy balance.” . Journal of Quantitative Spectroscopy and Radiative Transfer , 14.9 (1974): 861-871.
6. Charney, J. (1979). Carbon dioxide and climate: a scientific assessment. Washington DC: National Academy of Sciences.
7. Christy/Spencer. (2018). UAH-CDC MSU dataset v6. . Retrieved from UAH: http://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.tx
   4 https://drive.google.com/open?id=1G97o9qEwictDWgwVkB8SO5b7_bpLN0yp
8. CSIRO. (2017). CSIRO. Retrieved from CSIRO: https://www.csiro.au/
   Curry, J. (2011). Reasoning about climate uncertainty. Climatic Change, 108.4 (2011): 723.
9. Curry/Webster. (2011). Climate science and the uncertainty monster. Bulletin of the American Meteorological Society, 92.12 (2011): 1667-1682.
10. Danabasoglu, G. (2009). Equilibrium climate sensitivity: Is it accurate to use a slab ocean model? Journal of Climate , 22.9 (2009): 2494-2499.
11. Draper&Smith. (1998). Applied Regression Analysis. . Wiley.
12. Hansen, B. (1992). Testing for parameter instability in linear models. Journal of policy Modeling , 14.4 (1992): 517-533.
13. Hansen-Lacis. (1984). Climate sensitivity: Analysis of feedback mechanisms. Climate processes and climate sensitivity, 130-163.
14. IPCC. (2007). IPCC AR4 The Physical Science Basis. Retrieved 2017, from IPCC: https://www.ipcc.ch/report/ar4/wg1/
15. IPCC. (2013). AR5 The Physical Science Basis. Retrieved 2017, from IPCC: http://www.ipcc.ch/report/ar5/wg1/
16. Keeling, C. (1978). The Influence of the Mauna Loa Observatory on the Development of Atmospheric CO2 Research. San Diego: Scripps Institution of Oceanography, University of California San Diego.
17. Keeling, C. (1982). Measurements of the concentration of carbon dioxide at Mauna Loa Observatory, Hawaii. Carbon dioxide review.
18. Keeling, C. (1986). Reassessment of late 19th century tmospheric carbon dioxide variations. Tellus, 38B: 87-105.
19. Kuder, G. (1937). The theory of the estimation of test reliability. Psychometrika, 2.3(1937): 151-160.
20. Lacis, A. (2010). Atmospheric CO2: Principal control knob governing Earth’s temperature. Science , 330.6002 (2010): 356-359.
21. Munshi, J. (2017-5). Correlation of Regional Warming with Global Emissions. SSRN, https://ssrn.com/abstract=3033001.
22. Munshi, J. (2018). From Equilibrium Climate Sensitivity to Carbon Climate Response. SSRN, https://ssrn.com/abstract=3142525.
23. Munshi, J. (2018). Uncertainty in Empirical Climate Sensitivity Estimates 1850-2017. SSRN, https://ssrn.com/abstract=3117385.
24. Scripps. (2017). Scripps. Retrieved from CO2: scrippsco2.ucsd.edu
25. Shumway, R. (2011). Time series analysis. . NY: Springer .
26. Stainforth, D. (2005). Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature , 433.7024 (2005): 403.
27. Wigley, T. (1983). The pre-industrial carbon dioxide level. Climate Change, 5: 315-320.
28. WMO. (1983). CO2 Concentrations from pre-industrial times. Geneva: WMO.