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Redefining Normal Temperatures

Resource planning and forecasting in a changing climate.

Fortnightly Magazine - May 2013
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where they don’t properly account for the resource risks they face. Thus, failing to account for the trends in climate can have large and material implications.

Beyond temperature and precipitation—for example trends in high-impact storms including wind, snow, ice, and other short-term but highly damaging weather events—there’s little specific to recommend. At this time there’s no expert consensus or convincing evidence that the risks of these hazards are generally increasing. Arguments for their increase to date are largely anecdotal or supported by inadequate models. Continued climate warming will certainly lead to shifts in weather patterns, but there is a large uncertainty as to what these shifts will be and where. In the case of extreme weather, the best strategy for utilities is to continue to objectively monitor the sequence of year-to-year weather and the developing peer-reviewed science. 

Adapting to the current trends in climate requires accounting for weather in a way that’s consistent with the data. For utilities, one aspect of this is to properly account for what normal weather will be going forward. Two alternatives—OCNs and hinge-fits—both have a basis in climate science and empirical verification, and thus provide better estimates of normal weather than traditional 30-year averages do. The risks of improper accounting for changes in normal weather are real, and ignoring them is potentially costly.

Endnotes:

1. “Official” normals are 30-year averages that are updated at the end of every decade, e.g., 1971-2000, 1981-2010, etc.

2. See Wilks, D. S., and R. E. Livezey. “Performance of Alternative ’Normals‘ for Tracking Climate Changes, Using Homogenized and Non-homogenized Seasonal U.S. Surface Temperatures,” Journal of Applied Meteorology and Climatology , in press.

3. See Livezey, R.E., K.Y. Vinnikov, M.M. Timofeyeva, R. Tinker, and H.M. van den Dool. “Estimation and extrapolation of climate normals and climatic trends,” Journal of Applied Meteorology and Climatology , 46 (2007), 1759-1776.

4. The set of 1218 station records referred to as the U.S. Historical Climate Network (USHCN) is available in a fully homogenized version.

5. See Menne, M.J., and C.N. Williams. “Homogenization of temperature series via pairwise comparisons,” Journal of Climate , 22 (2009), 1700-1717. The website  has a less-technical description of the process.

6. See Livezey, et al. , op. cit.

7. See Wilks and Livezey, op. cit. and Wilks, D.S. “Projecting ’normals‘ in a nonstationary climate,” Journal of Applied Meteorology and Climatology , 52 (2013), 289-302.

8. See Wilks and Livezey, op. cit. , Wilks, op. cit. , and Huang, J., H.M. van den Dool, and A.G. Barnston. “Long-lead seasonal temperature prediction using optimal climate normals,” Journal of Climate , 9 (1996), 809-817.

9. See Huang, et al. , ibid.

10. See Wilks and Livezey, op. cit. and Wilks, op. cit.

11. See Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller, Eds., Climate Change 2007: The Physical Science Basis . Cambridge, UK: Cambridge University Press, 2007.

12. A data plot is termed “piecewise-continuous” if a function is defined throughout the interval, its constituent functions are continuous on that interval, and there’s no discontinuity at

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