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Climate Change, Climate Science and Economics pp 15–57Cite as

Weather and the Instrumental Record

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Abstract

Climate scientists use instrumental data from numerous weather stations to develop summary measures of regional and global temperatures. The difficulties of doing this are illustrated using both hypothetical data and information from two weather monitoring stations, where one of the stations is clearly influenced by non-climate factors to a greater extent than the other. Instrumental records are available from numerous weather stations around the globe, but whose numbers and quality have varied over time, and from satellite data. However, as demonstrated using simple data, efforts to remove non-climatic factors (such as the so-called urban heat island effect) from the surface temperature reconstructions prove to be unsuccessful. Statistical analyses indicate that, since the late 1970s, some 50 % of the temperature increase in the reconstructed data is attributable to socioeconomic factors, but that this is not true of temperatures derived from satellite data. The chapter ends by examining the potential for replacing traditional crop insurance, and its inherent drawbacks (adverse selection and moral hazard), with financial weather-based derivatives.

To kill an error is as good a service as, and sometimes better than, the establishing of a new truth or fact – Charles Darwin

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Notes

  1. 1.

    See http://data.giss.nasa.gov/gistemp/abs_temp.html (viewed February 18, 2010).

  2. 2.

    On August 13, 2009, Andrew Orlowski of The Register reported that the CRU had destroyed weather data. According to the CRU, “data storage availability in the 1980s meant that we were not able to keep the multiple sources for some sites, only the station series after adjustment for homogeneity issues. We, therefore, do not hold the original raw data but only the value added (i.e. quality controlled and homogenized) data” (viewed February 18, 2010 at: http://www.theregister.co.uk/2009/08/13/cru_missing/). Despite allegations, it is not entirely clear what has been lost – the actual raw data, the notes (and importantly the computer code used to generate the homogenized data), or both (e.g., see McKitrick 2010c). For example, in response to data requests, the CRU has claimed it downloaded data to the U.S. Department of Energy. Even if the data are available, McKitrick indicates that without knowing which weather stations are included in the reconstruction, it is impossible to check the CRU’s results. See also next note.

  3. 3.

    See “A Superstorm for Global Warming Research” by Marco Evers, Olaf Stampf and Gerald Traufetter, Spiegel Online, http://www.spiegel.de/international/world/0,1518,druck-686697,00.html (viewed April 6, 2010). Jones et al. (2010) claim that a detailed description of the weighting method used to construct the 5°  ×  5° latitude-longitude gridded boxes of temperatures, and thus their ‘temperature curve,’ can be found in an earlier paper (Jones et al. 2001). However, the current author could not determine from Jones et al. (2001) how it was done. This is an issue discussed in more detail in the next section.

  4. 4.

    The project was funded primarily by Canada’s Natural Sciences and Engineering Research Council (NSERC). Information on the schools (and some non-school ‘hosts’ of weather monitoring stations), along with weather data and graphs, are available from http://www.victoriaweather.ca/.

  5. 5.

    Data were obtained from the website indicted in the previous footnote (and were available March 6, 2012). A request for permission to display a chart illustrating the differences in average, maximum and minimum daily temperatures between the two schools was denied by the University of Victoria professor overseeing the school-based weather network.

  6. 6.

    The differences in average, maximum and minimum daily temperatures between the two schools are highly statistically significant – the chance that the Gordon Head temperatures might actually be higher is less than 0.005.

  7. 7.

    Given that temperature readings are taken to one decimal place, it might be more appropriate to consider only one rather than two significant decimals. However, climate scientists regularly provide summary measures ‘accurate’ to the thousandth degree Celsius or even higher (e.g., the CRU reports average temperature anomalies to the third significant decimal) (Jones et al. 2010).

  8. 8.

    The Berkeley Earth Surface Temperature project, which is discussed in the next section, claims to have accomplished this; see, e.g., Rohde et al. (2011).

  9. 9.

    “Climategate: So Jones Lost the Data? It Was Worthless, Anyway” by Vincent Gray, February 15, 2010: http://pajamasmedia.com/blog/climategate-so-jones-lost-the-data-it-was-worthless-anyway/ (viewed February 22, 2010). See also previous notes 2 and 3.

  10. 10.

    An excellent source of climate data is the KNMI website: http://climexp.knmi.nl/. Available information on weather station data and where it can be found is also provided by Steve McIntyre at http://climateaudit.org/station-data/ (viewed April 24, 2010).

  11. 11.

    See McIntyre, http://climateaudit.org/station-data/ (viewed April 24, 2010).

  12. 12.

    A description of the GHCN data can be found in Peterson and Vose (1997) and Peterson et al. (1998).

  13. 13.

    See Goddard (2011). As several commentators have already observed, this would appear to be a conflict of interest. How impartial can a climate-data gatekeeper be if that same person is a vociferous proponent of human driven global warming?

  14. 14.

    A list of weather stations and numbers is available from (viewed February 18, 2010): http://data.giss.nasa.gov/gistemp/station_data/.

  15. 15.

    See http://www.ncdc.noaa.gov/oa/usgcos/index.htm (viewed April 24, 2010).

  16. 16.

    From http://www.ncdc.noaa.gov/oa/usgcos/programdescription.htm (April 24, 2010).

  17. 17.

    From http://www.ncdc.noaa.gov/oa/climate/ghcn-monthly/index.php (April 24, 2010).

  18. 18.

    See http://data.giss.nasa.gov/gistemp/ (viewed March 9, 2010).

  19. 19.

    Indeed, Jones and Moberg (2003, p.208) admit that it is difficult to say what homogeneity adjustments have been applied since the original data sources do not always include this information.

  20. 20.

    See http://climateaudit.org/station-data/ (viewed April 24, 2010). As McKitrick (2010c), points out: The 1985 technical reports to the U.S. Department of Energy are indeed exhaustive, but they refer to data sets that have since been superseded, and thus are not adequate for understanding the post-1980 CRUTEM series (para 48, pp.26–27). “Following the publication of the CRUTEM3 data series (Brohan et al. 2006), it was not possible to discern from information on the CRU website, or in accompanying publications, which locations and weather stations had been used to produce the gridcell anomalies” (para 54, p.30).

  21. 21.

    Four papers have been submitted for potential publication to the Journal of Geophysical Research (Muller et al. 2011a, b; Rohde et al. 2011; Wickham et al. 2011).

  22. 22.

    Both researchers have questioned the role of humans in driving climate change, with Spencer recently arguing that three-quarters of the observed increase in temperatures is due to changes in natural cloud formation (i.e., of non-human origin) (Spencer 2010). This is discussed further in Chap. 5.

  23. 23.

    http://data.giss.nasa.gov/gistemp/station_data/ (viewed February 18, 2010).

  24. 24.

    Information found at http://www.cru.uea.ac.uk/cru/data/temperature/#datdow (viewed March 5, 2010). See also Brohan et al. (2006).

  25. 25.

    Quote by R. Muller, Wall Street Journal, October 21, 2011 (viewed November4, 2011):

    http://online.wsj.com/article/SB10001424052970204422404576594872796327348.html.

  26. 26.

    See http://www.spaceref.com/news/viewpr.html?pid=30000 (viewed March 9, 2010).

  27. 27.

    See http://www.uoguelph.ca/∼rmckitri/research/nvst.html (as viewed March 9, 2010). Data provided by R. McKitrick, University of Guelph. Also see D’Aleo and Watts (2010).

  28. 28.

    See ‘Answers to Frequently-asked Questions’ at the CRU website (viewed March 10, 2010): http://www.cru.uea.ac.uk/cru/data/temperature/#datdow. The HadCRUT3 data and other data products are also available from this website. See also http://climexp.knmi.nl/.

  29. 29.

    See www.surfacestations.org (viewed February 1, 2011). The rating system is provided in a manual by the National Oceanic and Atmospheric Administration (NOAA) and National Climatic Data Center (NCDC), entitled Climate Reference Network (CRN) Site Information Handbook and dated December 10, 2002. At (viewed 14 April 2010): www1.ncdc.noaa.gov/pub/data/uscrn/documentation/program/X030FullDocumentD0.pdf. It is worthwhile noting that the U.S. is the only country that attempts to rank the quality of its weather stations.

  30. 30.

    A network of ‘super’ stations that meet all of the proper siting criteria has now been established in the U.S., but data from this network are only available for about 2 years.

  31. 31.

    Data to construct Figure 2.3 are from http://www.hadobs.org/ (viewed August 26, 2010).

  32. 32.

    BEST data are available at http://www.berkeleyearth.org/ (viewed January 6, 2012).

  33. 33.

    These data differ slightly from that in Figure 2.4(a); they are from Jones et al. (2010), who prepared the data for the U.S. government.

  34. 34.

    When other lags were included in the regression, they turned out to be statistically insignificant, while their inclusion did not change the coefficients on the two lags of the dependent variable that were included.

  35. 35.

    Source: http://news.mongabay.com/2006/0926-oceans.html (viewed April 28, 2010).

  36. 36.

    McKitrick (2010b) provides an interesting and entertaining commentary on the attempts to prove his results and those of de Latt and Maurellis false. Some of this is discussed in the next several paragraphs. See also McKitrick (2010a), which addresses an error in the IPCC WGI (2007) report that pertains to his research. This paper was sent to seven journals – three journals would not even send it out for review, while a fourth journal would not even correspond with the author. The paper was examined by seven reviewers, six of whom agreed with the methods and results, and recommended publication. The editors of two journals turned down publication because, in one instance, the paper did not really address the journal’s aims and, in the other, the editor agreed with the one dissenting reviewer (despite evidence that the reviewer was not familiar with the statistical methods employed).

  37. 37.

    References in the above quote to other sections in the same chapter of the IPCC report were removed as they provide no evidence whatsoever on this matter (see also McKitrick 2010b).

  38. 38.

    The NAO is not to be confused with Atlantic Multi-decadal Oscillation (AMO); the former is caused by surface-atmospheric pressure changes, whereas the latter is the result of changes in ocean temperatures and currents and other factors that are not entirely known.

  39. 39.

    BEST data are available at http://www.berkeleyearth.org/ (viewed January 6, 2012).

  40. 40.

    Climate scientists continue to insist that the temperature reconstructions from surface-based observations are free of non-climate factors. What is perplexing is that, in making such claims, no statistical evidence is provided and there are no citations to peer-reviewed studies that do provide statistical evidence of contamination (see, e.g., Parker 2010).

  41. 41.

    The problem with a newly-installed, site specific monitoring station is the lack of a historical record of temperatures that the insurance company can use for calculating the insurance premium. The insurer will need to rely on information from nearby weather stations, which militates against the need for a site specific station.

  42. 42.

    Preliminary research by University of Victoria PhD student, Zhen Zhu, finds that the PNA, PDO and El Niño indexes predict wildfire intensity in British Columbia’s interior. With some indexes, however, the more important predictor is a lag of nearly 1½  years as opposed to the closer lag of 4–6 months. Perhaps it requires a longer period of warm dry weather before forests are susceptible to fire.

References

  • Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., & Jones, P. D. (2006). Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850. Journal of Geophysical Research-Atmospheres, 111, D12106.

    Article  Google Scholar 

  • Chantarat, S., Turvey, C. G., Mude, A. G., & Barrett, C. B. (2008). Improving humanitarian response to slow-onset disasters using famine-indexed weather derivatives. Agricultural Finance Review, 68(1), 169–195.

    Article  Google Scholar 

  • Chen, C.-C., McCarl, B., & Hill, H. (2002). Agricultural value of ENSO information under alternative phase definition. Climatic Change, 54(3), 305–325.

    Article  Google Scholar 

  • Chincarini, L. (2011). No chills or burns from temperature surprises: An empirical analysis of the weather derivatives market. Journal of Futures Markets, 31(1), 1–33.

    Article  Google Scholar 

  • D’Aleo, J., & Watts, A. (2010, January 27). Surface temperature records: Policy driven deception? (SPPI Original Paper, 111 pp). Retrieved January 29, 2010, from http://scienceandpublicpolicy.org/images/stories/papers/originals/surface_temp.pdf

  • De Laat, A. T. J., & Maurellis, A. N. (2004). Industrial CO2 emissions as a proxy for anthropogenic influence on lower tropospheric temperature trends. Geophysical Research Letters, 31, L05204. doi:10.1029/2003GL019024.

    Article  Google Scholar 

  • De Laat, A. T. J., & Maurellis, A. N. (2006). Evidence for influence of anthropogenic surface processes on lower tropospheric and surface temperature trends. International Journal of Climatology, 26(7), 897–913.

    Article  Google Scholar 

  • Essex, C., & McKitrick, R. (2002). Taken by storm: The troubled science, policy and politics of global warming. Toronto: Key Porter Books.

    Google Scholar 

  • Essex, C., McKitrick, R., & Andresen, B. (2007). Does a global temperature exist? Journal of Non-Equilibrium Thermodynamics, 32(1), 1–28.

    Article  Google Scholar 

  • Garnett, E. R. (2007). The use of El Niño information in forecasting grain yields in the Canadian prairie provinces. Winnipeg: Canadian Wheat Board.

    Google Scholar 

  • Goddard, S. (2011, February 15). The hottest year on record? SPPI Reprint Series (20 pp). Retrieved from http://scienceandpublicpolicy.org/images/stories/papers/reprint/the_hottest_year_ever.pdf

  • Hansen, J. E., Ruedy, R., Glascoe, J., & Sato, M. (1999). GISS analysis of surface temperature change. Journal of Geophysical Research, 104(D24), 30997–31022.

    Article  Google Scholar 

  • Hansen, J. E., Ruedy, R., Sato, M., Imhoff, M., Lawrence, W., Easterling, D., Peterson, T., & Karl, T. (2001). A closer look at United States and global surface temperature change. Journal of Geophysical Research, 106(D20), 23947–23963.

    Article  Google Scholar 

  • Hansen, J. E., Ruedy, R., Sato, M., & Lo, K. (2010). Global surface temperature change. Reviews of Geophysics, 48, RG4004. doi:10.1029/2010RG000345.

    Article  Google Scholar 

  • Hsieh, W. W., Tang, B., & Garnett, E. R. (1999). Teleconnections between Pacific sea surface temperatures and Canadian prairie wheat yield. Agriculture and Forest Meteorology, 96, 209–217.

    Article  Google Scholar 

  • IPCC WGI. (2007). Climate change 2007. The physical science basis. Working Group I contribution to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.

    Google Scholar 

  • Jones, P. D. (1988). Hemispheric surface air temperature variations: Recent trends and an update to 1987. Journal of Climate, 1(6), 654–660.

    Article  Google Scholar 

  • Jones, P. D. (1994). Hemispheric surface air temperature variations: A reanalysis and an update to 1993. Journal of Climate, 7(11), 1794–1802.

    Article  Google Scholar 

  • Jones, P. D., & Moberg, A. (2003). Hemispheric and large-scale surface air temperature va-riations: An extensive revision and update to 2001. Journal of Climate, 16(2), 206–223.

    Article  Google Scholar 

  • Jones, P. D., Raper, S. C. B., Santor, B., Cherry, B. S. G., Goodess, C., Kelly, P. M., Wigley, T. M. L., Bradley, R. S., & Diaz, H. F. (1985). A grid point surface air temperature data set for the Northern hemisphere, 1850–1984 (DoE Technical Report No. TR022). US. Department of Energy, Contract No. DE-AC02-79EV10098. DOE/EV/10098-2.

    Google Scholar 

  • Jones, P. D., Raper, S. C. B., Bradley, R. S., Diaz, H. F., Kelly, P. M., & Wigley, T. M. L. (1986a). Northern hemisphere surface air temperature variations: 1851–1984. Journal of Climate and Applied Meteorology, 25(2), 161–179.

    Article  Google Scholar 

  • Jones, P. D., Raper, S. C. B., Goodess, C., Cherry, B. S. G., & Wigley, T. M. L. (1986b, July). A grid point surface air temperature data set for the Southern hemisphere, 1850–1984 (DoE Technical Report No. TR027, p. 256). US. Dept. of Energy under Contract No. DE-AC02-79EV10098. DOE/EV/10098-2. Dist. Category UC-11.

    Google Scholar 

  • Jones, P. D., Raper, S. C. B., & Wigley, T. M. L. (1986c). Southern hemisphere surface air temperature variations: 1851–1984. Journal of Climate and Applied Meteorology, 25(9), 1213–1230.

    Article  Google Scholar 

  • Jones, P. D., New, M., Parker, D. E., Martin, S., & Rigor, I. G. (1999). Surface air temperature and its changes over the past 150 years. Reviews of Geophysics, 37(2), 173–199.

    Article  Google Scholar 

  • Jones, P. D., Osborn, T. J., Briffa, K. R., Folland, C. K., Horton, E. B., Alexander, L. V., Parker, D. E., & Rayner, N. A. (2001). Adjusting for sampling density in grid box land and ocean surface temperature time series. Journal of Geophysical Research-Atmospheres, 106(D4), 3371–3380.

    Article  Google Scholar 

  • Jones, P. D., Parker, D. E., Osborn, T. J., & Briffa, K. R. (2010). Global and hemispheric temperature anomalies – Land and marine instrumental records. Trends: A compendium of data on global change. From http://cdiac.ornl.gov/trends/temp/jonescru/jones.html

  • Khandekar, M. L. (2004). Canadian prairie drought: A climatological assessment (Pub. No: T/787, pp. 37). Edmonton: Environmental Policy Branch, Alberta Environment.

    Google Scholar 

  • Laidre, K. L., Heide-Jørgensen, M. P., Ermold, W., & Steele, M. (2010). Narwhals document continued warming of Southern Baffin Bay. Journal of Geophysical Research, 115, C10049. doi:10.1029/2009JC005820.

    Article  Google Scholar 

  • Manley, G. (1953). The mean temperature of Central England, 1698 to 1952. Quarterly Journal of the Royal Meteorological Society, 79(340), 242–261.

    Article  Google Scholar 

  • Manley, G. (1974). Central England temperatures: Monthly means 1659 to 1973. Quarterly Journal of the Royal Meteorological Society, 100(425), 389–405.

    Article  Google Scholar 

  • McKitrick, R. (2010a). Atmospheric circulations do not explain the temperature-industrialization correlation. Statistics, Politics and Policy, 1(1). doi:10.2202/2151-7509.1004 (Article 1).

  • McKitrick, R. (2010b, April 5). Circling the bandwagons: My adventures correcting the IPCC. SPPI Reprint Series. Retrieved January 19, 2011, from http://scienceandpublicpolicy.org/images/stories/papers/reprint/Circling_the_Bandwagons_Correcting_the_IPCC.pdf

  • McKitrick, R. (2010c, February 26). Evidence submitted to the independent climate change email review (ICCER), Sir M. Russell, Chairman (80 pp). Retrieved April 15, 2010, from http://sites.google.com/site/rossmckitrick/McKitrick-ICCER-Evidence.pdf

  • McKitrick, R., & Michaels, P. J. (2004). A test of corrections for extraneous signals in gridded surface temperature data. Climate Research, 26(2), 159–173.

    Article  Google Scholar 

  • McKitrick, R., & Michaels, P. J. (2007). Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data. Journal of Geophysical Research, 112, D24S09. doi:10.1029/2007JD008465.

    Article  Google Scholar 

  • McKitrick, R., & Nierenberg, N. (2011). Socioeconomic patterns in climate data. Journal of Economic and Social Measurement, 35(3–4), 149–175.

    Google Scholar 

  • McKitrick, R., & Vogelsang, T. J. (2012). Multivariate trend comparisons between autocorrelated climate series with possible intercept shifts (Working Paper). Guelph: Department of Economic, University of Guelph.

    Google Scholar 

  • Menne, M. J., Williams, C. N., Jr., & Palecki, M. A. (2010). On the reliability of the U.S. surface temperature record. Journal of Geophysical Research-Atmospheres, 115, D11108. doi:10.1029/2009JD01309.

    Article  Google Scholar 

  • Muller, R. A., Curry, J., Groom, D., Jacobsen, R., Perlmutter, S., Rohde, R., Rosenfeld, A., Wickham, C., & Wurtele, J. (2011a). Decadal variations in the global atmospheric land temperatures (Berkeley Earth Surface Temperature (BEST) Project working paper). Retrieved from http://www.berkeleyearth.org/

  • Muller, R. A., Curry, J., Groom, D., Jacobsen, R., Perlmutter, S., Rohde, R., Rosenfeld, A., Wickham, C., & Wurtele, J. (2011b). Earth atmospheric land surface temperature and station quality in the United States (Berkeley Earth Surface Temperature (BEST) project working paper). Retrieved from http://www.berkeleyearth.org/

  • Norton, M., Osgood, D., & Turvey, C. G. (2010). Weather index insurance and the pricing of spatial basis risk. Paper presented at the annual conference of the AAEA, CAES and WAEA, Denver.

    Google Scholar 

  • Parker, D. E. (2010). Urban heat island effects on estimates of observed climate change. WIREs Climate Change, 1(1, January/February), 123–133.

    Article  Google Scholar 

  • Parker, D. E., Legg, T. P., & Folland, C. K. (1992). A new daily Central England temperature series, 1772–1991. International Journal of Climatology, 12(4), 317–342.

    Article  Google Scholar 

  • Peterson, T. C., & Vose, R. S. (1997). An overview of the global historical climatology network temperature database. Bulletin of the American Meteorological Society, 78(12), 2837–2849.

    Article  Google Scholar 

  • Peterson, T. C., Vose, R. S., Schmoyer, R., & Razuvaev, V. (1998). Global Historical Climatology Network (GHCN) quality control of monthly temperature data. International Journal of Climatology, 18(11), 1169–1179.

    Article  Google Scholar 

  • Rohde, R., Curry, J., Groom, D., Jacobsen, R., Muller, R. A., Perlmutter, S., Rosenfeld, A., Wickham, C., & Wurtele, J. (2011). Berkeley earth temperature averaging process (Berkeley Earth Surface Temperature (BEST) project working paper). Retrieved from http://www.berkeleyearth.org/

  • Shabbar, A., Bonsal, B., & Khandekar, M. (1997). Canadian precipitation patterns associated with the Southern Oscillation. Journal of Climate, 10(12), 3016–3027.

    Article  Google Scholar 

  • Skees, J. R. (2008). Innovations in index insurance for the poor in lower income countries. Agricultural and Resource Economics Review, 37(1), 1–15.

    Google Scholar 

  • Spencer, R. W. (2010). The great global warming blunder. How Mother Nature fooled the world’s top climate scientists. New York: Encounter Books.

    Google Scholar 

  • Sussman, B. (2010). Climategate. A veteran meteorologist exposes the global warming scam. New York: WND Books.

    Google Scholar 

  • Turvey, C. G. (2001). Weather derivatives for specific event risks in agriculture. Review of Agricultural Economics, 23(2), 333–351.

    Article  Google Scholar 

  • Turvey, C. G. (2005). The pricing of degree-day weather options. Agricultural Finance Review, 65(1), 59–85.

    Article  Google Scholar 

  • Turvey, C. G., Weersink, A., & Chiang, S.-H. C. (2006). Pricing weather insurance with a random strike price: The Ontario ice-wine harvest. American Journal of Agricultural Economics, 88(3), 696–709.

    Article  Google Scholar 

  • Vedenov, D. V., & Barnett, B. J. (2004). Efficiency of weather derivatives as primary crop insurance instruments. Journal of Agricultural and Resource Economics, 29(3), 387–403.

    Google Scholar 

  • Vélez-Belchí, P., Hernández-Guerra, A., Fraile-Nuez, E., & Benítez-Barrios, V. (2010). Changes in temperature and salinity tendencies of the upper subtropical North Atlantic ocean at 24.5°N. Journal of Physical Oceanography, 40(11), 2546–2555. doi:10.1175/2010JPO4410.1.

    Article  Google Scholar 

  • Watts, A. (2009). Is the U.S. surface temperature record reliable? Chicago: The Heartland Institute and SurfaceStations.org.

    Google Scholar 

  • Wickham, C., Curry, J., Groom, D., Jacobsen, R., Muller, R. A., Perlmutter, S., Rosenfeld, A., & Wurtele, J. (2011). Influence of urban heating on the global temperature land average using rural sites identified from MODIS classifications (Berkeley Earth Surface Temperature (BEST) project working paper). Retrieved from http://www.berkeleyearth.org/

  • Williams, C. N., Menne, M. J., Vose, R. S., & Easterling, D. R. (2008). United States historical climatology network monthly temperature and precipitation data (ORNL/CDIAC-87, NDP-019). Oak Ridge: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory.

    Google Scholar 

  • Woodward, J. D., & Garcia, P. (2008). Basis risk and weather hedging effectiveness. Agricultural Finance Review, 68(1), 99–118.

    Article  Google Scholar 

  • Xu, W., Odening, M., & Mußhoff, O. (2008). Indifference pricing of weather derivatives. American Journal of Agricultural Economics, 90(4), 979–993.

    Article  Google Scholar 

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  1. Department of Economics, University of Victoria, Victoria, BC, Canada

    G. Cornelis van Kooten (Professor of Economics and Geography Canada Research Chair in Environmental Studies)

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van Kooten, G.C. (2013). Weather and the Instrumental Record. In: Climate Change, Climate Science and Economics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4988-7_2

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