Editorial Type:
Article
Article Type:
Research Article

Uncertainties, Trends, and Hottest and Coldest Years of U.S. Surface Air Temperature since 1895: An Update Based on the USHCN V2 TOB Data

Samuel S. P. Shen Department of Mathematics and Statistics, San Diego State University, San Diego, California

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Christine K. Lee Department of Mathematics and Statistics, San Diego State University, San Diego, California

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Jay Lawrimore NOAA/National Climatic Data Center, Asheville, North Carolina

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Print Publication:
15 Jun 2012
DOI:
https://doi.org/10.1175/JCLI-D-11-00102.1
Page(s):
4185–4203
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Abstract

This paper estimates the sampling error variances of gridded monthly U.S. Historical Climatology Network, version 2 (USHCN V2), time-of-observation-biases (TOB)-adjusted data. The analysis of mean surface air temperature (SAT) assesses uncertainties, trends, and the rankings of the hottest and coldest years for the contiguous United States in the period of 1895–2008. Data from the USHCN stations are aggregated onto a 2.5° × 3.5° latitude–longitude grid by an arithmetic mean of the stations inside a grid box. The sampling error variances of the gridded monthly data are estimated for every month and every grid box with data. The gridded data and their sampling error variances are used to calculate the contiguous U.S. averages and their trends and associated uncertainties. The sampling error variances are smaller (mostly less than 0.2°C2) over the eastern United States, where the station density is greater and larger (with values of 1.3°C2 for some grid boxes in the earlier period) over mountain and coastal areas. In the period of 1895–2008, every month from January to December has a positive linear trend. February has the largest trend of 0.162°C (10 yr)−1, and September has the smallest trend at 0.020°C (10 yr)−1. The three hottest (coldest) years measured by the mean SAT over the United States were ranked as 1998, 2006, and 1934 (1917, 1895, and 1912).

Corresponding author address: Samuel S. P. Shen, Dept. of Mathematics and Statistics, San Diego State University, 5500 Campanile Dr., San Diego, CA 92182. E-mail: shen@math.sdsu.edu

Abstract

This paper estimates the sampling error variances of gridded monthly U.S. Historical Climatology Network, version 2 (USHCN V2), time-of-observation-biases (TOB)-adjusted data. The analysis of mean surface air temperature (SAT) assesses uncertainties, trends, and the rankings of the hottest and coldest years for the contiguous United States in the period of 1895–2008. Data from the USHCN stations are aggregated onto a 2.5° × 3.5° latitude–longitude grid by an arithmetic mean of the stations inside a grid box. The sampling error variances of the gridded monthly data are estimated for every month and every grid box with data. The gridded data and their sampling error variances are used to calculate the contiguous U.S. averages and their trends and associated uncertainties. The sampling error variances are smaller (mostly less than 0.2°C2) over the eastern United States, where the station density is greater and larger (with values of 1.3°C2 for some grid boxes in the earlier period) over mountain and coastal areas. In the period of 1895–2008, every month from January to December has a positive linear trend. February has the largest trend of 0.162°C (10 yr)−1, and September has the smallest trend at 0.020°C (10 yr)−1. The three hottest (coldest) years measured by the mean SAT over the United States were ranked as 1998, 2006, and 1934 (1917, 1895, and 1912).

Corresponding author address: Samuel S. P. Shen, Dept. of Mathematics and Statistics, San Diego State University, 5500 Campanile Dr., San Diego, CA 92182. E-mail: shen@math.sdsu.edu
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  • Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, and P. D. Jones, 2006: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. J. Geophys. Res., 111, D12106, doi:10.1029/2005JD006548.

    • Search Google Scholar
    • Export Citation

  • Carroll, R. J., D. Ruppert, L. A. Stefanski, and C. M. Crainiceanu, 2006: Measurement Error in Nonlinear Models: A Modern Perspective. 2nd ed. Chapman and Hall, 455 pp.

  • Cochran, W. G., 1977: Sampling Techniques. 3rd ed. Wiley, 428 pp.

  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 1–28.

  • D’Ippoliti, D., and Coauthors, 2010: The impact of heat waves on mortality in 9 European cities: Results from the EuroHEAT project. Environ. Health, 9, doi:10.1186/1476-069X-9-37.

    • Search Google Scholar
    • Export Citation

  • Easterling, D. R., T. R. Karl, E. H. Mason, P. Y. Hughes, and D. P. Bowman, 1996: United States Historical Climatology Network (U.S. HCN) monthly temperature and precipitation data. Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center Rep. ORNL/CDIAC-87, NDP-019/R3, 280 pp.

  • Folland, C. K., and Coauthors, 2001: Global temperature change and its uncertainties since 1861. Geophys. Res. Lett., 28, 2621–2624.

  • Gaffen, D. J., and R. J. Ross, 1998: Increased summertime heat stress in the US. Nature, 396, 529–530.

  • Giese, B. S., and S. Ray, 2011: El Niño variability in simple ocean data assimilation SODA). J. Geophys. Res., 116, C02024, doi:10.1029/2010JC006695.

    • Search Google Scholar
    • Export Citation

  • Gleason, K. L., J. H. Lawrimore, D. H. Levinson, T. R. Karl, and D. J. Karoly, 2008: A revised U.S. climate extremes index. J. Climate, 21, 2124–2137.

    • Search Google Scholar
    • Export Citation

  • Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345.

  • Huang, N. E., and S. S. P. Shen, Eds., 2005: Hilbert–Huang Transform and Its Applications. Interdisciplinary Mathematical Sciences, Vol. 5, World Scientific, 360 pp.

  • Jones, P. D., P. Ya. Groisman, M. Coughlin, N. Plummer, W.-C. Wang, and T. R. Karl, 1990: Assessment of urbanization effects in time series of surface air temperatures over land. Nature, 347, 169–172.

    • Search Google Scholar
    • Export Citation

  • Jones, P. D., T. J. Osborn, and K. R. Briffa, 1997: Estimating sampling errors in large-scale temperature averages. J. Climate, 10, 2548–2568.

    • Search Google Scholar
    • Export Citation

  • Jones, P. D., D. E. Parker, T. J. Osborn, and K. R. Briffa, cited 2011: Global and hemispheric temperature anomalies - Land and marine instrumental records. [Available online at http://cdiac.ornl.gov/trends/temp/jonescru/jones.html.]

  • Karl, T. R., C. N. Williams Jr., P. J. Young, and W. M. Wendland, 1986: A model to estimate the time of observation bias associated with monthly mean maximum, minimum, and mean temperature for the United States. J. Climate Appl. Meteor., 25, 145–160.

    • Search Google Scholar
    • Export Citation

  • Karl, T. R., J. M. Melillo, and T. C. Peterson, Eds., 2009: Global Climate Change Impacts in the United States. Cambridge University Press, 188 pp.

  • Mann, M. E., 2004: On smoothing potentially non-stationary climate time series. Geophys. Res. Lett., 31, L07214, doi:10.1029/2004GL019569.

    • Search Google Scholar
    • Export Citation

  • Menne, M. J., and C. N. Williams Jr., 2009: Homogenization of temperature series via pairwise comparisons. J. Climate, 22, 1700–1717.

  • Menne, M. J., C. N. Williams Jr., and R. S. Vose, 2009: The U.S. Historical Climatology Network monthly temperature data, version 2. Bull. Amer. Meteor. Soc., 90, 993–1007.

    • Search Google Scholar
    • Export Citation

  • Parker, D. E., 2006: A demonstration that large-scale warming is not urban. J. Climate, 19, 2882–2895.

  • Parker, D. E., and B. Horton, 2005: Uncertainties in central England temperature 1878–2003 and some improvements to the maximum and minimum series. Int. J. Climatol., 25, 1173–1188.

    • Search Google Scholar
    • Export Citation

  • Peterson, T. C., K. P. Gallo, J. Lawrimore, T. W. Owen, A. Huang, and D. A. McKittrick, 1999: Global rural temperature trends. Geophys. Res. Lett., 26, 329–332.

    • Search Google Scholar
    • Export Citation

  • Regele, T., 2010: USHCN data gridding, error estimation, and the changes of the extreme weather over the US since 1895. M.S. thesis, Dept. of Mathematics and Statistics, San Diego State University, 103 pp.

  • Shen, S. S. P., T. M. Smith, C. F. Ropelewski, and R. E. Livezey, 1998: An optimal regional averaging method with error estimates and a test using tropical Pacific SST data. J. Climate, 11, 2340–2350.

    • Search Google Scholar
    • Export Citation

  • Shen, S. S. P., H. Yin, and T. M. Smith, 2007: An estimate of the error variance of the gridded GHCN monthly surface air temperature data. J. Climate, 20, 2321–2331.

    • Search Google Scholar
    • Export Citation

  • Shen, S. S. P., A. B. Gurung, H.-S. Oh, T. Shu, and D. R. Easterling, 2011: The twentieth century contiguous US temperature changes indicated by daily data and higher statistical moments. Climatic Change, 109, 287–317.

    • Search Google Scholar
    • Export Citation

  • Smith, T. M., R. W. Reynolds, and C. F. Ropelewski, 1994: Optimal averaging of seasonal sea surface temperatures and associated confidence interval (1860–1989). J. Climate, 7, 949–964.

    • Search Google Scholar
    • Export Citation

  • Solomon, S., D. Qin, M. Manning, M. Marquis, K. Averyt, M. M. B. Tignor, H. L. Miller Jr., and Z. Chen, Eds., 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1297–1300.

    • Search Google Scholar
    • Export Citation

  • Titchner, H. A., P. W. Thorne, M. P. McCarthy, S. F. B. Tett, L. Haimberger, and D. E. Parker, 2009: Critically reassessing tropospheric temperature trends from radiosondes using realistic validation experiments. J. Climate, 22, 465–485.

    • Search Google Scholar
    • Export Citation

  • Vose, R. S., C. N. Williams Jr., T. C. Peterson, T. R. Karl, and D. R. Easterling, 2003: An evaluation of the time of observation bias adjustment in the U.S. Historical Climatology Network. Geophys. Res. Lett., 30, 2046, doi:10.1029/2003GL018111.

    • Search Google Scholar
    • Export Citation

  • Wackerly, D. D., W. Mendenhall III, and R. L. Scheaffer, 2002: Mathematical Statistics with Applications. 6th ed. Duxbury, 853 pp.

  • Wang, X., and S. S. P. Shen, 1999: Estimation of spatial degrees of freedom of a climate field. J. Climate, 12, 1280–1291.

  • Washington, W. M., and C. L. Parkinson, 1986: An Introduction to Three-Dimensional Climate Modeling. University Science Books, 422 pp.

  • Wei, W. W. S., 2006: Time Series Analysis: Univariate and Multivariate Methods. 2nd ed. Pearson Addison Wesley, 614 pp.

  • Weithmann, A., 2011: Optimal averages of U.S. temperature, error estimates and inferences. M.S. thesis, Dept. of Mathematics and Statistics, San Diego State University, 96 pp.

  • Wigley, T. M. L., K. R. Briffa, and P. D. Jones, 1984: On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Climate Appl. Meteor., 23, 201–213.

    • Search Google Scholar
    • Export Citation

  • Williams, C. N., M. J. Menne, and P. W. Thorne, 2012: Benchmarking the performance of pairwise homogenization of surface temperatures in the United States. J. Geophys. Res., 117, D05116, doi:10.1029/2011JD016761.

    • Search Google Scholar
    • Export Citation

  • Willmott, C. J., C. M. Rowe, and W. D. Philpot, 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Amer. Cartogr., 12, 5–16.

    • Search Google Scholar
    • Export Citation
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