Editorial Type:
Article
Article Type:
Research Article

What Do Rain Gauges Tell Us about the Limits of Precipitation Predictability?

Dan Gianotti Department of Earth and Environment, Boston University, Boston, Massachusetts

Search for other papers by Dan Gianotti in
Current site
Google Scholar
PubMed Close
,
Bruce T. Anderson Department of Earth and Environment, Boston University, Boston, Massachusetts

Search for other papers by Bruce T. Anderson in
Current site
Google Scholar
PubMed Close
, and
Guido D. Salvucci Department of Earth and Environment, Boston University, Boston, Massachusetts

Search for other papers by Guido D. Salvucci in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2013
DOI:
https://doi.org/10.1175/JCLI-D-12-00718.1
Page(s):
5682–5688
Restricted access

Abstract

A generalizable method is presented for establishing the potential predictability for seasonal precipitation occurrence using rain gauge data. This method provides an observationally based upper limit for potential predictability for 774 weather stations in the contiguous United States. It is found that the potentially predictable fraction varies seasonally and spatially, and that on average 30% of year-to-year seasonal variability is potentially explained by predictable climate processes. Potential predictability is generally highest in winter, appears to be enhanced by orography and land surface coupling, and is lowest (stochastic variance is highest) along the Pacific coast. These results depict “hot” spots of climate variability, for use in guiding regional climate forecasting and in uncovering processes driving climate. Identified “cold” spots are equally useful in guiding future studies as predictable climate signals in these areas will likely be undetectable.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00718.s1.

Corresponding author address: Dan Gianotti, Boston University, Department of Earth and Environment, 685 Commonwealth Ave., Boston, MA 02215. E-mail: gianotti@bu.edu

Abstract

A generalizable method is presented for establishing the potential predictability for seasonal precipitation occurrence using rain gauge data. This method provides an observationally based upper limit for potential predictability for 774 weather stations in the contiguous United States. It is found that the potentially predictable fraction varies seasonally and spatially, and that on average 30% of year-to-year seasonal variability is potentially explained by predictable climate processes. Potential predictability is generally highest in winter, appears to be enhanced by orography and land surface coupling, and is lowest (stochastic variance is highest) along the Pacific coast. These results depict “hot” spots of climate variability, for use in guiding regional climate forecasting and in uncovering processes driving climate. Identified “cold” spots are equally useful in guiding future studies as predictable climate signals in these areas will likely be undetectable.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00718.s1.

Corresponding author address: Dan Gianotti, Boston University, Department of Earth and Environment, 685 Commonwealth Ave., Boston, MA 02215. E-mail: gianotti@bu.edu

Supplementary Materials

    • Supplemental Materials (PDF 7.36 MB)
Save
Cover Journal of Climate
Journal of Climate

  • Boer, G., 2004: Long time-scale potential predictability in an ensemble of coupled climate models. Climate Dyn., 23, 29–44.

  • Bohr, G., and E. Aguado, 2001: Use of April 1 SWE measurements as estimates of peak seasonal snowpack and total cold-season precipitation. Water Resour. Res., 37, 51–60.

    • Search Google Scholar
    • Export Citation

  • Cayan, D., M. Dettinger, H. Diaz, and N. Graham, 1998: Decadal variability of precipitation over western North America. J. Climate, 11, 3148–3166.

    • Search Google Scholar
    • Export Citation

  • Cayan, D., K. Redmond, and L. Riddle, 1999: ENSO and hydrologic extremes in the western United States. J. Climate, 12, 2881–2893.

  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 4605–4630.

  • DelSole, T., and M. Tippett, 2007: Predictability: Recent insights from information theory. Rev. Geophys., 45, RG4002, doi:10.1029/2006RG000202.

    • Search Google Scholar
    • Export Citation

  • D'Odorico, P., L. Ridolfi, A. Porporato, and I. Rodriguez-Iturbe, 2000: Preferential states of seasonal soil moisture: The impact of climate fluctuations. Water Resour. Res., 36, 2209–2219.

    • Search Google Scholar
    • Export Citation

  • Feldl, N., and G. H. Roe, 2011: Climate variability and the shape of daily precipitation: A case study of ENSO and the American West. J. Climate, 24, 2483–2499.

    • Search Google Scholar
    • Export Citation

  • Ferguson, I., P. Duffy, T. Phillips, X. Liang, J. Dracup, S. Schubert, and P. Pegion, 2011: Non-stationarity of the signal and noise characteristics of seasonal precipitation anomalies. Climate Dyn., 36, 739–752.

    • Search Google Scholar
    • Export Citation

  • Hurvich, C. M., and C.-L. Tsai, 1989: Regression and time series model selection in small samples. Biometrika, 76, 297–307.

  • Karl, T., and R. Knight, 1998: Secular trends of precipitation amount, frequency, and intensity in the United States. Bull. Amer. Meteor. Soc., 79, 231–241.

    • Search Google Scholar
    • Export Citation

  • Katz, R., 1977: Precipitation as a chain-dependent process. J. Appl. Meteor., 16, 671–676.

  • Katz, R., and M. Parlange, 1993: Effects of an index of atmospheric circulation on stochastic properties of precipitation. Water Resour. Res., 29, 2335–2344.

    • Search Google Scholar
    • Export Citation

  • Katz, R., and M. Parlange, 1998: Overdispersion phenomenon in stochastic modeling of precipitation. J. Climate, 11, 591–601.

  • Kirtman, B., and P. Schopf, 1998: Decadal variability in ENSO predictability and prediction. J. Climate, 11, 2804–2822.

  • Koster, R., M. Suarez, R. Higgins, and H. Van den Dool, 2003: Observational evidence that soil moisture variations affect precipitation. Geophys. Res. Lett., 30, 1241, doi:10.1029/2002GL016571.

    • Search Google Scholar
    • Export Citation

  • Koster, R., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140.

  • Leith, C., 1978: Predictability of climate. Nature, 276, 352–355.

  • Lorenz, E. N., 1973: Predictability and periodicity: A review and extension. Proc. Third Conf. on Predictability and Statistics in the Atmospheric Sciences, Boulder, CO, Amer. Meteor. Soc., 1–4.

  • MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symp. on Mathematical Statistics and Probability, Vol. 1, Berkeley, CA, University of California, 281–297.

  • Madden, R., and D. Shea, 1978: Estimates of the natural variability of time-averaged temperatures over the United States. Mon. Wea. Rev., 106, 1695–1703.

    • Search Google Scholar
    • Export Citation

  • McCabe, G., and M. Dettinger, 1999: Decadal variations in the strength of ENSO teleconnections with precipitation in the western United States. Int. J. Climatol., 19, 1399–1410.

    • Search Google Scholar
    • Export Citation

  • Niziol, T., W. Snyder, and J. Waldstreicher, 1995: Winter weather forecasting throughout the eastern United States. Part IV: Lake effect snow. Wea. Forecasting, 10, 61–77.

    • Search Google Scholar
    • Export Citation

  • NRC, 2010: Assessment of intraseasonal to interannual climate prediction and predictability. National Research Council, 173 pp.

  • Palmer, T., 2006: Predictability of weather and climate: From theory to practice. Predictability of Weather and Climate, Cambridge University Press, 1–29.

  • Ropelewski, C. F., and M. S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño–Southern Oscillation (ENSO). Mon. Wea. Rev., 114, 2352–2362.

    • Search Google Scholar
    • Export Citation

  • Rowell, D., 1998: Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11, 109–120.

    • Search Google Scholar
    • Export Citation

  • Sibson, R., 1981: A brief description of natural neighbor interpolation. Interpreting Multivariate Data, V. Barnett, Ed., Wiley, 21–36.

  • Somerville, R., 1987: The predictability of weather and climate. Climatic Change, 11, 239–246.

  • Sun, Y., S. Solomon, A. Dai, and R. Portmann, 2006: How often does it rain? J. Climate, 19, 916–934.

  • Wang, H., S. Schubert, M. Suarez, and R. Koster, 2010: The physical mechanisms by which the leading patterns of SST variability impact U.S. precipitation. J. Climate, 23, 1815–1836.

    • Search Google Scholar
    • Export Citation

  • Wang, J., B. Anderson, and G. Salvucci, 2006: Stochastic modeling of daily summertime rainfall over the southwestern United States. Part I: Interannual variability. J. Hydrometeor., 7, 739–754.

    • Search Google Scholar
    • Export Citation

  • Wilks, D., and R. Wilby, 1999: The weather generation game: A review of stochastic weather models. Prog. Phys. Geogr., 23, 329–357.

  • Williams, C. N., R. S. Vose, D. R. Easterling, and M. J. Menne, 2006: United States historical climatology network daily temperature, precipitation, and snow data. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. [Available online at http://cdiac.ornl.gov/ftp/ushcn_daily/.]

  • Zhang, X., F. Zwiers, G. Hegerl, F. Lambert, N. Gillett, S. Solomon, P. Stott, and T. Nozawa, 2007: Detection of human influence on twentieth-century precipitation trends. Nature, 448, 461–465.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 580 307 24
PDF Downloads 247 62 7