Statistical Downscaling Using Localized Constructed Analogs (LOCA)

David W. Pierce Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

Search for other papers by David W. Pierce in
Current site
Google Scholar
PubMed
Close
,
Daniel R. Cayan Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography, University of California, San Diego, and U.S. Geological Survey, La Jolla, California

Search for other papers by Daniel R. Cayan in
Current site
Google Scholar
PubMed
Close
, and
Bridget L. Thrasher Climate Analytics Group, Menlo Park, California

Search for other papers by Bridget L. Thrasher in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-14-0082.s1.

Corresponding author address: David W. Pierce, Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography, University of California, San Diego, Mail Stop 0224, La Jolla, CA 92093-0224. E-mail: dpierce@ucsd.edu

Abstract

A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-14-0082.s1.

Corresponding author address: David W. Pierce, Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography, University of California, San Diego, Mail Stop 0224, La Jolla, CA 92093-0224. E-mail: dpierce@ucsd.edu

Supplementary Materials

    • Supplemental Materials (PDF 2.40 MB)
Save
  • Abatzoglou, J. T., and Brown T. J. , 2012: A comparison of statistical downscaling methods suited for wildfire applications. Int. J. Climatol., 32, 772780, doi:10.1002/joc.2312.

    • Search Google Scholar
    • Export Citation
  • Chen, J., Brissette F. P. , and Leconte R. , 2014: Assessing regression-based statistical approaches for downscaling precipitation over North America. Hydrol. Processes, 28, 34823504, doi:10.1002/hyp.9889.

    • Search Google Scholar
    • Export Citation
  • Das, T., Maurer E. P. , Pierce D. W. , Dettinger M. D. , and Cayan D. R. , 2013: Increases in flood magnitudes in California under warming climates. J. Hydrol., 501, 101110, doi:10.1016/j.jhydrol.2013.07.042.

    • Search Google Scholar
    • Export Citation
  • Fowler, H. J., Blenkinsop S. , and Tebaldi C. , 2007: Linking climate change modeling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol., 27, 15471578, doi:10.1002/joc.1556.

    • Search Google Scholar
    • Export Citation
  • Goodess, C. M., and Palutikof J. P. , 1998: Development of daily rainfall scenarios for southeast Spain using a circulation-type approach to downscaling. Int. J. Climatol., 18, 10511083, doi:10.1002/(SICI)1097-0088(199808)18:10<1051::AID-JOC304>3.0.CO;2-1.

    • Search Google Scholar
    • Export Citation
  • Gutmann, E. D., Pruitt T. , Clark M. P. , Brekke L. , Arnold J. R. , Raff D. A. , and Rasmussen R. M. , 2014: An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resour. Res., 50, 71677186, doi:10.1002/2014WR015559.

    • Search Google Scholar
    • Export Citation
  • Hidalgo, H. G., Dettinger M. D. , and Cayan D. R. , 2008: Downscaling with constructed analogues: Daily precipitation and temperature fields over the United States. CEC PIER Project Rep. CEC-500-2007-123, 48 pp. [Available online at www.energy.ca.gov/2007publications/CEC-500-2007-123/CEC-500-2007-123.PDF.]

  • Hwang, S., and Graham W. D. , 2014: Assessment of alternative methods for statistically downscaling daily GCM precipitation outputs to simulate regional streamflow. J. Amer. Water Resour. Assoc.,50, 1010–1032, doi:10.1111/jawr.12154.

  • Livneh, B., Rosenberg E. A. , Lin C. , Nijssen B. , Mishra V. , Andreadis K. , Maurer E. P. , and Lettenmaier D. P. , 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Updates and extensions. J. Climate, 26, 93849392, doi:10.1175/JCLI-D-12-00508.1.

    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., Torney A. , and Field C. B. , 2009: Climate extremes in California agriculture. California Energy Commission Rep. CEC-500-2009-040-F, 8 pp. [Available online at www.energy.ca.gov/2009publications/CEC-500-2009-040/CEC-500-2009-040-F.PDF.]

  • Maraun, D., 2013: Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue. J. Climate, 26, 21372143, doi:10.1175/JCLI-D-12-00821.1.

    • Search Google Scholar
    • Export Citation
  • Maraun, D., and Coauthors, 2010: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys., 48, RG3003, doi:10.1029/2009RG000314.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., Hidalgo H. G. , Das T. , Dettinger M. D. , and Cayan D. R. , 2010: The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol. Earth Syst. Sci., 14, 11251138, doi:10.5194/hess-14-1125-2010.

    • Search Google Scholar
    • Export Citation
  • Parry, M. L., Canziani O. F. , Palutikof J. P. , van der Linden P. J. , and Hanson C. E. , Eds., 2007: Climate Change 2007: Impacts, Adaptation, and Vulnerability. Cambridge University Press, 976 pp.

  • Piani, C., Haerter J. , and Coppola E. , 2010: Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol., 99, 187192, doi:10.1007/s00704-009-0134-9.

    • Search Google Scholar
    • Export Citation
  • Pierce, D. W., and Cayan D. R. , 2013: The uneven response of different snow measures to human-induced climate warming. J. Climate, 26, 41484167, doi:10.1175/JCLI-D-12-00534.1.

    • Search Google Scholar
    • Export Citation
  • Pierce, D. W., and Coauthors, 2013: The key role of heavy precipitation events in climate model disagreements of future annual precipitation changes in California. J. Climate, 26, 58795896, doi:10.1175/JCLI-D-12-00766.1.

    • Search Google Scholar
    • Export Citation
  • Schoof, J. T., and Pryor S. C. , 2001: Downscaling temperature and precipitation: A comparison of regression-based methods and artificial neural networks. Int. J. Climatol., 21, 773790, doi:10.1002/joc.655.

    • Search Google Scholar
    • Export Citation
  • Thrasher, B., Maurer E. P. , McKellar C. , and Duffy P. B. , 2012: Technical note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci.,16, 3309–3314, doi:10.5194/hess-16-3309-2012.

  • Trewin, B. C., 2007: The role of climatological normals in a changing climate. WCDMP-61/WMO-TD-1377, 45 pp. [Available online at www.wmo.int/datastat/documents/WCDMPNo61_1.pdf.]

  • van den Dool, H. M., 1994: Searching for analogues, how long must we wait? Tellus, 46A, 314324, doi:10.1034/j.1600-0870.1994.t01-2-00006.x.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., 1999: On the use of “inflation” in statistical downscaling. J. Climate, 12, 35053506, doi:10.1175/1520-0442(1999)012<3505:OTUOII>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., Zorita E. , and Cubasch U. , 1993: Downscaling of global climate change estimates to regional scales: An application to Iberian rainfall in wintertime. J. Climate, 6, 11611171, doi:10.1175/1520-0442(1993)006<1161:DOGCCE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., Wigley T. M. L. , Conway D. , Jones P. D. , Hewiston B. C. , Main J. , and Wilks D. S. , 1998: Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Res., 34, 29953008, doi:10.1029/98WR02577.

    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., Charles S. P. , Zorita E. , Timbal B. , Whetton P. , and Mearns L. O. , 2004: Guidelines for use of climate scenarios developed from statistical downscaling methods. IPCC Doc., 27 pp. [Available online at www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf.]

  • Wilks, D. S., 2012: Stochastic weather generators for climate-change downscaling, part II: Multivariable and spatially coherent multisite downscaling. Wiley Interdiscip. Rev.: Climate Change, 3, 267278, doi:10.1002/wcc.167.

    • Search Google Scholar
    • Export Citation
  • Wood, A. W., Leung L. R. , Sridhar V. , and Lettenmaier D. P. , 2004:Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189216, doi:10.1023/B:CLIM.0000013685.99609.9e.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., and Georgakakos A. P. , 2012: Joint variable spatial downscaling. Climatic Change, 111, 945972, doi:10.1007/s10584-011-0167-9.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 11993 4426 292
PDF Downloads 5170 1498 132