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Bivariate downscaling with asynchronous measurements. (English) Zbl 1302.62258

Summary: Statistical downscaling is a useful technique to localize global or regional climate model projections to assess the potential impact of climate changes. It requires quantifying a relationship between climate model output and local observations from the past, but the two sets of measurements are not necessarily taken simultaneously, so the usual regression techniques are not applicable. In the case of univariate downscaling, the Statistical Asynchronous Regression (SAR) method of T. P. O’Brien, D. Sornette, and R. L. McPherron [“Statistical asynchronous regression: determining the relationship between two quantities that are not measured simultaneously”, J. Geophys. Res. 106, 13247–13259 (2001; doi:10.1029/2000JA900193)] provides a simple quantile-matching approach with asynchronous measurements. In this paper, we propose a bivariate downscaling method for asynchronous measurements based on a notion of bivariate ranks and positions. The proposed method is preferable to univariate downscaling, because it is able to preserve general forms of association between two variables, such as temperature and precipitation, in statistical downscaling. This desirable property of the bivariate downscaling method is demonstrated through applications to simulated and real data.

MSC:

62P12 Applications of statistics to environmental and related topics

Software:

SDSM
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[1] Buja, A., Logan, B. F., Reeds, J. A., and Shepp, L. A. (1994), ”Inequalities and Positive-Definite Functions Arising From a Problem in Multidimensional Scaling,” Annals of Statistics, 22, 406–438. · Zbl 0834.62060
[2] Chaudhuri, P. (1996), ”On a Geometric Notion of Quantiles for Multivariate Data,” Journal of the American Statistical Association, 91, 862–872. · Zbl 0869.62040
[3] Dettinger, M., Cayan, D., Meyer, M., and Jeton, A. (2004), ”Simulated Hydrologic Responses to Climate Variations and Change in the Merced, Carson, and American River Basins, Sierra Nevada, California, 1900–2099,” Climate Change, 62, 283–317.
[4] Guillas, S., Bao, J., Choi, Y., and Wang, Y. (2007), ”Statistical Correction and Downscaling of Chemical Transport Model Ozone Forecasts Over Atlanta,” Atmospheric Environment, 42, 1338–1348.
[5] Hayhoe, K. (2010), ”A Standarized Framework for Evaluating the Skill of Regional Climate Downscaling Techniques,” Doctoral Thesis, University of Illinois at Urbana-Champaign.
[6] Karl, T. R., Wang, W. C., Schlesinger, M. E., Knight, R. W., and Portman, D. (1990), ”Method of Relating General Circulation Model Simulated Climate to the Observed Local Climate.1.Seasonal Statistics,” Journal of Climate, 3 (10), 1053–1079.
[7] Liang, X.-Z., Kunkel, K. E., Meehl, G. A., Jones, R. G., and Wang, J. X. L. (2008), ”Regional Climate Models Downscaling Analysis of General Circulation Models Present Climate Biases Propagation Into Future Change Projections,” Geophysical Research Letters, 35, L08709.
[8] Marden, J. I. (2004), ”Positions and QQ Plots,” Statistical Science, 19, 606–614. · Zbl 1100.62502
[9] Maurer, E. P. (2008), ”Utility of Daily vs. Monthly Large-Scale Climate Data: An Intercomparison of Two Statistical Downscaling Methods,” Hydrology and Earth System Sciences, 12, 551–563.
[10] Nakičenovič, and Swart, R. (eds.) (2000), Special Report on Emission Scenarios, New York: Cambridge Univ. Press.
[11] O’Brien, T. P., Sornette, D., and McPherro, R. L. (2001), ”Statistical Asynchronous Regression Determining: The Relationship Between Two Quantities That Are Not Measured Simultaneously,” Journal of Geophysical Research, 106, 13247–13259.
[12] Pilipenkoa, V., Yagovab, N., Romanovab, N., and Allenc, J. (2005), ”Statistical Relationships Between Satellite Anomalies at Geostationary Orbit and High-Energy Particles,” Advances in Space Research, 37, 1192–1205.
[13] Wigley, T. M. L. (1990), ”Obtaining Sub-Grid-Scale Information From Coarse-Resolution General Circulation Model Output,” Journal of Geophysical Research. Atmospheres, 95 (D2), 1943–1953.
[14] Wilby, R. L. (2002), ”SDSM–A Decision Support Tool for the Assessment of Regional Climate Change Impacts,” Environmental Modelling & Software, 17, 147–159.
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