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Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques

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Abstract

One of the main challenges in climate change studies is accurate projection of the global warming impacts on the probabilistic behaviour of hydro-climate processes. Due to the complexity of climate-associated processes, identification of predictor variables from high dimensional atmospheric variables is considered a key factor for improvement of climate change projections in statistical downscaling approaches. For this purpose, the present paper adopts a new approach of supervised dimensionality reduction, which is called “Supervised Principal Component Analysis (Supervised PCA)” to regression-based statistical downscaling. This method is a generalization of PCA, extracting a sequence of principal components of atmospheric variables, which have maximal dependence on the response hydro-climate variable. To capture the nonlinear variability between hydro-climatic response variables and projectors, a kernelized version of Supervised PCA is also applied for nonlinear dimensionality reduction. The effectiveness of the Supervised PCA methods in comparison with some state-of-the-art algorithms for dimensionality reduction is evaluated in relation to the statistical downscaling process of precipitation in a specific site using two soft computing nonlinear machine learning methods, Support Vector Regression and Relevance Vector Machine. The results demonstrate a significant improvement over Supervised PCA methods in terms of performance accuracy.

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Acknowledgments

We thank the associate editor and anonymous reviewer whose suggestions helped improve the paper. We acknowledge the CMIP5 climate coupled modelling groups, for producing and making their model output (listed in Table 2 of this paper) available, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison (PCMDI), which provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The CMIP5 model outputs used in the present study are available from http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. The NCEP/NCAR reanalysis records are also available through http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml. We also thank the Iran Meteorological Organization (IRIMO) for providing rainfall data recorded at the Tehran synoptic station.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, N2L-3G1, Canada

    Ali Sarhadi & Donald H. Burn

  2. Department of Statistics and Actuarial Science, School of Computer Science, University of Waterloo, Waterloo, ON, N2L-3G1, Canada

    Ge Yang & Ali Ghodsi

Authors
  1. Ali Sarhadi
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  2. Donald H. Burn
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  4. Ali Ghodsi
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Correspondence to Ali Sarhadi.

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Sarhadi, A., Burn, D.H., Yang, G. et al. Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques. Clim Dyn 48, 1329–1351 (2017). https://doi.org/10.1007/s00382-016-3145-0

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