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Credibility of statistical downscaling under nonstationary climate

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Abstract

Statistical downscaling (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.

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Acknowledgments

The authors thank the anonymous reviewers for valuable comments. KS and SG acknowledge funding from the Space Technology Cell of Indian Institute of Technology Bombay and Indian Space Research Organization while ARG acknowledges support from the United States National Science Foundation (NSF) Expeditions in Computing Grant Award No. 1029166 titled “Understanding climate change: a data driven approach”. The collaborative activity between IIT Bombay and Northeastern University was supported through Interdisciplinary Program in Climate Studies, IIT Bombay, funded by Department of Science and Technology, Government of India. The authors thank Amey Pathak of the Indian Institute of Technology, Bombay, as well as Udit Bhatia, Thomas Vandal and Evan Kodra, all of Northeastern University, for helpful suggestions.

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  1. Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India

    Kaustubh Salvi & Subimal Ghosh

  2. Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India

    Subimal Ghosh & Auroop R. Ganguly

  3. Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, Boston, MA, 02115, USA

    Auroop R. Ganguly

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Salvi, K., Ghosh, S. & Ganguly, A.R. Credibility of statistical downscaling under nonstationary climate. Clim Dyn 46, 1991–2023 (2016). https://doi.org/10.1007/s00382-015-2688-9

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