Climate Dynamics

, Volume 46, Issue 5–6, pp 1991–2023 | Cite as

Credibility of statistical downscaling under nonstationary climate

  • Kaustubh Salvi
  • Subimal Ghosh
  • Auroop R. Ganguly
Article

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.

Keywords

Statistical downscaling Stationarity Climate change 

Supplementary material

382_2015_2688_MOESM1_ESM.docx (2.8 mb)
Supplementary material 1 (DOCX 2882 kb)
382_2015_2688_MOESM2_ESM.pdf (292 kb)
Supplementary material 2 (PDF 291 kb)
382_2015_2688_MOESM3_ESM.pdf (4.9 mb)
Supplementary material 3 (PDF 5056 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Kaustubh Salvi
    • 1
  • Subimal Ghosh
    • 1
    • 2
  • Auroop R. Ganguly
    • 2
    • 3
  1. 1.Department of Civil EngineeringIndian Institute of Technology BombayPowai, MumbaiIndia
  2. 2.Interdisciplinary Program in Climate StudiesIndian Institute of Technology BombayPowai, MumbaiIndia
  3. 3.Sustainability and Data Sciences Laboratory, Civil and Environmental EngineeringNortheastern UniversityBostonUSA

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