Climate Dynamics

, Volume 49, Issue 9–10, pp 3587–3604 | Cite as

Decadal temperature predictions over the continental United States: Analysis and Enhancement

  • Kaustubh SalviEmail author
  • Gabriele Villarini
  • Gabriel A. Vecchi
  • Subimal Ghosh


Increases in global temperature over recent decades and the projected acceleration in warming trends over the 21 century have resulted in a strong need to obtain information about future temperature conditions. Hence, skillful decadal temperature predictions (DTPs) can have profound societal and economic benefits through informed planning and response. However, skillful and actionable DTPs are extremely challenging to achieve. Even though general circulation models (GCMs) provide decadal predictions of different climate variables, the direct use of GCM data for regional-scale impacts assessment is not encouraged because of the limited skill they possibly exhibit and their coarse spatial resolution. Here, we focus on 14 GCMs and evaluate seasonally and regionally averaged skills in DTPs over the continental United States. Moreover, we address the limitations in skill and spatial resolution in the GCM outputs using two data-driven approaches: (1) quantile-based bias correction and (2) linear regression-based statistical downscaling. For both the approaches, statistical parameters/relationships, established over the calibration period (1961–1990) are applied to retrospective and near future decadal predictions by GCMs to obtain DTPs at ‘4 km’ resolution. Predictions are assessed using different evaluation metrics, long-term statistical properties, and uncertainty in terms of the range of predictions. Both the approaches adopted here show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DTPs from 14 GCMs with a spatial resolution of 4 km, which can be used as a key input for impacts assessments.


Decadal temperature predictions Continental United States Bias correction Statistical downscaling 



This work is funded in part by the Broad Agency Announcement (BAA) Program and the Engineer Research and Development Center (ERDC)–Cold Regions Research and Engineering Laboratory (CRREL) under Contract No. W913E5-16-C-0002. Gabriele Villarini also acknowledges financial support from the USACE Institute for Water Resources.

Supplementary material

382_2017_3532_MOESM1_ESM.docx (12.6 mb)
Supplementary material 1 (DOCX 12927 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Kaustubh Salvi
    • 1
    Email author
  • Gabriele Villarini
    • 1
  • Gabriel A. Vecchi
    • 2
  • Subimal Ghosh
    • 3
  1. 1.IIHR-Hydroscience & Engineering, The University of Iowa, 100 C. Maxwell Stanley Hydraulics LaboratoryIowa CityUSA
  2. 2.NOAA/Geophysical Fluid Dynamics LaboratoryPrincetonUSA
  3. 3.Department of Civil EngineeringIndian Institute of Technology BombayMumbaiIndia

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