Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US

Davide Cammarano, Lydia Stefanova, Brenda V. Ortiz, Melissa Ramirez-Rodrigues, Senthold Asseng, Vasubandhu Misra, Gail Wilkerson, Bruno Basso, James W. Jones, Kenneth J. Boote, Steven DiNapoli

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models' historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or downscaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments.

Original languageEnglish
Pages (from-to)101-110
Number of pages10
JournalRegional Environmental Change
Volume13
Issue numberSUPPL.1
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Climate variability
  • Crop simulation models
  • Global circulation models
  • Maize
  • Reanalysis
  • Wheat

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