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Author Cammarano, D; Stefanova, L; Ortiz, BV; Ramirez-Rodrigues, M; Asseng, S; Misra, V; Wilkerson, G; Basso, B; Jones, JW; Boote, KJ; DiNapoli, S
Title Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US Type Journal Article
Year 2013 Publication Regional Environmental Change Abbreviated Journal Reg. Environ. Change
Volume 13 Issue 1 Pages 101-110
Keywords Crop simulation models; Climate variability; Global circulation models; Reanalysis; Wheat; Maize
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.
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Call Number FCI @ refbase @ Serial 347
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Author Tian, D.; Asseng, S.; Martinez, C.J.; Misra, V.; Cammarano, D.; Ortiz, B.V.
Title Does decadal climate variation influence wheat and maize production in the southeast USA? Type Journal Article
Year 2015 Publication Agricultural and Forest Meteorology Abbreviated Journal Agricultural and Forest Meteorology
Volume 204 Issue Pages 1-9
Keywords Crop simulation models; Decadal climate variability; Wavelet analysis; Atlantic Multi-decadal Oscillation; Pacific Decadal Oscillation; North Atlantic Oscillation
Abstract Linking decadal variability with short-term variability could be potentially exploited for improving seasonal climate forecasting for assisting crop management decisions. The objective of this study was to explore whether there are decadal variations in wheat (winter crop) and maize (summer crop) production and whether these decadal variations correlate with any known variations of climate. Over one hundred years of wheat and maize yields were simulated using process-based crop models with dynamically downscaled daily reanalysis data over four locations in the southeast USA. Using wavelet and cross-wavelet analysis, we found that winter crop yields were dominated by 10- and 22-year decadal oscillations; the decadal variations of winter crop yields were driven by decadal variations of winter temperature and spring precipitation; no decadal variations were detected for summer crop yields and summer precipitation and temperature. Cross-wavelet analysis showed that the decadal variations of winter crop yields were correlated with indices of the annual Atlantic Multi-decadal Oscillation (AMO), the annual Pacific Decadal Oscillation (PDO), and the winter North Atlantic Oscillation (NAO). Therefore, this knowledge of decadal climate variability could potentially be leveraged to predict winter seasonal yields of crops.
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ISSN 0168-1923 ISBN Medium
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Notes Approved no
Call Number FCI @ refbase @ Serial 689
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