Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6537378 | Agricultural and Forest Meteorology | 2015 | 12 Pages |
Abstract
The method is not predictive in the deterministic sense, but rather attempts to characterize uncertainty in near-term future climate, taking into account both forced trends and unforced, natural climate fluctuations. It differs from typical downscaling methods in that GCM information is utilized only at the regional scale, subregional variability being modeled based on the observational record. Output, generated on the monthly time scale, is disaggregated to daily values with a weather generator and used to drive soybean yields in the crop model DSSAT-CSM, for which preliminary results are discussed. The simulations produced permit assessment of the interplay between long-range trends and near-term climate variability in terms of agricultural production.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Atmospheric Science
Authors
Arthur M. Greene, Lisa Goddard, Paula L.M. Gonzalez, Amor V.M. Ines, James Chryssanthacopoulos,