Publication Abstracts
Ruane et al. 2015
, , and , 2015: Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agric. Forest Meteorol., 200, 233-248, doi:10.1016/j.agrformet.2014.09.016.
The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.
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BibTeX Citation
@article{ru02200w,
author={Ruane, A. C. and Goldberg, R. and Chryssanthacopoulos, J.},
title={Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation},
year={2015},
journal={Agricultural and Forest Meteorology},
volume={200},
pages={233--248},
doi={10.1016/j.agrformet.2014.09.016},
}
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RIS Citation
TY - JOUR ID - ru02200w AU - Ruane, A. C. AU - Goldberg, R. AU - Chryssanthacopoulos, J. PY - 2015 TI - Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation JA - Agric. Forest Meteorol. JO - Agricultural and Forest Meteorology VL - 200 SP - 233 EP - 248 DO - 10.1016/j.agrformet.2014.09.016 ER -
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