Publication Abstracts
Ruane et al. 2015
, , , and , 2015: AgMIP climate datasets and scenarios for integrated assessment. In Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments, Part 1. C. Rosenzweig and D. Hillel, Eds., ICP Series on Climate Change Impacts, Adaptation, and Mitigation, vol. 3, Imperial College Press, pp. 45-78, doi:10.1142/9781783265640_0003.
Climate change presents a great challenge to the agricultural sector as changes in precipitation, temperature, humidity, and circulation aptterns alter the climatic conditions upon which many agricultural systems rely. Projections for future climate conditions are inherently uncertain owing to a lack of clarity on how society will develop, policies that may be implemented to reduce greenhouse-gas (GHG) emissions, and complexitie in modeling the atmosphere, ocean, land, cryosphere, and biosphere components of the climate system. Global climate models (GCMs) are based on wellestablished physics of each climate component that enable the models to project climate responses to GHG concentraion scenarios. The most recent iteration of the Coupled Model Intercomparison Project (CMIP5) utilized representative comcetration pathways (RCPs) to cover the range of plausible GHG concentrations out past the year 2100, with RCP8.5 representing an extreme scenario and RCP4.5 representing a lower concentrations scenario.
GCMS were designed to capture the response of the climate system to changes in major climate forcings, such as GHG concentrations; however, substantial biases (e.g., in mean climate, climate variability, and extreme events) limit their direct use in climate change impact assessments and applications. Considerable research in the climate community has therefore focused on bias-correction and downscaling of GCM outputs to add some combination of increased horizontal and temporal resolution, better statistics for climate variability and extremes, and interactions betweem higher spatial- and temporal-scale dynamics and long-term climate changes. Downscaling methods may be dynamical (e.g., utilizing regional climate models, RCMs), statistical (i.e., utilizing historical climate statistics and climate change information), or some combination of the two. For the purposes of agricultural modeling we pay close attention to model biases (which may impede the use of RCMs without bias correction) and non-stationarity within the climatesystem (which may hinder statistical scenario applications). It is important to consider whether the scenario-generation methodology employed captures changes to the variables and time-scales affecting agricultural systems without introducing any compromising biases.
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BibTeX Citation
@inbook{ru06200a,
author={Ruane, A. C. and Winter, J. M. and McDermid, S. P. and Hudson, N. I.},
editor={Rosenzweig, C. and Hillel, D.},
title={AgMIP climate datasets and scenarios for integrated assessment},
booktitle={Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments, Part 1},
year={2015},
volume={3},
pages={45--78},
publisher={Imperial College Press},
address={London},
series={ICP Series on Climate Change Impacts, Adaptation, and Mitigation},
doi={10.1142/9781783265640_0003},
}
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RIS Citation
TY - CHAP ID - ru06200a AU - Ruane, A. C. AU - Winter, J. M. AU - McDermid, S. P. AU - Hudson, N. I. ED - Rosenzweig, C. ED - Hillel, D. PY - 2015 TI - AgMIP climate datasets and scenarios for integrated assessment BT - Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments, Part 1 T3 - ICP Series on Climate Change Impacts, Adaptation, and Mitigation VL - 3 SP - 45 EP - 78 DO - 10.1142/9781783265640_0003 PB - Imperial College Press CY - London ER -
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