Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator

A - Papers appearing in refereed journals

Ma, D., Jing, Q., Xu, Y.-P., Cannon, A. J., Dong, T., Semenov, M. A. and Qian, B. 2021. Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator . Climate Research. 83, pp. 161-171. https://doi.org/10.3354/cr01646

AuthorsMa, D., Jing, Q., Xu, Y.-P., Cannon, A. J., Dong, T., Semenov, M. A. and Qian, B.
Abstract

Using climate scenarios from only 1 or a small number of global climate models (GCMs) in climate change impact studies may lead to biased assessment due to large uncertainty
in climate projections. Ensemble means in impact projections derived from a multi-GCM ensemble are often used as best estimates to reduce bias. However, it is often time consuming to run process-based models (e.g. hydrological and crop models) in climate change impact studies using numerous climate scenarios. It would be interesting to investigate if using a reduced number of climate scenarios could lead to a reasonable estimate of the ensemble mean. In this study, we generated a single ensemble-mean climate scenario (En-WG scenario) using ensemble means of the change factors derived from 20 GCMs included in CMIP5 to perturb the parameters in a weather generator, LARS-WG, for selected locations across Canada. We used En-WG scenarios to drive crop growth models in DSSAT ver. 4.7 to simulate crop yields for canola and spring wheat under RCP4.5 and RCP8.5 emission scenarios. We evaluated the potential of using the En-WG scenario to simulate crop yields by comparing them with crop yields simulated with the LARS-WG generated climate scenarios based on each of the 20 GCMs (WG scenarios). Our results showed that simulated crop yields using the En-WG scenarios were often close to the ensemble means of simulated crop yields using the 20 WG scenarios with a high probability of outperforming simulations based on a randomly selected GCM. Further studies are required, as the results of the proposed approach may be influenced by selected crop types, crop models, weather generators, and GCM ensembles.

KeywordsGlobal climate models; LARS-WG; DSSAT; Wheat; Canola; Simulation
Year of Publication2021
JournalClimate Research
Journal citation83, pp. 161-171
Digital Object Identifier (DOI)https://doi.org/10.3354/cr01646
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeDesigning Future Wheat - WP1 - Increased efficiency and sustainability
Publisher's version
Output statusPublished
Publication dates
Online06 May 2021
PublisherInter-Research
ISSN0936-577X

Permalink - https://repository.rothamsted.ac.uk/item/985q8/using-ensemble-mean-climate-scenarios-for-future-crop-yield-projections-a-stochastic-weather-generator

39 total views
3 total downloads
7 views this month
1 downloads this month
Download files as zip