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Implications of climate model biases and downscaling on crop model simulated climate change impacts
- Source :
- ASA/CSSA/SSSA/ESA 2015 Joint Annual Meeting, ASA/CSSA/SSSA/ESA 2015 Joint Annual Meeting, Nov 2015, Minneapolis, MN, United States. 3 p, ASA/CSSA/SSSA/ESA 2015 Joint Annual Meeting, Minneapolis, MN, USA, 2015-11-15-2015-11-18, European Journal of Agronomy, European Journal of Agronomy, Elsevier, 2017, 88, pp.63-75. ⟨10.1016/j.eja.2016.05.012⟩
- Publication Year :
- 2015
- Publisher :
- HAL CCSD, 2015.
-
Abstract
- International audience; In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate sources of uncertainty. This paper considers the specific aspect of input weather data quality from a Regional Climate Model (RCM) leading to differences in estimates made by three crop models. The availability of hindcast RCM estimates enables comparison of crop model outputs derived from observed and modelled weather data. Errors in estimating the past climate implies biases in future projections, and thus affect modelled crop responses. We investigate the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed (12 UK sites), original (50 x 50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 RCM. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop model estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite evaluated (observed-hindcast) differences. This is likely due to compensating errors in the input weather data and non-linearity in the crop models processes, making interpretation of results problematic. Understanding how biases in climate data manifest themselves in individual crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections and crop model ensembles. The results have implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles. Crown Copyright (C) 2016 Published by Elsevier B.V. All rights reserved.
- Subjects :
- Matching (statistics)
010504 meteorology & atmospheric sciences
[SDE.MCG]Environmental Sciences/Global Changes
Soil Science
Climate change
Plant Science
01 natural sciences
Climate model
Crop
modelling
Bias
Crop simulation models
Downscaling Bias correction
Hindcast
Milieux et Changements globaux
global change
0105 earth and related environmental sciences
modélisation
2. Zero hunger
changement climatique
production agricole
Simulation modeling
Uncertainty
04 agricultural and veterinary sciences
15. Life on land
régionalisation
Agronomy
13. Climate action
Climatology
agricultural production
regionalization
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Environmental science
Crop simulation model
Agronomy and Crop Science
Downscaling
Subjects
Details
- Language :
- English
- ISSN :
- 11610301
- Database :
- OpenAIRE
- Journal :
- ASA/CSSA/SSSA/ESA 2015 Joint Annual Meeting, ASA/CSSA/SSSA/ESA 2015 Joint Annual Meeting, Nov 2015, Minneapolis, MN, United States. 3 p, ASA/CSSA/SSSA/ESA 2015 Joint Annual Meeting, Minneapolis, MN, USA, 2015-11-15-2015-11-18, European Journal of Agronomy, European Journal of Agronomy, Elsevier, 2017, 88, pp.63-75. ⟨10.1016/j.eja.2016.05.012⟩
- Accession number :
- edsair.doi.dedup.....dc2c97f798757c9c8e09c3b59f507918