Propagation of climate model biases to biophysical modelling can complicate assessments of climate change impact in agricultural systems
- Publisher:
- Wiley
- Publication Type:
- Journal Article
- Citation:
- International Journal of Climatology, 2019, 39, (1), pp. 424-444
- Issue Date:
- 2019-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Liu_et_al_IJC_2018_smallest.pdf | Published version | 1.92 MB |
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© 2018 Royal Meteorological Society Regional climate model (RCM) simulations are being increasingly used for climate change impact assessments, but their application is challenging due to considerable biases inherited from global climate model (GCM) simulations and generated from dynamical downscaling processes. This study assesses the biases in NARCliM (NSW and ACT regional climate modelling) simulations and quantifies the consequence of the climate biases in the downstream assessment of climate change impact on wheat crop system, using the Agricultural Production System sIMulator (APSIM). Results showed that post-processing bias-corrected temperature and rainfall data from NARCliM had small annual mean biases but large biases in the crop growing season (CGS). During the CGS, the mean bias error of rainfall was generally positive for rainfall probability and negative for intensity, which subsequently resulted in APSIM simulating negative biases for runoff and deep drainage and positive bias in soil evaporation. Bias in soil water balance and water availability resulted in less plant transpiration and less N uptake, ultimately, leading to large negative biases in crop yields. A simple bias correction of the simulated crop yield driven by RCMs could result in a largely consistent distribution with those generated with APSIM simulations forced by observed climate. Our results showed that RCM simulation biases could confound with the climate change signal and produced an unreliable estimate of the effects of the changes in climate and farm management variables on crop yields. The results suggested that RCM simulations with the current bias correction on the RCM-simulated outputs applied on an annual basis were inadequate for climate change assessments which involve biophysical models. Our study highlights the need for improved RCM simulations by eliminating the systemic biases associated with rainfall characteristics, although suitable post-processing bias correction on a seasonal or monthly basis may result in improved RCM simulations for agricultural impacts of climate change.
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