Propagation of climate model biases to biophysical modelling can complicate assessments of climate change impact in agricultural systems.

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 conse- quence 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 rain- fall 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 gener- ally 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 gener- ated with APSIM simulations forced by observed climate. Our results showed that RCM simulation biases could confound with the climate change signal and pro- duced an unreliable estimate of the effects of the changes in climate and farm man- agement 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 eliminat- ing 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.

Figure 12. Spatial distribution of the coefficients of multiple least squared regression of wheat yield change (ΔY, %) as a function of changes in climate (radiation: ΔR, %; temperature: ΔT,
C; rainfall: ΔP, %; [CO 2]: ΔCO2, 100 ppm) and management (crop residue incorporation: ΔRI, t/ha; N-uses: ΔNU, 2kgN/ha) in the formula, for data without SBC (NonSBC) and with SBC (SBCMnSD). Also included are the coefficient of determination (R ) and SE of the regression analyses