Uncertainty decomposition for yield for the time period 2050–2069
Figure 2. Uncertainty decomposition for yield for the time period 2050–2069. Rows separate simulations using the two bias-corrected climate model outputs (QUMP BC, QUMP CF). The columns are the different sources of uncertainty: 'climate' =QUMP17 ensemble (17×), 'lethal' =lethal temperature limits of 40, 45, 50 ° C and 1–5 days of exceedence (16×), 'thermal' =crop thermal time development (3×), 'optimization' =using observed weather, ERA40-reanalysis or climate model data for optimization of GLAM (3×), 'planting' =Sacks planting dates  ±14 days (29×).
As climate changes, temperatures will play an increasing role in determining crop yield. Both climate model error and lack of constrained physiological thresholds limit the predictability of yield. We used a perturbed-parameter climate model ensemble with two methods of bias-correction as input to a regional-scale wheat simulation model over India to examine future yields. This model configuration accounted for uncertainty in climate, planting date, optimization, temperature-induced changes in development rate and reproduction. It also accounts for lethal temperatures, which have been somewhat neglected to date. Using uncertainty decomposition, we found that fractional uncertainty due to temperature-driven processes in the crop model was on average larger than climate model uncertainty (0.56 versus 0.44), and that the crop model uncertainty is dominated by crop development. Simulations with the raw compared to the bias-corrected climate data did not agree on the impact on future wheat yield, nor its geographical distribution. However the method of bias-correction was not an important source of uncertainty. We conclude that bias-correction of climate model data and improved constraints on especially crop development are critical for robust impact predictions.