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

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 scenarios 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.

A Stepwise-Clustered Simulation Approach for Projecting Future Heat Wave Over Guangdong Province

“…The results implied that the model ensemble can be an effective tool in the case of reproducing the observed Tmax at the 16 selected weather stations over Guangdong. Previous studies have also found the use of model ensemble reduced more the bias in the evaluation than using only a single GCM, thereby improving the reliability of climate projections (Webber et al, 2018;Qian et al, 2020;Ma et al, 2021). Similarly, it can be found that the developed approach also demonstrates significant performance in reproducing the observed Tmax from the four GCMs at the 16 selected weather stations (Figure 5).…”

“…In detail, Tmax is somewhat overestimated by the CNRM-CM5 from March to April (e.g., Guangning and Shanwei), and underestimated by the MIROC5 during July to August (e.g., Nanxiong and Zijin). This is mainly due to the uncertainty of the model, which may be induced by differences in the model parameterization, emission concentration scenarios, and internal climate variability (Zhai et al, 2019;Ma et al, 2021). Therefore, it is essentially recommended to present the Tmax with the model ensemble from multiple GCMs' outputs to reduce potential biases.…”

Managing mineral phosphorus application with soil residual phosphorus reuse in Canada

“…However, given the great variety of crop yields and livestock productions in our model, it is probably impractical to use crop models to predict yields for all of them in the short term. While a few crop model studies have projected yields for a single or three typical Canadian crops (Chipanshi et al., 2015; Ma et al., 2021), there is no national‐scale predictions for other crop yields. To address this, we used projected statistics from 2023 to 2030 collected from a report by Agriculture and Agri‐Food Canada, which forecasted increases in major crop yields, crop seeding areas, and primary livestock inventory for the 10‐year period (2021–2030) based on economic models (MTO, 2021) (Figure S3).…”