Internal climate variability (ICV) is one of the major sources of uncertainty in climate projections, yet it is seldom quantified for projections of crop production. Our study focuses on quantifying the uncertainty due to ICV in projections of crop productions in Canada. We utilize climate scenarios from two large ensembles (LEs, CanESM2-LE and CanRCM4-LE with 25 members each) as inputs to the crop models in the Decision Support System for Agrotechnology Transfer. We simulate crop yields for canola, maize and spring wheat under the future climates of four global warming levels. The coefficient of variation (CV) of the projected crop production across the LE members is used to quantify the uncertainty related to ICV and this is compared with the CVs generated using the 20 GCMs in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Crop production in Canada could increase with global warming, e.g. spring wheat production could increase by up to 21% at the warming level of 3...

中文翻译：

---

量化内部气候变化在加拿大作物产量预测中带来的不确定性

内部气候变异性（ICV）是气候预测中不确定性的主要来源之一，但很少对农作物产量的预测进行量化。我们的研究重点是量化加拿大作物产量预测中ICV引起的不确定性。我们利用来自两个大型集成体（LE，CanESM2-LE和CanRCM4-LE，每个具有25个成员）的气候情景作为农业技术转让决策支持系统中作物模型的输入。我们在四个全球变暖水平的未来气候下模拟油菜，玉米和春小麦的作物产量。LE成员之间预计作物产量的变异系数（CV）用于量化与ICV相关的不确定性，并将其与在耦合模型比较项目第5阶段（CMIP5）中使用20个GCM生成的CV进行比较。