List of physics options used in WRF downscaling.

Predicting changes in species distributions under climate change requires high-quality climate projections. In this case study of coastal British Columbia, we prepare and evaluate two sets of climate data - a priori bias corrected and non bias corrected dynamically downscaled historical projections of Community Earth System Model 2 simulations. We compare these datasets with downscaled ERA5 reanalysis focusing on commonly used inputs to species distribution models (SDM), namely, bioclimatic (BIOCLIM) variables and climate extreme indices. Our results show improvements for mean BIOCLIM variables when a priori bias correction is applied. However, modest improvements are observed in terms of variability and extreme indices. Overall, our findings suggest that a priori bias corrected dynamically downscaled climate projections provide more accurate input to SDMs, and thus can improve the reliability of these important ecological models.在气候变化背景下预测物种分布变化，需依托高质量的气候预测数据。本研究以不列颠哥伦比亚省沿海区域为研究案例，制备并评估了两套气候数据集：一套为经先验偏差校正的动态降尺度（dynamically downscaled）历史预测数据，另一套为未施加偏差校正的同类数据，二者均源自社区地球系统模型2（Community Earth System Model 2）的模拟结果。我们将这两套数据集与降尺度处理后的ERA5再分析数据进行比对，重点聚焦于物种分布模型（Species Distribution Models，SDM）的常用输入变量，即生物气候（bioclimatic，BIOCLIM）变量与气候极端指数。研究结果显示，施加先验偏差校正后，平均生物气候变量的模拟精度得到改善。不过，在气候变异性与极端指数方面仅观测到小幅提升。总体而言，本研究表明，经先验偏差校正的动态降尺度气候预测数据，可为物种分布模型提供更为精准的输入数据，进而提升这类重要生态模型的可靠性。