Over the past decade, the use of future climate projections from the coupled model intercomparison project (CMIP) has become central in biodiversity science. Pre-packaged datasets containing future projections of the widely used bioclimatic variables, for different times and socio-economic pathways, have contributed immensely to the study of climate change implications for biodiversity. However, these datasets lack the flexibility to obtain projections to other target years, and the use of raw data requires coding and spatial information systems expertise. The Python tool, ‘chelsa-cmip6', developed by Karger et al., provides the flexibility needed by allowing users to generate bioclimatic variables for the time of their choice provided the selected general circulation model and socioeconomic pathway combination exists. This is a fantastic step forward in bringing flexibility to the use of climate datasets in biodiversity and will allow for more widespread use of data provided by CMIP6. We hope it also will prompt the development of more user-friendly tools for the study of the effects of climate change on biodiversity.

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通过使用新的 Python 工具，改善生态研究气候预测的获取和使用

在过去的十年中，耦合模型比对项目（CMIP）对未来气候预测的使用已成为生物多样性科学的核心。预先打包的数据集包含不同时期和社会经济路径下广泛使用的生物气候变量的未来预测，为研究气候变化对生物多样性的影响做出了巨大贡献。然而，这些数据集缺乏获得对其他目标年份的预测的灵活性，并且原始数据的使用需要编码和空间信息系统专业知识。 Karger 等人开发的 Python 工具“chelsa-cmip6”提供了所需的灵活性，允许用户在选定的总体循环模型和社会经济路径组合存在的情况下生成其选择时间的生物气候变量。这是在生物多样性中使用气候数据集的灵活性方面向前迈出的一大步，并将允许更广泛地使用 CMIP6 提供的数据。我们希望它也将促进开发更多用户友好的工具来研究气候变化对生物多样性的影响。