Improving access and use of climate projections for ecological research through the use of a new Python tool

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 2023 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.

Methods
This is a jupiter notebook that allows the user to check if the desired combination of GCM and SSP is available for the selected time and area, and then uses the chelsa-cmip6 tool to create bioclimatic variables based on the user-selected scenarios. This notebook was created to facilitate use of the chelsa-cmip6 Python tool by Karger et al 2023.

过去十年间，耦合模式比较计划（Coupled Model Intercomparison Project, CMIP）产出的未来气候预估数据，已成为生物多样性科学研究的核心支撑手段。针对不同时间尺度与社会经济路径的主流生物气候变量未来预估预制数据集，极大推动了气候变化对生物多样性影响相关研究的开展。然而此类数据集无法灵活获取其他目标年份的预估结果，而直接使用原始数据则需要掌握编码与空间信息系统的专业技能。由Karger等人于2023年开发的Python工具chelsa-cmip6，恰好解决了上述痛点：只要用户选定的全球环流模型（General Circulation Model, GCM）与社会经济路径（Socioeconomic Pathway, SSP）组合存在，即可按需生成任意指定时段的生物气候变量。这一进展为生物多样性研究中气候数据集的灵活应用迈出了关键一步，也将推动CMIP6（Coupled Model Intercomparison Project Phase 6）产出数据的更广泛使用。我们同时期望，该工具能够进一步推动更多面向气候变化对生物多样性影响研究的易用型工具的开发。

方法
本项目提供一款Jupyter Notebook，可帮助用户核查目标时段与研究区域内所需的GCM与SSP组合是否可用，并基于用户选定的情景调用chelsa-cmip6工具生成生物气候变量。本Notebook旨在简化Karger等人2023年开发的chelsa-cmip6 Python工具的使用流程。