Data and code underlying the publication "Alternative climate metrics to the Global Warming Potential are more suitable for assessing aviation non-CO2 effects"

Data and code accompanying Megill et al. (2024): "Alternative climate metrics to the Global Warming Potential are more suitable for assessing aviation non-CO2 effects", published in <em>Communications Earth &amp; Environment</em>. This dataset contains all data and code developed during research towards the linked article and contains all elements required to reproduce the linked figures and analysis.<br>The data was generated using the V2.1 of the climate-chemistry response model AirClim (Grewe and Stenke, 2008; Dahlmann et al., 2016, see References). The dataset includes analyses of the full aviation industry (scenarios CORSIA, COVID15s, CurTec, Fa1 and FP2050 from Grewe et al., 2021, see References) and of individual, theoretical aircraft designs of Category 4 (152 - 201 seats). Trajectory data is taken from DLR WeCare (Grewe et al., 2017, see References). The analysis of the results is performed with four Jupyter notebooks running Python.<br>All data files are licensed under a CC-BY 4.0. All Jupyter notebooks and the Python script are licensed under an Apache License v2.0. <br>Please note: The software code AirClim is confidential proprietary information of the DLR and cannot be made available to the public or readers without restrictions. Licensing of the code to third parties is conditioned upon the prior conclusion of a licensing agreement with the DLR. Qualified researchers can request an agreement on reasonable request from the corresponding author.本数据集配套Megill等人（2024）发表于《Communications Earth & Environment》的论文《替代全球增温潜势（Global Warming Potential）的气候指标更适用于评估航空非CO₂排放影响》。本数据集包含支撑该关联论文的全部研究数据与代码，涵盖复现论文附图与分析结果所需的全部要素。本数据集的数据基于气候-化学响应模型AirClim V2.1生成（Grewe与Stenke, 2008；Dahlmann等人, 2016，详见参考文献）。数据集包含全航空业的分析结果（采用Grewe等人2021年提出的CORSIA、COVID15s、CurTec、Fa1及FP2050情景，详见参考文献），以及第4类（152至201座）单款理论航空器设计的相关分析。轨迹数据取自DLR WeCare项目（Grewe等人, 2017，详见参考文献）。结果分析依托4个运行Python语言的Jupyter Notebook完成。所有数据文件采用CC BY 4.0许可协议授权，所有Jupyter Notebook及Python脚本采用Apache License v2.0许可协议授权。请注意：AirClim软件代码属于DLR的保密专有信息，不得无限制向公众或读者公开。向第三方授权该代码需事先与DLR签署许可协议。符合条件的研究人员可通过通讯作者按合理流程申请签署协议。