Master Thesis: Climate Sensitivity as a Determinant of Climate Tipping Risk - Data

This dataset contains all data used and generated during the master’s thesis "Climate Sensitivity as a Determinant of Climate Tipping Risk". It includes input data and proceeded results. Related Code is found in the subsequent GitHub repository. Contents: Temperature: Temperature Data Output from FaIR. Number order is the order of scenarios, where T_0 is for the lowest carbon concentration scenario and T_6 for the highest tcrecs.txt: In FaIR generated TCR-ECS pair ensemble ecs_timeseries: contains rearranged temperature data to be used as a more efficient input into PyCascades. One file per ECS pycas_output.npy: collected PyCascades Output risks_data.npy: results tabel with tipping risks for each scenario, ECS, in total and for each element.Structure: [ECS, Scenario, Risk: at least 1 tipped, Risk: at least 2 tipped, Risk: at least 3 tipped, Risk: all 4 tipped, AMOC Risk, WAIS Risk, AMAZ Risk] all_amoc_oscillations.txt: Contains data of the cases with amoc oscillations本数据集包含硕士学位论文《气候敏感性作为气候临界点风险的决定因素》（英文标题："Climate Sensitivity as a Determinant of Climate Tipping Risk"）中使用及生成的全部数据，涵盖输入数据与经处理后的结果。相关代码可在后续的GitHub仓库中获取。 数据内容： Temperature：FaIR输出的温度数据。数据排序与情景顺序一致，其中T_0对应最低碳浓度情景，T_6对应最高碳浓度情景。 tcrecs.txt：FaIR生成的暂态气候响应（Transient Climate Response, TCR）与平衡气候敏感度（Equilibrium Climate Sensitivity, ECS）配对集合。 ecs_timeseries：包含经重排的温度数据，可作为更高效的输入用于PyCascades模型，每个ECS对应一个独立文件。 pycas_output.npy：收集得到的PyCascades输出结果。 risks_data.npy：包含各情景、ECS下临界点风险的结果表格，涵盖总体风险及各元素风险。数据结构为：[ECS, 情景, 至少1个临界点被触发的风险, 至少2个临界点被触发的风险, 至少3个临界点被触发的风险, 全部4个临界点被触发的风险, 大西洋经向翻转环流（Atlantic Meridional Overturning Circulation, AMOC）风险, 西南极冰盖（West Antarctic Ice Sheet, WAIS）风险, 亚马逊雨林（Amazon Rainforest, AMAZ）风险] all_amoc_oscillations.txt：包含出现大西洋经向翻转环流振荡的案例数据