Arctic Ocean dynamical downscaling data (ssp585) for understanding past and future climate change

The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community.

北极是地球上最易受气候变化影响的区域之一。然而，用于气候研究的现场长期观测资料在北冰洋海域相对匮乏，当前气候模式的模拟结果在北极区域存在显著偏差。本研究发布一套基于高分辨率冰-海耦合模式FESOM与气候模式FIO-ESM的北冰洋动力降尺度数据集。该数据集包含115年（1900年至2014年）的历史模拟结果，以及在SSP245和SSP585情景下完成的两组86年（2015年至2100年）未来情景模拟结果。历史模拟结果显示，本动力降尺度数据集的温度与盐度均方根误差远低于CMIP6（Coupled Model Intercomparison Project phase 6，耦合模式比较计划第六阶段）气候模式的模拟结果。气候模式中普遍存在的大西洋层过深、过厚等典型偏差，经动力降尺度处理后得到了显著改善。本数据集可为北冰洋气候变化评估与相关科学研究提供关键的长期数据源，为科研界提供宝贵的支撑资料。