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

• Main Authors: Qi, Shu, Qiang, Wang, Zhenya, Song, Gui, Gao, Hailong, Liu, Shizhu, Wang, Yan, He, Rongrong, Pan, Fangli, Qiao
• Format: Dataset
• Language: English
• Published: Science Data Bank 2024
• Subjects: Earth science; Arctic Ocean; dynamical downscaling; climate change; climate models; sea ice; ice-ocean model; SSP245; SSP585; CMIP6; FIO-ESM; FESOM; Arctic; Climate change; Sea ice
• Online Access: https://dx.doi.org/10.57760/sciencedb.16319; https://www.scidb.cn/en/detail?dataSetId=3f95f1f10c04474aa54ecdac63417253

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 ... : 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 ...