NUIST ESM v3 Dataset for the SSP2-com scenario (Part 2)

Ocean output is based on the ORCA1 tripolar grid, with a nominal horizontal resolution of about 1° × 1° and meridional refinement to approximately 1/3° near the equator, and 46 vertical levels. Sea-ice output follows the ocean/sea-ice grid. The temporal coverage of the simulations is 2015–2200, and the available temporal resolutions include monthly, daily, and 6-hourly data, depending on the variable.The deposited files include selected atmospheric, oceanic, and/or sea-ice variables from the SSP2-com simulations.

The data were post-processed following CMIP6 output requirements using CMOR, and are stored in standard NetCDF format. Each NetCDF file is self-describing and contains metadata for the variable name, units, dimensions, coordinates, time axis, model information, experiment name, ensemble member, and other relevant attributes. File names follow the CMIP-style convention and encode the variable, temporal frequency, model, experiment, ensemble member, grid information, and time period, which facilitates identification and reuse.

The data can be opened and analyzed with commonly used NetCDF-compatible tools, including Python packages such as xarray and netCDF4, Climate Data Operators (CDO), NetCDF Operators (NCO), Panoply, and other standard climate-data software. Part 2 in the current data refers to r3i1p1f2, r4i1p1f1, r4i1p1f2, r5i1p1f1 and r5i1p1f2 members of the SSP2-com scenario experiment.No artificial gap filling or bias correction was applied to the deposited model output.

Missing values, if present, follow the missing-value attributes recorded in the NetCDF files and usually correspond to physically undefined grid cells or variable-specific masks, such as land–sea or sea-ice masks. Users should note that the data are climate-model simulations rather than direct observations. Therefore, uncertainties arise from model structural uncertainty, internal climate variability, and scenario/forcing assumptions.

The ensemble design is intended to help characterize part of the uncertainty associated with future climate responses under the SSP2-com scenario. Users are encouraged to consider these uncertainties when applying the data to climate-change assessment, impacts studies, and mitigation-related analyses.