This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. The specific parameters for this dataset are: sea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019) ocean model: NEMOv3.6 period: 1960-2015 atmospheric forcing: DFS5.2 (Drakkar) domain: global grid resolution: 1deg ORCA The simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.

This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. The specific parameters for this dataset are: sea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019) ocean model: NEMOv3.6 period: 1969-2015 atmospheric forcing: DFS5.2 (Drakkar) domain: global grid resolution: 0.25deg ORCA The simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.

This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. The specific parameters for this dataset are: sea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology ocean model: NEMOv3.6 period: 1969-2015 atmospheric forcing: DFS5.2 (Drakkar) domain: global grid resolution: 0.25deg ORCA The simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.