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  • This dataset contains digital imagery from the University of Leed's three 'Mobotix MX-M24M IP' cameras mounted on board the Swedish Icebreaker Oden durning Arctic Cloud Summer Expedition (ACSE). ACSE took place in the Arctic during summer 2014. These imagery were used to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record. The three camera units were mounted pointing in the following directions: - Camera 1: pointing to starboard, - Camera 2: pointing to bow, - Camera 3: pointing to port. The Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat. ACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud. The UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF).

  • 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 stand-alone 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 uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). 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: mixed-layer period: 1980-2020 atmospheric forcing: NCEP2 domain: pan-Arctic 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: 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: CORE II surface data (Large & Yeager, 2009). 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: 1960-2009 atmospheric forcing: CORE II 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: CORE II surface data (Large & Yeager, 2009). 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: 1960-2009 atmospheric forcing: COREII 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 stand-alone 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 uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). 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: mixed-layer period: 1980-2020 atmospheric forcing: NCEP2 domain: pan-Arctic 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.

  • Monthly output from an integration of the GO6 configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model, forced by the CORE2 (Coordinated Ocean-ice Reference Experiments version 2.0) corrected inter-annual forcing (CIAF) surface field dataset. UK Global Ocean GO6 consists of version 3.6 of NEMO and version 5.2.1 of the CICE (Community Ice CodE) sea-ice model, and the present simulation is on the global eORCA025 1/4° grid. The ocean is initialised from a climatology based on the EN3 monthly objective analysis (Ingleby and Huddleston, 2007) averaged over years 2004–2008, and is integrated from 1958 to 2007. The model was run on the Archer supercomputing platform through the Rose/Cylc interface on Puma, and the run ID on the Puma system is u-ap795. The integrations were funded by the Natural Environment Research Council (NERC) under the Atlantic Climate System Integrated Study (ACSIS) project (NE/N018044/1).

  • 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.

  • 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 uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). 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: NCEP2 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.