Description: We present a dynamically consistent gridded data set of the global, monthly-mean oxygen isotope ratio of seawater (δ¹⁸Osw). The data set is created from an optimized simulation of an ocean general circulation model constrained by global monthly δ¹⁸Osw data collected from 1950 until 2011 and climatological salinity and temperature data collected from 1951 to 1980. The optimization was obtained using the adjoint method for variational data assimilation, which yields a simulation that is consistent with the observational data and the physical laws incorporated in the model. Our data set performs equally well as a previous data set in terms of model-data misfit and brings an improvement in terms of physical consistency and a seasonal cycle. The data assimilation method shows high potential for interpolating sparse data sets in a physical meaningful way. Comparatively big errors, however, are found in our data set in the surface levels in the Arctic Ocean mainly because there is no influence of isotopically highly depleted precipitation on the ocean in areas with sea-ice, and because of the low model resolution. The data set is the 100-year monthly-mean of the optimized 400-year equilibrium model simulation. It includes simulated δ¹⁸Osw, potential temperature, and salinity on the model grid. The model uses a cubed-sphere grid with a horizontal resolution of 2.8° and 15 vertical levels. We additionally provide the data interpolated onto a 1° lat-lon grid. Values at the edge of the ocean, which could not be interpolated, are set to the respective values in the raw data set on the model grid.

Description: An Arctic sea ice thickness record covering from 2010 to 2016 is generated by assimilating satellite thickness from CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS). The model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a local Error Subspace Transform Kalman filter (LESTKF) coded in the Parallel Data Assimilation Framework (PDAF).

Description: A key parameterization in sea ice models describes the sub-grid scale ice thickness distribution. Based on only a few observations, the ice thickness distribution model was shown to be consistent with field data and to improve the simulation's large scale properties. The available submarine and airborne observations enable to evaluate in greater detail the ability of a pan-Arctic sea ice - ocean model with an ice thickness distribution parameterization to reproduce observed thickness distributions in different regions and seasons. Many observations are reproduced accurately. Some cases of poorly simulated modes and tails of the distributions are tentatively attributed to simplified thermodynamics and inaccurate deformation fields. Variability on decadal timescales, however, is generally underestimated. Thickness distributions in individual grid cells of the model show similar differences between regions and seasons as observed regional mean distributions, but the modeled grid-scale variability is lower than observed. Simulated modal thicknesses of first-year ice are only insufficiently different from those of multi-year ice. The modal thickness proves to be a useful metric for quantifying model biases in both dynamics and thermodynamics. In addition to improving basin-wide mean variables, the ice thickness distribution parameterization provides reliable and valuable additional sub-grid scale data. At the same time the low climate sensitivity of the parameterization may affect longer simulations with strong climate change aspects. Archived are the data files necessary to run the simulation. The simulation was performed with the MITgcm (version checkpoint 66a), simulation geometry and boundary conditions were taken from [Nguyen et al., 2011, doi:10.1029/2010JC006573]

Description: The global ocean state for the modern age and for the Last Glacial Maximum (LGM) was dynamically reconstructed with a sophisticated data assimilation technique. A substantial amount of data including global seawater temperature, salinity (only for the modern estimate), and the isotopic composition of oxygen and carbon (only in the Atlantic for the LGM) were integrated into an ocean general circulation model with the help of the adjoint method, thereby the model was optimized to reconstruct plausible continuous fields of tracers, overturning circulation and water mass distribution. The adjoint-based LGM state estimation of this study represents the state of the art in terms of the length of forward model runs, the number of observations assimilated, and the model domain. Compared to the modern state, the reconstructed continuous sea-surface temperature field for the LGM shows a global-mean cooling of 2.2 K, and the reconstructed LGM ocean has a more vigorous Atlantic meridional overturning circulation, shallower North Atlantic Deep Water (NADW) equivalent, stronger stratification, and more saline deep water.

Description: The numerical model documented here is a regional coupled sea ice - ocean model based on the Massachusetts Institute of Technology General Circulation Model code - MITgcm (for details we refer to: http://mitgcm.org/public/r2_manual/latest/online_documents) with a model domain covering the Arctic Ocean, Nordic Seas and northern North Atlantic. The horizontal resolution is 1/4 degree (approx. 28 km) on a rotated grid with the grid equator passing through the geographical North Pole. The sea ice model is a dynamic-thermodynamic sea-ice model with a viscous-plastic rheology and has a landfast ice parametrization as described by Itkin et al [2015, see bellow], where more details about the model set-up can be found. The model is forced by the atmospheric reanalysis -- The Climate Forecast System Reanalysis from 1979 to 2010 and then from 2011 to 2014 with the NCEP Climate Forecast System Version 2. The model output provided here contains sea ice simulations used by Itkin and Krumpen, [2017, see bellow]. The control run (CTRL) is forced by the CFSR and CSFv2. In the climatological run (CLIM) the May-December fields are replaced by the climatology (1979-2013). On 1. January each year the run is restarted from CTRL. Initial years 1979-1991 are regarded as spin up are not included into the data set here.

Description: The simulated sea ice drift data is a by-product from a sea ice thickness assimilation system that generates the Arctic 'Combined Model and Satellite sea ice Thickness (CMST; doi:10.1594/PANGAEA.891475) ' dataset. The data also provide the ocean current velocity where ice free. To obtain the sea ice drift on the geographic coordinate, a transformation must be done as following: uE = AngleCS * SIuice - AngleSN * SIvice; vN = AngleSN * SIuice + AngleCS * SIvice; where uE and vN are two velocity components on the geographic coordinate; AngleCS and AngleSN can be found in 'grid.cdf'; SIuice and SIvice are sea ice velocity on model mesh.

Description: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation data for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008. A detailed description of the data-set and of the algorithms deriving it is provided in Hutter et al. (2019).

Description: The global ocean state for the modern age and for the Last Glacial Maximum (LGM) was dynamically reconstructed with a sophisticated data assimilation technique. A substantial amount of data including global seawater temperature, salinity (only for the modern estimate), and the isotopic composition of oxygen and carbon (only in the Atlantic for the LGM) were integrated into an ocean general circulation model with the help of the adjoint method, thereby the model was optimized to reconstruct plausible continuous fields of tracers, overturning circulation and water mass distribution. The adjoint‐based LGM state estimation of this study represents the state of the art in terms of the length of forward model runs, the number of observations assimilated, and the model domain. Compared to the modern state, the reconstructed continuous sea‐surface temperature field for the LGM shows a global‐mean cooling of 2.2 K, and the reconstructed LGM ocean has a more vigorous Atlantic meridional overturning circulation, shallower North Atlantic Deep Water (NADW) equivalent, stronger stratification, and more saline deep water.

Description: The impact of a subgrid-scale ice thickness distribution (ITD) and two standard ice strength formulations on simulated Arctic sea ice climate is investigated. To this end, different model configurations with and without an ITD were tuned by minimizing the weighted mean error between the simulated and observed sea ice concentration, thickness, and drift speed with an semiautomatic parameter optimization routine. The standard ITD and ice strength parameterization lead to larger errors when compared to the simple single-category model with an ice strength parameterization based on the mean ice thickness. Interestingly, the simpler ice strength formulation, which depends linearly on the mean ice thickness, also reduces the model-observation error when using an ITD. For the ice strength parameterization that makes use of the ITD, the effective ice strength depends strongly on the number of thickness categories, so that introducing more categories can lead to overall thicker ice that is more easily deformed.

Description: We present a dynamically consistent gridded data set of the global, monthly mean oxygen isotope ratio of seawater ( urn:x-wiley:jgrc:media:jgrc23118:jgrc23118-math-0001). The data set was created from an optimized simulation of an ocean general circulation model constrained by global monthly urn:x-wiley:jgrc:media:jgrc23118:jgrc23118-math-0002 data collected from 1950 to 2011 and climatological salinity and temperature data collected from 1951 to 1980. The optimization was obtained using the adjoint method for variational data assimilation, which yields a simulation that is consistent with the observational data and the physical laws embedded in the model. Our data set performs equally well as a previous data set in terms of model‐data misfit but brings an improvement in terms of the seasonal cycle and physical consistency. As a result the data set does not show any sharp transitions between water masses or in areas where the data coverage is low. The data assimilation method shows high potential for interpolating sparse data sets in a physically meaningful way. Comparatively big errors, however, are found in our data set in the surface levels in the Arctic Ocean mainly because the influence of isotopically highly depleted precipitation is not preserved in the sea ice model, and the low model resolution of about 285 km horizontally. The data set is publicly available, and it is anticipated to be useful for a large range of applications in (paleo‐) oceanographic studies.