A description and assessment of the first release of the Arctic Subpolar gyre sTate Estimate (ASTE_R1), a data-constrained ocean-sea ice model-data synthesis is presented. ASTE_R1 has a nominal resolution of 1/3o and spans the period 2002-2017. The fit of the model to an extensive (O(10^9)) set of satellite and in situ observations was achieved through adjoint-based nonlinear least-squares optimization. The improvement of the solution compared to an unconstrained simulation is reflected in misfit reductions of 77% for Argo, 50% for satellite sea surface height, 58% for the Fram Strait mooring, 65% for Ice Tethered Profilers, and 83% for sea ice extent. Exact dynamical and kinematic consistency is a key advantage of ASTE_R1, distinguishing the state estimate from existing ocean reanalyses. Through strict adherence to conservation laws, all sources and sinks within ASTE_R1 can be accounted for, permitting meaningful analysis of closed budgets at the grid-scale, such as contributions of horizontal and vertical convergence to the tendencies of heat and salt. ASTE_R1 thus serves as the biggest effort undertaken to date of producing a specialized Arctic ocean-ice estimate over the 21st century. Transports of volume, heat, and freshwater are consistent with published observation-based estimates across important Arctic Mediterranean gateways. Interannual variability and low frequency trends of freshwater and heat content are well represented in the Barents Sea, western Arctic halocline, and east subpolar North Atlantic. Systematic biases remain in ASTE_R1, including a warm bias in the Atlantic Water layer in the Arctic and deficient freshwater inputs from rivers and Greenland discharge.

Mixing parameters can be inaccurate in ocean data assimilation systems, even if there is close agreement between observations and mixing parameters in the same modeling system when data are not assimilated. To address this, we investigate whether there are additional observations that can be assimilated by ocean modeling systems to improve their representation of mixing parameters and thereby gain knowledge of the global ocean’s mixing parameters. Observationally-derived diapycnal diffusivities–using a strain-based parameterization of finescale hydrographic structure–are included in the Estimating the Circulation & Climate of the Ocean (ECCO) framework and the GEOS-5 coupled Earth system model to test if adding observational diffusivities can reduce model biases. We find that adjusting ECCO-estimated and GEOS-5-calculated diapycnal diffusivity profiles toward profiles derived from Argo floats using the finescale parameterization improves agreement with independent diapycnal diffusivity profiles inferred from microstructure data. Additionally, for the GEOS-5 hindcast, agreement with observed mixed layer depths and temperature/salinity/stratification (i.e., hydrographic) fields improves. Dynamic adjustments arise when we make this substitution in GEOS-5, causing the model’s hydrographic changes. Adjoint model-based sensitivity analyses suggest that the assimilation of dissolved oxygen concentrations in future ECCO assimilation efforts would improve estimates of the diapycnal diffusivity field. Observationally-derived products for horizontal mixing need to be validated before conclusions can be drawn about them through similar analyses.