Deep Learning-Based 4D Reconstruction of Arctic Ocean Hydrography and derived Geostrophic Currents from Satellite and In Situ Observations

Abstract. The Arctic Ocean plays a critical role in the global climate system through its influence on freshwater storage, ocean circulation, sea-ice dynamics, and air–sea heat exchanges. However, subsurface observations remain sparse in space and time, limiting the characterization of Arctic hydrographic variability and circulation changes. This study presents an observation-constrained Arctic Ocean reconstruction framework based on a Long Short-Term Memory neural network (LSTM), combining satellite-derived surface variables and in situ hydrographic profiles to infer subsurface temperature and salinity profiles.

The framework is applied over the 2011–2021 period to reconstruct four-dimensional temperature and salinity fields across the Arctic Ocean at 3-day temporal resolution and on 102 WOA standard depth levels. A pan-Arctic reconstruction is provided on a 25 km EASE grid, together with four higher-resolution regional reconstructions on 6.25 km EASE grids covering the main Arctic gateways: Bering Strait, Davis Strait, Fram Strait, and the Barents Sea Opening. The reconstructed temperature and salinity fields are also used to derive steric height, absolute dynamic height, and geostrophic currents.

Independent evaluation against withheld in situ observations shows that the reconstruction improves the representation of Arctic hydrography relative to the baseline reanalysis (GLORYS) across most regions and depth ranges, with the largest error reductions in the upper ocean and in ice-covered regions. The reconstructed fields reproduce the main large-scale hydrographic and dynamical structures of the Arctic Ocean, including the Beaufort Gyre, Arctic boundary currents, and the major gateways. They also enhance the representation of seasonal variability, freshwater accumulation, upper-ocean stratification, and Atlantic Water pathways entering through Fram Strait and the Barents Sea Opening.

The resulting five spatially and temporally continuous datasets provide observation-constrained estimates of Arctic hydrography and geostrophic circulation. They are intended to support studies of freshwater variability, Arctic circulation change, climate model evaluation, and data-driven Arctic Ocean research. The datasets are available at World Data Center for Climate (WDCC) via https://doi.org/10.26050/wdcc/dlrec-ao_v1.