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Abstract The Arctic has undergone dramatic changes in sea ice cover and the hydrologic cycle, both of which strongly impact the freshwater storage in, and export from, the Arctic Ocean. Here we analyze Arctic freshwater storage and fluxes in seven climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and assess their performance over the historical period (1980–2000) and in two future emissions scenarios, SSP1‐2.6 and SSP5‐8.5. Similar to CMIP5, substantial differences exist between the models' Arctic mean states and the magnitude of their 21st century storage and flux changes. In the historical simulation, most models disagree with observations over 1980–2000. In both future scenarios, the models show an increase in liquid freshwater storage and a reduction in solid storage and fluxes through the major Arctic gateways (Bering Strait, Fram Strait, Davis Strait, and the Barents Sea Opening) that is typically larger for SSP5‐8.5 than SSP1‐2.6. The liquid fluxes are driven by both volume and salinity changes, with models exhibiting a change in sign (relative to 1980–2000) of the freshwater flux through the Barents Sea Opening by mid‐century, little change in the Bering Strait flux, and increased export from the remaining straits by the end of the 21st century. In the straits west of Greenland (Nares, Barrow, and Davis straits), the models disagree on the behavior of the liquid freshwater export in the early‐to‐mid 21st century due to differences in the magnitude and timing of a simulated decrease in the volume flux.
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This content will become publicly available on March 1, 2026
Enhancing Sea Ice Concentration Resolution in a Northern Sea Route Strait Using a Generative Adversarial Network
Abstract Straits are strategically and economically vital due to their role as maritime choke points, controlling access to regions and resources. This is particularly pertinent in the Arctic, where navigability along critical shipping routes relies on access through straits that are frequently ice impacted. With the retreat of Arctic sea ice under anthropogenic climate change, scenarios using CMIP6 projections have the potential to provide valuable insights into future maritime accessibility regimes. However, typical climate model spatial resolutions limit the capacity to represent Arctic straits accurately. This study introduces a novel approach, the sea Ice Concentration Enhancement Generative Adversarial Network (ICE‐GAN), to enhance the spatial resolution of sea ice concentration (SIC) in Vilkitsky Strait, a passage along the Northern Sea Route (NSR). By employing the ICE‐GAN model, the spatial resolution is functionally increased from to . The approach is prototyped using ERA5 Reanalysis training data to predict ice cover for 2021 and 2022. The results indicate that the ICE‐GAN method outperforms, across multiple metrics, standard interpolation techniques such as Nearest Neighbor Interpolation and Bilinear Interpolation, both used in maritime accessibility models, as well as the super‐resolution convolutional neural network, the best practice method for super‐resolution in SIC. Importantly, the approach is robust to the non‐stationarity of the sea ice record. Moreover, by incorporating a physics‐informed approach into the ICE‐GAN, the model is able to further improve the accurate representation of sea ice cover in the studied Strait.
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- Award ID(s):
- 2334440
- PAR ID:
- 10618569
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Machine Learning and Computation
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2993-5210
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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