Physics-Knowledge-Guided Hybrid Neural Learning for Arctic Sea Ice Concentration Evolution and Short-Range Prediction

Maqun Zhang; Feng Gao; Wankun Chen; Hui Yu; Yanhai Gan; Junyu Dong

cs.AIAug 22, 2026

Physics-Knowledge-Guided Hybrid Neural Learning for Arctic Sea Ice Concentration Evolution and Short-Range Prediction

Authors: Maqun Zhang, Feng Gao, Wankun Chen, Hui Yu, Yanhai Gan, Junyu Dong

Organizations: State Key Laboratory of Physical Oceanography and Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China · School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QQ, U.K.

Abstract

Accurate modeling of sea ice concentration (SIC) evolution is essential for polar climate assessment and short?range sea ice prediction. Numerical and data-driven approaches constitute major foundations for SIC modeling, but the former often require complex parameterizations and substantial compu?tation, whereas the latter rarely encode physical dependencies explicitly. This study presents the Physics-Informed Hybrid Ice Model (PIHIM), a differentiable data-driven hybrid ice model for daily SIC evolution that organizes its network structure according to the physical dependencies encoded in the sea ice continuity equation and explicitly accounts for dynamical transport, ther?modynamically driven areal growth and loss, and unresolved local processes. PIHIM preserves the representation capacity of deep learning while providing a process-decomposed formulation of ice displacement, freeze-melt areal change, and local error closure. Two evaluation settings are adopted: reanalysis-forced simulation examines SIC evolution stability under reanalysis forcing, and forecast-forced prediction assesses short-range performance un?der forecast-forced conditions, with reanalysis and observational SIC serving as verification references. Results indicate enhanced ice-edge preservation and error-growth control in reanalysis?forced simulation, while PIHIM retains measurable short-range prediction skill under forecast-forced conditions. Our code will be made publicly available after the paper is accepted.

Figures & tables

Fig. 1: Conceptual overview. (a) SIC evolution is jointly driven by ice movement, freeze/melt processes, and local unresolved changes. (b) Black-box neural networks cannot explicitly attribute SIC changes to different physical processes. (c) PIHIM decomposes neural prediction into process-guided components for movement, freeze/melt, and local changes.

Panel of Figure 2 — Fig. 2: PIHIM network overview. (a) The conceptual structure links the SIC continuity-equation decomposition to movement, freeze/melt processes, and local unresolved changes. (b) The implementation-level architecture presents the corresponding module inputs, outputs, and autoregressive rollout for multi-day prediction.

Category	Setting	Role
Evaluation setting	reanalysis-forced simulation	Setting defined in Section 4.1 for assessing SIC evolution under reanalysis forcing
Evaluation setting	forecast-forced prediction	Setting defined in Section 4.1 for assessing D1-D9 short-range prediction under forecast-forced conditions
Reanalysis/reference	ERA5 [ 50 ] , GLORYS [ 51 ]	ERA5 provides atmospheric forcings; GLORYS provides SST, SIC, and sea ice velocity
Forecast forcings	AIFS [ 52 ] , RTOFS [ 53 ]	AIFS provides air temperature and wind speed, RTOFS provides SST, and the sea ice velocity channel is set to zero
Observational verification	OSI SAF [ 54 ]	Satellite SIC for assessing whether the results depend on the GLORYS reference
Numerical reference	SEAS5 [ 17 ]	Serves as a contextual numerical reference rather than a task-equivalent baseline

TABLE I: Summary of experimental settings, data, metrics, and baselines

Fig. 3: Seasonal cycles of SIA/SIE in calendar-year integrations from 2022 to 2024

Fig. 4: SIA/SIE trajectories in multi-year continuous integration over the 2022-2024 test period

Fig. 5: Rapid ice-edge retreat case in reanalysis-forced simulation. Panels (a)-(d) correspond to D2, D6, D10, and D14 from the July 2, 2024 initialization.

Fig. 6: Rapid ice-edge expansion case in reanalysis-forced simulation. Panels (a)-(d) correspond to D2, D6, D10, and D14 from the October 16, 2024 initialization.

Fig. 7: GLORYS-verified ice-edge case in 99-case forecast-forced prediction. Panels (a)-(c) correspond to D3, D6, and D9 from the May 22, 2024 initialization.

Fig. 8: OSI SAF-verified ice-edge case in 99-case forecast-forced prediction. Panels (a)-(c) correspond to D3, D6, and D9 from the May 22, 2024 initialization.

Fig. 9: GLORYS-verified ice-edge comparison in SEAS5-intersection 25-case forecast-forced prediction. Panels (a)-(c) correspond to D3, D6, and D9 from the April 1, 2026 initialization.

Fig. 10: OSI SAF-verified ice-edge comparison in SEAS5-intersection 25-case forecast-forced prediction. Panels (a)-(c) correspond to D3, D6, and D9 from the April 1, 2026 initialization.

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Accurate modeling of the daily evolution of sea ice concentration (SIC) is central to improving the credibility and operational forecasting capability of deep learning-based sea ice prediction. However, existing deep learning methods often couple the underlying sea ice evolution relationships and data errors within high-dimensional nonlinear mappings, making it difficult to construct a stable and verifiable evolution core and apply it reliably to practical forecasting. To address this issue, this study proposes a reanalysis-forecast dual-domain decoupled framework for learning sea ice evolution operators. The framework builds upon a lightweight physical baseline to generate daily evolution trajectories, employs a temporally constrained joint multi-lead compensation network to com?pensate for unresolved processes, and introduces an ice-mass?aware transport mechanism to suppress numerical dissipation. In the forecasting stage, the parameters of the base evolution core are fixed, while a lightweight variable-semantic adaptation mechanism calibrates inter-domain distributions and evolution responses, thereby separating forecast-domain errors from base evolution errors. Experiments show that the constructed base evolution core can accurately and stably simulate daily sea ice evolution at both short-term and annual scales under reanal?ysis forcing, and can be effectively transferred to the forecast domain through lightweight adaptation, achieving stable prac?tical forecasting capability while preserving the base evolution structure. The source code will be made publicly available at https://github.com/zhangmaqun65535/SNM upon acceptance of this manuscript.

Abstract

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