Gradient-infused climatic trajectory synthesis using assimilated remote-sensing environmental fields across the Strait of Hormuz system

The difficulty in modeling the long-term climatic variability in narrow marine corridors is that the strong air-sea coupling, large environmental gradients, and non-stationary behavior resulting in a non-stationary temporal behavior make this task difficult. The Strait of Hormuz is a dynamically controlled system in which seasonal forcing, variability in circulation and atmospheric structure interact in various time scales. Traditional statistical and deep learning methods tend to have difficulty in producing consistent climatic tracks using dissimilar remote-sensing variables in such circumstances. In this study, Strait-ClimateGradientSynthNet is presented as a gradient-aware deep learning model which is applied to the generation of long-horizon climatic trajectories based on assimilated multi-source reanalysis observations. This architecture makes use of hierarchical temporal coding which is based on recurrent neural networks to learn long-term climatic persistence along with regime switches based on gradient-sensitive features. The assessment of the framework is conducted with the help of multi-decadal ERA5 reanalysis data (including atmospheric, oceanic, radiative, and vertical-structure variables) over the Strait of Hormuz. The outcomes indicate that the suggested methodology has consistent and plausible trajectory reconstructions, and the prediction error is RMSE = 0.3973 °C, MAE = 0.3156 °C, and R 2 = 0.7390 to the monthly near-surface air temperature anomalies. Comparison analysis suggests better generalization compared to more complicated architectures when there is limited data. The results not only show that the proposed framework is effective in terms of parameter efficiency in synthesizing regional climate trajectories, but also drive the relevance of the proposed framework to climate risk assessment and long-term environmental surveillance in the dynamically constrained marine environment.