Climate change is a complex worldwide issue that is driving numerous changes in the composition and operation of the terrestrial and marine ecosystems. Energy balance models (EBMs) provide physically interpretable and computationally efficient frameworks for investigating climate variability and surface temperature responses to radiative forcing. This study presents a modified energy balance model for simulating seasonal sea surface temperature (SST) by explicitly incorporating oceanic thermal inertia and heat storage through a simplified perturbation approach. The new model framework is implemented on a two-dimensional spatial grid, enabling regional-scale climate analysis without the computational expense of full general circulation models. The model was validated for Banjul, The Gambia (13.45 \(\:^\circ\:\) N, 16.58 \(\:^\circ\:\) W), using historical and satellite-derived SST data spanning 1991–2025. Simulated monthly SSTs ranged from approximately 18.4 \(\:\:℃\) during the cool season to 31.2 \(\:℃\) during the warm season, closely matching observed climatological values of 20–31 \(\:℃\) . Model evaluation using statistical metrics yielded a Root Mean Square Error (RMSE) of 1.58 \(\:℃\:\) and a Nash–Sutcliffe Efficiency (NSE) of 0.779, indicating very good agreement with observed seasonal SST variability and capturing approximately 78% of the observed variance. Sensitivity analyses present the model’s approach in diagnosing climate change signals. A reduction in outgoing longwave radiation of 5 \(\:{\text{W}\:\text{m}}^{-2}\) increased peak warm-season SST by 0.83 \(\:℃\) , while a 20% decrease in ocean heat exchange efficiency amplified the seasonal temperature gradient by increasing warm-season SST to 31.7 \(\:℃\) and lowering cool-season SST to 18.9 \(\:℃\) . Decreasing surface albedo from 0.20 to 0.15 produced a 0.45 \(\:℃\) SST increase, whereas a 5% increase in ocean area slightly moderated extremes by approximately 0.10 \(\:℃\) . Overall, the proposed model balances physical realism with computational simplicity and is recommended as a valid tool for seasonal SST analysis, rapid sensitivity testing, and for regional climate change investigations.
