Climate change, driven by greenhouse gas emissions and rising temperatures, poses a significant environmental, societal, and economic threat. Accurate predictions of future climate trends are crucial for developing effective mitigation and adaptation strategies. This study presents a novel data-driven dynamical model to forecast climate trends in Nairobi, Kenya. The model uses historical climate data for Nairobi County to simulate future carbon dioxide (CO \(_2\) ) concentrations, photosynthetic biomass density, human population density, and atmospheric temperature. The model equations are solved numerically using fourth and fifth-order Runge–Kutta methods. The study employs a least-squares curve fitting technique implemented through MATLAB’s lsqcurvefit function to calibrate the model parameters and achieve a close fit to the historical data. This approach yields a mean absolute error (MAE) of 0.0884 for average atmospheric temperature (T) simulations, indicating a close fit to the historical data. Optimal values of the model parameters are then estimated from the fitting algorithm. The model projects a concerning scenario of potentially increasing CO \(_2\) emissions and atmospheric accumulation in the long term. However, a decrease in CO \(_2\) concentration below 400 ppm is predicted starting from May 2024. This finding necessitates further investigation into potential drivers like carbon sequestration and technological advancements in carbon capture. The model forecasts a rapid rise in average atmospheric temperature, reaching 32 \(^\circ\) C in January 2025 from a baseline of 17.5 \(^\circ\) C in September 2024, followed by a sharp decline. This suggests an accelerated warming trend, potentially linked to intensified ocean heat uptake or approaching climate tipping points. These findings provide valuable insights into potential climate trends specific to Nairobi, highlighting the urgency of immediate climate change mitigation actions.
