用机器学习模拟大气成分实时变化，提升气候模型效率与精度

Interactive composition simulations in Earth System Models (ESMs) are computationally expensive as they transport numerous gaseous and aerosol tracers at each timestep. This limits higher-resolution transient climate simulations with current computational resources. ESMs like NASA GISS-ModelE3 (ModelE) often use pre-computed monthly-averaged atmospheric composition concentrations (Non-Interactive Tracers or NINT) to reduce computational costs. While NINT significantly cuts computations, it fails to capture real-time feedback between aerosols and other climate processes by relying on pre-calculated fields. We extended the ModelE NINT version using machine learning (ML) to create Smart NINT, which emulates interactive emissions. Smart NINT interactively calculates concentrations using ML with surface emissions and meteorological data as inputs, avoiding full physics parameterizations. Our approach utilizes a spatiotemporal architecture that possesses a well-matched inductive bias to effectively capture the spatial and temporal dependencies in tracer evolution. Input data processed through the first 20 vertical levels (from the surface up to 656 hPa) using the ModelE OMA scheme. This vertical range covers nearly the entire BCB concentration distribution in the troposphere, where significant variation on short time horizons due to surface-level emissions is observed. Our evaluation shows excellent model performance with R-squared values of 0.92 and Pearson-r of 0.96 at the first pressure level. This high performance continues through level 15 (808.5 hPa), then gradually decreases as BCB concentrations drop significantly. The model maintains acceptable performance even when tested on data from entirely different periods outside the training domain, which is a crucial capability for climate modeling applications requiring reliable long-term projections.