Advancing Sophisticated Photochemistry Simulation in Atmospheric Numerical Models With Artificial Intelligence PhotoChemistry (AIPC) Scheme Using the Feature-Mapping Subspace Self-Attention Algorithm

Accurate simulation of atmospheric photochemistry is essential for air quality and climate studies but computationally expensive in three-dimensional atmospheric models. Artificial intelligence (AI) algorithms show promise for accelerating photochemical simulations, but integrating them reliably into numerical models as replacements for complex mechanisms has been challenging, with success mostly limited to simplified schemes (e.g., 12 species). We present a novel AI PhotoChemistry (AIPC) scheme using the Feature-Mapping Subspace Self-Attention (FMSSA) algorithm, enabling fast, accurate, and stable online simulation of the full SAPRC-99 mechanism (79 species, 229 reactions) within WRF-Chem. Feature-mapping subspace self-attention reduces computational cost by 91% versus standard attention architectures via global feature mapping and subspace attention decomposition while maintaining high fidelity to nonlinear chemistry. Offline evaluations show FMSSA's superior accuracy (mean NRMSE = 3.09% for 69 species) over Multi-Layer Perceptron and Residual Neural Network baselines, especially for ozone. Ablation experiments confirm the critical role of attention and LayerNorm modules for accuracy and generalizability. Monthly-scale online simulations conducted in August show stable FMSSA-AIPC performance, accurately reproducing species spatiotemporal distributions with 77% faster computation than the numerical solver. However, simulations conducted in February show performance degradation for all AIPC schemes, with FMSSA-AIPC exhibiting unique synchronous errors, highlighting generalization challenges across significantly distinct atmospheric regimes. This work advances integrating sophisticated chemical processes in weather and climate models, with future efforts targeting expanded training data sets, architectural refinements and broader spatiotemporal testing.