Super-Resolution Surrogate Downscaling of MRI-ESM2(CMIP6) Black Carbon Surface Concentration Using Attention Based Convolutional Neural Networks

Tropospheric Black Carbon (BC) aerosols are short lived positive radiative forcer with critical impacts on cardiovascular and pulmonary health. The MRI-ESM2(CMIP6) delivers the BC monthly global surface concentration from 1950-2100 at coarse special resolution of 1.875° × 1.875° which does not capture the city level BC hotspots at the global scale. The BC hotspots are essential for reducing the BC aerosols induced health burden, implementing air quality management policies and regional planning. The downscaling algorithm is an enhanced U-net with attention-based convolution neural network (Super Resolution Convolution Neural Network (SRCNN)). The SRCNN model executes downscaling of MRI-ESM2(CMIP6) BC monthly surface concentration with special resolution of 1.875° × 1.875° to NASA’s MERRA2 reanalysis BC monthly average surface concentration at a spatial resolution of 0.5° × 0.625°, thus achieving 3.6 times downscaling for identification city level BC-hotspots and cold spots at a global scale. The SRCNN model is trained on global monthly average BC surface concentration data from MRI-ESM2(CMIP6) and NASA’s MERRA2 reanalysis product. The model training is spread from 1980-2012(31 years) with validation from 2013-2016(4 years) and testing for 2017-2020(4 years). We have also examined the effects of Channel Based Attention Module (CBAM) with and without Residual Block (RB) and their effectiveness and efficacy in climate data downscaling with data-scarce condition. The training results showes that CBAM with RB based CNN outperforms then both (CNN without CBAM and without CBAM & RB) in the benchmarks such as stability, overfitting, validation losses etc. The training results for SRCNN (with CBAM and RB) shows a final validation losses of 0.0028, final R² value of 0.7162, final Pearson-r value of 0.8467 with Structural Similarity Index Measure (SSIM) at 0.9954. The SRCNN (with CBAM & RB) model testing reveals it performs exceptionally well in the identification of hotspots and cold-spots, with final testing RSME at 0.0015, final R² at 0.88, final Pearson-r values at 0.94 and final SSIM at 0.99. Furthermore, testing outputs of SRCNN with attention module and residual blocks shows close fidelity with MERRA2 reanalysis vis-à-vis MRI-ESM2(CMIP6) at both seasonal and annual temporal resolution thus reducing systematic bias between ground truth and global climate models.