PADNet constrained machine learning projections of global land fAOD and cAOD driven by six CMIP6 GCMs from 2030 to 2100

This dataset provides PADNet constrained machine learning projections of global land fine mode aerosol optical depth (fAOD) and coarse mode aerosol optical depth (cAOD) from 2030 to 2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The projections were generated by training machine learning models with PADNet derived size resolved aerosol products and climate related predictors, and then applying the trained models to future climate variables from six CMIP6 global climate models. The released products are six GCM multi model mean estimates, designed to provide an observation constrained representation of future aerosol size mode variability. The dataset is intended for research on future aerosol change, size resolved aerosol projections, compound fine and coarse aerosol extremes, aerosol climate interactions, land atmosphere coupling and climate related aerosol risk. By providing future fAOD and cAOD separately, the dataset enables analyses of aerosol modal partitioning and compound aerosol behavior that cannot be resolved from total aerosol optical depth alone.