A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing.

Uncertainties in estimates of climate cooling by anthropogenic aerosols have not decreased significantly in the last two decades, partly because observational constraints on crucial aerosol properties simulated in Earth System Models are insufficient. To help address this insufficiency in aerosol observations, we describe a paradigm for deriving higher-level aerosol properties with machine learning algorithms that use only lidar observations and reanalysis data as predictors. Our paradigm employs high-accuracy suborbital lidar and collocated in situ measurements to train and test two fully-connected neural network algorithms. We use two lidar data sets as input to our machine learning algorithms. The first data set consists of suborbital lidar observations not previously used in the training of the machine learning algorithms. The second data set consists of simulated UV-only observations to preview the algorithms' predictive capabilities in anticipation of data from the ATmospheric LIDar system on the EarthCARE satellite, which was launched in May 2024. Here we show that our algorithms predict two crucial aerosol properties, aerosol light absorption and cloud condensation nuclei concentrations with unprecedented accuracy, yielding mean relative errors of 21% and 13%, respectively, when suborbital lidar data are used as predictors. These errors represent significant improvements over conventional aerosol retrievals. Applied to future satellite missions, the paradigm presented here has great potential for constraining Earth System Models and reducing uncertainties in their estimates of aerosol climate forcing and future global warming.