Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes

Abstract

Rationale: Aerosols perturb the atmosphere and climate both directly and indirectly. Indirectly, they cause changes in cloud properties, including cloud formation, cloud lifetime, and cloud radiative properties (Twomey, 1974 and Albrecht, 1989). Directly, aerosols can impact the frequency and intensity of water cycle-related extreme events, such as drought and flooding (Reed et al., 2019; Sillmann et al., 2019). Research linking observed aerosol-cloud-precipitation processes with numerical modeling has experienced much improvement in the treatment of ice nucleation, warm rain processes, secondary-organic aerosol formation, and convection (Fan et al., 2018; Zhao et al 2019, Mülmenstädt et al. 2020, Quaas et al. 2020). However, our knowledge on how aerosols mix with clouds, and thus on how aerosols impact the water-cycle, is still limited. Large uncertainties in these processes remain due to both difficulties in measuring a mixed, non-idealized atmosphere, due to asynchronous model-observation linkages, and missing or incomplete parameterizations linking aerosol-cloud interactions. This knowledge gap has led to large uncertainties in climate predictions, e.g. 20-year extreme precipitation predictions of the tropics and subtropics (Kharin et al., 2013). “How are cloud processes affected by changes in anthropogenic aerosol production and how do these fluctuations translate to extreme precipitation events?” are nontrivial and transformational science questions that could greatly benefit from improved understanding and modeling of complex aerosol-cloudprecipitation interactions. Such improvements would necessarily reduce uncertainty in these predictions, leading to fewer model discrepancies, more confident predictions of extreme events (e.g. floods and droughts) and more informed decision making on climate policy. Aerosol effects on cloud processes are dynamic, multi-faceted, and interdependent, resulting in some of the largest uncertainties in climate models (IPCC, 2013). Expected effects of aerosol emissions on cloud systems include changes in cloud condensation nuclei, liquid water content, precipitation and albedo, to name a few (Durkee et al., 2018 and Possner et al., 2018). High frequency and high-resolution data collection and monitoring further challenges the study of these complex relationships. These rich data, which include in situ, ground-based, and space-based measurements, as well as data from multiresolution numerical simulations, need to be integrated to improve our understanding of aerosol processes within clouds. While a wealth of high-resolution measurements has been collected with the objective of monitoring aerosol and cloud processes, especially by BER Atmospheric Radiation Measurement (ARM) facilities, NASA and NOAA field campaigns, and satellite observations, these data have been historically underutilized to improve our knowledge of the earth system. For example, in the weather forecasting community, only 3-5% of the vast amount of satellite data are used in numerical forecasts due in large part to the complexity of the science. This complexity presents significant “challenges to properly interpret and exploit the most interesting and potentially most valuable satellite data” (Boukabara, 2019). Recent and continuing advances in satellite observational capabilities have