Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing

Abstract

Atmospheric aerosols affect the Earth's climate in many ways, including acting as the seeds on which cloud droplets form. Since a large fraction of these particles is anthropogenic, the clouds' microphysical and radiative characteristics are influenced by human activity on a global scale leading to important climatic effects. The respective change in the energy budget at the top of the atmosphere is defined as the effective radiative forcing due to aerosol‐cloud interaction (ERFaci). It is estimated that the ERFaci offsets presently nearly 1/4 of the greenhouse‐induced warming, but the uncertainty is within a factor of two. A common method to calculate the ERFaci is by the multiplication of the susceptibility of the cloud radiative effect to changes in aerosols by the anthropogenic change of the aerosol concentration. This has to be done by integrating it over all cloud regimes. Here we review the various methods of the ERFaci estimation. Global measurements require satellites' global coverage. The challenge of quantifying aerosol amounts in cloudy atmospheres are met with the rapid development of novel methodologies reviewed here. The aerosol characteristics can be retrieved from space based on their optical properties, including polarization. The concentrations of the aerosols that serve as cloud drop condensation nuclei can be also estimated from their impact on the satellite‐retrieved cloud drop number concentrations. These observations are critical for reducing the uncertainty in the ERFaci calculated from global climate models (GCMs), but further development is required to allow GCMs to properly simulate and benefit these novel observables.

Cloud Susceptibility to Aerosols: Comparing Cloud‐Appearance Versus Cloud‐Controlling Factors Regimes

“…We summarized and presented the estimated cloud susceptibilities to N d between two different definitions of cloud regimes in Figure 10. Considering that the effective radiative forcing of aerosol‐cloud interactions (ERF aci ) necessitates computation across all cloud regimes weighted by their frequency of occurrence (Rosenfeld et al., 2023), we calculated and compared appearance‐weighted and LTS‐weighted averages of cloud susceptibilities to N d . The results revealed that the absolute values of precipitation and CF susceptibilities to N d , aggregated over cloud‐appearance regimes, notably reduced compared to those in LTS regimes.…”

“…Cloud adjustments to aerosols are strongly dependent on specific cloud regimes, which are representative of distinct cloud states and meteorological conditions (Douglas & L'Ecuyer, 2019; Stevens & Feingold, 2009). Traditionally, cloud regimes have been dynamically identified using a single meteorological parameter or combinations that are independent of aerosols, such as lower‐tropospheric stability (LTS) or sea surface temperatures (SST) (Bony et al., 2004; Klein & Hartmann, 1993; Medeiros & Stevens, 2011; Wood & Bretherton, 2006; Zhang et al., 2016), which is termed here a “cloud‐controlling factors regime” (Rosenfeld et al., 2023). However, those approaches may not fully capture the complexity and variability of the cloud field driven by the combined effects of meteorological conditions and background aerosols (Nam & Quaas, 2013).…”

Potential Modulation of Aerosol on Precipitation Efficiency in Southwest China

“…The direct and indirect effects of aerosol on clouds and precipitation have very prominent uncertainty, which is mainly restricted by aerosol type, cloud regimes, meteorological environment conditions, and so on [48][49][50]. Aerosols act as cloud condensation nuclei and ice nuclei, influencing cloud microphysical processes and subsequently impacting precipitation, which is the aerosol microphysical effect [51][52][53].…”

Revisiting Aerosol–Cloud Interactions From Weekly Cycles

“…For estimating ACI from observations, the most commonly used approach is to correlate aerosol-cloud quantities through spatio-temporal variability (Quaas et al, 2008). However, inferring causality from correlations is challenging (Gryspeerdt et al, 2019;McCoy et al, 2020), particularly when other confounders, such as meteorological variability and satellite retrieval errors play a role (Rosenfeld et al, 2023). In this regard, opportunistic experiments (volcanic eruptions, ship tracks, industrial sources, long-term trends, weekly cycles, etc.)…”