Abstract
Abstract. Aerosol–cloud interactions represent the leading uncertainty in our ability to infer climate sensitivity from the observational record. The forcing from changes in cloud albedo driven by increases in cloud droplet number (Nd) (the first indirect effect) is confidently negative and has narrowed its probable range in the last decade, but the sign and strength of forcing associated with changes in cloud macrophysics in response to aerosol (aerosol–cloud adjustments) remain uncertain. This uncertainty reflects our inability to accurately quantify variability not associated with a causal link flowing from the cloud microphysical state to the cloud macrophysical state. Once variability associated with meteorology has been removed, covariance between the liquid water path (LWP) averaged across cloudy and clear regions (here characterizing the macrophysical state) and Nd (characterizing the microphysical) is the sum of two causal pathways linking Nd to LWP: Nd altering LWP (adjustments) and precipitation scavenging aerosol and thus depleting Nd. Only the former term is relevant to constraining adjustments, but disentangling these terms in observations is challenging. We hypothesize that the diversity of constraints on aerosol–cloud adjustments in the literature may be partly due to not explicitly characterizing covariance flowing from cloud to aerosol and aerosol to cloud. Here, we restrict our analysis to the regime of extratropical clouds outside of low-pressure centers associated with cyclonic activity. Observations from MAC-LWP (Multisensor Advanced Climatology of Liquid Water Path) and MODIS are compared to simulations in the Met Office Unified Model (UM) GA7.1 (the atmosphere model of HadGEM3-GC3.1 and UKESM1). The meteorological predictors of LWP are found to be similar between the model and observations. There is also agreement with previous literature on cloud-controlling factors finding that increasing stability, moisture, and sensible heat flux enhance LWP, while increasing subsidence and sea surface temperature decrease it. A simulation where cloud microphysics are insensitive to changes in Nd is used to characterize covariance between Nd and LWP that is induced by factors other than aerosol–cloud adjustments. By removing variability associated with meteorology and scavenging, we infer the sensitivity of LWP to changes in Nd. Application of this technique to UM GA7.1 simulations reproduces the true model adjustment strength. Observational constraints developed using simulated covariability not induced by adjustments and observed covariability between Nd and LWP predict a 25 %–30 % overestimate by the UM GA7.1 in LWP change and a 30 %–35 % overestimate in associated radiative forcing.
Highlights
Uncertainty in the radiative forcing due to aerosol–cloud interactions is the leading uncertainty limiting our ability to accurately diagnose the Earth’s climate sensitivity from the observational record (Forster, 2016)
As a function of Nd we can see that the coefficient relating liquid water path (LWP) to Nd is greater than zero at 99 % confidence, once variability associated with meteorological predictors is removed (Fig. 5)
As the possible range for the radiative forcing from the first indirect effect has narrowed, aerosol–cloud adjustments have become an increasingly central source of uncertainty in aerosol–cloud radiative forcing (Bellouin et al, 2020)
Summary
Uncertainty in the radiative forcing due to aerosol–cloud interactions is the leading uncertainty limiting our ability to accurately diagnose the Earth’s climate sensitivity from the observational record (Forster, 2016). McCoy et al.: Untangling causality in midlatitude aerosol–cloud adjustments ing from aerosol–cloud interactions (Storelvmo, 2017; Bellouin et al, 2020), but the sign and strength of the forcing due to changes in cloud macrophysical properties in response to aerosol (aerosol–cloud adjustments) remain uncertain (Bellouin et al, 2020). This uncertainty reflects the difficulty in disentangling the many factors that determine cloud macrophysical properties. Unlike cloud droplet number concentration (Nd), which is primarily driven by the availability of suitable aerosol, cloud macrophysical properties are primarily determined by the state of the atmosphere but may be modulated by Nd (Stevens and Feingold, 2009)
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