Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes

Abstract

Global climate models (GCMs) disagree with other lines of evidence on the rapid adjustments of cloud cover and liquid water path to anthropogenic aerosols. Attempts to use observations to constrain the parameterizations of cloud processes in GCMs have failed to reduce the disagreement. We propose using observations sensitive to the relevant cloud processes rather than only to the atmospheric state and focusing on process realism in the absence of aerosol perturbations in addition to the process susceptibility to aerosols. We show that process-sensitive observations of precipitation can reduce the uncertainty on GCM estimates of rapid cloud adjustments to aerosols. The feasibility of an observational constraint depends on understanding the precipitation intensity spectrum in both observations and models and also on improving methods to compare the two.

The prevalence of precipitation from polar supercooled clouds

“…Based on these statistics, we conclude that the prevalent weak R CB (Fig. 3a) can be important cloud moisture sinks especially for low temperature and low LWP regimes that are common over polar regions (e.g., Nomokonova et al, 2019;Shupe, 2011;Silber et al, 2018a;Zhang et al, 2010). We postulate that such fluxes are also important to below-cloud moisture budgets owing in part to the likely commonality of continued growth of ice precipitation in sub-cloud ice supersaturated conditions, which will serve to enhance moisture transport even in cases of low cloud base Z e (e.g., just above Z e min ; see Appendix D).…”

The Impact of Resolving Subkilometer Processes on Aerosol‐Cloud Interactions of Low‐Level Clouds in Global Model Simulations

“…Climate models, in general, show a tendency to overpredict the probability of precipitation (POP; Stephens et al, 2010), and even in SPCAM (Kooperman et al, 2016). Furthermore, Mülmenstädt et al (2020) point out the importance of establishing the baseline precipitation frequency to better constrain the ACIs. L'Ecuyer et al (2009) provide such an estimate of POP based on CloudSat, and a comparison of Figure 6 of this study with Figure 1 of L'Ecuyer et al (2009) suggests that the precipitation fraction in UPCAM is more consistent with the POP from L'Ecuyer et al (2009).…”

“…The study of L'Ecuyer et al (2009) also encompasses a larger region over the oceans, compared to the focus of NH clouds in this study. Mülmenstädt et al (2020) further report the potential importance of differentiating between drizzle and rain to better constrain model behavior. Exploring these are beyond the scope of this study, but highlight observational estimates that will be important for better assessing ACIs in models.…”

“…Facilitating the comparison of global ACI simulations with high‐resolution model simulations or with observations requires analyses beyond just examining the aggregated cloud radiative changes due to aerosol perturbations. On the one hand, analyses using process‐oriented diagnostics highlight the importance of precipitation‐forming microphysical processes in the models (Jing & Suzuki, 2018; Michibata et al, 2016; Mülmenstädt et al, 2020; Suzuki et al, 2013; Wang et al, 2012). Progress has also been made in finding meteorological regimes in which GCMs respond similarly to aerosol perturbations (e.g., S. Zhang et al, 2016), but we still struggle to identify which processes cause the response of GCMs to diverge in other meteorological regimes.…”

Snow-induced buffering in aerosol–cloud interactions

“…However, the results presented here are based on a single GCM framework and need to be replicated using other GCMs as they incorporate prognostic precipitation frameworks in the future (Li et al, 2020). This is particularly true because little is known about aerosol influences on mixedand ice-phase clouds as well as deep convective clouds (Rosenfeld et al, 2014;Fan et al, 2018) and cirrus clouds (Penner et al, 2018) at a fundamental process level, and the degree of microphysical complexity differs widely among GCMs (Heyn et al, 2017). Although the responses of clouds and precipitation to aerosol perturbations are therefore likely to be model dependent, the sign of the response of ERF aci to the precipitation framework and microphysical processes is consistent with a previous assessment using CAM5/MG2 ), suggesting that the major findings of this study will apply across the models.…”