Exploring satellite-derived relationships between cloud droplet number concentration and liquid water path using a large-domain large-eddy simulation

<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses adiabatic assumption to derive the Nd (NAd), we have also considered NAd from the LES model for comparison. The NAd-LWP relationship in the satellite and the LES model show similar, generally positive, but non-monotonic relations. This case over continent thus behaves differently compared to previously-published analysis of oceanic clouds, and the analysis illustrates a regime dependency (marine and continental) in the NAd-LWP relation in the satellite retrievals. The study further explores the impact of the satellite retrieval assumptions on the Nd-LWP relationship. When considering the relationship of the actually simulated cloud-top Nd, rather than NAd, with LWP, the result shows a much more nonlinear relationship. The difference is much less pronounced, however, for shallow stratiform than for convective clouds. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear Nd-LWP relationship. Hence a regime based Nd-LWP analysis is even more relevant when it comes to continental clouds.</p> </div> </div> </div>

Addressing the difficulties in quantifying the Twomey effect for marine warm clouds from multi-sensor satellite observations and reanalysis

<p>Aerosol–cloud interaction is the most uncertain component of the overall anthropogenic forcing of the climate, inwhich the Twomey effect plays a fundamental role. Satellite-based estimates of the Twomey effect are especially challenging, mainly due to the difficulty in disentangling aerosol effects on cloud droplet number concentration (Nd) from possible confounders. By combining multiple satellite observations and reanalysis, this study investigates the impacts of a) updraft, b) precipitation, c) retrieval errors, as well as (d) vertical co-location between aerosol and cloud, on the assessment of Nd-to-aerosol sensitivity (S) in the context of marine warm (liquid) clouds. Our analysis suggests that S increases remarkably with both cloud base height and cloud geometric thickness (proxies for vertical velocity at cloud base), consistent with stronger aerosol-cloud interactions at larger updraft velocity. In turn, introducing the confounding effect of aerosol–precipitation interaction can artificially amplify S by an estimated 21 %, highlighting the necessity of removing precipitating clouds from analyses on the Twomey effect. It is noted that the retrieval biases in aerosol and cloud appear to underestimate S, in which cloud fraction acts as a key modulator, making it practically difficult to balance the accuracies of aerosol–cloud retrievals at aggregate scales (e.g., 1◦ × 1◦ grid). Moreover, we show that using column-integrated sulfate mass concentration (SO4C) to approximate sulfate concentration at cloud base (SO4B) can result in a degradation of correlation with Nd, along with a nearly two fold enhancement of S, mostly attributed to the inability of SO4C to capture the full spatio-temporal variability of SO4B. These findings point to several potential ways forward to account for the major influential factors practically by means of satellite observations and reanalysis, aiming at an optimal observational estimate of global radiative forcing due to the Twomey effect.</p>

Impact of Holuhraun volcano aerosol on clouds in cloud-system resolving simulations

<p>Since increased anthropogenic aerosol result in an enhancement in cloud droplet number concentration, cloud and precipitation process are modified. It is unclear how exactly cloud liquid water path (LWP) and cloud fraction respond to aerosol perturbations. A large volcanic eruption may help to better understand and quantify the cloud response to external perturbations, with a focus on the short-term cloud adjustments . Volcloud is one of the research projects in the Vollmpact collaborative German research unit which aims to the improve understanding of how the climate system responds to volcanic eruptions. This includes skills in satellite remote sensing of atmospheric composition, stratospheric aerosol parameters and clouds as well as in modelling of aerosol microphysical and cloud processes, and in climate modelling. The goal of VolCloud is to understand and quantify the response of clouds to volcanic eruptions and to thereby advance the fundamental understanding of the cloud response to external forcing, particularly aerosol-cloud interactions. In this study we used ICON-NWP atmospheric model at a cloud-system-resolving resolution of 2.5 km horizontally, to simulate the region around the Holuhraun volcano for the duration of one week (1 – 7 September 2014). The pair of simulations, with and without the volcanic aerosol emissions allowed us to assess the simulated effective radiative forcing and its mechanisms as well as its impact on adjustments of cloud liquid water path and cloud fraction to the perturbations of cloud droplet number concentration. In this case studies liquid water path positively correlates with enhanced cloud droplet concentration.</p>

The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data

Cloud droplet number concentration (Nd) is of central importance to observation-based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases. A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of insitu measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5–0.8). This is lower in other cloud regimes, but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol-cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling.

Evaluation of the contribution of new particle formation to cloud droplet number concentration in the urban atmosphere

The effect of new particle formation (NPF) on cloud condensation nuclei (CCN) varies widely in diverse environments. CCN or cloud droplets from NPF sources remain highly uncertain in the urban atmosphere; they are greatly affected by the high background aerosols and frequent local emissions. In this study, we quantified the effect of NPF on cloud droplet number concentration (CDNC, or Nd) at typical updraft velocities (V) in clouds based on field observations on 25 May–18 June 2017 in urban Beijing. We show that NPF increases the Nd by 32 %–40 % at V=0.3–3 m s−1 during the studied period. The Nd is reduced by 11.8 ± 5.0 % at V=3 m s−1 and 19.0 ± 4.5 % at V=0.3 m s−1 compared to that calculated from constant supersaturations due to the water vapor competition effect, which suppresses the cloud droplet formation by decreasing the environmental maximum supersaturation (Smax). The effect of water vapor competition becomes smaller at larger V that can provide more sufficient water vapor. However, under extremely high aerosol particle number concentrations, the effect of water vapor competition becomes more pronounced. As a result, although a larger increase of CCN-sized particles by NPF events is derived on clean NPF days when the number concentration of preexisting background aerosol particles is very low, no large discrepancy is presented in the enhancement of Nd by NPF between clean and polluted NPF days. We finally reveal a considerable impact of the primary sources on the evaluation of the contribution of NPF to CCN number concentration (NCCN) and Nd based on a case study. Our study highlights the importance of full consideration of both the environmental meteorological conditions and multiple sources (i.e., secondary and primary) to evaluate the effect of NPF on clouds and the associated climate effects in polluted regions.

On the Relation among Satellite Observed Liquid Water Path, Cloud Droplet Number Concentration and Cloud Base Rain Rate and Its Implication to the Auto-Conversion Parameterization in Stratocumulus Clouds

Precipitation processes play a critical role in the longevity and spatial distribution of stratocumulus clouds through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. One of the difficulties in understanding these processes is the limited amount of observational data. In this study, robust relations among liquid water path (LWP), cloud droplet number concentration (Nd) and cloud base rain rate (Rcb) from three subtropical stratocumulus decks are obtained from A-Train satellite observations in order to obtain a broad perspective on warm rain processes. Rcb has a positive correlation with LWP/Nd and the increase of Rcb becomes larger as LWP/Nd increases. However, the increase of Rcb with respect to LWP/Nd becomes more gradual in regions with larger Nd, which indicates the relation is moderated by Nd. These results are consistent with our theoretical understanding of warm rain processes and suggest that satellite observations are capable of elucidating the average manner of how precipitation processes are modulated by LWP and Nd. The sensitivity of the auto-conversion rate to Nd is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on Rcb. The upper limit of the dependency of auto-conversion rate on Nd is assessed from the relation between Rcb and Nd, since the sensitivity is exaggerated by the accretion process, and was found to be a cloud droplet number concentration to the power of −1.44 ± 0.12.

Quantification of aerosol effects on marine boundary layer clouds with machine learning

<p>Satellite observations are used in regional machine learning models to quantify sensitivities of marine boundary-layer clouds (MBLC) to aerosol changes.</p><p>MBLCs make up a large part of the global cloud coverage as they are persistently present over more than 20% of the Earth’s oceans in the annual mean.They play an important role in Earth’s energy budget by reflecting solar radiation and interacting with thermal radiation from the surface, leading to a net cooling effect. Cloud properties and their radiative characteristics such as cloud albedo, horizontal and vertical extent, lifetime and precipitation susceptibility are dependent on environmental conditions. Aerosols in their role as condensation nuclei affect these cloud radiative properties through changes in the cloud droplet number concentration and subsequent cloud adjustments to this perturbation. However, the magnitude and sign of these effects remain among the largest uncertainties in future climate predictions.</p><p>In an effort to help improve these predictions a machine learning approach in combination with observational data is pursued:</p><p>Satellite observations from the collocated A-Train dataset (C3M) for 2006-2011 are used in combination with ECMWF atmospheric reanalysis data (ERA5) to train regional Gradient Boosting Regression Tree (GBRT) models to predict changes in key physical and radiative properties of MBLCs. The cloud droplet number concentration (N<sub>d</sub>) and the liquid water path (LWP) are simulated for the eastern subtropical oceans, which are characterised by a high annual coverage of MBLC due to the occurrence of semi-permanent stratocumulus sheets. Relative humidity above cloud, cloud top height and temperature below the cloud base and at the surface are identified as important predictors for both N<sub>d</sub> and LWP.  The impact of each predictor variable on the GBRT model's output is analysed using Shapley values as a method of explainable machine learning, providing novel sensitivity estimates that will improve process understanding and help constrain the parameterization of MBLC processes in Global Climate Models.</p>

The hemispheric contrast in cloud microphysical properties constrains aerosol forcing

The change in planetary albedo due to aerosol−cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth’s climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol−cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm−3and 24 cm−3. By extension, the radiative forcing since 1850 from aerosol−cloud interactions is constrained to be −1.2 W⋅m−2to −0.6 W⋅m−2. The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol−cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.

On the relationship between cloud water composition and cloud droplet number concentration

Aerosol–cloud interactions are the largest source of uncertainty in quantifying anthropogenic radiative forcing. The large uncertainty is, in part, due to the difficulty of predicting cloud microphysical parameters, such as the cloud droplet number concentration (Nd). Even though rigorous first-principle approaches exist to calculate Nd, the cloud and aerosol research community also relies on empirical approaches such as relating Nd to aerosol mass concentration. Here we analyze relationships between Nd and cloud water chemical composition, in addition to the effect of environmental factors on the degree of the relationships. Warm, marine, stratocumulus clouds off the California coast were sampled throughout four summer campaigns between 2011 and 2016. A total of 385 cloud water samples were collected and analyzed for 80 chemical species. Single- and multispecies log–log linear regressions were performed to predict Nd using chemical composition. Single-species regressions reveal that the species that best predicts Nd is total sulfate (Radj2=0.40). Multispecies regressions reveal that adding more species does not necessarily produce a better model, as six or more species yield regressions that are statistically insignificant. A commonality among the multispecies regressions that produce the highest correlation with Nd was that most included sulfate (either total or non-sea-salt), an ocean emissions tracer (such as sodium), and an organic tracer (such as oxalate). Binning the data according to turbulence, smoke influence, and in-cloud height allowed for examination of the effect of these environmental factors on the composition–Nd correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between Nd with both total sulfate and sodium increased at higher turbulence conditions, consistent with turbulence promoting the mixing between ocean surface and cloud base. Considering the influence of smoke significantly improved the correlation with Nd for two biomass burning tracer species in the study region, specifically oxalate and iron. When binning by in-cloud height, non-sea-salt sulfate and sodium correlated best with Nd at cloud top, whereas iron and oxalate correlated best with Nd at cloud base.