Anthropogenic aerosol emissions lead to an increase in the amount of cloud condensation nuclei and consequently an increase in cloud droplet number concentration and cloud albedo. The corresponding ...negative radiative forcing due to aerosol cloud interactions (RFFormula: see text) is one of the most uncertain radiative forcing terms as reported in the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Here we show that previous observation-based studies underestimate aerosol-cloud interactions because they used measurements of aerosol optical properties that are not directly related to cloud formation and are hampered by measurement uncertainties. We have overcome this problem by the use of new polarimetric satellite retrievals of the relevant aerosol properties (aerosol number, size, shape). The resulting estimate of RFFormula: see text = -1.14 WmFormula: see text (range between -0.84 and -1.72 WmFormula: see text) is more than a factor 2 stronger than the IPCC estimate that includes also other aerosol induced changes in cloud properties.
Previous global satellite studies into the indirect aerosol effect have relied on determining the sensitivity of derived Cloud Droplet Number Concentration (Nd) to co‐located Aerosol Optical Depth ...(AOD). These studies generally find a positive Nd sensitivity to AOD changes over ocean, but some find a negative sensitivity over land, in contrast to that predicted by models and theory. Here we investigate the Ndsensitivity to AOD in different cloud regimes, determined using a k‐means clustering process on retrieved cloud properties. We find the strongest positive Nd sensitivity in the stratiform regimes over both land and ocean, providing the majority of the total sensitivity. The negative sensitivity previously observed over land is generated by the low cloud fraction regimes, suggesting that it is due to the difficulty of retrieving Nd at low cloud fractions. When considering a mean sensitivity, weighted by liquid cloud fraction to account for sampling biases, we find an increased sensitivity over land, in some regions becoming positive. This highlights the importance of regime based analysis when studying aerosol indirect effects.
Key Points
Sensitivity of cloud droplet number to aerosol optical depth differs by regime
The negative sensitivity observed over land is from low cloud fraction regimes
High cloud fraction regimes are the largest proportion of the total sensitivity
The impact of aerosols on cloud properties is one of the largest uncertainties in the anthropogenic radiative forcing of the climate. Significant progress has been made in constraining this forcing ...using observations, but uncertainty remains, particularly in the magnitude of cloud rapid adjustments to aerosol perturbations. Cloud liquid water path (LWP) is the leading control on liquid-cloud albedo, making it important to observationally constrain the aerosol impact on LWP.
The number concentration of cloud particles is a key quantity for understanding aerosol-cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with ...recent advances for liquid clouds, few observational constraints exist regarding the ice crystal number concentration (N.sub.i). This study investigates how combined lidar-radar measurements can be used to provide satellite estimates of N.sub.i, using a methodology that constrains moments of a parameterized particle size distribution (PSD). The operational liDAR-raDAR (DARDAR) product serves as an existing base for this method, which focuses on ice clouds with temperatures T.sub.c < -30 °C.
The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation ...error for a range of timescales and length scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site or in situ remote sensing (PM2. 5, black carbon mass or number concentrations), satellite remote sensing with imagers or lidar (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly gridded satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. However, temporal collocation of data (possible when observations are compared to model data or other observations), combined with temporal averaging, can be very effective at reducing representation errors. We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors, although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce, even with substantial temporal averaging.
Much of the uncertainty in estimates of the anthropogenic forcing of climate change comes from uncertainties in the instantaneous effect of aerosols on cloud albedo, known as the Twomey effect or the ...radiative forcing from aerosol–cloud interactions (RFaci), a component of the total or effective radiative forcing. Because aerosols serving as cloud condensation nuclei can have a strong influence on the cloud droplet number concentration (Nd
), previous studies have used the sensitivity of the Nd
to aerosol properties as a constraint on the strength of the RFaci. However, recent studies have suggested that relationships between aerosol and cloud properties in the present-day climate may not be suitable for determining the sensitivity of the Nd
to anthropogenic aerosol perturbations. Using an ensemble of global aerosol–climate models, this study demonstrates how joint histograms between Nd
and aerosol properties can account for many of the issues raised by previous studies. It shows that if the anthropogenic contribution to the aerosol is known, the RFaci can be diagnosed to within 20% of its actual value. The accuracy of different aerosol proxies for diagnosing the RFaci is investigated, confirming that using the aerosol optical depth significantly underestimates the strength of the aerosol–cloud interactions in satellite data.
Abstract
One major source of uncertainty in the cloud-mediated aerosol forcing arises from the magnitude of the cloud liquid water path (LWP) adjustment to aerosol-cloud interactions, which is poorly ...constrained by observations. Many of the recent satellite-based studies have observed a decreasing LWP as a function of cloud droplet number concentration (CDNC) as the dominating behavior. Estimating the LWP response to the CDNC changes is a complex task since various confounding factors need to be isolated. However, an important aspect has not been sufficiently considered: the propagation of natural spatial variability and errors in satellite retrievals of cloud optical depth and cloud effective radius to estimates of CDNC and LWP. Here we use satellite and simulated measurements to demonstrate that, because of this propagation, even a positive LWP adjustment is likely to be misinterpreted as negative. This biasing effect therefore leads to an underestimate of the aerosol-cloud-climate cooling and must be properly considered in future studies.
The interaction between aerosols and clouds is one of the major uncertainties in past climate change, affecting the accuracy of future climate projections. Ship tracks, trails left in clouds through ...the addition of aerosol in the ship exhaust plume, have become a key observational tool for constraining aerosol‐cloud interactions. However, many expected tracks remain undetected, presenting a significant gap in current knowledge of aerosol forcing. Here we leverage a plume‐parcel model to simulate the impact of aerosol dispersion for 2,957 cases off California's coast on cloud droplet number concentration (CDNC) enhancements. Plume‐parcel models show a large sensitivity to updraft uncertainties, which are found to be a primary control on track formation. Using these plume‐parcel models, updraft values consistent with observed CDNC enhancements are recovered, suggesting that relying solely on cloud‐top radiative cooling may overestimate in‐cloud updrafts by around 50%, hence overstating the cloud sensitivity to aerosols.
Plain Language Summary
Here we investigate the interactions between aerosols and clouds and their impact on climate change. Ship tracks were used to study these interactions, and aerosol emissions from ships were modeled. It was shown that the increase in cloud droplets from ship aerosol was highly sensitive to the speed of the updraft in the clouds. A method was developed to fit the updraft to the observed cloud enhancements; the resultant updrafts were smaller than current estimates, suggesting that the clouds may be less sensitive to aerosols than previously thought.
Key Points
Using ship emission data, a plume‐parcel model was implemented to study aerosol availability and cloud sensitivity in ship track formation
Cloud droplet number enhancements are very sensitive to uncertainties in the in‐cloud updraft
The retrieved plume‐parcel‐based updrafts suggest that clouds may be less sensitive to aerosols than implied by other estimates
The radiative forcing from aerosols (particularly through their interaction with clouds) remains one of the most uncertain components of the human forcing of the climate. Observation-based studies ...have typically found a smaller aerosol effective radiative forcing than in model simulations and were given preferential weighting in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). With their own sources of uncertainty, it is not clear that observation-based estimates are more reliable. Understanding the source of the model and observational differences is thus vital to reduce uncertainty in the impact of aerosols on the climate. These reported discrepancies arise from the different methods of separating the components of aerosol forcing used in model and observational studies. Applying the observational decomposition to global climate model (GCM) output, the two different lines of evidence are surprisingly similar, with a much better agreement on the magnitude of aerosol impacts on cloud properties. Cloud adjustments remain a significant source of uncertainty, particularly for ice clouds. However, they are consistent with the uncertainty from observation-based methods, with the liquid water path adjustment usually enhancing the Twomey effect by less than 50%. Depending on different sets of assumptions, this work suggests that model and observation-based estimates could be more equally weighted in future synthesis studies.
The ice crystal number concentration (N.sub.i) is a key property of ice clouds, both radiatively and microphysically. Due to sparse in situ measurements of ice cloud properties, the controls on the ...N.sub.i have remained difficult to determine. As more advanced treatments of ice clouds are included in global models, it is becoming increasingly necessary to develop strong observational constraints on the processes involved.
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