Aerosols are thought to havea large effect on the climate, both through the direct effect on radiation, andthrough their interaction with clouds. In our atmosphere, aerosols refer to finesolid particles or liquid droplets that are typically smaller than 1 ?m and canbe natural (dust, etc) or anthropogenic (haze, smoke, etc). Many aerosols canact as cloud condensation nuclei (CCN).
This is where we see cloud droplets ableto form on aerosol particles. Thus, any change in the aerosol numberconcentration or aerosol properties may drastically influence cloud properties.Several different indirect effects have been theorized in regions of increasedaerosols, including increases in cloud albedo (Twomey 1977), cloud fraction (Albrecht 1989), and decreases ofcloud top pressure (Koren et al. 2005).
These effectsinfluence the radiative forcing of clouds and have been shown to be asignificant component of the total environmental anthropogenic radiativeforcing. However, the radiative forcing due to anthropogenicaerosols is the most uncertain component of anthropogenic radiative forcing (Boucher 2013) with the interactionbetween aerosols and clouds generating much of this uncertainty. It is thusimportant and necessary to understand aerosol-cloud interactions to gain morecertain estimates of the radiative forcing they provide and thus a more certainestimate of our future climate. One person trying to tacklethis problem is Dr Edward Gryspeerdt. A recent seminar made by Gryspeerdt atthe University of Reading discussed his recent work on the role of aerosols incloud processes. In this report, an analysis of the work discussed by Gryspeerdtwill be made with a discussion on how this fits into the larger body ofresearch and suggesting where future work may be necessary.
1. DiscussionGryspeerdt and Stier (2012)focussed on the relationship between aerosol concentration and cloud-albedo.The “Twomey effect” theorises that increasing aerosol concentration at constantcloud water content will decrease droplet size, which in turn increases cloud albedodue to the increased scattering of smaller, more numerous droplets generatingan instantaneous radiative forcing (Twomey 1974). Previous globalsatellite studies into the indirect aerosol effect have found positive relationshipbetween aerosol concentration and cloud albedo over ocean (Quaas et al.
2008). However, some finda negative relationship over land, in contrast to models and theory (Quaas et al. 2009). To study this further,Gryspeerdt investigated the relationship in different cloud regimes. The findingsshowed the strongest positive relationship in stratiform regimes over both landand ocean.
The Negative relationship previously observed over land was shown tobe due to the low cloud fraction (CF) regimes. This prompted Gryspeerdt tofirst suggest that previous negative relationship findings are due to thedifficulty of retrieving cloud droplet number concentration (Nd) atlow cloud fractions. Without accounting for thedifferent cloud regimes, the study found a negative sensitivity over land, inthe location of the strongest anthropogenic aerosol perturbations. Gryspeerdt andStier (2012) provided a way of compensating for the sampling bias by providinga weighting to each cloud regime by its frequency. The weighting strengthensthe relationship over land, in some regions changing the sign to positive,putting it in better agreement with the theory. The Increased sensitivity overland is significant given that the short lifetime of many aerosols leads tothem to be concentrated near sources, often over land. The results firsthighlighted the importance of regime based analysis when studying aerosol effectsdue to the differing interaction strengths of the different regimes allowingmore accurate analysis of aerosol anthropogenic forcing.
Following these results, Gryspeerdt et al. (2014) aimed to furtherconstrain the influence of aerosols in different cloud regimes. Rather than the”snapshot” study in Gryspeerdt and Stier (2012), this study looked at theinfluence of aerosols on cloud regime development.
To do this, Gryspeerdt etal. (2014) made use of multiple temporally-spaced satellite retrievals toobserve the development of cloud regimes. The observation of cloud regimedevelopment allowed the ability to account for the influences of CF and meteorologicalfactors on the aerosol-satellite retrieval, thus, reducing errors. This workwas important as hypothesised aerosol effects that modify the CF in some mannercannot easily be separated from CF-related errors in the aerosol retrieval.
Byaccounting for the aerosol-CF relationship, the influence of meteorologicalcorrelations compared to ”snapshot” studies were reduced, finding that simplecorrelations over-estimate any aerosol effect on CF by a factor of two.Furthermore, Gryspeerdt Investigatedthe transitions between cloud regimes to allow a direct view of clouddevelopment in different aerosol environments. The results found an increasedoccurrence of transitions into stratocumulus regimes over ocean with increasedaerosols, consistent with the hypothesis that aerosols increase stratocumuluspersistence. An increase in transitions into the deep convective regime overland was also observed, consistent with the aerosol invigoration hypothesis (Koren et al. 2005). The increasedtransitions between regimes with increasing aerosol, are consistent withpreviously hypothesized effects of aerosols on cloud development (Rosenfeld et al.
2006), even after accountingfor the influence of CF and meteorological parameters at the time of aerosolretrieval. These results Illustrated the importance of accounting for CF andmeteorological influences on aerosol retrieval. The study demonstrates thepowerful possibilities of studying cloud development for controlling formeteorological effects when investigating for aerosol cloud interactions.Further work necessary to understand the relationship between developmentmethods and “snapshot” studies so that the advantages of each method can beused in conjunction with each other. While the so the results likely upperbound on the effect of aerosols on cloud development and CF. Whilst the resultsof this study reduced the error due to meteorological and CF effects on theaerosol retrieval, meteorological covariation with the cloud and aerosolproperties is harder to remove, so estimates were likely the upper bound. Due to the difficulty in separating aerosol effects onclouds from correlations generated by local meteorology, there was stillsignificant uncertainty.
Gryspeerdt et al. (2016) attempts toconstrain the influence of aerosols on cloud fraction to further reduce thisuncertainty. The relationship between aerosol and CF is particularly importantto determine, due to the strong correlation of CF to other cloud properties andits large impact on radiation. Strong correlations between aerosols and CF havebeen made (Shinozuka et al. 2015) however the magnitude of the influence in previous studies werealso still highly uncertain. Gryspeerdt et al. (2016)used a new method of analysing the relationship (Pearl 1994) using the clouddroplet number concentration (CDNC).
Using this method, the impact of themeteorological covariations was significantly reduced allowing for a morecertain aerosol-CF relationship. The method shows much of the aerosol-CFcorrelation is explained by relationships other than that mediated by CDNC. Thestrength of the global mean aerosol-CF relationship was reduced by 80% suggestingthat most of the aerosol-CF relationship is due to meteorological covariations,especially in the shallow cumulus regime. The method provided an estimate of anthropogenicradiative forcing from an aerosol influence of -0.48 Wm-2 although uncertaintyremained due to possible biases in the CDNC retrievals in broken cloud cases.
By using extra informationabout cloud properties, the impact of meteorological covariations was shown tohave been reduced. In doing so, an improved observational estimate of thepossible aerosol influence on CF was determined that was in better agreementwith estimates from models and inverse studies. This study demonstrated how amethod for determining causality in complex systems can reduce the impact ofmeteorological covariations on the aerosol-CF relationship. It was first shownto have many advantages when investigating aerosol-cloud interactions, due tothe complex nature of the relationships involved and the difficulty inexplicitly accounting for every possible meteorological covariation. Theimplications of this provide future studies with a tested method for isolatingaerosol influences on cloud properties and the resulting radiative forcing. As aerosols can serve as CCN,they can have a strong influence on cloud droplet number concentration (Nd).In previous studies, the sensitivity of Nd to aerosol properties hadbeen used as a constraint on the strength of the radiative forcing fromaerosol-cloud interactions (Feingold et al.
2003). In said studies(and ones discussed previously in this report), AOD has been used as a proxyfor CCN concentration. However, (Shinozuka et al.
2015) called thisassumption into question showing a disconnect between AOD and CCN. Consideringthis, (Gryspeerdt et al. 2017)continued his work with aerosol-cloud interactions attempting to furtherconstrain the relationship between aerosols and cloud albedo. To do this, newtechniques previously unavailable were taken advantage of to combat theseproblems.
An ensemble of global aerosol-climate models was used to demonstratehow joint histograms between Nd and aerosol-properties can accountfor issues highlighted by previous studies. The accuracy of usingdifferent aerosol proxies for diagnosing the aerosol anthropogenic forcing wasinvestigated, confirming that using the AOD significantly underestimates thestrength of the aerosol-cloud interactions in satellite data throwing intoquestion the results from previous studiesusing this method. These results imply that information about the aerosolsize and distribution makes a dominant contribution to the accuracy of thepredictions made. Use of the Aerosol Index (AI) showed significant gains over usingAOD, similar to Stier (2016). Using AI has theadvantage over using CCN as it Is currently retrieved by satellite instruments.
This suggests that AI is potentially a useful parameter to use when calculatingobservational constraints for future studies and model inputs. A revised aerosolanthropogenic forcing estimate is made of around -0.4 Wm-2 which islower than the estimate provided in Gryspeerdt et al. (2016) of -0.48 Wm-2, although there is a large diversity between modelestimates, ranging from -0.18 Wm-2 to -1.01 Wm-2 which isa larger range than the Gryspeerdt et al. (2016) estimate range of -0.
1 Wm-2 to -0.64 Wm-2. Thismay seem like a step backwards however, the results from this study provide anew technique to constrain the aerosol cloud relationship that has thepotential to be refined in future work.
2. ConclusionsClimate change is the one ofthe biggest problems that faces humanity in the modern day. The first step tocombatting climate change is to understand the various interactions andforcing’s in play. One aspect is made up by influence of anthropogenic aerosolsthrough direct influence on radiation and their interaction with clouds. Theradiative forcing from anthropogenic aerosols remains the one of the mostuncertain components of the total anthropogenic radiative forcing. Thus,gaining a more certain view of aerosol-cloud interactions allows a more certainestimate of the total anthropogenic forcing for our future climate.
The work done by Gryspeerdthas made significant advances in furthering the field of knowledge ofaerosol-cloud interactions and, reducing the uncertainty on the anthropogenicaerosol radiative forcing. The need to account for differing cloud regimes washighlighted by showing increased agreement with models and the theory whendoing so. Previous studies results showing negative relationships betweenaerosol concentration and cloud albedo were shown to be reversed to a positiveone when accounting for cloud regime. Furthermore, Gryspeerdt was able toconstrain aerosols influence on cloud regime development.
Both results show thepowerful possibilities of using cloud regime in the analysis of aerosol-cloudinteractions to reduce the effect of meteorological and CF influences on cloud,reducing overall uncertainty. Gryspeerdt also showed thepossibilities in using further information about cloud properties, in this caseCDNC, to further reduce the uncertainty on anthropogenic aerosol radiativeforcing. In addition, AOD was shown to be a bad proxy for aerosolconcentration, showing the satellite retrieved AI was a much better candidate.These results help reduce the large error currently assigned to anthropogenicradiative forcing.
The work provides future studies with the tools and theknowledge to study aerosol-cloud interactions more accurately and providingfuture climate prediction better estimates. Into the future, furtherrefining of aerosol retrieval techniques will provide studies an ever-improvingability to estimate aerosol-cloud interactions. The contradictions to previousstudies presented by Gryspeerdt that agree more with the theory show the needto return to previous studies data.