Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint

Abstract

We use the Global Modelling Initiative (GMI) chemical transport model with a cloud droplet parameterisation adjoint to quantify the sensitivity of cloud droplet number concentration to uncertainties in predicting CCN concentrations. Published CCN closure uncertainties for six different sets of simplifying compositional and mixing state assumptions are used as proxies for modelled CCN uncertainty arising from application of those scenarios. It is found that cloud droplet number concentrations (<i>N</i>_d) are fairly insensitive to the number concentration (<i>N</i>_a) of aerosol which act as CCN over the continents (&part;ln<i>N</i>_d/&part;ln<i>N</i>_a ~10–30%), but the sensitivities exceed 70% in pristine regions such as the Alaskan Arctic and remote oceans. This means that CCN concentration uncertainties of 4–71% translate into only 1–23% uncertainty in cloud droplet number, on average. Since most of the anthropogenic indirect forcing is concentrated over the continents, this work shows that the application of Köhler theory and attendant simplifying assumptions in models is not a major source of uncertainty in predicting cloud droplet number or anthropogenic aerosol indirect forcing for the liquid, stratiform clouds simulated in these models. However, it does highlight the sensitivity of some remote areas to pollution brought into the region via long-range transport (e.g., biomass burning) or from seasonal biogenic sources (e.g., phytoplankton as a source of dimethylsulfide in the southern oceans). Since these transient processes are not captured well by the climatological emissions inventories employed by current large-scale models, the uncertainties in aerosol-cloud interactions during these events could be much larger than those uncovered here. This finding motivates additional measurements in these pristine regions, for which few observations exist, to quantify the impact (and associated uncertainty) of transient aerosol processes on cloud properties

A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network

“…Since the overwhelming majority of measurements analysed in this paper were conducted on land, and the overall fit results in an uncertainty of the predicted annual mean N CCN of up to ∼ 45 %, for many sites the use of the overall fit would yield a deviation of the predicted average CDNC of approximately less than 10 %. CDNC, however, is more sensitive to N CCN in cleaner regions with low total particle number concentrations, such as the Alaskan Arctic and remote oceans (Moore et al, 2013). In such areas the use of the overall fit may not be appropriate.…”

“…This uncertainty decreases exponentially for S eff levels above 0.3 %. A global modelling study conducted by Moore et al (2013) reported that CDNC over the continental regions is fairly insensitive to N CCN , where a 4-71 % uncertainty in N CCN leads to a 1-23 % uncertainty in CDNC. Since the overwhelming majority of measurements analysed in this paper were conducted on land, and the overall fit results in an uncertainty of the predicted annual mean N CCN of up to ∼ 45 %, for many sites the use of the overall fit would yield a deviation of the predicted average CDNC of approximately less than 10 %.…”

Chemical and physical influences on aerosol activation in liquid clouds: a study based on observations from the Jungfraujoch, Switzerland

“…The closer the ACI is to unity, the less competition effects are present, linearity applies, and vice versa. The ACI can be calculated either numerically with a parcel model (Reutter et al, 2009) or with a parametrization adjoint Moore et al, 2013;Morales Betancourt and Nenes, 2014). The latter is used here to establish the degree to which linearity holds for the conditions at the JFJ.…”

Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects

“…4). This sensitivity of radiative forcing in oceanic regions to aerosol activation is due to two main reasons: (1) the low penetration of shortwave radiation through stratocumulus decks covering large areas of the ocean and (2) the sensitivity of marine cloud albedo to changes in CDNC (Twomey, 1991;Platnick and Twomey, 1994;Moore et al, 2013). The updates to the FN05 scheme do not substantially affect the spatial distribution of SWCF changes relative to the change from AR-G00 to FN05.…”

“…The insensitivity of tropical cloud properties to the various aerosol activation parameterizations is likely due to the abundance of convective clouds not treated by the aerosol activation schemes and high frequency of strong updrafts in the region which have been shown to have a lower variance in the activated fraction from different parameterizations than do weak updrafts . Predictions of CF, COT, and LWP in the AR-G00 and FN05 series of simulations are most different in polar regions because of the sensitivity of Arctic and Antarctic CDNC (and corresponding cloud properties) to slight changes in aerosol and ice nuclei number concentration and lack of sensitivity to aerosol activation treatment Moore et al, 2013). Mixedphase clouds, which are found in polar regions, are particularly difficult to simulate because they are affected by both aerosol activation and ice nucleation (Lance et al, 2011;Xie et al, 2013).…”