Studies of heterogeneous freezing by three different desert dust samples

P. J. Connolly; O. Möhler; P. R. Field; H. Saathoff; R. Burgess; T. Choularton; M. Gallagher

doi:10.5194/acp-9-2805-2009

Connolly, P. J.;Möhler, O.;Field, P. R.;Saathoff, H.;Burgess, R.;Choularton, T.;Gallagher, M.;

2009-04-27 00:00:00

Atmos. Chem. Phys., 9, 2805–2824, 2009 Atmospheric www.atmos-chem-phys.net/9/2805/2009/ Chemistry © Author(s) 2009. This work is distributed under the Creative Commons Attribution 3.0 License. and Physics Studies of heterogeneous freezing by three different desert dust samples 1 2 3 2 1 1 1 P. J. Connolly , O. Mohler ¨ , P. R. Field , H. Saathoff , R. Burgess , T. Choularton , and M. Gallagher School of Earth, Atmospheric and Environmental Sciences, The University of Manchester, UK IMK-AAF Forschungszentrum Karlsruhe, Germany Met Ofﬁce, Exeter, UK Received: 13 October 2008 – Published in Atmos. Chem. Phys. Discuss.: 8 January 2009 Revised: 7 April 2009 – Accepted: 7 April 2009 – Published: 27 April 2009 Abstract. We present results of experiments at the aerosol sations in atmospheric cloud models where cooling rates of ◦ −1 interactions and dynamics in the atmosphere (AIDA) cham- approximately 1 C min or more are present to predict the ber facility looking at the freezing of water by three different concentration of ice crystals forming by the condensation- types of mineral particles at temperatures between −12 C freezing mode of ice nucleation. Finally a polynomial is ﬁt- and −33 C. The three different dusts are Asia Dust-1 (AD1), ted to all three samples together in order to have a parameter- Sahara Dust-2 (SD2) and Arizona test Dust (ATD). The dust isation describing the average ice-active surface site density samples used had particle concentrations of sizes that were vs. temperature for an equal mixture of the three dust sam- log-normally distributed with mode diameters between 0.3 ples. and 0.5 μm and standard deviations, σ , of 1.6–1.9. The re- sults from the freezing experiments are consistent with the singular hypothesis of ice nucleation. The dusts showed dif- 1 Introduction ferent nucleation abilities, with ATD showing a rather sharp increase in ice-active surface site density at temperatures less Recently Ansmann et al. (2008) presented lidar observations than −24 C. AD1 was the next most efﬁcient freezing nuclei demonstrating that altocumulus (Ac) and layer clouds inﬂu- and showed a more gradual increase in activity than the ATD enced by desert dust over the African continent, close to the sample. SD2 was the least active freezing nuclei. source, seldom show any signs of glaciation for tempera- We used data taken with particle counting probes to de- tures warmer than −20 C. This is apparently contradictory rive the ice-active surface site density forming on the dust as to the numerous observations by other authors in cumulus a function of temperature for each of the three samples and (Cu) clouds (see Hobbs and Rangno, 1985, 1990, for exam- polynomial curves are ﬁtted to this data. The curve ﬁts are ple). Another interesting ﬁnding was that in this temperature then used independently within a bin microphysical model to ◦ ◦ regime (−30 C<T <0 C), liquid drops were apparently re- simulate the ice formation rates from the experiments in or- quired before the formation of ice. The measurements of der to test the validity of parameterising the data with smooth Ansmann et al. therefore suggest that the freezing modes of curves. Good agreement is found between the measurements ice nucleation, i.e. condensation-freezing/immersion freez- and the model for AD1 and SD2; however, the curve for ATD ing and not deposition are important ice formation mecha- does not yield results that agree well with the observations. nisms in layer clouds. The reason for this is that more experiments between −20 ◦ A further perplexing piece in the puzzle of atmospheric and −24 C are needed to quantify the rather sharp increase dust as ice nuclei (IN) comes from measurements made dur- in ice-active surface site density on ATD in this temperature ing the Cirrus Regional Study of Tropical Anvils and Cirrus regime. The curves presented can be used as parameteri- Layers-Florida Area Cirrus Experiment CRYSTAL-FACE project, which demonstrated a possible link between the con- Correspondence to: P. J. Connolly centration of desert dust that advected across the Atlantic ([email protected]) Ocean and the glaciation of layer clouds near the Florida Published by Copernicus Publications on behalf of the European Geosciences Union. 2806 P. J. Connolly et al.: Freezing on dust coast (DeMott et al., 2003; Sassen et al., 2003). In the case classical nucleation theory. In classical nucleation theory ice reported by Sassen et al. desert dust particles were inferred germs are assumed to be spherical caps in contact with the to glaciate a cloud at temperatures from −5.2 to −8.8 C. nucleating material (i.e. the dust). The three assumptions Numerous laboratory observations have shown that when were: (1) that each particle of ATD had the same contact an- a sample of liquid drops that contain IN are subject to a fast gle (stochastic hypothesis); (2) that the contact angle varied cooling they freeze at a rate that is approximately propor- between particles (singular hypothesis-a); and (3) that there tional to the cooling rate. They also show that if this cooling was a distribution of active sites with different contact angles is stopped the rate at which the drops freeze is much slower on each particle (singular hypthesis-b). Their basic ﬁnding than when the drops are being cooled. To explain these ob- was that the singular hypothesis best describes their results. servations Vali (1994) presented the time-dependent freezing However, neither of the approaches could reproduce the mea- rate (TDFR) theory for heterogeneous drop freezing. TDFR surements in their entirety, which highlights the inadequacies theory allows one to calculate the drop freezing rate of a sam- of the classical approach. ple in which there is a distribution of different IN contained Mohler ¨ et al. (2006) were motivated by the potential im- within the drops; each different type of IN having a different portance of dust as atmospheric IN; they studied and de- temperature-dependent ice nucleation rate. scribed heterogeneous deposition nucleation for cirrus (Ci) From TDFR theory two approximations can be made: (1) temperatures in the AIDA laboratory by the same three dust each sample unit (drop) is the same (i.e. the IN the drops con- samples used in this paper – so called AD1; ATD and SD2. tain all have the same ice nucleation rate). Under this approx- They found that to within their instrumental error, this “depo- imation, known as the “stochastic hypothesis”, the freezing sition” nucleation mode acted only while the supersaturation of individual drops can be viewed as a Poisson distributed with respect to ice was increasing, and there was little explicit variable with respect to time and a nucleation rate equation time dependence on the ice particle formation rate. This ice can be applied to explain this, similar to that for radioactive nucleation behaviour is consistent with the dust samples hav- decay. (2) The nucleation rates of the spectrum of the dif- ing a distribution of supersaturations at which they become ferent IN contained in the drops are not smooth functions, active as IN – i.e. it is consistent with the singular nucleation but sharp transitions with respect to temperature; so sharp hypothesis. that the nucleation rate for one type of nucleus can be rep- Since the study by Mohler ¨ et al., Zimmermann et al. resented by a step function – i.e. ice-nucleation happens at a (2008) investigated efﬁciency as IN of numerous minerals ﬁxed temperature on a given type of nucleus. In this case the at different temperatures using an Environmental Scanning freezing rate can be described from the distribution of freez- Electron Microscope (ESEM) to quantify the onset relative ing temperatures of the nuclei within the drops – i.e. “the humidity of ice nucleation. They showed that in some cases −3 ◦ −1 nucleus content” in the drops – K(T ) (ice germs m C ) the nucleation efﬁciency may also be a function of tempera- and the cooling rate, T . ture. Drop freezing experiments were also conducted by Vali Here we present further results from three campaigns at (1994) who studied the freezing rate of water containing sus- the AIDA facility to attempt to quantify ice nucleation be- pended foreign material due to heterogeneous nucleation. He haviour on the three different types of dust particles in the found that for water drops cooled at rates of the order of temperature range T >235 K. We also present the ice crys- ◦ −1 −1 C min , the “nucleus content” (distribution of freezing tal habits, that were observed with the CPI during the ex- temperatures in the nuclei) of the drops predicts the freezing periments, mainly as supporting measurements, but also to rate well – i.e. the singular hypothesis holds. However, for look into any effects that nucleation may have on resulting samples with ﬁxed temperatures, the stochastic, time depen- ice crystal habit (e.g. Bailey and Hallett, 2002). dent nature, although small, becomes non-negligible. Section 2 describes the experiments; Sect. 3 gives an out- This conclusion is also supported by the more recent work line of the methods of data analysis we are using; Sect. 4 is of Vali (2007), who investigated the freezing temperatures the results and 5 and 6 are discussion and conclusion sec- of drops of water containing IN from two soil samples. Vali tions. ’s experiments had the drops placed on a cold stage and, dur- ing several cycles, he repeatedly lowered the temperature un- til they froze and then increased the temperature until they 2 Experiments melted. He found evidence supporting a modiﬁed singular hypothesis. The ﬁnding that the temperature at which drop 2.1 Laboratory experiments and data collection containing IN froze changed by very little upon repeated cy- cles led Vali to conclude that a modiﬁed singular hypothesis In order to investigate heterogeneous freezing we conducted is appropriate. experiments at the large AIDA cloud chamber. Cloud for- Marcolli et al. (2007) looked at the freezing spectrum of mation and evolution were simulated in the laboratory at the drops containing so called ATD and analysed their results AIDA (see Fig. 1 for a schematic of the AIDA); the exper- by comparing with three assumptions that were based on the iments aimed to form clouds under natural and controlled Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ P. J. Connolly et al.: Freezing on dust 19 P. J. Connolly et al.: Freezing on dust 2807 Table 1. Log-normal ﬁts to the PSD measured with a SMPS. The total particle number, N was generally variable between experiments and taken from the in situ CPC measurements for every experiment. Dust sample Median diameter, D (μm) Standard deviation, σ Total particle number, N g L AD1 0.40±0.05 1.70±0.05 measured with in-situ CPC SD2 0.35±0.05 1.85±0.05 measured with in-situ CPC ATD 0.35±0.05 1.65±0.05 measured with in-situ CPC In our experiments dust aerosol samples (AD1, SD2 and Temperature Controlled Housing -90 to +30°C ATD) were prepared with a PALAS rotating brush generator in the way described by Mohler ¨ et al. (2006, p. 1545) and Aerosol T,p AIDA Aerosol Generator Chilled Mirror were introduced into the chamber (see schematic in Fig. 1); Vessel Hygrometer Filter a mechanical fan mixed the air at the start of the experi- TDL Water M ment giving homogeneous conditions within the chamber. Vapour Detection CPC 3010 Scanning mobility particle sizer (SMPS) measurements con- CPC 3010 ducted separate to this work (Mohler ¨ et al., 2006) found the Small Ice SID Detector (SID) dust particle size distribution (PSD) of the different dust sam- ples to be log-normally distributed in size with ﬁt parameters given in Table 1. Expan- sion To simulate cloud formation, the chamber volume is ex- Volume panded using a mixture of Vacuum pump 1, 2 and the expan- Vacuum Synthetic Vacuum Cryostat Pump 2 Pump 1 Liquid Nitrogen sion volume (see Fig. 1). The time at which the pumps start Air Supply to expand the volume is set to t=0 s and typically the exper- iments last 600 s. Combinations of these pumps to expand Fig. 1. This shows a schematic of the AIDA facility. The aerosol Fig. 11. This shows a schematic of the AIDA facility. The aerosol the volume are able to yield cooling rates in the chamber (by vessel is cooled inside an insulated cold box by ventilation and liq- Fig. 1. This shows a schematic of the AIDA facility. The aerosol vessel is cooled inside an insulated vessel is cooled inside an insulated cold box by ventilation and liq- −1 quasi adiabatic expansion) of up to 4 K min . As cooling cold box by ventilation and liquid nitrogen cooling. A variety of pumps and an expansion volume is uid nitrogen cooling. A variety of pumps and an expansion volume used uidtonitrogen evacuate the cooling. air fromA thevaerosol arietyvof esselpumps at different andrates, an esimulating xpansionquasi-adiabatic volume expan- takes place, conditions of water vapour saturation (liquid or is used to evacuate the air from the aerosol vessel at different rates, sion. Dust aerosols are introduced into the chamber using a brush disperser from PALAS and are is used to evacuate the air from the aerosol vessel at different rates, sampled with a CPC 3010 and the WELAS probe. Total water and water vapour are measured with ice) are reached and a cloud is formed on the aerosol particles simulating quasi-adiabatic expansion. Dust aerosols are introduced the simulating chilled mirror quasi-adiabatic and a TDL hygrometer expansion. . CloudDust particles aerosols are sampled arewith introduced the CPI, the SID, the within the chamber. into the chamber using a brush disperser from PALAS and are sam- WELAS and the CDP. into the chamber using a brush disperser from PALAS and are sam- pled with a CPC 3010 and the WELAS probe. Total water and water The interior wall of the AIDA is ice coated and the tem- pled with a CPC 3010 and the WELAS probe. Total water and water vapour are measured with the chilled mirror and a TDL hygrometer. perature of the wall stays relatively constant, while during 2 Experiments vapour are measured with the chilled mirror and a TDL hygrometer. Cloud particles are sampled with the CPI, the SID, the WELAS and the experiment the gas is generally colder than the wall. This Cloud particles are sampled with the CPI, the SID, the WELAS and the CDP. 2.1 Laboratory experiments and data collection results in a ﬂux of water vapour from the interior wall of the the CDP. AIDA to the gas, which is not large, but important enough to In order to investigate heterogeneous freezing we conducted experiments at the large AIDA signiﬁcantly alter the relative humidity with respect to liquid cloud chamber. Cloud formation and evolution were simulated in the laboratorywat ater the (RH) in the chamber during the expansion. conditions. The AIDA consists of a cylindrical (with rounded The aerosol, liquid and ice PSD – 0.5 μm<D <50 μm – 85 AIDends), A (see Figure 7 m by 1 for 4 m, a schematic 84 m vessel of the encased AIDA); the ineaxperiments large cold aimed box. to form clouds p are sampled using the white-light aerosol spectrometer (WE- The vessel itself is connected to a vacuum and air supply under natural and controlled conditions. The AIDA consists of a cylindrical (with rounded LAS) optical particle counter (OPC) from PALAS, which is system and can be evacuated to a pressure below 0.1 hPa and ends), 7 m by 4 m, 84 m vessel encased in a large cold box. The vessel itself is connected situated at the bottom of the AIDA vessel (see Fig. 1); to- ﬁlled with particle free synthetic air (see Fig. 1). This en- to a vacuum and air supply system and can be evacuated to a pressure below 0.1 hPa and tal number concentration of particles (0.01 μm<D <3 μm) sures that background particle concentrations, measured with p −3 ﬁlled with particle free synthetic air (see Figure 1). This ensures that background particle is measured with a modiﬁed CPC 3010, able to sample at a condensation particle counter (CPC), are less than 0.1 cm reduced pressures (see Fig. 1). 90 concentrations, measured with a Condensation Particle Counter (CPC), are less than 0.1 (see Mohler ¨ et al., 2006). −3 For a small subset of these experiments we were able to Experiments are prepared by injecting humid air into the cm (see Mohler ¨ et al., 2006). use the small ice detector (SID) probe (Hirst et al., 2001) for chamber and then slowly cooling throughout the night to the sampling the size and concentration of the cloud and for de- required temperature for the experiment. The reason for the termining cloud phase (liquid or ice). The SID was placed at slow cooling of the cold box to the required temperature is the side of the AIDA (see Fig. 1). The basis for the discrim- that the air can saturate slowly (eventually resulting in frost ination of phase is the assumption that liquid particles are forming on the interior of the aerosol vessel). The frost coat- ing on the chamber wall results in conditions close to ice spherical and ice particles are non-spherical. The probe nor- saturation at the start of the experiment. mally uses six detectors arranged azimuthally at a forward scattering angle of 30 , with a seventh detector mounted www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 Welas CDP CPI 2808 P. J. Connolly et al.: Freezing on dust directly in front of the laser. However, for the AIDA conﬁgu- of the ice saturation vapour pressure formulation of Murphy ration it was decided that one of the azimuthal detectors per- and Koop (2005). In some situations it can be seen that there formed better than the standard design so the probe was con- is a systematic error in the values of saturation ratio calcu- ﬁgured to use ﬁve azimuthal detectors for sizing and shap- lated from the TDL data. These problems are being looked ing and the remaining sixth azimuthal detector for triggering. at with on going inter-comparisons between various water When a particle passes through the system, the response of vapour probes at the AIDA – they do not affect our conclu- scattered light falling on the detectors is recorded. Spherical sions. As mentioned above, we also measured the total water particles result in light falling relatively uniformly on all ﬁve (vapour plus liquid plus ice) using the chilled mirror hygrom- azimuthal signal detectors, while aspherical particles record eter with a heated inlet that evaporated all cloud particles be- a non-uniform signal on the detectors. This is quantiﬁed by fore they entered the sensor. For more information on the using the asphericity factor, A , for each particle measured. instrumental techniques and limitations the reader is referred The discrimination between liquid and ice particles is fairly to Mohler ¨ et al. (2006, 2004). clear as two regimes can be seen, liquid having small A and ice having large A . The A is calculated by: f f 3 Methods of data analysis (hEi − E ) i 3.1 Basic assumptions and deﬁnitions i=1 A = κ (1) hEi This paper considers the behaviour of the three dust samples in the freezing mode at warmer temperatures than former ex- where κ=22.361, E are the detector values and hEi is the periments that investigated the deposition mode of ice nu- mean of all detector values. For more information see Sect. 4 cleation of the same dust samples (Mohler ¨ et al., 2006). In of Field et al. (2006) and also Hirst et al. (2001). contrast to the deposition mode nucleation the freezing mode A cloud particle imager (CPI) was available for all of the nucleation is mainly driven by the temperature of the water measurements within this paper. The CPI images particles drops, with no explicit dependence on the water vapour su- (10<D <2300 μm) by use of a 20 ns pulsed 100 W laser persaturation. diode. Images from a charge-coupled device (CCD) camera Our main assumption is that ice nucleation occurs at the are recorded with a frame-rate of 40 Hz (see Lawson et al., interface between a dust particle and the liquid drop it is im- 2001). The time series of images were used to calculate parti- mersed in. The dust particles are assumed to have a char- cle concentrations and the PSD using the calibration method acteristic number density of sites on their surface at which described in Connolly et al. (2007) to correct the raw data. ice germs form at deﬁnite temperatures. Our assumption is This calibration corrects over sizing and under sampling of slightly different to that of Marcolli et al. (2007), who at- the particles relative to their true size by using scalar diffrac- tempted to deﬁne a range of nucleation rates for different ar- tion theory. Connolly et al. show that using these correc- eas on individual IN using the classical spherical cap model. tions gives good agreement for the cloud PSD when com- The main difference being that, in this model, ice crystal for- pared with other cloud spectrometers. mation occurs instantaneously at a deﬁned temperature. The CPI was placed at the bottom of the AIDA vessel (see −1 This assumption follows the concept of the singular hy- Fig. 1) and the airﬂow through the CPI tube was ≈5 ms . pothesis for heterogeneous ice nucleation as described in Asphericity is also the criteria by which CPI images are used Sect. 1. The number of these sites per surface area of the dust to discriminate between liquid or ice. Particles from the CPI that are active at temperature T is referred to as the ice-active that have size greater than 40 μm and a roundness, A (see surface site density (IASSD), and given the symbol n (T ). Eq. 2), less than 0.75 and a maximum deviation from the s We also deﬁne the IASSD that become active as the tem- mean radius of 0.1 times the mean radius are classiﬁed as ice perature is lowered by dT and give it the symbol k(T ). Note crystals. that n and k are related by: 4 × Area A = (2) min π × d n (T ) = − k(T )dT (3) s min here, d and Area are the maximum length and the projected area of the particle, respectively. where T is the minimum temperature reached during the min The chamber also has instrumentation to measure water experiment and k(T ) is inferred from the experimental data vapour – a tunable diode laser (TDL) system. The TDL mea- – see Sect. 3.2; n (T ) is the IASSD between 0 C and s min dn (T ) surement is scaled to the water vapour concentration inferred T . Note also that k(T )= and is analogous to a time- min dT from the frost point measured by a chilled mirror hygrom- independent concentration function or “nucleus content” de- eter in the absence of cloud. The partial pressure of water ﬁned by Vali (1971), but in our case has units of germs −2 ◦ −1 vapour is calculated from the frost point using ice saturation m C . vapour pressures by Buck research, which agree within 0.1% Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ 20 P. J. Connolly et al.: Freezing on dust P. J. Connolly et al.: Freezing on dust 2809 Now provided the singular hypothesis holds, the rate of change of ice concentration with respect to temperature can be written as: dN i,j = N A k(T ) (4) d,j j dT where N is the drop number concentration of mass cate- d,j gory j (unfrozen), A is the surface area of the aerosol in this drop mass category, N is the ice number concentration i,j of drops in category j and k(T ) is the IASSD (per unit area of the dust) per temperature interval, which is a function of temperature, T . Note also that the liquid and ice mass grids are assumed to be the same. Another assumption in this paper is that for a particular dust sample n (T ) – the IASSD that form between T =0 s min and T =T – is constant for all sizes of the dust sample. min Using the same n value for all sizes of dust particles may not strictly be valid due to a size dependent mineralogical composition or surface structure. However, for this paper it was deemed acceptable to assume a constant n for all sizes to avoid insurmountable complications. 3.2 Using the ice-active surface site density to compute the ice particle concentration in a cloud We will now consider an experiment (Fig. 2) that starts at temperature T at sub water saturated conditions (region i, init Fig. 2) in which the air is expanded until the point of liquid Fig. 2. Shows a schematic of the freezing experiments and is used to illustrate how the ice concentra- Fig. 12. A schematic of the freezing experiments to illustrate how drop formation on the dust particles at which point the tem- tion is calculated. (a) shows a temperature time series starting at t = 0, with decreasing temperature Fig. 2. A schematic of the freezing experiments to illustrate how until time t is reached at temperature T , where the saturation ratio, s = 1.0–see (b). The cooling 1 1 w perature is T and the time is t . The air continues to cool the ice concentration is calculated. (a) shows a temperature time 1 1 continues, with ice forming until s goes below 1.0 and all drops evaporate at time t , tempera- w 2 the ice concentration is calculated. (a) shows a temperature time ture, T , or T . After this point, no more ice can form from the freezing of drops. (c) shows a 1 min by expansion and liquid remains in the cloud (region ii) until series starting at t=0, with decreasing temperature until time t is hypothetical series starting value for IASSD, at t=0, in this with scenario decreasing the value istemperature above zero before until dropstime form (in t re isgion 1 i) and consequently as soon as the drops form they start to freeze instantly and then continuously time t at temperature T – also referred to as T . At this 2 2 min reached at temperature T , where the saturation ratio, s =1.0 – 1 w reached as the temperature at temperatu is decreasedrefurther T ,(rewhere gion ii). (d)the showssaturation the correspondingratio, ice particlesnumber =1.0 – 1 w concentration for Scenario 1. (e) shows the same but for a scenario where the value is zero until time, all of the liquid drops evaporate or freeze and the RH see (b). The cooling continues, with ice forming until s goes be- some time after drops form; in this case the ice crystals start to form continuously, part way through see (b). The cooling continues, with ice forming until s goes be- drops below 1.0 (region iii). This is depicted by the schemat- region lowiii,1when .0 and theall temperature drops ethreshold vaporate foranucleation t time t is, met. temperature, (f) shows theTcorrespondi , or T ng. ice 2 1 min particle number concentration for Scenario 2. low 1.0 and all drops evaporate at time t , temperature, T , or T . 2 1 min After this point, no more ice can form from the freezing of drops. ics in Fig. 2a and b. Note, T is not necessarily the min- min After this (c) sho point, ws a hypothetical no more ice valuecan for IASSD, form from in thisthe scenario freezing the value of drops. imum temperature of the experiment, but it is the minimum is above zero before drops form (in region i) and consequently as temperature where drops are still present, not having frozen (c) shows a hypothetical value for IASSD, in this scenario the value soon as the drops form they start to freeze instantly and then contin- or evaporated. is above zero before drops form (in region i) and consequently as uously as the temperature is decreased further (region ii). (d) shows In order to calculate the time dependent ice particle con- soon as the drops form they start to freeze instantly and then contin- the corresponding ice particle number concentration for Scenario 1. centration in this experiment we need to consider two scenar- uously(e) assho the wstemperature the same but for is decreased a scenario where further the(re value gion is zero ii). un (d) - shows ios. (1) is that the IN become active freezing nuclei (i.e. the til some time after drops form; in this case the ice crystals start to the corresponding ice particle number concentration for Scenario 1. IASSD is greater than 0) at a time before t ; (2) is that the form continuously, part way through region iii, when the tempera- (e) shows the same but for a scenario where the value is zero un- IN become active freezing nuclei at t <time<t . These two 1 2 ture threshold for nucleation is met. (f) shows the corresponding ice til some time after drops form; in this case the ice crystals start to scenarios are depicted in Fig. 2c and e with the correspond- particle number concentration for Scenario 2. ing ice particle number concentrations in Fig. 2d and f. W form e continuously, part way through region iii, when the tempera- will refer back to this “experiment” throughout this section. ture threshold for nucleation is met. (f) shows the corresponding ice here, 1N(T ) is the number of ice crystals formed by active In order to calculate the time dependent formation rate of particle number concentration for Scenario 2. IN between 0 C and T , where T is the temperature when ice crystals we can multiply Eq. (4) by the cooling rate to 1 1 the drops ﬁrst formed. This is the case for scenario 1 de- obtain time derivatives (instead of wrt. temperature): scribed above where IN are potentially active at times <t . dN dT i,j In scenario 1, even though the IN are potentially active for = N A k(T ) (5) d,j j dt dt times <t , no ice particles can form because there are no liq- dn (T ) substituting k(T )=− into Eq. (5) and integrating uid drops present; however, when liquid drops form at time dT yields: =t , this built-up reservoir of potential IN becomes active t=t instantly (the reservoir is shown by the light-grey shading in dn (T ) dT N (t → t ) = 1N(T ) + N A dt (6) i,j 1 2 1 d,j j Fig. 2c). dT dt t=t www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 2810 P. J. Connolly et al.: Freezing on dust In order to compute this 1N term we note that initially the Sect. 2.1). This enabled us to calculate the time series of the only important transformation process affecting number con- product of the IASSD per temperature interval, k(T ), and the dT dT centrations of aerosol and ice crystals is the formation of ice cooling rate, . The product k(T ) can be calculated by dt dt particles; aggregation, coalescence and washout are negligi- rearranging Eq. (5): ble. Therefore we can substitute N =N −N – here, N and d s i d M M X X N are the drop and ice particle number concentrations, re- i dT dN i,j k(t) = (N × A ) (11) d,j j spectively; N is the starting number of drops (constant with dt dt j j time) – in Eq. (4) and integrate wrt. T . Equation (11) was then integrated between times t and dN 1 = (N − N )Ak(T ) (7) s i t (which is equivalent to the integral in Eq. 3) to yield the dT IASSD, n (T ). This method was repeated for all the ex- s min integrating Eq. (7) yields an equation for the number of ice periments providing enough points to ﬁt a polynomial to n crystals at time =t : vs. T . Admittedly other functional forms could also be min Z Z used with this method, but we decided on a polynomial as it N T i 1 dN = A k(T )dT (8) ﬁtted the data well enough. N − N 0 s i 0 There are other ways that could have been used to estimate or n , for instance, one could estimate the surface area of dust in contact with the drops by ﬁnding the average surface area N (0 → t ) = 1N = N (1 − exp[−An (T )]) (9) i 1 s s 1 of the dust distribution via Table 1 (i.e. the second moment of the dust distribution) and inverting Eq. (9), therefore not where requiring a model. However, we feel our method is the best T =T n (T ) = − k(T )dT (10) for this application. s 1 T =0 An advantage of our method is that we are able to take into For times >t , the increase in ice particle number concen- account the modelled surface area of dust in contact with in- tration can be computed from the second term on the rhs of dividual drops. For instance the larger dust particles freeze Eq. (6). This results in the IASSD increasing wrt. time (de- the drops ﬁrst as they contain larger surface area – and thus noted by the darker shading in Fig. 2c). a larger IASSD (meaning that the average surface area in the For scenario 2, where IN become active after t , the ice drops decreases with time); also, the larger dust particles ac- particle number concentration is also computed from the sec- tivate as cloud condensation nuclei (CCN) before the smaller ond term on the rhs of Eq. (6) but there is no need to calculate particles so adding ﬂaws to the assumption that the surface the 1N term. area of the dust in contact with the drops is just the average surface area of the distribution in Table 1. 3.3 Deriving the dependence of the ice-active germ den- sity on temperature 3.3.2 Heterogeneous deposition 3.3.1 Heterogeneous freezing In some experiments, where RH<1.0, on ATD we noted sig- niﬁcant nucleation due to heterogeneous deposition and in The main tool used in this analysis is the aerosol-cloud- this case we inferred the IASSD n as a function of supersat- precipitation interaction model (ACPIM), which has been de- uration with respect to ice, s . The theory used is analogous veloped at the University of Manchester (UoM) in collabo- to that described in Sect. 3, except that all occurences of tem- ration with the Forschungszentrum Karlsruhe; it is described perature, T , are substituted for ice supersaturation, s . Also in the Appendix. instead of the minimum temperature reached determining the In order to derive the value of n we adopted the follow- IASSD it is the maximum ice supersaturation reached s i,max ing method – note the actual AIDA experiments in general during the experiment – i.e. n (s ). Since heterogeneous s i,max followed the same life cycle to the schematic experiment de- deposition does not require the presence of water drops the scribed in Fig. 2. For every experiment in Tables 2, 3 and 1N in the analogous Eq. (6) is set to zero for the case of 4 (see Sect. 4) we initialised ACPIM with the aerosol PSD heterogeneous deposition. parameters in Table 1 with the total aerosol number from the in situ CPC measurements. We then constrained the ACPIM 3.4 Quality control to the measured time-series of T , P and total water mass content as described in the Appendix. The drop number con- This last step was performed to quality control the derived centration was predicted by the ACPIM model and we calcu- parameterisations of n . We therefore ran the ACPIM in a lated the surface area of dust in contact with the liquid drops purely predictive mode, initialised with the dust PSD – see in the model. The ice formation rate in the ACPIM was con- Table 1 – and still constrained to the timeseries of T , P and strained to the measured ice formation rate with the CPI (see total water mass content. The model was used to predict the Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ P. J. Connolly et al.: Freezing on dust 2811 Table 2. Experiments for AD1 dust. Dual refers to the fact that deposition was observed before the formation of liquid. Date Experiment T Liquid Observed Comments min 24 September 2003 10:30:00 IN04 18 −30.0 C Yes Freezing 24 September 2003 12:15:02 IN04 19 −32.0 C Yes Freezing 24 September 2003 14:00:01 IN04 20 −32.0 C Yes Freezing 24 September 2003 15:45:00 IN04 21 −33.5 C Yes Freezing 16 November 2004 10:30:00 IN05 51 −27.0 C Yes Freezing 16 November 2004 12:45:00 IN05 52 −21.8 C Yes Dual 17 November 2004 10:30:00 IN05 55 −27.5 C Yes Freezing 17 November 2004 12:50:00 IN05 56 −18.5 C Yes Dual – very low 23 September 2003 10:31:40 IN04 15 −5.5 C Yes No Ice 23 September 2003 12:16:40 IN04 16 −6.5 C Yes No Ice 12 November 2004 11:10:00 IN05 45 −12.5 C Yes No Ice 12 November 2004 15:05:00 IN05 46 −12.5 C Yes No Ice 12 November 2004 16:30:00 IN05 47 −12.4 C Yes No Ice 15 November 2004 10:45:00 IN05 48 −18.5 C Yes Some ice by dep. 15 November 2004 12:40:00 IN05 49 −18.1 C Yes No Ice Table 3. Experiments for SD2 dust. Low aerosol refers to a case where ice was observed, but the statistics were poor due to low aerosol concentrations. This experiment was not used in the analysis. Date Experiment T Liquid Observed Comments min 17 September 2003 10:50:00 IN04 06 −27.5 C Yes Freezing 17 September 2003 12:16:00 IN04 07 −25.5 C Yes Freezing 29 September 2003 10:31:00 IN04 30 −26.3 C Yes Freezing 29 September 2003 12:15:00 IN04 31 −26.0 C Yes Freezing – IN04 32 – Yes Low aerosol 18 November 2004 10:35:00 IN05 58 −26.7 C Yes Freezing 18 November 2004 12:45:00 IN05 59 −25.5 C Yes Freezing 15 September 2003 11:50:00 IN04 01 −1.5 C Yes No Ice 15 September 2003 17:05:00 IN04 02 −2.9 C Yes No Ice 16 September 2003 14:01:00 IN04 03 −4.7 C Yes No Ice 16 September 2003 15:45:00 IN04 04 −7.8 C Yes No Ice 17 September 2003 10:50:00 IN04 05 −8.3 C Yes No Ice 10 November 2004 12:45:00 IN05 40 −5.0 C Yes No Ice 10 November 2004 14:15:00 IN05 41 −6.9 C Yes No Ice drop and ice particle concentration and the RH. The ice par- 4.1 Intercomparison of SID and CPI derived ice-active ticle concentration was predicted with Eq. (6) and the de- germ densities rived n polynomials. These were compared visually with the measurements in order to assess the validity of smooth- For small crystals the SID is better than the CPI for phase dis- ing of data with a polynomial function. crimination; however, in experiments where the ice crystals grow rapidly outside of the range observable by the SID the CPI is the better of the two instruments for determining ice 4 Results number concentrations providing the correction algorithms of Connolly et al. (2007) are used. The results are from three separate sets of experimental cam- The SID measurements were only available for a limited paigns lasting approximately 2 weeks each: IN02 in 2002, number of experiments during IN04 and it is desirable to use IN04 in 2003 and IN05 in 2004. Summaries of the experi- the larger, more complete dataset of the CPI, collected for our ments used in the analysis are shown in Tables 2, 3 and 4. experiments, for determining ice concentrations. However, www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 2812 P. J. Connolly et al.: Freezing on dust Table 4. Experiments for ATD dust. Low aerosol refers to a case where ice was observed, but the statistics were poor due to low aerosol concentrations. This experiment was not used in the analysis. Homogeneous freezing refers to an experiment where the supercooling was below that required for homogeneous freezing to take place. Date Experiment T Liquid Observed Comments min 17 September 2003 16:30:00 IN04 09 −27.9 C Yes Freezing 17 September 2003 17:30:00 IN04 10 −26.2 C Yes Freezing 04 July 2002 15:04:00 IN02 74 – No Homogeneous freezing 05 July 2002 13:38:00 IN02 79 −27.0 C Yes Freezing 08 July 2002 11:45:00 IN02 83 −19.3 C Yes Freezing 08 July 2002 13:30:00 IN02 84 −18.1 C Yes Freezing 08 July 2002 14:42:00 IN02 85 −18.0 C Yes Freezing 08 July 2002 16:00:00 IN02 86 −17.9 C Yes Freezing 08 July 2002 16:57:00 IN02 87 −17.9 C Yes Freezing 11 July 2002 15:10:00 IN02 103 −12.4 C Yes No Ice 11 July 2002 16:30:00 IN02 104 −12.0 C Yes No Ice 04 July 2002 11:46:00 IN02 72 −34.5 C No Deposition 04 July 2002 13:18:00 IN02 73 −33.7 C No Low aerosol 04 July 2002 17:51:00 IN02 75 −34.9 C No Deposition 05 July 2002 10:35:00 IN02 77 −27.9 C No Deposition 05 July 2002 11:34:00 IN02 78 −26.5 C No Deposition 05 July 2002 14:48:00 IN02 80 −26.0 C No Deposition 05 July 2002 16:11:00 IN02 81 −25.0 C No Deposition since the CPI cannot observe the smallest ice crystals nucle- crystals (see Table 2). The IASSD increases markedly at ated at the start of the experiment we need to validate the CPI temperatures less than −30 C. against the SID. A polynomial ﬁt to the data for AD1 is shown by the grey dashed line and yields the following curves for T >−33 C: Figure 3 shows a comparison of the IASSD calculated with both probes with error bars . The comparison shows good linear agreement between the two methods with the a (T + a ) , T < −a 1 2 2 n (T ) = (12a) CPI tending to under predict the IASSD when compared to 0, T ≥ −a the SID probe. It is not clear whether this is due to problems with SID, CPI or both and so the offset should be kept in dn (T ) −k(T ) = 2 × a (T + a ), T < −a 1 2 2 = (12b) mind. −k(T ) = 0, T ≥ −a dT The Poisson uncertainty associated with the CPI data are larger than the SID errors and are partly because the air- 9 1 Here, a =6.723780×10 , a =2.078×10 C. 1 2 −1 ﬂow velocity was lower though the CPI (5 m s ) than it was For freezing on SD2 (Fig. 4b) the range in temperature −1 through the SID (10 m s ) and also because the sample vol- for the data was unfortunately not as large as for the AD1 ume of the CPI is smaller than SID due to probe dead-time. sample. If we look at the enlarged plot (Fig. 4b(ii)), we can see that the trend is for increasing IASSD with decreasing 4.2 Determination of ice-active germ density vs. T temperature. It should be noted that experiments were performed at The CPI data was used to infer the IASSD, n (T ), as a func- warmer temperatures (−1.5<T ≤8.5 C) than this (experi- tion of temperature in the manner described in Sect. 3.3. Fig- ments IN04 01, 02, 03, 04 and 05) and non of them yielded ure 4 shows the results of this analysis for these experiments. any ice to within the detection limits of the experiment (see For freezing on AD1 (Fig. 4a) we can see that the IASSD Table 3). A polynomial ﬁt to the data for SD2 is shown by the is negligible for temperatures warmer than −18 C and in- grey dashed line and when ﬁtted to Eq. (12) for T >−26.8 C 10 1 creases only gradually to temperatures of −27 C. Note that yields a =4.315221×10 , a =2.503×10 C. Note that the 1 2 experiments IN05 45, 46, 47, 48 and 49 were performed for ﬁtted curve is zero for T >−25.03 C unlike the data, which temperatures warmer than this (−12.5 C) and yielded no ice shows small, but ﬁnite values for n warmer than −25 C. For freezing on ATD (Fig. 4c) we noted that there was no freezing at temperatures warmer than −18 C to within The error bars assume Poisson counting errors at 5 and 95% conﬁdence. detection limits (this was also conﬁrmed by experiments Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ P. J. Connolly et al.: Freezing on dust 21 P. J. Connolly et al.: Freezing on dust 2813 IN04 103 and IN02 104 at temperatures of −12 C). At tem- CPI−SID intercomparison x 10 peratures colder than this there was a gradual increase in IN04_19 the IASSD. For the same temperatures, the freezing mode on ATD showed the highest IASSD compared to the other two desert dusts. A polynomial ﬁt to the data for ATD is shown by the grey dashed line and when ﬁtted to Eq. (12) for ◦ 9 1 T >−27 C yields a =2.019153×10 , a =1.515×10 C. 1 2 No heterogeneous freezing was observed on ATD for ex- IN04_18 periments that started at temperatures colder than −24 C and this was probably due to the fact that heterogeneous de- position became very efﬁcient at temperatures colder than −25 C, as evident in experiments IN02 72, 73, 75, 77, 78, 6 80 and 81 (see Table 4). This creates a large vapour sink to the particles and impedes liquid drop formation. Figure 4d shows results at two different temperatures for IN04_30 deposition nucleation on ATD – see Sect. 3.3.2. The lines IN04_31 with triangles show experiments at −33 C and lines with 0 2 4 6 8 10 12 14 16 pluses show experiments at −25 C. Experiment 81 reached −2 11 n (# m ) x 10 s,cpi a lower supersaturation (s =0.16) with respect to ice than experiment 80 (s =0.21) and yet shows a higher IASSD Fig. 3. This shows an inter-comparison of calculated IASSD between CPI and SID for the available Fig. 3. This shows an inter-comparison of calculated IASSD be- 11 −2 11 −2 experiments. Error bars are 5 and 95 conﬁdence limits for a Poisson distribution. It can be seen (0.5×10 m against 0.37×10 m ). Both values are Fig. 13. This shows an inter-comparison of calculated IASSD be- that the errors associated with the CPI data are higher than the SID. This is mainly because a lower tween CPI and SID for the available experiments. Error bars are 5 air velocity was used to calculate the errors in counting with the CPI. Also, there is in general a within the Poisson uncertainty at the 90% level and we can- tween CPItendenc and y forSID the CPI tofor undercount theiceacrystals vailable relative toe thexp SIDeriments. probe. It is not clearError whether thisbars are 5 and 95 conﬁdence limits for a Poisson distribution. It can be seen is a problem with SID or the CPI but it should be kept in mind when considering the results. not say if there are pre-nucleation effects occurring between that the errors associated with the CPI data are higher than the SID. and 95 conﬁdence limits for a Poisson distribution. It can be seen IN02 80 and IN02 81. This is mainly because a lower air velocity was used to calculate the that the errors associated with the CPI data are higher than the SID. For the heterogeneous deposition experiments in Fig. 4d errors in counting with the CPI. Also, there is in general a tendency This is mainly because a lower air velocity was used to calculate the for the CPI to undercount ice crystals relative to the SID probe. It is the dependence of IASSD on ice supersaturation is consistent not clear whether this is a problem with SID or the CPI but it should with the analysis at Ci temperatures by Mohler ¨ et al. (2006). errors in counting with the CPI. Also, there is in general a tendency be kept in mind when considering the results. for the CPI to undercount ice crystals relative to the SID probe. It is 4.3 Testing the parameterization not clear whether this is a problem with SID or the CPI but it should be kept in mind when considering the results. served by Bailey and Hallett (2004) in experiments at −20 C The IASSD determined in the previous section (see Fig. 4a– (see Fig. 5, right panel). c) were quality controlled using the ACPIM model in a pre- It can be seen that there is reasonable agreement between dictive mode as described in Sect. 3.4. the modelled ice concentration and that observed with the Our aim was to test the parameterizations for experiments −3 −3 CPI (0.1 cm and 0.25 cm , respectively). The starting observed at both extremes of the curves for n in Fig. 4 – i.e. total water concentration has to be increased in this simu- experiments near the onset of ice formation and examples at lation relative to that measured so that the simulated appear- the low temperature end of the parameterization. We have ance of drops was in accord with the observations from the done this by visually comparing the concentration timeseries CPI. from the model and data. Note that toward the end of all ex- The IN04 10 experiment started at −19 C and during periments the measured ice concentration decreases whereas the experiment the temperature was reduced to −26 C (see the modelled value stays constant. The reasons for this are Fig. 6a, left panel). Liquid drops formed at about t=80 s (see (1) fall out of the largest crystals to the chamber ﬂoor as they the WELAS plot – Fig. 6f) and no signiﬁcant freezing was grow to large sizes; and at the very end (2) sublimation of observed with either the CPI (Fig. 6b) or SID (Fig. 6c) until some ice crystals to sizes not observable by the instruments. about t=130 s. The ice crystal habits observed in this exper- iment were similar to side planes, overlapping parallel plates 4.3.1 ATD and possibly bare spearheads observed by Bailey and Hallett (2004) at −20 and −30 C. Firstly we shall evaluate the ATD n against T curve (Fig. 4c) It appears that in Fig. 6b and c the model over-predicts the by looking at experiments IN02 86 and IN04 10. IN02 86 concentration of ice crystals initially, but the concentrations started at −10.8 C and during the experiment the temper- agree at the end of the IN04 10 experiment. Also evident in ature was reduced to −17.9 C (see Fig. 5a). Liquid drops formed at about t=140 s following which some of them The cause of this is a systematic error (i.e. offset) in the instru- froze. The ice crystal habits observed with the CPI in this ment that measures total water. The implications for the quality of experiment were similar to the overlapping parallel plates ob- the simulation are insigniﬁcant. www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 −2 n (# m ) s,sid 22 P. J. Connolly et al.: Freezing on dust 2814 P. J. Connolly et al.: Freezing on dust AD1 freezing mode SD2 freezing mode IN04_21 IN04_20 Enlarged IN04_19 −27 −30 −30 IN04_18 IN05_58 IN05_58 IN04_30 −26 IN04_30 IN04_51 IN04_31 IN04_31 −25 −25 IN04_07 IN04_06 IN05_59 −25 IN04_07 IN04_55 IN04_06 IN04_52 IN05_59 −24 −20 −20 0 1 2 3 −2 11 IN04_56 n (# m ) x 10 2 2 Curve fit: 6.723780e+009(T+2.078e+001) , T< −2.078e+001 Curve fit: 4.315211e+010(T+2.503e+001) , T< −2.503e+001 −15 −15 0, T≥ −2.078e+001 0, T≥ −2.503e+001 Deriv fit: 1.344756e+010(T+2.078e+001), T< −2.078e+001 Deriv fit: 8.630422e+010(T+2.503e+001), T< −2.503e+001 0, T≥ −2.078e+001 0, T≥ −2.503e+001 −10 −10 0 5 10 15 0 5 10 15 −2 −2 11 11 n (# m ) n (# m ) x 10 x 10 s s (a) Freezing on AD1 (b) Freezing on SD2 ATD deposition mode ATD freezing mode 1.3 1.28 −30 IN02_73 1.26 IN02_79 IN04_09 Simple fit: 0.3/2 × (24.0−T), T< −24.0 0, T≥ −24.0 IN02_72 IN04_10 1.24 −25 1.22 IN02_80 −20 IN02_83 1.2 IN02_84 IN02_85 IN02_87 IN02_86 1.18 Curve fit: 2.019153e+009(T+1.515e+001) , T< −1.515e+001 −15 0, T≥ −1.515e+001 Deriv fit: 4.038305e+009(T+1.515e+001), T< −1.515e+001 1.16 IN02_81 0, T≥ −1.515e+001 IN02_103 1.14 −10 0 5 10 15 0 1 2 3 4 5 6 −2 11 −2 11 n (# m ) n (# m ) x 10 x 10 s s (c) Freezing on ATD (d) Deposition on ATD Fig. 4. This shows results from the ice nucleation experiments in the AIDA. (a) shows the curve of IASSD between 0 C and the temperature Fig. 14. This shows results from the ice nucleation experiments Fig. 4. This shows results from the ice nucleation experiments in the AIDA. (a) shows the curve of on the y-axis for AD1; in all graphs, error bars assume 5 and 95 conﬁdence intervals of the Poisson distribution based on the ice concentration IASSD between 0C and the temperature on the y-axis for AD1; in all graphs, error ◦ bars assume 5 in the AIDA. (a) shows the curve of IASSD between 0 C and the from the CPI. The gray dashed line shows a robust ﬁt to the data and equations for the curves and their derivatives wrt. T are shown for and 95 conﬁdence intervals of the Poisson distribution based on the ice concentration from the CPI. the freezing experiments. (b) (i) shows the same for IASSD between 0 C and the temperature on the y-axis for SD2, while (b) (ii) is an The gray dashed line shows a robust ﬁt to the data and equations for the curves and their derivatives temperature on the y-axis for AD1; ◦ in all graphs, error bars assume enlargement of this. (c) shows the same for IASSD between 0 C and the temperature on the y-axis for ATD. For this experiment the ﬁt did wrt T are shown for the freezing experiments. b(i) shows the same for IASSD between 0C and not yield good agreement with the data since there was a large gap in measurements between −18 and −25 C. A simple visual ﬁt (shown by the temperature on the y-axis for SD2, while b(ii) is an enlargement of this. (c) shows the same 5 and 95 conﬁdence intervals of the Poisson distribution based on the black dashed line) yielded a good comparison with the experiments. (d) shows the IASSD between 0 and RH on the y-axis for ATD in ice for IASSD between 0C and the temperature on the y-axis for ATD. For this experiment the ﬁt did experiments below water saturated conditions (i.e. nucleation due to heterogeneous deposition). the ice concentration from the CPI. The gray dashed line shows not yield good agreement with the data since there was a large gap in measurements between -18 and -25C. A simple visual ﬁt (shown by the black dashed line) yielded a good comparison with the a robust ﬁt to the data and equations for the curves and their deriva- experiments. (d) shows the IASSD between 0 and RH on the y-axis for ATD in experiments ice below water saturated conditions (i.e. nucleation due to heterogeneous deposition). tives wrt. T are shown for the freezing experiments. (b)(i) shows Fig. 6e is the fact that the modelled supersaturation with re- ◦ quickly: there are no drops after t=130 s in the model, but in the same for IASSD between 0 C and the temperature on the y- spect to ice is too low when compared to the water vapour the observations they last until t=220 s. TDL measurement after t=150 s, which also suggests prob- axis for SD2, while (b)(ii) is an enlargement of this. (c) shows the The reason for this poor agreement seems to be due to lems with the prediction of the ice crystal concentration. This the fact that there is missing data in the freezing curve pa- same for IASSD between 0 C and the temperature on the y-axis for has the effect of evaporating the liquid drops in the model too rameterisation in the temperature regime −20 to −25 C (see ATD. For this experiment the ﬁt did not yield good agreement with the data since there was a large gap in measurements between −18 Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ and −25 C. A simple visual ﬁt (shown by the black dashed line) yielded a good comparison with the experiments. (d) shows the IASSD between 0 and RH on the y-axis for ATD in experiments ice below water saturated conditions (i.e. nucleation due to heteroge- neous deposition). T (°C) T (°C) RH T (°C) ice T (°C) P. J. Connolly et al.: Freezing on dust 23 P. J. Connolly et al.: Freezing on dust 2815 Fig. 5. Experiment IN02 86 showing freezing on ATD at −16 C. (a) shows the measured (black line) and modelled air temperature (thick black dashed line); (b) shows the CPI measured total concentration (grey dotted line) and ice (black solid line), the modelled liquid and Fig. 15. Experiment IN02 86 showing freezing on ATD at −16 C. ice concentrations are shown by the thicker dotted grey and dashed black lines, respectively; (c) shows the measured total water content (a) shows the measured (black line) and modelled air temperature converted to an equivalent saturation ratio wrt. ice (black dashed line) and saturation wrt. liquid (grey dashed line), the thicker dotted line is the modelled RH (no TDL measurements were available for this experiment). Ice crystal images observed are shown on the right. (thick black dashed line); (b) shows the CPI measured total con- centration (grey dotted line) and ice (black solid line), the modelled Fig. 4c). If we use a different freezing curve that also ﬁts There is very good agreement between the modelled ice liquid and ice concentrations are shown by the thicker dotted grey the data well, but has a lower IASSD at −24 C, we are able concentration and the observed CPI concentration with both −3 to get betterand agreement. dashed Thisblack curve is lines, shown byrespecti the black vely; showing (c) around shows 2 cmthe ofmeasured ice crystals near total the end of the dashed line in Fig. 4c and is given by Eq. (13) experiment (t=300 s). For this simulation, the total water water content converted to an equivalent saturation ratio wrt. ice ( content had to be slightly adjusted in the model from that 0.3×10 1 1 ◦ × (2.4 × 10 − T ), T < −2.4 × 10 C 2 measured so that liquid water appeared at the correct time. n (T ) = (black dashed line) and saturation (13) wrt. liquid (grey dashed line), s,ATD 1 ◦ 0, T ≥ −2.4 × 10 C This can be seen by the offset between the modelled RH and the thicker dotted line is the modelled RH (no TDL measurements the measured RH at t=40 s (see Fig. 8c). The total concen- Figure 7 shows the result of using the above equation in- tration measured from the WELAS OPC agrees reasonably were available for this experiment). Ice crystal images observed are stead of the ﬁtted polynomial in Sect. 4. We see that there is well with the concentration of drops at the start of liquid drop much better agreement with the ice concentration, drop con- shown on the right. formation (see Fig. 8d). centration and RH. Experiment IN04 18 started at −20 C and during the ex- periment the temperature was reduced to −30 C (see Fig. 9a, 4.3.2 AD1 left panel). Liquid drops formed at about t=140 s (see the We shall now evaluate the AD1 curve WELAS plot – Fig. 9f) and freezing was observed to com- by looking at experiments IN05 51 and mence just after t=150 s as was evident from the CPI and IN04 18, since these experiments were performed at 2 SID time series (Fig. 9b and c). The crystals in this ex- quite different temperatures (see Fig. 4a). IN05 51 started periment were small and it is almost impossible to tell what at −17.5 C and during the experiment the temperature was they are from the CPI imagery (Fig. 9, right panel); but they reduced to −27.5 C (see Fig. 8a). Liquid drops formed at are likely to be overlapping parallel plates like observed in about t=40 s following which there was a small amount of IN05 51. freezing. The ice crystal habits observed in this experiment The starting total water content had to be adjusted slightly were quite similar to those observed on ATD during experi- in the simulations from the observed value in order that liq- ment IN04 10; that is similar to the side planes, overlapping uid water in the model appeared at the same time as that ob- parallel plates and possible bare spear heads observed by served with the WELAS probe (see Fig. 9f). However, in ◦ ◦ Bailey and Hallett (2004) at −20 C and −30 C (see Fig. 8, right panel). The cause of this is a systematic error (i.e. offset) in the instru- ment that measures total water. The implications for the quality of the simulation are insigniﬁcant. www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 24 P. J. Connolly et al.: Freezing on dust 2816 P. J. Connolly et al.: Freezing on dust Fig. 6. Experiment IN04 10 showing ice nucleation on ATD at −24 C. (a) shows the measured (black line) and modelled air temperature (thick dashed line); (b) shows the CPI measured total concentration (grey dotted line) and ice (black solid line), the modelled liquid and Fig. 16. Experiment IN04 10 showing ice nucleation on ATD at ice concentrations are shown by the thicker dotted grey and dashed black lines, respectively; (c) shows the SID concentrations: grey dotted line is total liquid, ◦ black solid line is ice cloud and the modelled liquid and ice concentrations are shown by the thicker dotted grey and −24 C. (a) shows the measured (black line) and modelled air tem- dashed black lines, respectively. (d) shows the individual counts of particle size from the SID probe and over laid concentration contours from the CPI. (e) shows the measured saturation ratio and total water content converted to an equivalent saturation ratio: solid black line perature (thick dashed line); (b) shows the CPI measured total con- is the saturation ratio wrt. ice, grey solid line wrt. liquid, while the black dashed line is the total water content saturation ratio wrt. ice and the grey dashed line wrt. liquid. The modelled saturation ratio wrt. liquid is shown by the thicker black dotted line. (f) shows the WELAS centration (grey dotted line) and ice (black solid line), the modelled concentration: black solid line is total concentration (aerosol+cloud), and grey dashed line is the cloud concentration. The modelled liquid and ice concentration are shown by the thicker grey dotted and black dashed lines, respectively. Ice crystal images observed are shown on liquid and ice concentrations are shown by the thicker dotted grey the right. and dashed black lines, respectively; (c) shows the SID concentra- tions: grey dotted line is total liquid, black solid line is ice cloud ◦ ◦ comparison with other experiments the starting RH was low at −20 C and −30 C, but there were only a small amount of in this experiment and is the reason why the ice crystals do crystals in total (see Fig. 10, right panel). and the modelled liquid and ice concentrations are shown by the not grow to be so large. Figure 9d shows individual size in- There is very good agreement between the modelled ice ferredthick from the erSID dotted probe with gre theyPSD and contours dashed from theblack lines, respectively. (d) shows concentration and the observed CPI concentration with both CPI overlaid; these too show good agreement. In this exper- −3 showing around 0.1 cm of ice crystals near the end of the the individual counts of particle size from the SID probe and over iment we have good agreement for the concentration of ice experiment (t=300 s). However, near the start of the exper- and the times at which liquid appears and evaporates. This iment, just after liquid drops form at t=50 s, the SID probe laid concentration contours from the CPI. (e) shows the measured suggests that the parameterized curve that was ﬁtted (Fig. 4a) observes low concentrations of small ice crystals. The reason describes the data quite well. saturation ratio and total water content converted to an equivalent these crystals are not nucleated in the model is because the value of n in the polynomial ﬁt is zero in this temperature 4.3.3 SD2 saturation ratio: solid black line is the saturation ratio wrt. ice, grey regime; however, the data does show low values of IASSD 11 −2 of about 0.1×10 m (see Fig. 4b). For predictions of ice We shall now evaluate the SD2 curve by looking at experi- solid line wrt. liquid, while the black dashed line is the total water number concentration in this temperature regime on SD2, ments IN05 58 and IN04 31, since these experiments were 11 −2 a value of n =0.1×10 m could be used instead of the content saturation ratio wrt. ice and the grey dashed line wrt. liquid. performed at 2 different temperatures within the range of ob- curve. servations (see Fig. 4b). IN04 31 started at −17 C and dur- The modelled saturation ratio wrt. liquid is shown by the thicker ing the experiment the temperature was reduced to −26 C For this simulation, the total water content had to be (see Fig. 10a). Liquid drops formed at about t=50 s follow- slightly adjusted in the model from that measured so that black dotted line. (f) shows the WELAS concentration: black solid ing which there was a very small amount of freezing. The ice liquid water appeared at the correct time. This can be seen crystal habits observed in this experiment were quite similar by the offset between the modelled RH and the measured line is total concentration (aerosol+cloud), and grey dashed line is to those observed on ATD during experiment IN04 10; that is RH at t=40 s (see Fig. 10e). The total concentration mea- the cloud concentration. The modelled liquid and ice concentra- similar to the side planes, overlapping parallel plates and pos- sured from the WELAS OPC agrees reasonably well with the sible bare spear heads observed by Bailey and Hallett (2004) concentration of drops at the start of liquid drop formation tion are shown by the thicker grey dotted and black dashed lines, Atmos. respecti Chem. Phv ys.,ely 9, 2805– . Ice 2824crystal , 2009 images observed are sho wwwwn .atmos-chem-ph on theys.right. net/9/2805/2009/ P. J. Connolly et al.: Freezing on dust 25 P. J. Connolly et al.: Freezing on dust 2817 IN04_10 HetIN_ATD 2003−09−17 17:30:00.0 P2 @ 60% −15 a) AIDA core T (° C) gas gas −20 model −25 −30 10 CPI−total −3 b) CPI (cm ) CPI−ice Model−water Model−ice 3 0 10 10 SID−spheres −3 c) SID (cm ) SID−ice Model−water Model−ice 0 2 10 10 d) SID Raw (µ m) 2 10 s−wrt ice e) TDL s−wrt liquid 1.5 Total−wrt ice 1 Total−wrt liquid Model s−wrt liquid 0.5 10 WELAS−total −3 f) WELAS (cm ) WELAS−drops 10 Model−water Model−ice 10 −100 0 100 200 300 400 500 600 700 800 900 1000 Time [ s ] Fig. 7. Experiment IN04 10 showing ice nucleation on ATD using a better ﬁt. Plot captions are as for Fig. 6 (see Fig. 10f) andFig the. sizes 17. Experiment of individual particles IN04from 10 the showing in theice regime nucleation where −24o.n 4>T ATD >−25using .8 C, n should be set 11 −2 SID probe agree well with the PSD contours from the CPI to a constant (0.1×10 m ). a better ﬁt. Plot captions are as for Fig. 16 (Fig. 10d shows these sizes with the contours of the CPI PSD 4.4 Characterization of SD2 and ATD composition overlaid in black). Experiment IN05 58 started at −17.5 C and during the It is clear that the three dusts exhibit different nucleation ef- experiment the temperature was reduced to −27 C (see ﬁciencies at the 90% certainty level, as noted by the Poisson Fig. 11a, left panel). Liquid drops formed at about t=40 s uncertainties in Fig. 15a–c. The purpose of this analysis was (see the WELAS plot – Fig. 11d) and freezing was observed to see if any large differences could be attributed to the ele- to commence just after t=150 s as was evident from the CPI mental composition of the dust samples. time series (Fig. 11b). The crystals in this experiment had An analysis of the elemental composition of Saharan min- the appearance of overlapping parallel plates, and bare spear eral dusts similar to those used here has been presented previ- heads, consistent with ice crystal habits observed by Bailey ously (Linke et al., 2006). This analysis was provided by X- and Hallett (2004) at −20 and −30 C (see Fig. 11, right Ray Fluorescence Analysis (XRF, Bruker AXS, SRS 303AS) panel). for bulk samples preheated to 1000 C and for particle sizes The starting total water content had to be adjusted slightly D <20 μm. Here we will focus brieﬂy on speciﬁc aspects in the simulations from the observed value in order that liq- of a further morphological and elemental composition anal- uid water in the model appeared at the same time as that ob- ysis conducted on samples of SD2 and ATD using an envi- served with the WELAS probe (see Fig. 11c and d). The total ronmental scanning electron microscope (ESEM) – Phillips cloud concentration measured with the WELAS OPC shows XL30 ESEM-FG – which was used to isolate and image in- good agreement with the modelled drop concentration also. dividual dust particles. Target images were then compared In this experiment we have good agreement for the concen- with spectra collected using the ESEM associated energy dis- tration of ice and the times at which liquid appears and evap- persive X-ray (EDX) analysis system. Dust samples were orates. This suggests that the parameterized curve that was mounted onto a standard aluminium stub following dispersal ﬁtted (Fig. 4c) describes the data reasonably well; however, onto double sided carbon ﬁlm. Excess dust was blown or vi- brated off the ﬁlm. ESEM images were then taken of an area The cause of this is a systematic error (i.e. offset) in the instru- of the stub where an even and almost complete coverage by ment that measures total water. The implications for the quality of dust particles was observed. the simulation are insigniﬁcant. www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 −3 T [ ° C ] s Conc [ cm ] gas −3 −3 Size [ µ m ] Conc [ cm ] Conc [ cm ] 26 P. J. Connolly et al.: Freezing on dust P. J. Connolly et al.: Freezing on dust 27 2818 P. J. Connolly et al.: Freezing on dust Fig. 8. Experiment IN05 51 showing freezing on AD1 at −22 C. (a) shows the measured (black line) and modelled air temperature (thick dashed line); (b) shows the CPI measured total concentration (grey dotted line) and ice (black solid line), the modelled liquid and ice Fig. 18. Experiment IN05 51 showing freezing on AD1 at −22 C. concentrations are shown by the thicker dotted grey and dashed black lines, respectively; (c) shows the measured saturation ratio and total water content converted to an equivalent saturation ratio: solid black line is the saturation ratio wrt. ice, grey solid line wrt. liquid, while the (a) shows the measured (black line) and modelled air temperature black dashed line is the total water content saturation ratio wrt. ice and the grey dashed line wrt. liquid. The modelled saturation ratio wrt. liquid is shown by the thicker black dotted line. (d) shows the WELAS concentration: black solid line is total concentration (aerosol+cloud). (thick dashed line); (b) shows the CPI measured total concentration The modelled liquid and ice concentration are shown by the thicker grey dotted and black dashed lines, respectively. Ice crystal images observed are shown on the right. (grey dotted line) and ice (black solid line), the modelled liquid and ice concentrations are shown by the thicker dotted grey and dashed black lines, respectively; (c) shows the measured saturation ratio and total water content converted to an equivalent saturation ratio: solid black line is the saturation ratio wrt. ice, grey solid line wrt. liquid, while the black dashed line is the total water content saturation ratio wrt. ice and the grey dashed line wrt. liquid. The modelled saturation ratio wrt. liquid is shown by the thicker black dotted line. (d) shows the WELAS concentration: black solid line is total concentration (aerosol+cloud). The modelled liquid and ice concentration are shown by the thicker grey dotted and black dashed lines, respectively. Ice crystal images observed are shown on the right. Fig. 9. Experiment IN04 18 showing ice nucleation on AD1 at −26 C. Plot captions are as for Fig. 6. Fig. 19. Experiment IN04 18 showing ice nucleation on AD1 at −26 C. Plot captions are as for Fig. 16. Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ 28 P. J. Connolly et al.: Freezing on dust P. J. Connolly et al.: Freezing on dust 29 P. J. Connolly et al.: Freezing on dust 2819 Fig. 10. Experiment IN04 31 showing ice nucleation on SD2 at −25 C. Plot captions are as for Fig. 6. Fig. 110. Experiment IN04 31 showing ice nucleation on SD2 at −25 C. Plot captions are as for Fig. 16. Fig. 11. Experiment IN05 58 showing freezing on SD2 at −26 C. Plot captions are as for Fig. 8. Fig. 111. Experiment IN05 58 showing freezing on SD2 at −26 C. Plot captions are as for Fig. 18. Figure 12 shows an ESEM image of a typical ATD sam- dominantly Si. This was typical of the composition of many ple . The particles are characterized by relatively uniform of the larger (D >1 μm) particles observed. However, the smooth faceted ensembles with strong fracture lines possi- morphology of the ATD could occasionally be highly var- bly the result of mechanical deformation. Full frame EDX ied presenting both smooth faceted, e.g. the target particle analysis of this image conﬁrmed the composition to be pre- labelled “c” in Fig. 12, as well as granular or “shocked”-like appearances (target particle labelled “l”). Particles marked Reference: ATD0801 “a”, “e” and “l” (selected as being representative of particle www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 30 P. J. Connolly et al.: Freezing on dust 2820 P. J. Connolly et al.: Freezing on dust Table 5. Atomic elemental percentages as determined by EDX spot P. J. Connolly et al.: Freezing on dust 31 analysis of particles “a” to “l” in Fig. 12a. SAMPLE ATD Target Atomic % Element mean σ C 71.65 13.95 O 19.94 11.61 Mg 0.42 0.74 Al 0.66 0.50 Si 5.98 3.28 S 0.07 0.06 K 1.10 1.61 Ca 0.85 1.64 Fe 0.23 0.51 Fig Fig ..12. 112. ATD ATD ESEM ESEM image image (Sample (Sample ATD0811) ATD0811) showing showing both gr both an- granular (or shocked, e.g. “l”) and smooth faceted morphologies. ular (or shocked, e.g. “l”) and smooth faceted morphologies. Par- Particles labelled “a” to “l” represent selected locations for EDX ticles labelled “a” to “l” represent selected locations for EDX spot spot elemental analysis (scale 2 μm). elemental analysis (scale 2 μm). Table 6. Mean elemental atomic % composition of SD2 samples SD8030 and SD8032 based on multiple target EDX spot analyses. σ is the standard deviation of the sample. SAMPLE SD2 Target Atomic % Element mean σ C 78.44 11.00 O 17.35 9.58 Mg 0.11 0.10 Al 0.73 0.65 (a) Optical microscope image for scale equal to (b) Optical microscope image for scale equal to Cl 0.04 0.06 120μm 30μm Si 1.99 0.94 S 0.03 0.03 P 0.01 0.01 Ni 0.03 0.04 K 0.08 0.14 (c) ESEM image for (d) ESEM image for (e) ESEM image for (f) ESEM image for Ca 1.13 1.42 scale equal to 50μm scale equal to 20μm scale equal to 5μm scale equal to 2μm Fig. 13. Optical and scanning electron microscope images of SD2. Fe 0.07 0.08 Fig. 13. Optical and scanning electron microscope images of SD2. Fig. 113. Optical and scanning electron microscope images of SD2. seen by Bailey and Hallett (2004) at -25 C, which were mostly plates and plate-like poly- sizes in the range 1<D <2 μm) in Fig. 12 revealed signiﬁ- crystals including overlapping parallel plates, side-planes, and spear heads. in fact they were 4.5 Other interesting experiments cant Ca loadings compared to the large particles. The reason actually a combination of needle-like crystals, T shaped crystals and perhaps rosette-like for this is unclear. Table 5 shows the elemental summary of 475 habits. Some aggregation was observed and could have been enhanced due to interlocking Experiments IN02 77, 78, 80 and 81 were experiments on the EDX analysis by atomic percentage of the main elements. of the crystal shapes. The crystals observed during IN02 77 and 78 are shown in Figure 14a ATD where deposition nucleation was the mode of ice for- Figure 13a and b are optical microscope images taken and b respectively. mation at T =−25 C (see Table 4). These experiments had of the raw dust samples, showing the slightly rounded ap- To the authors’ knowledge, this is the ﬁrst time crystals of this habit have been observed no liquid water present throughout the run and yielded very pearance of the primary “sand” granules, much larger than to form at -25 C. Some of these crystals have appearances of sheaths, needles and rosettes different ice crystal habits to those observed in the freez- would have been passed by the chamber pre-ﬁlter system. ◦ ◦ 480 that Bailey and Hallett (2004) observed at temperatures of -40, -50, -60 and -70 C. ing experiments and other deposition experiments at −33 C These large particles are loosely coated with aggregates of The largest crystals in these sets of experiments were observed in experiments IN02 77 (IN02 72, IN02 73). much smaller granular particles some of which have been and IN02 78 and smaller, but similar examples of these crystals were observed in exper- The ice crystal habits observed during these experiments dislodged from the surface in the image. Figure 13c–f shows iments IN02 80 and 81. More work is needed to test the exact range of conditions that were not consistent with those seen by Bailey and Hal- the corresponding ESEM images at increasing magniﬁca- produce these interesting crystals at -25 C. They seem to be formed by deposition nucle- lett (2004) at −25 C, which were mostly plates and plate- tions highlighting the sub 2 μm and coarse mode distribu- 485 ation on ATD at temperatures of around -25 C. like poly-crystals including overlapping parallel plates, side- tions. EDX for SD2 is summarized in Table 6. planes, and spear heads. in fact they were actually a combi- nation of needle-like crystals, T shaped crystals and perhaps Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ 32 P. J. Connolly et al.: Freezing on dust Table 6. Mean elemental atomic % composition of SD2 samples SD8030 and SD8032 based on multiple target EDX spot analyses. σ is the standard deviation of the sample SAMPLE SD2 Target Atomic % Element mean σ C 78.44 11.00 O 17.35 9.58 Mg 0.11 0.10 Al 0.73 0.65 Cl 0.04 0.06 Si 1.99 0.94 S 0.03 0.03 P 0.01 0.01 Ni 0.03 0.04 K 0.08 0.14 Ca 1.13 1.42 Fe 0.07 0.08 P. J. Connolly et al.: Freezing on dust 2821 P. J. Connolly et al.: Freezing on dust 33 (a) (b) Fig. 14. Ice crystal habits observed at −25 C for deposition nucleation on ATD. (a) shows experiment IN02 77 and (b) shows experiment Fig. 14. Ice crystal habits observed at -25C for deposition nucleation on ATD. (a) shows experiment IN02 78. Fig. 114. Ice crystal habits observed at −25 C for deposition nu- IN02 77 and (b) shows experiment IN02 78. cleation on ATD. (a) shows experiment IN02 77 and (b) shows ex- periment IN02 78. range of conditions that produce these interesting crystals at 5 Discussion −25 C. They seem to be formed by deposition nucleation on ATD at temperatures of around −25 C. The polynomial curves (see Section 4) for describing the nucleation efﬁciency of AD1, 5 Discussion ATD and SD2 may be used as parameterisations for ice formation rates within atmospheric models in the freezing mode. However it should The polynomial be noted that curvesfor (seethe Sect. SD2 4) for experiments describing the nu- cleation efﬁciency of AD1, ATD and SD2 may be used as 490 the range of observations with respect to temperature is quite small and therefore does not parameterisations for ice formation rates within atmospheric models in the freezing mode. However it should be noted that for the SD2 experiments the range of observations with respect to temperature is quite small and therefore does not show the variability of IASSD with temperature. Figure 15 shows a summary of all three curves, with the ﬁtted polyno- mial which may also be used for simulations of ice formation in clouds. However, the differences between the different samples are signiﬁcant; as noted from the “error” bars. Sassen et al. (2003) noted an Ac cloud in the Florida region during CRYSTAL-FACE that was glaciated at temperatures Fig. 115. This ﬁgure shows all of the ﬁts for the three different dusts Fig. 15. This ﬁgure shows all of the ﬁts for the three different dusts between −5 and −8 C. This observation was coincident with a large amount of dust being advected by long range transport into the Florida region from the Sahara desert. The observation does not agree with the freezing parameteriza- rosette-like habits. Some aggregation was observed and tion in Fig. 4b, which showed that the IASSD was negligible could have been enhanced due to interlocking of the crys- in this temperature regime. Aircraft measurements with a tal shapes. The crystals observed during IN02 77 and 78 are continuous-ﬂow diffusion chamber (CFDC) showed IN con- shown in Fig. 14a and b, respectively. centrations to be very large within the dust layer at heights To the authors’ knowledge, this is the ﬁrst time crystals corresponding to between −5 and −8 C (see DeMott et al., of this habit have been observed to form at −25 C. Some 2003); however, it should be noted that in this case the pro- of these crystals have appearances of sheaths, needles and cessing conditions of the IN chamber were much colder than rosettes that Bailey and Hallett (2004) observed at tempera- the ambient conditions (about −36.5 C). tures of −40, −50, −60 and −70 C. In addition to this there is also the possibility that the dust The largest crystals in these sets of experiments were ob- aerosols become more efﬁcient as IN as they undergo pro- served in experiments IN02 77 and IN02 78 and smaller, but cessing in the atmosphere when they are blown across the At- similar examples of these crystals were observed in experi- lantic Ocean. Ansmann et al. (2008) have hinted that a pos- ments IN02 80 and 81. More work is needed to test the exact sible reason for the discrepancy between their measurements www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 2822 P. J. Connolly et al.: Freezing on dust and the results of DeMott et al. (2003); Sassen et al. (2003) very strong deposition mode that was observed at tempera- was that tures colder than −24 C. We could not quantify this over a wide range of conditions. A polynomial ﬁtted to the IASSD “when the desert dust was advected over the ocean for the freezing mode needed adjustment in the regime where it could have been mixed with maritime particles ◦ ◦ there were few observations (−20 C to −26 C) to get good and may have been inﬂuenced by anthropogenic agreement with between modelled and experimental data. pollution”. For AD1, we ﬁnd that freezing nucleation is negligible (less than 1%) at temperatures warmer than −20 C, while for It has been shown by Krueger et al. (2004) that certain Ca ◦ ◦ temperatures between −29.5 C and −33 C the IASSD in- containing compounds such as calcite and dolomite may re- creases, doubling over the temperature range of 3.5 C. Some act with nitric acid in the atmosphere to form nitrate salts; activity in the deposition ice nucleation mode was noted for hence potentially modifying the chemical and physical prop- temperatures colder than −16 C, this was not observable at erties of the dust. However, one might expect that the nitrate ◦ ◦ −12 C and was not observed at −26 C; however, this was salts would reduce the IN activity. Another process that may typically very low (less than 1%). be important to increasing the IN activity is chemical aging For SD2 we found an increase in freezing efﬁciency be- due to oxidation of the mineral surface by ozone. More work ◦ ◦ tween −24 C and −27 C. No freezing was observed for is needed to understand the impacts of such chemical aging temperatures warmer than −24 C at least detectable to in- processes on the dusts ability to act as an IN. strumental accuracy. Other possible reasons for this could be that the dust sam- The results from this paper are supported by a recent li- ple we collected is not representative of all Sahara dust. In- dar study by Ansmann et al. (2008) that freezing on Sa- deed the large particles in the sample are sifted out before hara dust is not efﬁcient for T >−20 C. However, numerous introduction into the AIDA chamber in our experiments and observations suggest there is little doubt that there are pro- there have been suggestions that large particles may have cesses that result in ice particle formation at warmer temper- a higher IASSD, as noted by the size dependent nucleation atures in many cloud types (Hobbs and Rangno, 1985, 1990). rates measured by Archuleta et al. (2005) for aluminium ox- Whether this is due to contact nucleation or some other, more ide (Al O ), alumina-silicate (3Al O :2SiO ), and iron ox- 2 3 2 3 2 efﬁcient freezing IN that are abundant in the atmosphere is a ide (Fe O ) particles. However, the EDX analysis for the 2 3 question that needs further research to answer. the SD2 sample is in reasonable agreement with the values This study has brought up several questions that need to measured from aircraft samples (see McConnell et al., 2008; be addressed in order to reconcile ice crystal concentrations Krueger et al., 2004; Formenti et al., 2003) in terms of the in atmospheric models. Al:Si (0.37), Mg:Si (0.06) and Ca:Si (0.57) ratios. The main difference between the ATD sample and the SD2 sample was 1. If the Sahara dust sample we collected is representative the Si content with ATD having around 4 times more Si by of the Sahara dust observed in the Florida clouds, then mol. what was responsible for the glaciation of the Ac ob- served by Sassen et al. (2003)? 6 Conclusions 2. To what extent does atmospheric processing or coatings by other material affect the freezing efﬁciency of these This has been a study of ice nucleation by three different nuclei? ◦ ◦ dust samples in the temperature regime 0 C to −33 C. It 3. Can the largest coarse mode aerosols explain the glacia- was found that at temperatures warmer than −12 C, freez- tion of the Ac observed by Sassen et al. (2003)? ing on AD1, SD2 and ATD dusts was below our instrument detection threshold – which typically equates to less than An additional question that we ﬁnd intriguing is what 0.01% of dust particles being active as IN. All three sam- caused the appearance of thin columnar ice habits at −25 C ples showed increasing freezing efﬁciency with decreasing in the ATD deposition experiments? And are these habits temperature. Deposition nucleation was negligible for tem- observed in the atmosphere under any conditions? peratures warmer than −12.5 C (not shown). In the experi- ments shown here all of the dust particles in the AIDA acted as CCN, leaving no interstitial dust particles that could act Appendix A as deposition nuclei. In the atmosphere however, it is rea- sonable to assume that this would happen and signiﬁcant de- Equations and description of parcel model position nucleation could take place before the formation of liquid drops. The ACPIM code is a bin microphysical code including For ATD, we noted that freezing never took place at tem- aerosol thermodynamics following Topping et al. (2005a,b). peratures warmer than −12.5 C and increased by an order Solid inclusions within the solution can be taken into account of magnitude at temperatures of −27 C. ATD also had a such as dust particles. The model includes descriptions of the Atmos. Chem. Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/ P. J. Connolly et al.: Freezing on dust 2823 important liquid and ice phase microphysical processes; acti- pressure over an ice surface, m is the mass of the j th ice ice,j vation of drops; ice nucleation; aggregation, coalescence and bin and C is the capacitance factor. The derivatives are in- riming. For this study we have neglected collisions and co- tegrated using the DLSODAR numerical integrator available alescences between the different hydrometeor species since from netlib. this was of negligible importance for the experiments. In the AIDA the chamber wall is an additional source of In a closed parcel, the total water content remains a con- heat and moisture to the air inside. The reduction in pres- stant and can be partially converted between water vapour, sure causes the air temperature to initially fall almost adia- liquid or ice. The temperature of the air is calculated by con- batically, but the chamber wall temperature stays relatively sideration of the 1st law of thermodynamics for a closed par- constant. There is therefore a heat ﬂux into the gas from the cel: chamber wall, which increases as the temperature difference between the wall and the gas increases. The fact that the wall dT R dP L dr L dr P m v v f i resides at a warmer temperature than the gas means the frost = − + (A1) dt P dt T dt T dt c pm layer on the wall will tend to dry, acting as a vapour source to the gas inside the chamber. where R is the gas constant for moist air, L is the latent m v We could attempt to model these complexities, but that heat of vapourisation, L is the latent heat of fusion, c is f pm is not the focus of this paper. Instead we have chosen to the speciﬁc heat of moist, cloudy air, r is the vapour mixing use measured T , P and total water mixing ratio to drive ratio and r is the ice mixing ratio (actually the rate of change the ACPIM model. The time series of the measured T , P due to an internal phase change). and total water mixing ratio, r were used to calculate time Also, the total water content within the parcel remains con- derivatives by ﬁtting parabolas to the data over 10 s worth of stant: data and differentiating this function analytically. This re- dr dr dr v l i moves instrumental noise from the data, which would other- + + = 0 (A2) wise cause problems with the numerical ordinary differential dt dt dt equation (ODE) solver. These derivatives are used directly where r is the water vapour mixing ratio. The time deriva- for the calculation of T and P in the model rather than using tives for r and r are calculated from the drop growth equa- l i the above equation. The absolute starting value of the total tions for different size bins (for r , see Pruppacher and Klett, water measurement was adjusted by a small amount so that 1997) and the ice growth and nucleation equations for the in the model, liquid water condensed at the same time as in different size bins (for r , see Pruppacher and Klett, 1997). the observations. For total water, r , the above equation is modiﬁed to take dD 4D M e e j w eq = − (A3a) the additional ﬂux in to account: dt D Rρ T T j j p,j dr dr dr dr v l i t,meas + + = (A5) 2L dm v j T = T + (A3b) dt dt dt dt p,j 4πD k dt where r is the measured total water. In the model this is t,meas dm dD dm π dD j j j j achieved by adjusting the water vapour derivative, r so that = ≈ ρ D (A3c) dt dt dD 2 dt the above equation is satisﬁed. where the subscript j refers to a size bin, D is the particle Acknowledgements. Skillful support by the AIDA team is grate- fully acknowledged. We thank L. Schutz ¨ from the University size, M is the molecular mass of water, R is the gas con- Mainz, Germany, for providing the AD1 sample, and Khaled stant, ρ is the density of the solution, e is the water vapour Megahed for collecting the SD2 sample. We would like to pressure, e is the equilibrium vapour pressure (calculated eq acknowledge funding from Atmospheric Composition Change using Kohler theory, with parameters supplied by a thermo- the European NeTwork of excellence (ACCENT). The CPI was dynamic model), T is the air temperature and T is the tem- provided through the University Facility of Atmospheric Measure- perature of the particle. The equations above are solved iter- ment (UFAM) infrastructure and the SID probe is the property atively using Broydens method. of the University of Hertfordshire. The ﬁrst author would like to A simpler equation is used for the growth rate of ice parti- acknowledge J. Hallett and M. Bailey for interesting discussions cles by vapour deposition, following the electrostatic analogy on ice crystal habit growth. Additional support from the NERC (see Pruppacher and Klett, 1997, page 547): APPRAISE-CLOUDS consortium is gratefully acknowledged (grant reference number NE/E01125X/1). dm 4πC s ice,j j v,i = (A4) L L M dt RT s s w Edited by: D. Cziczo + − 1 ∗ ∗ e (T )D M k T RT sat,i where L is the latent heat of sublimation, s is the supersat- s v,i uration with respect to ice, e (T ) is the saturation vapour sat,i www.atmos-chem-phys.net/9/2805/2009/ Atmos. Chem. Phys., 9, 2805–2824, 2009 2824 P. J. Connolly et al.: Freezing on dust References Marcolli, C., Gedamke, S., Peter, T. and Zobrist, B.: Efﬁciency of immersion mode ice nucleation on surrogates of mineral dust, Ansmann, A., Tesche, M., Althausen, D., Muller ¨ , D., Seifert, P., Atmos. Chem. 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Phys., 9, 2805–2824, 2009 www.atmos-chem-phys.net/9/2805/2009/

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Studies of heterogeneous freezing by three different desert dust samples

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DOI: 10.5194/acp-9-2805-2009
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Studies of heterogeneous freezing by three different desert dust samples

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Studies of heterogeneous freezing by three different desert dust samples

Studies of heterogeneous freezing by three different desert dust samples

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