Aerosol-Cloud Interactions - A Directed Programme to Reduce Uncertainty in Forcing
(ACID-PRUF) through a Targeted Laboratory and Modelling Programme.
Case for Support; Part 1: University of Manchester (UMan) Track Record
Formerly the Atmospheric Physics Group in the UMIST Physics Department, the Centre for
Atmospheric Sciences, CAS, in the School of Earth, Atmospheric & Environmental Sciences at
UMan comprises 12 members of academic staff, 30 PDRAs and 23 PG students. CAS currently
holds over £9M in NERC grants (2006-present) with recently refurbished laboratories. CAS hosts 6
members of National Centre for Atmospheric Sciences (NCAS) staff, three are Fellows supported by
NCAS Composition Directorate, providing strategic input into UK aerosol science; two, Drs Alfarra
and Topping, will work on the ACID-PRUF Programme as will two FGAM Instrument Scientists, Drs
Dorsey and Williams. CAS has many years experience in the field of cloud physics and chemistry.
Over the last decade it has recently expanded its capabilities in aerosol composition measurements
and modelling, contributing significantly to gas-aerosol and aerosol-cloud interaction and aerosol
transformation process understanding using laboratory, field and modelling studies.
PI Gordon McFiggans (GBM) has interests in gas-aerosol and aerosol-cloud interactions from a
field, laboratory and modelling perspective. He has 76 peer-reviewed publications (including an
overview of impacts of aerosol properties on cloud droplet activation2), 9 in-press or in review, 9
current NERC or EU grants, 6 as main PI including 2 consortia (incl. the APPRAISE-funded Aerosol
Coupling in the Earth System (ACES) chamber, field, process & regional modelling programme).
Tom Choularton (TWC): Prof Choularton has over 35 years of research experience and is author of
over 160 papers in the peer reviewed literature. In 2008 he was awarded the Mason Gold medal for
his work on mixed phase clouds and precipitation. He currently leads the APPRAISE consortium
concerning the influence of aerosol on the glaciation of supercooled layer and convective clouds.
Paul Connolly (PJC): Lecturer in Atmospheric Science, with interests in aerosol cloud interactions,
convective cloud processes and ice microphysics; experienced in working with cloud chambers, and
cloud imaging probes, publishing a detailed paper on their calibration and one describing nucleation
of ice by natural dust. Has worked extensively with the main beneficiaries, UKMO, on: i) EPSRCfunded improvement of the UKMO large eddy model (LEM); ii) assessing whether aerosol-cloud
interactions should be included in NWP models iii) developing parameterisations of ice nucleation
from laboratory data. Has 25 peer reviewed publications and 5 in press. PI for the SIMPLEX project
improving understanding of ice crystal growth rates in clouds and creating broad public interest with
publications in New Scientist, Science Magazine and features in several BBC documentaries
M. Rami Alfarra (MRA): NCAS Fellow in aerosol chamber studies: interests in aerosol chemical
composition and physical properties, formation and transformation of their organic components as a
function of location and photochemical age. Previously at Paul Scherrer Institute, Switzerland, he
designed and implemented a field, chamber and controlled emission study programme to
characterise and quantify primary and secondary organic aerosol (SOA) sources. He investigated
atmospheric relevance of smog chamber generated SOA and linkage between SOA chemical
composition, hygroscopicity and volatility, receiving the 2008 Atmospheric Chemistry and Physics
Award from the Swiss Academy of Sciences. Participated in 21 major field and smog chamber
campaigns and has 41 peer-reviewed publications. MRA will direct the chamber experiments in WP1.
David O. Topping (DOT): NCAS Fellow in aerosol modelling, determining and quantifying processes
dictating aerosol transformation, assessing methods for process representation in models. He
constructed the widely used diameter dependent thermodynamic equilibrium model for mixed
inorganic / organic aerosols (ADDEM), used to predict the water uptake and CCN behaviour in WP2.
PI on 2 NERC grants: i) improving laboratory datasets for aerosol property prediction models, ii)
developing an informatics package for property prediction. 15 peer-reviewed publications.
Paul Williams (PIW) and James Dorsey (JD): FGAM Instrument Scientists, responsible for NCAS'
aerosol and cloud microphysical equipment respectively with extensive experience of maintaining
and operating the NCAS instrumentation in the laboratory, ground based field experiments and on
the FAAM BAe146 aircraft, and of designing tools to analyse and visualise data. The instruments
used in the chamber experiments are either shared with field experiments or are identical duplicates.
The groups of Hugh Coe (HC) and Martin Gallagher (MWG) are vastly experienced in field
measurements and analysis of aerosol composition and physical characterisation and of cloud
microphysics respectively and will supply instrumental support for the chamber work in ACID-PRUF.
CAS are involved in international field campaigns measuring aerosols and clouds in mixed phase and
ice conditions (e.g. CLACE, ACTIVE and EMERALD campaigns24, Allen et al, 2008). They have
shown through modelling and observations conducted in deep tropical convective clouds that there
may be a sizeable effect of aerosols on the strength of deep convective cloudse.g.33. The group has
strong collaboration with project partners, the KIT (Moehler and co-workers), with whom PJC has
worked extensively to derive ice nucleation parameterisations that allow derivation of accurate ice
formation rates on mineral particles26,28. They have worked to reduce uncertainties associated with
measuring ice particles in a variety of clouds27 and developed the MICC cloud chamber facility at
UMan29 (http://data.cas.manchester.ac.uk/micc/micc.htm). The group demonstrated the importance
of the HM process in deep convective clouds33,34 and recently showed from field measurements
secondary ice formation by the HM process to occur on riming snow particles more efficiently than
previously thought32. PDRA Dearden, under PJC’s supervision, developed a consistent model
framework for comparing and improving bin and bulk cloud microphysics parameterisations30.
CAS has been at the forefront of Aerosol Mass Spectrometer (AMS) developments since its
introductione.g.19,20,21, conducting online aerosol measurements in numerous field, laboratory and
chamber experiments since 2001. Since the ACE Asia project in Cheju, Korea in 20018,14, the group
has characterised online and offline aerosol composition in cloud processing experiments, expanding
this to reconcile composition and sub-saturated water uptake in many field studies (TORCH11,6;
NAMBLEX7; TORCH210; RHaMBLe15). The group have developed and built two Hygroscopicity
Tandem Differential Mobility Analysers (HTDMAs) and developed widely used state-of-the-science
inversion algorithms4,16. Since 2005, the group have been involved in investigations aiming to
reconcile sub- and supersaturated water uptake (by commercial CCN counter) in chambers3,11 (Good
et al, 2010a, Hamilton et al., 2010) and various field environments (Good et al, 2010b,c (RHaMBLe,
MAP); Irwin et al., 2010a,b (COPS, OP3)). The group hosts the Manchester photochemical aerosol
chamber and coordinates aerosol chamber modelling in the EU FP7 EUROCHAMP2 programme.
1. McFiggans, G., M. R. Alfarra, et al., Faraday Discussions, 130, 341, 2005, 2. McFiggans, G. et al., The Effect of Physical and Chemical
Aerosol Properties on Warm Cloud Droplet Activation, Atmos. Chem. Phys., 6, 2593, 2006, 3. Duplissy, J., M. Gysel, M. R. Alfarra, …, G.
McFiggans et al., Cloud forming potential of secondary organic aerosol under near atmospheric conditions. Geophys. Res. Letts, 35,
L03818, doi:10.1029/2007GL031075, 2008, 4. Swietlicki, E., …, G. McFiggans, et al., Hygroscopic Properties of Sub-Micrometer
Atmospheric Aerosol Particles Measured with H-TDMA Instruments in Various Environments - A Review, Tellus 1B, BACCI Special Issue
2, TeB-07-11-0072, 2008, 5. Topping, D. O., G. B. McFiggans et al., Surface tensions of multi-component mixed inorganic/organic aqueous
systems of atmospheric significance: Measurements, model predictions and importance for cloud activation predictions, Atmos. Chem.
Phys., 7, 2371, 2007, 6. Cubison, M. J., M. R. Alfarra, …, H. Coe, G. McFiggans, …, P. I. Williams, Q. Zhang, et al., The characterisation of
pollution aerosol in a changing photochemical environment, Atmos. Chem. Phys., 6, 5573-5588, 2006, 7. Coe, H., …, M. R. Alfarra, …, G.
McFiggans, D. O. Topping, P. I. Williams, et al., Chemical and Physical Characteristics of Aerosol Particles at a remote Coastal location,
Mace Head, Ireland, during NAMBLEX, Atmos. Chem. Phys., 6, 3289-3301, 2006, 8. Decesari, S., …, G. McFiggans et al., The watersoluble organic component of size-segregated aerosol, cloud water and wet depositions from Jeju Island during ACE-Asia, Atmos.
Environ., 39, 211, 2005, 9. Hanford, K. L., …, J. P. Reid, …, D. O. Topping G. B. McFiggans et al., Comparative Thermodynamic Studies of
Aqueous Glutaric Acid, Ammonium Sulphate and Sodium Chloride Aerosol at High Humidity, J. Phys. Chem., 112, 9413, 2008, 10. Gysel,
M., …D. O. Topping, …, P. I. Williams, …, G. McFiggans, and H. Coe, Closure study between chemical composition and hygroscopic
growth of aerosol particles during TORCH2, Atmos. Chem. Phys., 7, 6131-6144, 2007, 11. Meyer, N. K., …, Alfarra, M. R., ..., McFiggans,
G. et al., Analysis of the hygroscopic and volatile properties of ammonium sulphate seeded and un-seeded SOA particles, Atmos. Chem.
Phys. Discuss., 8, 8629, 2008, 12. Rissman, T. A., …, D. O. Topping, G. McFiggans et al., Cloud condensation nucleus (CCN) behavior of
organic aerosol particles generated by atomization of water and methanol solutions, Atmos. Chem. Phys., 7, 2949, 2007, 13. Wex, H. F.
Stratmann, D. Topping, G. McFiggans, The Kelvin versus the Raoult term in the Köhler equation, J. Atmos. Sci., 2008, 14. Topping, D. O.,
H. Coe, G. McFiggans, …, M. R. Alfarra, …, T. W. Choularton, et al., Aerosol chemical characteristics from sampling conducted on the
island of Jeju, Korea, during ACE Asia, Atmos. Env., 38, 2111-2123, 2004, 15. Allan, J. D., D. O. Topping, …, P. I. Williams, H. Coe, … and
G. McFiggans, Composition and properties of atmospheric particles in the eastern Atlantic and impacts on gas phase uptake rates, Atmos.
Chem. Phys., 9, 9299-9314, 2009, 16. Gysel, M., G. B. McFiggans & H. Coe, Inversion of Tandem Differential Mobility Analyser (TDMA)
Measurements, J. Aerosol Sci., 40, 134-151, 2009, 17. Alfarra, M. R. et al., A Mass Spectrometric Study Of Secondary Organic Aerosols
Formed From The Photooxidation Of Anthropogenic And Biogenic Precursors In A Reaction Chamber. Atmos. Chem. Phys., 6, 5279, 2006.
18. Dommen, J., …, M. R. Alfarra et al., Laboratory observation of oligomers in the aerosol from isoprene/NOx photooxidation. Geophys.
Res. Letts., 33, L13805, doi:10.1029/2006GL026523, 2006. 19. M. R. Alfarra et al.,. Characterisation of Urban and Rural Organic
Particulate In the Lower Fraser Valley Using Two Aerodyne Aerosol Mass Spectrometers. Atmospheric Environment 38(34): 5745, 2004.
20. Jimenez, J..., H. Coe, …, M. R. Alfarra et al., Evolution of Organic Aerosols in the Atmosphere, Science, 326, 1525-1529, 2009. 21.
Zhang, Q., J. …, M. R. Alfarra et al. Ubiquity and Dominance of Oxygenated Species in Organic Aerosols in Anthropogenically Influenced
Northern Hemisphere Mid-latitudes. Geophys. Res. Lett., 34, L13801, doi:10.1029/2007GL029979, 2007. 22. Baltensperger, U., .., M. R.
Alfarra et al., Secondary organic aerosols from anthropogenic and biogenic precursors, Faraday Disc., 130, 265, 2005. 23. Gross, D. S.,
…, M. R. Alfarra et al., Real-Time Measurement of Oligomeric Species in Secondary Organic Aerosol with the Aerosol Time-of-Flight Mass
Spectrometer, Anal. Chem., DOI: 10.1021/ac0601381, 2006. 24. Choularton, T. W., …, Alfarra, M. R. et al. The influence of small aerosol
particles on the properties of water and ice clouds. Faraday Disc., 137, 205, 2008. 25. Field, P. R., O. Mohler, P. J. Connolly, M. Kramer, R.
J. Cotton, A. J. Heymsfield, H. Saathoff, and M. Schnaiter, 2006: Some ice nucleation characteristics of asian and saharan desert dust.
Atmos. Chem. Phys., 6, 2991–3006. 26. Moehler, O, P. J. Connolly, et al., 2006: Efficiency of the deposition mode ice nucleation on
mineral dust particles. Atmos. Chem. Phys., 6, 3007–3021. 27. Connolly, P. J., …, Z. Ulanowski, …, M. W. Gallagher, and T. W.
Choularton, 2007c: Calibration of the cloud particle imager probes using calibration beads and ice crystal analogs: The depth-of-field. J.
Atmos. Ocean. Technol., 24, 1860–1879. 28. Connolly, P. J., O. Mohler, P. R. Field, H. Saathoff, R. Burgess, M. W. Gallagher, and T. W.
Choularton, 2009: Studies of ice nucleation on three different types of dust particle. Atmos. Chem. Phys., 9, 2805–2824. 29. Kaye, P. H. E.
Hirst, R. S. Greenaway, Z. Ulanowski, E. Hesse, P. J. DeMott, C. P. R. Saunders, P. J. Connolly, 2008: Classifying atmospheric ice
crystals by spatial light scattering, Optics letters, 33, 1545-1547. 30. Dearden, C., P. J. Connolly, T.W. Choularton and P. R. Field,
Evaluating the effects of microphysical complexity in idealized simulations of trade wind cumulus using the factorial method, 2010, ACPD,
10, 23497-23537. 31. Dearden, C. Investigating the simulation of cloud microphysical processes in numerical models using a 1-d
dynamicsal framework, Atmos Sci Lett, 2009, 10, 207-214. 32. Crosier, J. K. N. Bower, T. W. Choularton, C. D. Westbrook, P. J. Connolly,
Z. Q. Cui, I. P. Crawford, G. L. Capes, H. Coe, J. R. Dorsey, P. I. Williams, A. J. Illingworth, M. W. Gallagher, and A. M. Blyth, Observations
of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus, 2010, ACPD, 10, 19381-19427. 33. Connolly,
P. J., T. W. Choularton, M. W. Gallagher, K. N. Bower, M. J. Flynn, and J. Whiteway, 2007a: Cloud resolving simulations of intense
tropical, hector thunderstorms: Implications for aerosol cloud interactions. Quart. J. Roy. Meteor. Soc., 132, 3079-3106. 34. Connolly, P. J.,
A. J. Heymsfield, and T. W. Choularton, 2007b: Modelling the influence of rimer surface temperature on the glaciation of intense
thunderstorms: The rime-splinter mechanism of ice multiplication. Quart. J. Roy. Meteor. Soc., 132, 3059–3077.
ACID-PRUF Case for Support; Part 2: The Science Case
Executive Summary: Aerosol particles act as sites for cloud droplet and ice particle formation.
Cloud properties can be perturbed through the addition of aerosol particles into the atmosphere from
anthropogenic and natural processes. This addition influences cloud microphysical properties, and
subsequently affects cloud dynamics and thermodynamics, and the way the cloud interacts with
radiation. The Earth’s radiation budget is very greatly affected by clouds, and human-induced
changes to the particle loading affecting them, known as indirect effects, are large and highly
uncertain. A large part of this uncertainty is the result of poor knowledge of the fundamental aerosol
and cloud properties and processes, leading to their poor representation in models. A programme of
research is proposed here to i) directly investigate these processes in the laboratory, ii) evaluate the
sensitivity of climate relevant parameters to the studied processes, iii) interpret the laboratory studies
with detailed model investigations and iv) to incorporate and test new descriptions of the studied
processes in cloud-scale and, where possible, global scale models. The programme will thereby
reduce the uncertainty in estimates of radiative forcing and climate feedbacks relating to aerosol and
cloud processes. The studies are split into those affecting warm clouds (those containing only liquid
droplets) and those affecting clouds containing ice particles. The programme brings together an
interdisciplinary team of researchers with expertise in “warm” and “cold” cloud and aerosol processes
combining laboratory and multiscale modelling activities to deliver the improved predictive capability.
Rationale & Motivation: Current estimates of indirect aerosol forcings show the largest
uncertainty among anthropogenic climate perturbations (Forster et al., 2007) with scientific
understanding of the aerosol-cloud interactions and cloud albedo radiative forcing rated as “low”.
Additional fast feedbacks not included in the stringent IPCC forcing definition, such as cloud
dynamics and lifetime effects, introduce further significant uncertainty (e.g. Lohmann and Feichter,
2005). In the absence of global observations of sufficient coverage and accuracy, global aerosol
models are the prime tool for their assessment. The influence of pollution on warm clouds, that is
clouds containing only liquid water, are the largest source of attributable uncertainty in radiative
forcing estimates in global models and uncertainties surrounding ice and mixed phase clouds are so
large that no estimates of the uncertainty in their radiative forcing have been made. Clouds may
either have a cooling effect by reflecting short wave radiation or warming effect by trapping long wave
radiation. The sign of the cloud radiative forcing is strongly dependent on the number, size, phase
and shape of cloud particles. Various processes occurring within the cloud determine these
properties and it is recognised that our best descriptions of some of these processes, are not well
constrained. Significant fundamental research into aerosol and cloud properties and processes is
necessary in order to reduce systematic errors in climate models. The proposed work will conduct
such investigations into processes that demonstrably have the potential to reduce uncertainty in
climate models and assist in the interpretation of the results of major field studies in a coupled
laboratory and modelling programme.
The number of cloud droplets in warm clouds is determined by the ambient dynamical and
environmental conditions as well as the number of “seeds” or cloud condensation nuclei (CCN); the
number and growth rate of growing droplets in the cloud determining the supersaturation. Whilst
there are a wide number of aerosol properties that can potentially influence their ability to form cloud
droplets (e.g. McFiggans et al., 2006), it has been known for many years that the size of the subset
of the aerosol particles acting as CCN is largely dominant. Feingold et al. (2003, 2009), Ervens et al.
(2005) and Reutter et al. (2009) have investigated the sensitivity of cloud droplet number to various
aerosol parameters. The largest potential influence of aerosol properties is the poorly understood
uptake rate of water onto growing droplets though substantial unexplained discrepancies have been
found when reconciling water uptake in laboratory and field studies (e.g. Good et al., 2010a,b; Irwin
et al., 2010). There is emerging and convincing evidence of real physical and atmospherically
important reasons for the discrepancies, such as particle semi-volatility (Topping et al. (2010) or
kinetic limitations to the approach to equilibrium behaviour of highly viscous particles (Zobrist et al.,
2008; Murray, 2008), rather than instrumental artefacts being responsible.
Ice formation rate in clouds is poorly quantified and in some cases poorly understood. Most
laboratory studies of ice formation investigate the role of particles as Ice Nuclei (IN), exposing them
to changes in temperature and humidity to represent vertical ascent and then observing when a
given aerosol type allows ice to form, (e.g., Mohler et al., 2006; Connolly et al., 2009). Slightly
supercooled clouds are generally assumed to contain very few active ice nuclei and hence little ice
would be expected to form (Meyers et al., 1992). Microphysical schemes used in Cloud Resolving
Models (CRMs) and Single Column Models (SCMs) predict major differences in cloud microphysical
properties for slightly (>-15°C) supercooled marine stratocumulus clouds (Klein et al., 2009).
Fit to Programme Requirements & Requirement for a Consortium Approach: A
series of cross-linked tasks are proposed to directly address the specified programme requirements.
The laboratory tasks have been targeted at those properties and processes at the core of the
uncertainties in aerosol and cloud impacts on climate. Coupled with proposed model sensitivity
analyses, cloud resolving model simulations and large-scale model improvements, the programme
will directly lead to improved quantification of, and reduction in, the uncertainties in estimates of
radiative forcing and climate feedbacks relating to aerosol and cloud processes. The consortium
draws on the extensive existing UK capacity built in part through the NERC APPRAISE Research
Programme. The various proposed studies could not be carried out by a standard grant and a
consortium is required. The system under investigation is fundamentally complex and tightly coupled
across the boundaries of disciplines conventionally regarded as distinct. ACID-PRUF brings together
partners of appropriate expertise from within the UK community but from widely different disciplines
and experience along with International partners of acknowledged expertise.
Objectives: With the stated overarching aim of the Aerosols and Clouds Programme being to
reduce the uncertainty in estimates of radiative forcing and climate feedbacks relating to
aerosol and cloud processes, the programme has at its core the primary interest in quantification of
i) indirect effects of aerosols on the radiative forcing due to their interaction with clouds and the
underlying cloud/aerosol processes; and
ii) ice particle processes in deep frontal, convective clouds and cirrus clouds.
Specific objectives to address this goal are to quantify in the laboratory
1) the accommodation coefficient of water vapour on liquid droplets,
2) the impacts of semi-volatile components on aerosol water uptake,
3) the impacts of aerosol phase on water uptake,
4) the propensity for mineral particles to act as heterogeneous ice nuclei,
5) the impacts of aerosol phase on their IN behaviour,
6) kinetic limitations to water uptake by ice crystals,
7) the impacts of ice crystal roughness,
8) the impacts of ice multiplication processes on ice crystal number,
Each of these property and process measurements will be interpreted directly by a model of
appropriate complexity. Those processes that can be reasonably included in cloud parcel, cloud
resolving and larger scale models will be directly constrained by the laboratory measurements and
used to predict the impacts of the process quantification improvements on cloud properties and
radiative forcing under a range of conditions. Sensitivity of climate important parameters to the
improvement in the laboratory quantification of the investigated parameter will be evaluated
systematically using a model emulator. Specific objectives that draw the laboratory studies through to
impact evaluation are therefore:
9) prediction of the impacts of the laboratory investigated parameters on cloud properties (e.g. cloud
droplet number) at cloud-resolving and global scales,
10) prediction of the impacts of the laboratory investigated parameters on radiative forcing at cloudresolving and global scales,
11) evaluation of the sensitivity of climate-relevant parameters to the process quantification
improvements at all scales,
12) quantification of the reduction in uncertainties in estimates of radiative forcing and climate
feedbacks relating to the investigated aerosol and cloud processes.
Workplan: The work programme focuses on addressing properties and processes that can exhibit
the potential to lead to reduced uncertainties in the climate impacts of aerosols and clouds. This does
not imply that the outputs from all laboratory process investigations will progress all the way to
explicit (or traceable) inclusion in climate models. The extent to which the properties and processes
can be carried through to reduced uncertainties must reflect the maturity and uncertainty of the
processes under investigation, the accessibility of the process to direct laboratory quantification and
the availability of the structural framework in the models. However, each laboratory investigation will
lead to substantial quantitative improvements in process understanding that can lead to a
significantly improved representation at the appropriate scale of model. Each of the activities and its
cross-linkages use this criterion as the primary test for its inclusion. Table 1 lists the processes and
properties under investigation and the tasks in which they are being explored.
Table 1: parameters and processes to be investigated within the ACID-PRUF programme
Parameter
Kinetic limitations to water uptake by liquid droplets
Impacts of semi-volatile aerosol components on water uptake
Impacts of aerosol phase on water uptake
Heterogeneous nucleation of ice by mineral particles
Impacts of aerosol properties on IN behaviour
Kinetic limitations to water uptake by ice crystals
Ice crystal roughness
Ice multiplication processes
Work packages
WP1 ST1, WP3, WP4, WP5
WP1 ST2 & ST3, WP3, WP5
WP1 ST2 & ST3, WP3, WP5
WP2 ST1, WP3, WP4, WP5
WP2 ST2, WP3, WP4, WP5
WP2 ST3, WP3, WP4, WP5
WP2 ST4, WP3, WP5
WP2 ST5, WP3, WP4, WP5
Principal Partner roles and activities: UMan: Project PI and coordination; Laboratory studies of
aerosol water uptake, chamber operation and aerosol measurements (WP1); ice chamber operation
and cloud microphysics measurements, for mineral dust, glass, ice uptake limitation (WP2), aerosol,
chamber and cloud modelling (WP2 and 3), data interpretation & synthesis. UBris: Water
accommodation coefficient experiment design and measurements (WP1); data interpretation &
synthesis. ULeeds: heterogeneous mineral dust and glassy aerosol ice nucleation labwork (WP2),
CRM and large-scale modelling (WP3 and 4), data interpretation & synthesis UHerts: Ice chamber
measurements, rough ice investigation (WP2); data interpretation & synthesis. UYork: Offline
analysis from chamber experiments (WP1); data interpretation & synthesis. ULeic: Online chamber
measurements (WP1); data interpretation & synthesis. UCam: Offline analysis from chamber
experiments (WP1); heterogeneous ice nucleation labwork (WP2); data interpretation & synthesis.
UOx: Improved large-scale model representations of aerosol-cloud interactions; quantification of
indirect aerosol radiative forcing and quantification of the impact of laboratory studies on uncertainty
reduction. UEx / Met Office: Sub-grid parameterisation development and Pathways to Impact.
Laboratory and Modelling Tools Employed: The laboratory tools to be employed comprise a range
of state-of-the science laboratory infrastructure including two large and extensively instrumented
chambers (the photochemical aerosol chamber and Manchester Ice Cloud Chamber (MICC): online
chamber instrumentation described in UMan and UHerts Justification of Resources), the optical
tweezers (UBris), electrodynamic balance (EDB, UCam) and the cold stage at ULeeds. The models
include the aerosol-cloud and precipitation interaction model (ACPIM), microphysical cloud resolving
model (MCRM) and UK Clouds and Aerosol model coupled to the Hadley Centre dynamics model
(HadGEM-UKCA). All tools are described in their respective work packages.
WP1 Aerosol Interactions with Liquid Water (MRA coordinating)
Contributing Partners: UBris, UMan, ULeeds, UYork, ULeic, UCam
This work package comprises a series of activities aiming to quantify remaining uncertainties related
to the uptake of water to growing liquid cloud droplets and the properties of aerosol particles
determining their ability to form liquid droplets under ambient atmospheric conditions.
ST1 (UBris) Kinetic limitations to water partitioning between condensed and gas phases
Knowledge of the mass (αM) and thermal (αT) accommodation coefficients is required to quantify the
rate of condensational droplet growth around the point of activation and thereafter (Kolb et al., 2010).
αM determines the degree to which the competition for water vapour changes supersaturation and the
number of droplets formed on a given cloud condensation nucleus (CCN) population. Measurements
of αM over the last decade span the range 0.04-1.0 with conflicting temperature dependencies (Kolb
et al., 2010). The influence of the near-surface concentration of ions and surface active organics
under dilute conditions near activation remains unclear. Further, resolving the interplay of surface
and bulk processes is crucial for interpreting kinetic limitations imposed on hygroscopicity
measurements under subsaturated conditions, both for concentrated solutions of organic/inorganic
solutes and amorphous aerosol.
Activity: Condensational growth rates will be measured on dilute solution aerosol at high relative
humidity and between 2 to 100 kPa using the method recently described by Reid and coworkers
(Miles et al., 2010): αM is determined from rates of relaxation to equilibrium size (typically a few
nanometres size change) following small temperature perturbations (typically a few milli-Kelvin) from
measurements on optically tweezed aerosol droplets. Numerical and analytic treatments of the heat
and mass transfer (with I. Riipinen, U. Helsinki) will be compared with measured growth and
evaporation rates, leading to advances in the model treatment of condensational growth and
evaporation. Technique developments will be followed by temperature dependent measurements.
The influence of surface-active organics (ranging in complexity from benchmark to atmospheric
mixed component) and solute concentration on surface uptake will be investigated. Kinetic limitations
imposed on bulk uptake for amorphous and glassy aerosol under subsaturated conditions will be
explored to provide parameterisations for ST2.
Deliverables: i) Measurements of water αM as a function of temperature and surface composition
(eg. surfactants). ii) Improved model descriptions of surface and bulk accommodation for water
adsorption coupled to heat transfer for aerosol at activation and under subsaturated conditions.
These will feed directly into cloud parcel models (by assuming the model parameter that is required
for the models is the actual αM value) and further into CRM and GCM models for sensitivity analyses
of global estimates of indirect aerosol radiative forcing in WP4 ST2.
ST2 (UMan, ULeeds, UBris). Quantify factors affecting particle ensemble water uptake
CCN and IN activities of aerosol particles are frequently determined in the laboratory (and invariably
in the field) by measurements of the water uptake behaviour of particle ensembles under
subsaturated conditions (using Hygroscopicity Tandem Differential Mobility Analyser, HTDMA; see
e.g. Swietlicki et al., 2009) and above water (by CCN counter, CCNc) and ice supersaturation (by
INc). It is not possible to reconcile such measurements in many cases (e.g. Good et al., 2010a,b;
Irwin et al., 2010) in either the laboratory or field. There is evidence of real physical and
atmospherically important reasons for the discrepancies, such as particle semi-volatility (Topping et
al. (2010) or kinetic limitations to the approach to equilibrium behaviour of highly viscous particles
(Zobrist et al., 2008; Murray, 2008), rather than instrumental artefacts being responsible.
First, reconciliation of composition with sub-saturated water uptake has demonstrated that the
inorganic to organic ratio is the primary quantity determining water partitioning (see e.g. McFiggans
et al., 2005), but instrumental evaporation of semi-volatile inorganic components was likely to
influence the ability to predict water uptake (Gysel et al., 2007). There is good reason to believe that
organic semi-volatile material will behave similarly and a substantial fraction of ambient aerosol mass
may comprise semi-volatile organic materials (so-called “SV-OOA”). In addition to the influence of
semi-volatiles, the ability for particles to behave as cloud condensation nuclei (CCN) will also be
influenced by the amount of condensed phase semi-volatile material present. First, the degree of
evaporative equilibration on drying the aerosol prior to measurement will influence the measurement
of CCN behaviour. Second, the presence of gaseous semi-volatile material can lead to substantial
impacts on CCN behaviour through co-condensation during the droplet activation process
(Romakkaniemi et al., 2005a,b; 2009). Potentially even greater impact on the apparent water uptake
behaviour will result from kinetic limitation of the mass transfer of water, influencing water partitioning
in the atmosphere and in the analysis of aerosol using conventional instrumentation. Kinetic limitation
can result from both gaseous “resistance” to uptake (Kolb et al., 2010) or from condensed phase
diffusional resistance in particles of substantial viscosity. Such particles have been postulated to be
present in the atmosphere in the form of glasses (e.g. Zobrist et al., 2008) and it may be reasonably
assumed that gels and rubbers are similarly plausible (Mikhailov et al., 2009), all potentially
introducing kinetic limitations to mass transfer of semi-volatile material such as water vapour.
Virtanen et al. (2010) demonstrated that amorphous solid particles are formed from plant emissions
in chamber experiments and so their occurrence in the atmosphere may be of large significance.
Discrepancies between CCN behaviour of chamber generated SOA attributed to the volatilisation of
hygroscopic semi-volatile components (Asa-Awuku et al., 2009) may alternatively be interpreted as
slow water uptake by a solid less volatile residual or a combination of both effects.
Activity: A measurement programme of water uptake behaviour under subsaturated conditions (by
Hygroscopicity Tandem Differential Mobility Analyser (HTDMA) as f(RH) in deliquescence and
metastable efflorescence branches)) and water uptake behaviour under supersaturated conditions
(by CCNc) of particle ensembles of known composition and concentration will be undertaken.
Instrument residence time in humidification and drying stages will be varied and phase of the
particles will be diagnosed (by difference in Electrostatic Low Pressure Impactor, ELPI and DMPS
determined size - see e.g. Virtanen et al., 2010) for particles comprising a range of components that
may be expected to exhibit limitations to mass transfer according to recent literature (e.g. sucrose,
raffinose, fulvic acid solutions) and for compounds with lower O:C ratios more representative of SOA
(e.g. camphoric and pinonic acid) as well as for polymeric material e.g. PEG, algal biopolymer
material (Fuentes et al., 2010a,b,c)) and their mixtures with inorganic salts. Measurements of the
same particles will explore the “operational” volatility, by thermal denuder as well as those of
solutions of multicomponent mixtures of components of known vapour pressures. The latter
measurements will be referenced to KEMS-determined saturation vapour pressures (and DSC
determined phase state) of components (Booth et al., 2009, 2010a,b) determined in an ongoing
programme. This comparison will allow a resolution of kinetic limitation to evaporation in
thermodenuder and will lead to indirect determination of kinetic parameters and hygroscopicity and
CCN behaviour of components as a function of operationally-defined “effective volatility”. A table of
instrumentation for the ST3 (and WP2) studies is provided in the Appendix.
To complement the ensemble studies, the nucleation rates of crystallisation of micron sized droplets
as a function of RH for the systems investigated in the ensemble studies will be determined using an
existing experimental set up with optical or Raman microscope (Kumar et al., 2010). From the
fraction of droplets which are crystalline, measured as f(RH), the RH for crystallisation on different
time scales and in smaller droplets can be predicted (e.g. citric acid and iodic acid solution droplets
remain non-crystalline to ~1 % RH (Kumar et al., ACPD, 2010)) and any crystalline phase (i.e. a
hydrate or an anhydrous crystal) identified using Raman spectroscopy. In addition, ensemble
measurements of equilibration timescales for the benchmark systems will be compared directly with
single particle equilibration measurements made as part of sub-task 1 by optical tweezers, through
the dedicated support of a PhD student by the UBris.
Deliverables: The activity will provide a database of aerosol physical properties (including phase
state inference and “effective volatility”) and associated water uptake reconciliation for all systems,
directly interpreting the impacts of semi-volatile components or solid particle on CCN behaviour.
These will be directly used to interpret sub- and supersaturated water uptake behaviour of SOA and
their reconciliation in chamber activities ST3. Interpretation of the kinetic limitations to water
partitioning will draw on the single particle measurements made in ST1. The equilibrium diameter
growth factor with varying RH (GFD(RH)) and critical supersaturation (Scrit) will be predicted from
known and measured particle composition assuming instantaneous equilibration with water and all
semi-volatile components. This will be compared with the measurement derived direct description of
the “effective” critical supersaturation as a function of phase state and volatility and the modified Scrit
description incorporated within ACPIM for sensitivity analyses in WP3 ST1, comparing with an
explicit kinetically limited mass transfer of semi-volatile components.
ST3 (UMan, UYork, ULeic, UCam). Quantify factors affecting CCN and IN number by SOA
formation from VOC oxidation
The influence of SOA components on CCN and IN behaviour of aerosol particles is highly uncertain
and a substantial fraction of sub-micron mass is known to be organic (Jimenez et al., 2009). SOA has
considerable potential to influence cloud droplet and ice particle formation (Murray et al., 2010).
Virtanen et al. (2010) recently demonstrated formation of amorphous solid particles from emissions of
plants in chamber experiments suggesting their occurrence in the atmosphere is the rule rather than
exception. This activity will explore the types of precursors that lead to solid particle formation and
the impacts on CCN and IN properties of the particles, characterising particle phase in a similar
manner to the Virtanen et al. study. It is critical to establish whether the hypothesised impacts of
kinetic limitation to water uptake and consequent impacts on water uptake of the simpler aerosol
investigated in previous studies (Zobrist et al., 2008; Murray, 2008) is reproduced in more realistic
anthropogenic and biogenic mixtures. The coupling of the photochemical and cloud chambers will
provide a unique combination to investigate this behaviour.
Activity: Water uptake behaviour of multicomponent particle ensembles generated by VOC
photooxidation under subsaturated conditions (using HTDMA) and above water (by CCNc and
linkage of the Manchester photochemical and cloud chambers) and ice supersaturation (by the
linkage of the photochemical and cloud chambers in Manchester - as described in WP2, ST2) will be
investigated. The particle and gas-phase composition will be characterised in a suite of unseeded
and inorganic seeded chamber experiments using a number of parent VOCs of anthropogenic and
biogenic origin, alone and in mixtures (e.g. "anthro" (toluene, alkane, PAH) and "bio" (isoprene,
mono- and sesqui-terpene)). Molecular identification of the gaseous and particle phase components
is essential to diagnose the degree of partitioning of the components and the degree to which they
equilibrate. The ability to reconcile the water uptake measurements will be related to particle semivolatility and possible kinetic limitation to equilibration by virtue of particle phase state. Introduction of
SOA into the cloud chamber for warm and cold expansions will further allow quantification of the
CCN and IN behaviour in a unique coupled investigation of photo-oxidative aerosol transformation
and subsequent impact on cloud behaviour. The thermodenuder and the comparison of impactor and
mobility size distributions will be employed as in ST2 to infer the “effective volatility” and phase state
of the particles. Any discrepancies in the reconciliation of sub- (HTDMA) and supersaturated (CCN)
water uptake will be evaluated in the context of the particle phase and volatility, informed by ST2
(and hence the single particle measurements made in ST1).
Deliverables: Database of gaseous and aerosol composition from anthropogenic and biogenic
mixtures under a range of representative conditions, physical property (including phase state
inference and “effective volatility”) and associated water uptake reconciliation for all systems. The
equilibrium diameter growth factor with varying RH (GFD(RH)) and critical supersaturation (Scrit) will
be predicted from measured particle composition assuming instantaneous equilibration with water
and all semi-volatile components. This will be compared with the measurement derived direct
description of the “effective” critical supersaturation as a function of phase state and volatility for
anthropogenic and biogenic particles and the modified Scrit description incorporated within ACPIM for
interpretation of warm cloud chamber expansions using the coupled chambers in WP3 ST1.
WP2 Ice Nucleation and Microphysics (PJC coordinating)
Contributing Partners: UMan, ULeeds, UHerts, UCam
There has been widespread interest in the role of mineral dust and recently biological aerosol as ice
nuclei. Both of these particle types have been shown to be efficient ice nuclei (IN) at colder
temperature (Connolly et al 2009, DeMott et al 2010). Studies have demonstrated the ability of some
organic compounds to form a glassy state at low temperatures that can act as IN (Murray et al.,
2010b). Glassy organic aerosol particles have recently been detected at room temperatures
(Virtanen et al., 2010), which could also provide a necessary source of efficient IN at relatively warm
temperatures. Only selected biological particles are currently known to act as IN at temperatures
above -15°C, (DeMott and Prenni, 2010). Secondary ice production (SIP) may occur without any
additional IN. Splinter ejection during riming of ice crystals, known as the Hallett-Mossop (HM, Hallett
and Mossop, 1974) process is one such SIP mechanism and is effective in the temperature range -3
to -8°C, being most active at -5°C). It can lead to the formation of large concentrations of ice at
slightly supercooled temperatures. Several aircraft and modelling studies suggest the HM process
can be responsible for the formation of the majority of the ice in convective clouds (Harris-Hobbs and
Cooper, 1987; Blyth and Latham, 1997; Hogan et al., 2002; Clark et al., 2005; Huang et al.,2008),
although not all (Rangno and Hobbs, 2001). This process needs to be better quantified especially in
conditions where snow crystals rather than graupel particles act as the main rimer (Crosier et al.
2010). Development of convection can be inhibited by temperature inversions. SIP is an important
process to understand since in marginal cases of convection that may not otherwise penetrate
through the temperature inversion, the additional latent heat energy released from ice formation can
provide enough buoyancy to overcome such barriers (Clark et al., 2005). WP 2 addresses areas of
uncertainty relating to ice microphysics, aiming to better quantify the controlling parameters of: the
rates of ice particle formation on natural dust particles; the influence of SOA on ice particle formation;
the rate of uptake of water to growing ice particles and the rate of secondary ice production by
splintering during riming (the HM process) and also to understand the environmental factors
responsible for generating rough ice crystals.
ST1 (UMan, UHerts, ULeeds, UCam). Heterogeneous nucleation of ice by mineral particles.
There is mounting evidence that the singular approximation to ice nucleation (see Vali, 1994), where
ice nuclei become active at well defined environmental conditions, reasonable over a large range of
atmospheric conditions (Vali, 2010, Connolly et al. 2009, Phillips et al. 2008). However, nucleation is
a stochastic process and it may be important to include the time dependence in some situations.
Laboratory experiments described by Murray et al. (2010a) suggest that nucleation by some minerals
should be described by a stochastic rather than singular model. Recent observations in atmosphere
suggest that steady (stochastic) ice production could occur in alto-stratus (Westbrook and Illingworth,
2009). This contrasts with the above chamber experiments that are consistent with the singular
approximation. Whether singular or stochastic approximations best describe nucleation in the
atmosphere is extremely important to the way clouds are parameterised in models, as use of the
singular approximation may result in a vast underestimation of the ability of natural IN to nucleate the
ice phase in clouds and vice-versa. It is only recently that the time and temperature dependence of
nucleation can be studied in isolation using a cold stage (Murray et al., 2010a) and parameterisations
developed using such a cold stages need to be tested for their applicability to the atmosphere using
chamber expansions to rule out possible artefacts. Further our best parameterisations of contact
nucleation rely on experiments in which collision frequency was not determined (Diehl et al. 2006).
Recent developments of an EDB during the APPRAISE activity has provided the capability to revisit
this and make large steps in reducing uncertainty in this microphysical process.
Activity: An experimental programme is proposed to investigate natural mineral dusts from a range
of geographical locations as well as pure minerals, size segregating the material, determining their
mineralogical composition of the dusts using a combination of X-ray diffraction and Raman
microscopy and then testing their ice nucleation ability by: (i) printing water drops containing
dusts/mineral particles onto a hydrophobic surface and cooling using a cold stage; (ii) levitating
supercooled drops in an electrodynamic balance and quantifying the fraction of collisions between
drops and mineral particles that result in freezing by contact nucleation and (iii) testing derived
nucleation rates using dynamical expansions in the MICC while monitoring all standard state
variables or testing nucleation rates using data from the AIDA chamber provided by project partners,
KIT. (i) will involve monitoring the ice formation rate on the cold stage for different rates of cooling or
indeed with the temperature held constant. Analysis of these data will allow us to derive nucleation
rates or activity spectra for each of the dusts investigated. For (ii) monodisperse mineral dust
particles will pass through the EDB in a continuous flow, conditioned to the appropriate temperature
and humidity. The precise size and number of contact nuclei that pass through the EDB and interact
with the droplet will be determined and selected by a scanning mobility particle sizer (TSI 3936). The
collision rate of contact nuclei with the water droplet will be estimated from directly measured SMPS
number concentrations (charge neutralisation minimising the influence of the electric field on contact
nuclei trajectory). Possible deviations in trajectory due to electric fields will be assessed, and
calibrated, via the use of commercially available charged particles. Water droplets and solid ice
particles have distinct elastic scattering signals and this will be used to unambiguously observe the
onset of ice nucleation after collision between mineral dust particle and droplet (Kramer, 1999). This
technique has previously used successfully within the Cambridge laboratory to size aerosols and
record phase changes (Pope et al. 2010). For (iii) we will test nucleation rates derived using the
above against previous experiments done in the AIDA chamber, but a similar set of experiments
lasting a period of two to three weeks will also be conducted using MICC by introducing the same
mineral particle samples as in (i) and (ii) into the chamber at different temperatures and then partially
evacuating the air from the chamber to create a super-cooled water cloud. The ice crystal formation
rate will then be monitored using both a WELAS OPC and the University of Hertfordshire SID probe.
Using the ACPIM model the parameterisation of ice nucleation developed based on the experiments
in Leeds and Cambridge will be tested against the laboratory expansion experiments in both the
AIDA and MICC chambers. This will give greater confidence that results derived from the cold stage
experiments can be applied to the atmosphere and inform whether the stochastic nucleation
approximation is the more general scheme to adopt for cloud parameterisation.
Deliverables: Quantification of, and thereby reduction of the uncertainty in, nucleation rates for
mineral particles; evaluation of mixing rules for parameterisation of nucleation rates of dusts from
pure mineral nucleation rates; evaluation / reduction in uncertainty of contact nucleation by mineral
particles, examined in the sensitivity WP5. ACPIM will be used to interpret the laboratory experiments
and to develop parameterisations of ice nucleation for use in the MCRM model in WP3 ST2 and
large-scale modelling in WP4 ST2. The ability of ice nucleation parameterisations to reproduce ice
number concentration in the MICC will be assessed in ST3.
ST2 (UMan, UHerts, ULeeds). Importance of ultra viscous or glassy solutions for ice
nucleation in photochemically generated SOA.
The freezing behaviour of liquid aqueous solution droplets is thought to be relatively well understood
(Koop et al., 2000), but Murray el al. (2010b) showed that when solution droplets are in a glassy state
rather than being liquid they nucleate ice heterogeneously at substantially lower humidity. In this
study it was shown that heterogeneous nucleation on glassy aerosol substantially modifies cirrus
cloud, reducing the ice number density by an order of magnitude and increasing ice particle size and
fall rates with significant impact on radiative properties. This activity will investigate the impacts of
particle components that more closely approximate the natural aerosol found in the atmosphere than
the simpler proxies previously investigated.
Activity: SOA generated in the photooxidation of “biogenic” and “anthropogenic” mixtures described
in WP1 ST3 will be transferred directly into the MICC chamber, assessing ice-nucleating properties
over a range of temperatures down to -55oC. Humidity will be altered by controlled expansion. Open
path TDL and optical particle counters will be used to measure water vapour concentrations, aerosol
and ice crystal concentration. System hysteresis will be explored through multiple expansions
Deliverables: The ACPIM model will be constrained by size and aerosol property measurements
made from the aerosol chamber and the environmental conditions of MICC providing quantification of
the IN behaviour of multicomponent SOA particles produced under atmospherically representative
photo-oxidation conditions as a function of RH and temperature. Case studies will investigate ice
formation under conditions representative of alto stratus. The ice results from this will feed into the
modelling activities in WP4, making use of the existing model described by Murray et al. (2010).
ST3 (UMan, UHerts, KIT). Kinetic limitations of ice crystal and liquid water growth.
Uncertainty in our knowledge of the mass and thermal accommodation coefficient of growing water
drops and ice crystals severely limits our ability to predict ice crystal number, size, and the withincloud water vapour ice supersaturation. Ice supersaturation can be parameterised in global models
thus demonstrating potential to improve GCM representations. Gierens et al (2003) have shown that
uncertainty in the knowledge of these accommodation coefficients may lead to uncertainty in ice
crystal number concentrations in cirrus clouds by 2 orders of magnitude. Lohmann et al., 2008
showed that varying the accommodation coefficient from 0.006 to 0.5 can lead to a change in cloud
ice particle number by a factor of 14, caused by persistent elevated supersaturation. The reason for
the difference between the two studies is that the sensitivity depends on temperature. Common to
both studies is the conclusion that the sensitivity of ice formation rate to the kinetic limitations of ice
growth is high, hence reduction in the uncertainty in this parameter is required.
Activity: This activity will analyse experiments done in the AIDA chamber using the Aerosol-Clouds
and Precipitation Interactions Model to narrow down the uncertainty in the mass and thermal
accommodation coefficients for ice. We will analyse several campaigns of AIDA data that have used
well characterised ice nucleating substances and that simultaneously measured the resulting ice
number concentration, humidity and total water content, T and P. To do this we will constrain ACPIM
to all measurements except humidity and alter the accommodation coefficients until the modelled and
measured humidity agrees. This will allow us to characterise the accommodation coefficient of water
vapour onto ice. Although the required accuracy in RH may be too high to repeat this analysis to
determine the accommodation coefficients for liquid drops we will perform an analysis, in a similar
way to that for ice, of experiments conducted in AIDA quantifying liquid drop activation to assess if
these experiments are consistent with the mass and thermal accommodation coefficients derived
from WP1 ST1. This will give greater confidence that we can apply the results from WP1 to an
ensemble of cloud particles that are in competition for the available water vapour.
Deliverables: The ACPIM model will be used to explore sensitivity to accommodation coefficients
and quantify reduction in uncertainty over a range of conditions, delivering i) reduced uncertainty in
the accommodation coefficients for liquid and ice and ii) evaluated consistency between mass and
thermal accommodation coefficients derived for liquid water with high temperature activation studies
in the AIDA and the single particle measurements of WP1.
ST4 (UHerts, UMan, IfT, KIT). Ice crystal roughness
Surface roughness can profoundly affect scattering properties of ice such as the asymmetry
parameter and can make ice clouds more reflective (Yang 2008b, Ulanowski 2006). Until recently in
situ observations confirming the presence of rough ice particles in the atmosphere have been indirect
(Shcherbakov 2006, Garrett 2008). However, the Small Ice Detector-3 probe developed at
Hertfordshire (SID-3, Kaye 2008) is sensitive to fine detail such as roughness. Results from the
APPRAISE / CONSTRAIN campaign in 2010 that involved SID-3 indicated that the majority of ice
crystals in both cirrus and mixed phase clouds had strongly rough surfaces (Ulanowski 2010d). This
is estimated to reduce shortwave forcing by as much as 40 W/m2 for tropical cirrus. Evidence from
cloud chambers and the in situ observations indicates that the roughness can arise away from
equilibrium water vapour pressure.
Activities: Ice crystal growth will be studied for a wide range of supersaturations in cloud chambers
(MICC, AIDA, LACIS), electrodynamic balance (KIT), and diffusion chambers (Herts) and
characterised using the SID-3 probe. Rough ice analogue particles will be produced using crystal
growth methods and Focussed Ion Beam (FIB) milling for comparison with ice and for levitated
particle phase function measurements. Roughness will be quantified through speckle analysis
techniques and ice and ice analogue surfaces will be imaged at high-resolution using electron
microscopy.
Deliverables: i) Quantification of ice surface roughness, ii) sensitivity of roughness to prognostic
variables; iii) improved library of single scattering properties of ice particles; sensitivity cases of
radiative forcing to ice roughness; sensitivity of remote sensing retrievals to roughness;
parameterisations linking prognostic variables, through ice roughness, to the scattering asymmetry
parameter; GCM radiative forcing uncertainty reduction; a by-product will be crystal growth rate data.
ST5 (UMan). Quantification of the HM-process in the laboratory
Many studies have shown that the Hallett-Mossop (HM) process (Hallett and Mossop, 1974) is
especially important in the glaciation of cumulus clouds with cloud base temperatures higher than
0ºC (e.g. Connolly et al 2007a,b). In cumulus clouds containing graupel particles the ice
concentrations are seemingly consistent with the HM process (e.g. Hogan et al, 2002). Ice
multiplication by the HM process has been recently observed in ICEPIC and COPS cumulus clouds
and APPRAISE mixed-phase clouds (Crosier et al. 2010, Huang et al. 2008, 2010), but the
necessary conditions required for this process to occur are still poorly understood. While a clear ice
multiplication process was present in the study of Crosier et al it could only be explained by allowing
the HM process to occur on riming snow (not just graupel) and also by allowing all riming drops to
contribute to the necessary conditions for multiplication, where previously these two criteria have not
been considered. The impact it had in the case studied by Crosier et al (2010) was striking and
resulted in an increase in ice number concentration of 3 orders of magnitude over natural IN
concentrations. Saunders and Housseini (2001) have shown that the rates of splinter production due
to the HM process are indeed dependent on the impact velocity between drops and ice particles and
hypothesized an interdependency between drop size and impact velocity where large drops and high
impact velocities resulted in the drops spreading before they freeze (reducing splinter production
rates) and vice versa. An added complication was that low impact velocities resulted is low collection
efficiencies of drops to take part in the riming process. However, following the recent observations,
more work is required in order to investigate this interdependency of droplet size and impact velocity
so that we can have a firm handle on understanding the sources of ice particles in clouds.
Activities: This activity will make use of the MICC. We will provide droplets of known size from a
droplet nebuliser to investigate the interdependence of drop size and their impact velocity on splinter
production rates. We will measure the droplet size distribution during the course of the experiment
using the WELAS OPC and a DMT CDP. The ice crystal number concentration will be measured
during the course of the experiment using both the CPI (SPEC Inc), CAPS depol (DMT) and the SID
(for some experiments). CAS-depol uses single particle depolarisation to unambiguously discriminate
between liquid and ice particles, whereas the SID uses spatial light scattering. A large set of
experiments will be done for different temperatures (within the HM regime), different droplet mean
sizes and different impact velocities (as in the study by Saunders and Housseini, 2001).This will
enable us to determine the splinter production rate as a function of temperature, droplet size
distribution and velocity of the rimer. This will simulate riming by graupel particles (high velocity) and
snow flakes (low velocity). This will give us tighter bounds on splinter production rates by the HM
process in a wide range of conditions. We will apply the model with the best description of the HM
process to date and evaluate its ability to reproduce the measurements. In order to gain mechanistic
understanding of the HM process and how drop size and impact velocity affect it (due to drop
spreading for instance) we will conduct a smaller number of separate experiments using a high
speed video camera (Phantom model xxxx) to observe individual riming events.
Deliverables: mechanistic understanding of the processes through analysis of high speed video of
single particle collisions; confirmation or not whether the HM process occurs on riming snow particles
for all drop sizes; better quantification of the rates of ice crystal production due to changes in drop
size distribution, impact velocity and temperature. Modelling of APPRAISE-CASE where riming snow
was important; Process modelling using ACPIM to improve parameterizations of the HM process and
derive uncertainty before and after – links to WP 5.
WP3 Process and Cloud Resolving Modelling (AMB coordinating)
Contributing Partners: UMan, ULeeds, UHerts, Met Office
This work package will assess the impact that lack of knowledge in heterogeneous ice nucleation
rates, accommodation coefficients and the HM process has at the cloud-scale. In order to do this we
will make use of available field data from several campaigns. The VOCALS dataset covers warm
marine Sc clouds off the Chilean coast; ICEPIC; COPS and APPRAISE cases cover cumulus clouds
and mixed phase layer clouds over the UK; RICO cases are of warm trade wind cumuli over the
ocean; and the NAMMA campaign add dust laden convective cases. The results from the case study
simulations will be re-evaluated based on improvements made within the laboratory programme.
ST1 (UMan, Met Office). Interpretation of parameters investigated in WP1 using an aerosolcloud and precipitations interaction model (ACPIM)
ACPIM (Connolly et al. 2009, Dearden, 2008, 2010) is the primary model tool used to relate the rates
of different laboratory-measured microphysical processes to definable parameters in the model
microphysics schemes. ACPIM will be used as a bin microphysics model enabling traceable
representation of laboratory results into the MCRM in ST2. Similarly, improvements in bin models
cannot transfer directly to bulk microphysics models.
By using the detailed cloud microphysics model ACPIM to simulate the chamber data, the process
information will be best interpreted and the model parameters better constrained, allowing transferral
to the CRM modeling activity with improved confidence. The Met Office high resolution modeling,
during their CONSTRAIN project, will utilize a more parameterized, or `bulk’, microphysical approach
and improvements in the bin scheme will not be directly transferable to their modeling activity. Hence,
using ACPIM as a driver, we will assess the performance of the bulk model to reproduce the
laboratory results and make changes to the process rates (due to accommodation coefficients, the
HM-process and ice nucleation) where necessary, based on the bin modelling, so that the bulk model
may be improved; these improvements will then be directly transferable to the Met Office models.
ACPIM was developed during the APPRAISE core modelling activity and contains both detailed bin
microphysics and the option to run multi-moment bulk microphysics schemes appropriate for
inclusion in the LEM and UM. All processes under investigation in the ice lab activity are described in
the bin scheme and all, except mass and thermal accommodation, in the bulk scheme.
Activities: For all processes an initial set of simulations will be conducted to quantify the impact of
uncertainty on cloud properties for idealised cases due to the mass and thermal accommodation
coefficients for liquid and ice (WP1 and WP2); the ice nucleation rates for different mineral dust IN
(WP2); and the influence of drop size and impact velocity (Saunders and Housseini, 2001) on the
splinter production rate due to the HM process. Following improvements the reduction in uncertainty
will be quantified (WP 5). The activities include: testing of the stochastic nucleation and contact
nucleation parameterizations derived from cold stages and EDBs by using data from dynamical
expansions in MICC and constraining ACPIM to the state variables and aerosol size distributions
measured during the experiment, if shown to work this will reduce uncertainty in nucleation rates;
constraining the accommodation coefficient by constraining ACPIM to all state variables, ice number
concentrations and total water and allowing RH to be a free variable. Comparing the modelled RH
with the measured RH then allows constraint of the mass accommodation coefficient and
interpretation of HM process in chamber experiments using drop size and impact velocity to better
constrain the rate of splinter production.
Deliverables: Set of detailed microphysics model runs provided to feed into WP 5 to quantify
uncertainty and reduction in uncertainty; detailed testing of ice nucleation rates by mineral particles,
constrain the accommodation coefficient for ice, interpret / improve if possible representation of HM
process. Use of the ACPIM model in ST1 will provide detailed analysis of the laboratory studies.
ST2 (ULeeds, UMan, UHerts). Model uncertainty and cloud radiative forcing due to processes
at CRM scale
It is proposed to use a detailed cloud resolving model with coupled radiation to perform sensitivity
studies of the effect that our current lack of knowledge of each of the parameters we are investigating
in WP1 and 2 have on the cloud-scale. We will quantify this by looking at reduced outputs from the
model runs such as condensed water path and precipitation as well as the radiative flux divergence
for each case studied. Although we will also gain a more detailed appreciation by analysing the
output in detail to understand the interplay between microphysics, dynamics and radiation.
The Microphysics Cloud Resolving Model (MCRM) (http://cabernet.atmosfcu.unam.mx/ICCP2008/abstracts/Program_on_line/Poster_06/CuiEtAl_extended.pdf) based on the Met Office large
eddy model with the incorporation of advanced cloud bin-resolved microphysics (used for research)
and prognostic aerosol and also more parameterized bulk microphysics (used more operationally); it
is the primary tool used in this WP to link the laboratory measurements to field experiments and
make improvements to more widely used bulk microphysics schemes. Another application of the
model is to inform / direct laboratory experiments to make them more relevant to clouds. In the bin
microphysics version cloud particles include four types of hydrometeors: liquid drops, ice crystals,
graupel and snow particles. Both number concentration and specific mass are prognostic variables
for each species. Maintaining the balance between the different moments is based on the spectral
method of moments by Tzivion et al. (1987).
The model includes activation and impaction scavenging of aerosol, warm rain collision and
coalescence processes, primary freezing processes, production of splinters during riming (the HallettMossop process), other ice multiplication processes, melting of ice particles of all types, interactions
between cloud particles, and sedimentation of cloud particles.
The Met Office high resolution modeling, during their CONSTRAIN project, will utilise a more
parameterised, or `bulk’, microphysical approach and improvements in the bin scheme will not be
directly transferable to their modeling activity. Hence, the MCRM will be used in bulk mode to make
changes to the process rates (due to accommodation coefficients, the HM-process and ice
nucleation) where necessary, based on the bin modelling, so that the bulk model may be improved.
These improvements will then be directly transferable to the Met Office models. All processes under
investigation in the ice lab activity are described in the bin scheme and all, except mass and thermal
accommodation to liquid and ice, in the bulk scheme. These will be added in parameterised form to
the bulk scheme.
Activities: In this work, the MCRM will be configured to simulate cases from field campaigns: (i)
quantifying the importance of the mass/thermal accommodation coefficient for liquid water using
VOCALS data; (ii) repeating this in cumulus clouds using idealized cases from RICO; (iii)
investigation of mass/thermal accommodation coefficient and the HM process in mixed phase
cumulus clouds based on ICEPIC; (iv) investigation of the importance of SOA / viscous aerosol to
glaciation based on APPRAISE-CLOUDS mixed phase clouds; (v) investigation of the influence of
SOA on ice nucleation in cirrus; (vi) investigation of the importance of Sahara dust using idealized
cases from NAMMA. The case studies will be used to define the variability in aerosol /dust loading as
input to the model and the uncertainty in the summary metrics associated with those bounds in input
as well as the reduction in uncertainty will be derived using the both the MCRM and the technique
described in WP5. Frequency distributions of humidity will be obtained from these CRM model runs,
from the ice and mixed-phase cases, for a range of aerosol loadings from clean to polluted cases.
These will be used to guide the conditions used in the lab ice growth experiments and roughness
parametrizations for GCMs, since saturation ratio of water vapour influences ice crystal roughness.
The results from the above activities will be used to make improvements in the bulk microphysics
schemes by statistical comparison of the cloud microphysical properties, and their rates of change,
between the bin and bulk schemes for the same modelled case. This will allow us to adjust
parameters in the bulk scheme, such as the assumed size distribution parameters and those
parameters defining conversions between water species to best match the more explicit bin scheme
in a similar way to Dearden et al. (2010). This will link to the Met Office model analyses of
microphysics data collected from the airborne CONSTRAIN programme. A similar activity will take
place based on an upcoming campaign to investigate congestus cloud in the Caribbean. Use of the
MCRM in the way described in this WP thus enables the results from the WP1 and 2 activities to feed
into Met Office research and be tested in alternative environments.
Deliverables: i) Summary of the sensitivity of the case studies to bounds in current knowledge of
each process description due to processes investigated in WP1 & 2 (mass accommodation
coefficient, HM-process, ice nucleation rates, ice roughness), ii) supply data to WP5 for uncertainty
reduction quantification for idealised cases based on field projects; iii) RH data to guide rough ice
experiments in WP2, iv) assessment of whether roughness explains remote sensing observations.
ST3 (UEx, Met Office, UOx, ULeeds). Evaluation of sub-grid vertical velocity pdfs
In order for improvements in cloud microphysical processes to feed through to climate model studies
and influence policy, etc, it is essential that the specification of the sub-grid parameterisation
(especially of vertical velocity) used in the GCM work in WP4 is appropriate to handle the
parameterisations resulting from the experimental and modelling work from WP3 in this project. The
current climate version of the Unified Model (UM) is HadGEM2. where the aerosol-cloud interaction
scheme used in determining the aerosol indirect effects (change in the cloud droplet size and number
distribution with subsequent impacts on the cloud microphysical development) relies on empirical
relationships based on observations of the aerosol number concentration and the effective radius of
cloud particles. However, vertical velocity is key to determining activation of aerosol to form droplets
in liquid cloud and can modulate the background humidity in the upper troposphere leading to ice
nucleation. At GCM scales, resolved vertical velocities cannot be used to activate droplets and so the
interaction and feedbacks between aerosol-clouds-radiation-dynamics cannot be assessed
accurately or adequately. UOx is trialling the use of turbulent kinetic energy (TKE) diagnostics to
determine a subgrid vertical velocity pdf. However, this approach is only currently practical within
the boundary layer.
Activities: UEx in close collaboration with the Met Office, propose to use the UM at CRM scale and
the LEM to simulate cases in different regimes (e.g. Sc, Cu, Ci). This work will be supplemented by
pdfs derived by the ULeeds based ST1. Explicitly resolved vertical velocity pdfs will be parameterised
as a function of state variables prognosed by GCMs and where possible these will be evaluated
against existing aircraft observations. The same cases will then be run at GCM scale to test the effect
of the subgrid vertical velocity parameterisations against existing field campaign data (e.g. VOCALS,
RICO) and satellite observations of variables such as LWC, effective radius, and top of atmosphere
solar and terrestrial fluxes..
Deliverables: Sub grid vertical velocity distributions (pdfs) for use in the UM. At the end of the 18
month PDRA a preliminary global subgrid vertical velocity distribution parametrisation will be in place.
As the rest of the consortium progresses, modifications will be made to improve the representation.
By the end of the consortium, improved sub-grid parameterisations of vertical velocity will be
available to enable future projects to take the results from the laboratory work through to GCM scale.
WP4 Large-Scale Modelling (PS coordinating)
Contributing Partners: UOx, UExeter, ULeeds, Met Office
Current estimates of indirect aerosol forcings show the largest uncertainty among anthropogenic
climate perturbations (Forster et al., 2007). Additional fast feedbacks not included in the stringent
IPCC forcing definition, such as cloud dynamics and lifetime effects, introduce further significant
uncertainty [e.g. Lohmann and Feichter, 2005]. In absence of global observations of sufficient
coverage and accuracy, global aerosol models are the prime tool for their assessment. However,
current model parameterisations are underconstrained on the process level, with e.g. the mass
accommodation coefficient of water varying between 0.04 and 1.0. This WP will assess and quantify
the impact of parametric uncertainties on radiative forcing guiding laboratory measurements and
process studies throughout the project.
ST1 (UOx, Met Office). Quantitative uncertainty analysis of aerosol-cloud processes in global
models
We will utilize the unique ensemble of models participating in the international global aerosol model
intercomparison project AeroCom (http://dataipsl.ipsl.jussieu.fr/AEROCOM/) to deconvolve process
uncertainties in quantification of indirect aerosol effects. The detailed protocol established for
AeroCom Phase II allows assessing diversity of parameters in each step from the point of emission
to the forcing calculation for the ensemble of models. This ensemble diversity combines with the
parametric uncertainty analysis to provide estimates of total uncertainty.
However, only a limited set of uncertainty perturbations has been performed within AeroCom. Thus,
we will test uncertainty ranges in parameters identified jointly with WP5 as key contributors to forcing
uncertainty through dedicated sensitivity studies with the HadGEM-UKCA model with explicit aerosol
cloud coupling. Simulations will be performed combining the AeroCom microphysics, forcing and
indirect protocols, to allow detailed insights into the process understanding and direct comparability
with other state of the art global models participating in AeroCom. The individual simulations will be
performed for 27 months nudged for the period 10/2005-12/2007. The combination of instantaneous
forcing calculation with the analysis of top-of-atmosphere flux perturbations will allow separating
direct and indirect aerosol radiative forcings. Output from these perturbed aerosol/cloud microphysics
simulations will feed directly into the parametric uncertainty analysis in WP5 where the results will be
used to train the Gaussian Emulation Machine for Sensitivity Analysis to quantify sensitivities.
This analysis will identify key discrepancies among the full range of global models early in the project
and provide guidance to process (WP1,2) and cloud resolving modeling activities (WP3) identifying
regimes of significant susceptibility, ambient conditions of interest and the global context.
Deliverables: List of impact of key parametric uncertainties in global simulations of indirect aerosol
effects; Dedicated sensitivity runs with HadGEM-UKCA model.
ST2 (ULeeds, UOx, UMan, Met Office). Improvement of the representation of aerosol-cloud
processes in global models
Investigation of parametric uncertainties in droplet activation and growth processes
We will incorporate results from the measurement
activities to evaluate and improve activation
parameterisations with focus on UKCA. In
particular the uncertainties in mass and thermal
accommodation coefficients of
water are
fundamental to the evolution of maximum
supersaturation at the cloud base, which in turn
determines the spectrum of activated aerosols. The
significant impact of currently unconstrained
accommodation coefficient ranges on aerosol
activation is illustrated in Fig. 1, based on the state
of the art parameterisation by Barahona et al. Fig. 1: Fraction of activation as function of mass
ammonium[2010]. We will investigate the impact of the accommodation coefficient for log-normal
sulfate aerosol population (230 cm-3) with insoluble core
laboratory revised parameters through similar
and count median diameter of 0.5 μm, as simulated with
offline box-modelling studies spanning the full Barahona et al. [2010] activation parameterisation.
parameter range encountered in GCMs in
combination with cloud parcel model studies (WP3 ST1) and sensitivity studies with HadGEM-UKCA.
Exploratory studies on ice nucleation in the HadGEM-UKCA aerosol-climate model
In this WP we will build on the progress of our understanding in ice nucleation processes through
laboratory studies in WP2 and cloud resolving modelling in WP3 to introduce parameterisations of ice
nucleation into the HadGEM-UKCA model with revised multi-moment ice cloud microphysics. As the
related uncertainties remain high, we will initially focus on empirical parameterisations of low
complexity (e.g. as function of supersaturation, temperature and potential IN availability) and limit our
analysis to fixed supersaturations or limited supersaturation and temperature ranges (e.g. DeMott et
al., 2010, function of T and potential IN only, assuming liquid water 101%<RH<104%). This will
further support the basic evaluation with campaign data and establish comparability of the simulated
distribution of potential IN with other models, in analogy to the commonly performed evaluation of
simulated CCN at fixed supersaturation.
At a later stage, we will build on the existing infrastructure of sub-grid scale updrafts introduced in the
framework of the UKCA activation scheme and the refinements thereof in WP3 ST3 to perform
exploratory studies with the goal to establish a framework for more explicit ice nucleation schemes.
Recent global modelling work has ignored the rapid depletion of supersaturation during the
nucleation process (Hoose et al., 2008), which raises questions about the direct applicability of
laboratory measurements. However, there is the potential to use quasi-steady supersaturation theory
(Korolev & Mazin, 2003) or alternative approximations (Kärcher et al., 2006) to the supersaturation
balance equation to establish more consistent parameterisations to link with the laboratory
measurements. To explore the applicability in global models, we will test the quasi steady-state
theory against the ACPIM parcel model (WP3) and laboratory experiments (WP2). This will provide a
traceable transfer of process understanding from the laboratory measurements to the development of
early parameterisations of ice nucleation in HadGEM-UKCA.
Deliverables: Activation parameterisation for HadGEM-UKCA with improved representation of
supersaturation based on based on laboratory work in WP1; Exploratory studies on ice nucleation in
HadGEM-UKCA based on the laboratory work in WP2 and cloud resolving / parcel modelling in WP3.
ST3 (UOx, Met Office). Quantification of uncertainty reductions in aerosol indirect radiative
forcing
In this WP we will synthesise knowledge from laboratory measurements, process modelling and
cloud resolving modelling into the global HadGEM-UKCA aerosol-climate model to quantify the
impact of the advancement in process understanding on radiative forcing.
Activities: We will perform a series of forcing type simulations with HadGEM-UKCA to quantify the
impact of progress in process understanding on radiative forcing. In this analysis we will focus on the
impact of improved estimates of the accommodation coefficients (WP1), sub-grid scale updraft
velocities (WP3.3) as well as other parameters identified of key importance by the laboratory studies
(WP1, WP2) and the uncertainty analysis in WP5. The simulation setup and emissions will be
identical to the initial assessment of indirect aerosol effects in ST1, allowing assessment of the
impact of subsequent model improvements. To investigate the overall impact of non-linearity on
aerosol radiative forcing (e.g. Stier et al., 2006), we will compare the impact of a series of simulations
with one-at-a-time improvements to a simulation in which all improvements are combined.
Deliverables: Publication on uncertainty reduction in global aerosol indirect radiative forcing through
dedicated laboratory and process modelling studies. This activity will synthesise the impact of
laboratory revised parameter ranges on global indirect aerosol radiative forcing.
WP 5. Quantification of uncertainty (KC coordinating)
Contributing Partners: All
The overall objective of this work package is to quantify the uncertainty in model predictions of the
climate-relevant aerosol and cloud quantities that are the focus of the project. Uncertainty analysis
techniques will be used to determine the uncertainty at the start of the project and as a result of
improved understanding derived from the project.
The overarching objective of the aerosol programme is to reduce the uncertainty in estimates of
radiative forcing and climate feedbacks relating to aerosol and cloud processes… To achieve
this objective it is necessary to quantify the uncertainties prior to the completion of activities and
again at the end of the project, taking into account new knowledge. The “uncertainty” is interpreted
here as the range of model predictions of a given quantity (e.g., cloud drop or ice concentrations,
global indirect forcing, etc) taking into account the structural and parametric uncertainties. The
structural uncertainties refer to the choices or weaknesses of the model design (e.g., representation
of unresolved processes such as entrainment or updraft velocities, inadequate resolution of clouds),
while the parametric uncertainties derive from incomplete knowledge of the model parameters (e.g.,
mass accommodation coefficient for water uptake, ice nucleation rates, etc). The focus of the project
is on improvement of process understanding through laboratory studies, and will deliver an improved
quantification of several important model parameters. The key objective of this WP is therefore to
quantify what effect this improvement in knowledge of parameters has on the overall prediction
uncertainty. The analysis will focus on four key quantities predicted by the models being used in the
project: CCN concentration, cloud drop concentration, ice particle concentration, and indirect forcing.
Methodology: We will use an emulator technique to quantify model prediction uncertainty. Emulator
techniques are becoming increasingly widely used in environmental science and are ideally suited to
uncertainty analysis of complex models. We will use new techniques that have been developed in the
RCUK project Managing Uncertainty in Complex Models (www.mucm.ac.uk), which are ideally suited
to complex models that are time consuming to run. Similar emulator techniques are being applied in
the Newton Institute project on climate model uncertainty (http://tinyurl.com/3ayamte) and in the
Leeds/Oxford AEROS project (Aerosol Model Robustness and Sensitivity Analysis). The approach,
based on the work of Oakley and O’Hagan (2004), allows the sensitivity to a large number of model
parameters to be quantified with the minimum number of model simulations. The model runs (training
data) are used to produce an interpolated output surface filling the space of the input uncertainty (an
emulator of the model) for a particular variable of interest (at one model location, or averaged over a
domain), in this case cloud drop number, indirect forcing, etc. Readily available software, the
Gaussian Emulation Machine for Sensitivity Analysis (GEM-SA), is then used to quantify sensitivities,
including main effects and interactions. The basic output is a quantitative measure of how much
the overall uncertainty would be reduced if a particular parameter were known perfectly. The
interaction terms quantify how much of that uncertainty is due to combinations of parameters.
The overall methodology involves running the complex model multiple times with a wide range of
parameter combinations, each spanning the likely range of uncertainty. The parameter under
investigation is just one of many others, so in each model it will be necessary to define uncertainties
in a wide range of important parameters. This “simulator” data is used as input to the emulator, which
is very fast to run, and generates model outputs at untried parameter combinations. The emulator
also quantifies how much of the error is due to the simulator and how much is due to the emulator
(which is kept to a minimum). Efficient parameter sampling techniques based on Latin-Hypercube
designs are used to generate the best possible combination of model parameter choices for the
simulator. Typically the number of runs is <10* the number of parameters. Once the simulator has
been run, the fast emulator can be run for any model diagnostic (e.g., droplet number, ice number,
radiative effect).
To quantify the reduction in uncertainty, we will do a “before and after” analysis. The “before” analysis
will calculate the overall prediction uncertainty based on the estimated range of a particular
parameter (e.g., based on literature or expert judgement). The “after” analysis is a recalculation using
a new (and hopefully reduced) uncertainty range based on results of the project. The project may
also deliver new methods of calculating a particular quantity (e.g., a new equation for accommodation
coefficient in terms of new physical processes). In this case, a modification will be made to the model
and a new estimate of uncertainty made.
ST1 (ULeeds / UMan) Uncertainties in global CCN
Fields of size resolved aerosol composition distributions and condensable organic vapours produced
by oxidation of biogenic and anthropogenic emissions will be diagnosed under a range of scenarios
using GLOMAP. The number of CCN at a range of supersaturations will be calculated from assuming
no equilibration and instantaneous equilibration with assumed semi-volatile vapours on activation
informed by the output from WP1 ST2 and ST3. This will provide the bounds of the impact of
disequilibrium of the ambient semi-volatile aerosol components on CCN number. Coupled with the
sensitivity analyses of uptake rates, this will enable the impacts of aerosol properties on global
droplet number to be evaluated.
ST2 (Leeds, all). Uncertainties in cloud drop & ice crystal concentrations and radiative effects
Activity: The project will deliver new knowledge on the mass (αM) and thermal (αT) accommodation
coefficients, the impacts of volatile components and particle phase on CCN number, ice crystal
nucleation rates, and rates of the Hallett-Mossop process. The aim here will be to quantify how
improved knowledge of these processes/rates affects the uncertainty in droplet and ice crystal
concentrations as well as short and longwave radiative effect predicted by the cloud resolving model.
ULeeds will work with the laboratory and modelling groups to define the input parameters and their
uncertainties based on expert judgement or literature. ULeeds will define the parameter
combinations, the modelling groups will do the runs, then ULeeds will use the results to create the
emulator and determine the contribution of each parameter to the uncertainty. The successful
outcome of the laboratory experiments (i.e., better constrained input parameters) will then allow us to
quantify the reduction in model uncertainty as a result of the project.
ST3 (Leeds + all). Uncertainties in global indirect aerosol radiative forcing
Activity: This activity will focus on the uncertainty in the first indirect radiative forcing predicted by the
global climate model. Here the uncertain parameters of interest include the updraught velocity
(WP3.3, WP4.2), the CCN activation (see ST1), and the water mass accommodation coefficient. UOx
will be responsible for doing the climate model runs based on one-year simulations (both present day
and pre-industrial emissions). The primary diagnostics of interest are the cloud top droplet
concentration and the top of the atmosphere radiative perturbations.
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Appendix: Table A1: Instruments for the Chamber Experiments, Further details on the MICC and aerosol
chamber instrumentation can be found in the UMan, UHerts, ULeic and UYork JoR and Track Records.
Instrument / Technique(references)
Differential Mobility Particle Sizer (DMPS)1
Aerosol Mass Spectrometers (CToF-AMS2 and HRToF-AMS3)
Hygroscopicity Tandem Differential Mobility Analyser
(HTDMA)4
Cloud Condensation Nucleus (CCN) counter5
downstream of DMPS
Thermal denuder
Electrostatic Low Pressure Impactor
Chemical Ionisation Reaction Time of Flight Mass
Spectrometry (CIR-ToF-MS)6
High Sensitivity Proton Transfer Reaction Quadrupole
Mass Spectrometer (PTR-QMS)(MKS Inc.)
Hadamard Transform CIR-ToF-MS (HT-CIR-ToF-MS)7
LoD
>100 p/cc
19 / 22/ 360
ng m-3
aerosol growth factor with RH ~ 5 p/cc per
size
cloud activation potential as ~ 1 p/cc
f(Dp)
Particle volatility as f(T)
n/a
0.1 - 50 /cc
particle N(Dp), 7nm - 10m
VOCs, OVOCs
~ 500 pptv 10 ppbv
VOCs, OVOCs
100 - 500
pptv
VOCs, OVOCs
100 pptv - 1
ppbv
Gas Chromatograph Ion Trap Mass Spectrometer (GC- VOCs, OVOCs
500 pptv
ITMS)(Varian Inc.)
UV absorption analyser, USA, Model 49C(Thermo Scientific) Ozone
Chemiluminescence analyzer, Model 42i(Thermo Scientific)
NO, NO2
400 pptv
Particle into liquid sampler (PILS)
water-soluble OA
Comprehensive 2-D Gas Chromatography coupled to organic aerosol components
0.2 ng m-3
Time of Flight Mass Spectrometry (GCxGC-ToF/MS)8
GCXGC coupled to a Nitrogen Chemiluminscence organic aerosol components
0.5 ng m-3
9
Detector (GCxGC-NCD)
Liquid Chromatography coupled to Ion Trap Mass organic aerosol components
2 ng m-3
Spectrometry (LC-MS)10
Fourier Transform Ion Cyclotron Resonance Mass Organic aerosol components 2 ng m-3
Spectrometer (FT-ICR/MS)
SPEC Cloud Particle Imager (CPI)
Images particles of sizes ~5 L-1
10<D<1500 m
WELAS white light OPC
Aerosol
/
cloud
size ~10 L-1
distributions
SID3 / PPD (Small Ice Detector)
Liquid and ice particle concn, ~10 L-1
size, shape, roughness
CAS-depol
Single particle depolarisation ~10 L-1
for phase discrimination
1
Target Species
particle N(Dp)
major component masses
Williams, P., PhD thesis, University of Manchester, 2000
Drewnick, F., et al., Aerosol Science and Technology, 39(7), 637-658, 2005
3
DeCarlo, P. F., et al. (2006), Analytical Chemistry, 78(24), 8281-8289
4
Cubison, M. J., et al. (2005), Journal of Aerosol Science, 36(7), 846-865
5
Roberts, G. C., and A. Nenes (2005), Aerosol Science and Technology, 39(3), 206 - 221
6
Blake et al., Analytical Chemistry 76, 3841-3845, 2004.
7
Wyche et al., Chapter 5, p. 64-76, Advanced Environmental Monitoring, Springer-Verlag GmbH, 2008.
8
Hamilton et al., Atmospheric Environment 2005, 39, 7263-75
9
Ochiai et al., J. Chromatogr. A., 1150, 13-20, 2007
10
Hamilton et al., ACPD, 9, 3921-3943, 2009
2
Int. t
6 min
1 min
Institute
UMan
UMan
10 min UMan
/ size
10 min UMan
/ SS
n/a
UMan
UMan
1 - 10 ULeic
min
1s - 1 ULeic
min
1 s - 1 ULeic
min
10 min ULeic
10 s
UMan
10s
UMan
20 min UYork
UYork
UYork
UYork
UYork
10s
UMan
10s
UMan
10s
UHerts
10s
UMan