constraints on aerosol processes in climate models from vertically

Atmos. Chem. Phys., 13, 5969–5986, 2013www.atmos-chem-phys.net/13/5969/2013/doi:10.5194/acp-13-5969-2013© Author(s) 2013. CC Attribution 3.0 License.

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Constraints on aerosol processes in climate models fromvertically-resolved aircraft observations of black carbon

Z. Kipling 1, P. Stier1, J. P. Schwarz2,3, A. E. Perring2,3, J. R. Spackman2,3, G. W. Mann4,5, C. E. Johnson6, andP. J. Telford7,8

1Department of Physics, University of Oxford, Oxford, UK2Co-operative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA3Chemical Sciences Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration,Boulder, Colorado, USA4National Centre for Atmospheric Science, University of Leeds, Leeds, UK5School of Earth and Environment, University of Leeds, Leeds, UK6Met. Office Hadley Centre, Exeter, UK7National Centre for Atmospheric Science, University of Cambridge, Cambridge, UK8Department of Chemistry, University of Cambridge, Cambridge, UK

Correspondence to:Z. Kipling ([email protected])

Received: 28 November 2012 – Published in Atmos. Chem. Phys. Discuss.: 7 January 2013Revised: 29 April 2013 – Accepted: 17 May 2013 – Published: 20 June 2013

Abstract. Evaluation of the aerosol schemes in current cli-mate models is dependent upon the available observationaldata. In-situ observations from flight campaigns can providevaluable data about the vertical distribution of aerosol that isdifficult to obtain from satellite or ground-based platforms,although they are localised in space and time. Using single-particle soot-photometer (SP2) measurements from the HI-APER Pole-to-Pole Observations (HIPPO) campaign, whichconsists of many vertical profiles over a large region of thePacific, we evaluate the meridional and vertical distributionof black carbon (BC) aerosol simulated by the HadGEM3–UKCA and ECHAM5–HAM2 models. Both models showa similar pattern of overestimating the BC column burdencompared to that derived from the observations, in many ar-eas by an order of magnitude. However, by sampling thesimulated BC mass mixing ratio along the flight track andcomparing to the observations, we show that this discrep-ancy has a rather different vertical structure in the two mod-els: in HadGEM3–UKCA the discrepancy is dominated byexcess aerosol in the tropical upper troposphere, while inECHAM5–HAM2 areas of discrepancy are spread acrossmany different latitudes and altitudes.

Using this methodology, we conduct sensitivity tests ontwo specific elements of the models: biomass-burning emis-

sions and scavenging by convective precipitation. We showthat, by coupling the convective scavenging more tightly withconvective transport, both the column burden and verticaldistribution of BC in HadGEM3–UKCA are much improvedwith respect to the observations, with a substantial and statis-tically significant increase in correlation – this demonstratesthe importance of a realistic representation of this process.In contrast, updating from GFED2 to GFED3.1 biomass-burning emissions makes a more modest improvement inboth models, which is not statistically significant. By com-paring our results with a more traditional approach usingregional- and monthly-mean vertical profile curves, we showthat the point-by-point analysis allows the model improve-ments to be demonstrated more clearly.

We also demonstrate the important role that nudged sim-ulations (where the large-scale model dynamics are continu-ously relaxed towards a reanalysis) can play in this type ofevaluation, allowing statistically significant differences be-tween configurations of the aerosol scheme to be seen wherethe differences between the corresponding free-running sim-ulations would not be significant.

Published by Copernicus Publications on behalf of the European Geosciences Union.

5970 Z. Kipling et al.: Constraints on aerosol processes in climate models

1 Introduction

Aerosol particles in the atmosphere play an important rolein the climate system on both global and regional scales,through several mechanisms: direct modification of theshort-wave radiation budget by scattering and absorption(Angstrom, 1962; Schulz et al., 2006; Myhre et al., 2013);effects on clouds and the hydrological cycle, indirectly mod-ifying the radiation budget (Twomey, 1977; Albrecht, 1989;Lohmann and Feichter, 2005); and “semi-directly” by al-tering the temperature profile of the atmosphere, and evap-orating or suppressing cloud, through absorption of radia-tion (Hansen, 1997; Koch and Del Genio, 2010). Consequentchanges to circulation patterns may lead to additional effects(e.g.Roeckner et al., 2006). The magnitudes of all these ef-fects are subject to considerable uncertainty.

Black carbon (BC) aerosol can contribute to all of theseclasses of effect, although its absorption of short-wave ra-diation makes it of particular interest in the context of thedirect and semi-direct effects (Stier et al., 2007; Ramanathanand Carmichael, 2008). The relative magnitudes of these ef-fects, and thus the sign of the net (semi-)direct forcing dueto BC, are thought to depend heavily on the vertical distri-bution of BC, and in particular its altitude relative to cloudlayers (Johnson et al., 2004; Zarzycki and Bond, 2010). Inaddition, “aged” BC particles with a soluble coating can actas cloud condensation nuclei (Penner et al., 1996; Lohmannet al., 2000), and thus contribute to indirect effects; ageingmay also reduce the lifetime of black carbon (by increasingsusceptibility to wet deposition) and enhance its absorptionof radiation (Ackerman and Toon, 1981; Stier et al., 2006;Schwarz et al., 2008).

Some progress has been made in analysing the relative po-sitions of BC and cloud layers, and the resulting radiative ef-fects, from satellite observations (Peters et al., 2011; Wilcox,2012). However, neither passive satellite remote sensing norground-based observations can provide well-resolved verti-cal profiles of BC (or aerosol in general), and thus we turnto in-situ aircraft observations. Although such observationsare limited in spatial and temporal coverage, they can pro-vide data with much better vertical resolution than can beobtained from other sources, as well as more direct mea-surements of the quantities (e.g. concentrations, mixing ra-tios, composition and particle size distributions) representedin aerosol models.

Previous studies using aircraft observations to evaluateaerosol models on a global scale have generally comparedmonthly-mean model profiles with campaign-mean profilesfrom a collection of separate campaigns (which may differin their methodology), each over a limited geographical area(e.g.Koch et al., 2009). Other studies have focused on moredetailed evaluation on a regional scale using individual flightcampaigns – e.g.Reddington et al.(2013), which also high-lights the importance of uncertainties in the size distributionof BC as well as its total mass.

The large-scale flight campaign conducted by the High-performance Instrumented Airborne Platform for Envi-ronmental Research (HIAPER) Pole-to-Pole Observations(HIPPO) of Carbon Cycle and Greenhouse Gases Study(Wofsy et al., 2011) provides the opportunity to evalu-ate against consistently-collected data from a single cam-paign over a large area of the Pacific region. The data aredescribed in more detail in Sect.2. The BC data fromthe first phase of the HIPPO campaign are analysed inSchwarz et al.(2010), where the observed vertical pro-files are used to evaluate the simulated BC profiles fromthe Aerosol Comparisons between Observations and Mod-els (AEROCOM;http://dataipsl.ipsl.jussieu.fr/AEROCOM)Phase I (Textor et al., 2006) models, comparing climatologi-cal monthly-mean model profiles against regional-mean pro-files from HIPPO. The model diversity is large – one to twoorders of magnitude over a wide altitude range, both in thePacific regions studied inSchwarz et al.(2010) and the conti-nental regions inKoch et al.(2009) – but both the mean andmedian of the model ensemble systematically overestimatethe BC mass mixing ratio (MMR) compared to the observa-tions.

In this study, we carry out a more detailed evaluationof the vertical distribution of BC in two particular models,HadGEM3–UKCA and ECHAM5–HAM2, against BC massmixing ratio data derived from the first three phases of theHIPPO campaign. These models and their configurations aredescribed in Sect.3. Rather than averaging the instantaneousobservations on a regional basis and comparing to model cli-matology, we use nudging and interpolation techniques tosample the models in time and space along the track of theflight campaign, as described in Sect.4.

We apply this approach to investigate and constrain the ef-fects of convective scavenging (which has an important rolein controlling vertical transport) and biomass-burning emis-sions (which are the most temporally and spatially variablesource of BC) on the vertical profile of BC in the models. Tothis end, we conduct a series of sensitivity tests, as describedin Sect.5, to assess how the agreement with the observationsis affected by the choice of convective scavenging schemeand emissions inventory.

2 Observational data

There have been five phases of the HIPPO campaigncompleted (http://hippo.ucar.edu/); data from the firstthree (in January 2009, October/November 2009 andMarch/April 2010) were available in time for this analysis.Each phase consists of an approximate meridional transectover the Pacific, with detours into neighbouring continentalregions – the flight tracks can be seen in Fig.1. Along eachtrack, a series of fairly regular ascents and descents weremade, providing vertically-resolved measurements, typicallyspanning 300 m above the surface to 8.5 km above sea level

Atmos. Chem. Phys., 13, 5969–5986, 2013 www.atmos-chem-phys.net/13/5969/2013/

http://dataipsl.ipsl.jussieu.fr/AEROCOM

http://hippo.ucar.edu/

Z. Kipling et al.: Constraints on aerosol processes in climate models 5971

Table 1. Differences relevant to black carbon between the aerosol schemes in HadGEM3–UKCA and ECHAM5–HAM2, in their BASEconfigurations.

Process HadGEM3–UKCA ECHAM5–HAM2

Biofuel emission size same as fossil fuel (60 nm diameter) same as biomass-burning (150 nm diameter)Fossil fuel and biofuel emissions added to lowest level applied as surface flux in vertical diffusionBiomass-burning emission height uniform in height over∼ 50 m to 3 km biome-dependent (Dentener et al., 2006)Ageing insoluble to soluble 10 monolayers required 1 monolayer requiredDry deposition Slinn (1982); Zhang et al.(2001) Ganzeveld et al.(1998)

operator-split as surface flux in vertical diffusionBelow-cloud scavenging Slinn (1984) Seinfeld and Pandis(1998)In-cloud nucleation scavenging 100% of soluble accumulation/coarse modes Prescribed fractions of all modes

Rain only Rain and snowImmediate removal Replaced where precipitation evaporates

Convective scavenging Operator-split, acting on grid-box means In-plume, acting on tracer fluxesAerosol feedbacks on meteorology(direct/semi-direct/indirect effects) Disabled Enabled

with some profiles extending to∼ 14km. In total, we identify184 separate vertical profiles suitable for our analysis (thecriteria used are discussed in Sect.4.1).

A wide range of instruments were carried on these flights,but for our purposes the most relevant data comes from aSingle Particle Soot Photometer (SP2;Schwarz et al., 2006),which measures the mass of BC in individual aerosol parti-cles. Particles were detected within a range of∼ 0.8 to 175fgBC (∼ 75 to 540 nm volume-equivalent diameter, assuminga void-free density of 1.8× 103 kg m−3).

FollowingSchwarz et al.(2010), we calculate the MMR ofBC in the atmosphere by aggregating the observed particlesover 1-minute intervals:

mBC = 1.1F

ρair

N∑i=1

Mi, (1)

where(M1, . . . ,MN ) are the masses of BC in each individualparticle observed by the SP2 instrument,F is the volumetricflow rate at which the air is sampled (4cm3s−1, constant) andρair is the density of the sampled air, derived from contempo-raneous measurements of ambient pressure from the HIPPOflight data, and a fixed temperature of 290K representing thecabin air temperature of the aircraft. (These are an approx-imation of the actual sampling conditions, but the resultingerror is small compared to that from other sources.) The fac-tor of 1.1 inflates the mass by 10 % to account for the portionof the aerosol size spectrum which the instrument does notdetect, as perSchwarz et al.(2010). We then produce a “cur-tain” plot of BC MMR against latitude and altitude to showthe distribution of BC over a vertical slice through the atmo-sphere (top row of Fig.2). We attach an uncertainty of±30 %to these MMR values (the±40 % quoted inSchwarz et al.isnow considered overly cautious). In the cleanest regions (BCMMR less than∼ 0.5ngkg−1), where only a small number ofparticles were detected per minute, the sampling uncertaintyof the observations is likely to contribute significantly to the

scatter in the results; however this is not considered furtherin the present study.

With the exception of HIPPO-2, which makes a detour toAustralia at about 30◦S, there is generally more BC seenin the Northern Hemisphere than the Southern Hemisphereat all levels (which is consistent with the greater anthro-pogenic emissions in the north) – this contrast is particularlystark for HIPPO-3, which spent very little time near land.While some BC is seen in the lower and mid tropical tro-posphere (∼ 10−11 kg kg−1 in places), very little is seen athigher levels in the tropics in any of the phases (typicallyless than 10−12 kg kg−1 above about 6 km); at higher lat-itudes, however, significant BC mass mixing ratios (above10−11kgkg−1) frequently extend into the upper troposphere.

3 Models

Two aerosol–climate models are considered here:HadGEM3–UKCA and ECHAM5–HAM2. These aredescribed in the following sections, and the major differ-ences relevant to black carbon aerosol are summarised inTable1.

3.1 HadGEM3–UKCA

HadGEM3 (Hewitt et al., 2011) is the latest version ofthe Hadley Centre Global Environmental Model developedat the UK Met. Office. Although the full model containsmany components (atmosphere, land surface, ocean, sea iceetc.), this study is concerned only with the uncoupled at-mosphere component, using prescribed sea-surface temper-ature (SST) and sea ice fields. The dynamical core (Davies,2005) is non-hydrostatic and fully compressible, with semi-Lagrangian advection and a hybrid sigma/height vertical co-ordinate. Large-scale cloud uses the bulk prognostic schemeof Wilson et al. (2008), with precipitation microphysics

www.atmos-chem-phys.net/13/5969/2013/ Atmos. Chem. Phys., 13, 5969–5986, 2013


HIPPO-1 HIPPO-2 HIPPO-3HadGEM3–UKCA

BASE

90°S60°S30°S

0°30°N60°N90°N

180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-610-1010-910-810-710-6

BC

burden

/kgm

−2

HadGEM3–UKCA

CVSCAV+G3M

90°S60°S30°S

0°30°N60°N90°N

180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-610-1010-910-810-710-6

BC

burden

/kgm

−2

ECHAM5–HAM2

G3M

90°S60°S30°S

0°30°N60°N90°N

180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-610-1010-910-810-710-6

BC

burden

/kgm

−2

AEROCOM

MEDIA

N

90°S60°S30°S

0°30°N60°N90°N

180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-6 180° 120°W 10-1010-8 10-610-1010-910-810-710-6

BC

burden

/kgm

−2

Fig. 1. Flight tracks for the first three phases of the HIPPO campaign (January 2009, October/November 2009 and March/April 2010,respectively). The circles show the BC burden (in kg m−2) estimated from the HIPPO SP2 observations over each vertical profile, while thebackground shading shows the monthly-mean BC burden from the HadGEM3–UKCA (BASE and CVSCAV+G3M) and ECHAM5–HAM2(G3M) simulations. The bottom row shows the burdens from the AEROCOM Phase I (Textor et al., 2006) median model (constructed fromthe ARQM, GISS, GOCART, GRANTOUR, KYU, LOA, MATCH, MPIHAM, MOZGN, PNNL, UIO CTM, UIO GCM, ULAQ and UMImodels). The side plots show the observed burdens (red bars, representing the range due to uncertainty in extrapolation of profiles to thesurface and a 15 km lid, plus the 30 % uncertainty in the mixing ratios used), the along-track model burden (blue line, two-valued due to thesouthbound and northbound legs) and the zonal range of the model burden between the map edges (shading).

based onWilson and Ballard(1999); sub-grid-scale convec-tion is based on the mass-flux scheme ofGregory and Rown-tree(1990) with subsequent modifications.

The standard tropospheric chemistry scheme in UKCA(O’Connor et al., 2013) is used. This includes oxidants (Ox,HOx and NOx) and hydrocarbons (CO, ethane and propane)with eight emitted species, 102 gas-phase reactions, 27 pho-

tolytic reactions and interactive wet and dry deposition. Anadditional aerosol-precursor chemistry scheme treats the ox-idation of sulphur compounds (SO2 and dimethyl sulphide)and monoterpene to form the sulphuric acid and organiccompounds which may condense to form secondary aerosolmaterial.



Fig. 2. Mass mixing ratio (kg kg−1) of BC in the atmosphere, from each phase of the HIPPO campaign, calculated by aggregating SP2 dataover 1-minute intervals, and from nudged HadGEM3–UKCA (BASE and CVSCAV) and ECHAM5–HAM2 (BASE) simulations, sampledalong the HIPPO flight track (also at 1-min intervals).

The aerosol scheme in UKCA (Mann et al., 2013) is thetwo-moment modal version of the Global Model of AerosolProcesses (GLOMAP-mode;Mann et al., 2010), which fol-lows the M7 framework (Vignati, 2004) in transporting fivecomponents (sulphate, sea salt, black carbon, particulate or-ganic matter and mineral dust) in seven internally-mixedlog-normal modes (four soluble and three insoluble; not allcomponents are found in all modes). Because mineral dustis transported by a separate scheme (Woodward, 2001) in

HadGEM3, only four components and five modes are en-abled in the UKCA configuration of GLOMAP-mode usedhere (omitting the two larger insoluble modes which containonly mineral dust). The representation of aerosol microphysi-cal processes is based on the sectional GLOMAP-bin scheme(Spracklen et al., 2005), with each process acting sequen-tially in an operator-split manner (except nucleation, coagu-lation and condensation which are solved iteratively).



Primary BC emissions use the AEROCOM recommendedsize distributions (Dentener et al., 2006), as modified byStieret al.(2005), but with biofuel emissions using the same distri-bution as fossil fuel rather than biomass burning. Fossil-fueland biofuel emissions are added to the lowest model levelwith a geometric mean diameter of 60 nm, while biomass-burning emissions have a geometric mean diameter of 150nmand are distributed uniformly in height over levels 2 to 12(∼ 50 m to 3 km, compressed over orography) – this is dif-ferent to the TOMCAT-based version of GLOMAP–MODEdocumented inMann et al.(2010), which uses the biome-dependent vertical profiles recommended inDentener et al.(2006). For all sources, the geometric standard deviation ofthe particle diameter is 1.59.

BC aerosol is initially insoluble, but can be “aged” intothe soluble Aitken mode following uptake of sulphuric acidand secondary organic material via condensation and coag-ulation. This ageing proceeds at a rate consistent with a 10-monolayer coating being required to make a particle soluble.

All sizes of soluble and insoluble aerosol particles maybe removed by dry deposition and below-cloud impactionscavenging; soluble accumulation- and coarse-mode parti-cles may also be removed by in-cloud nucleation scaveng-ing. Dry deposition and gravitational sedimentation are cal-culated following Slinn (1982) and Zhang et al.(2001).Below-cloud scavenging followsSlinn (1984), usingBeardand Grover(1974) scavenging coefficients and terminal ve-locities fromEaster and Hales(1983), assuming a modifiedMarshall-Palmer raindrop size distribution (Sekhon and Sri-vastava, 1971). In-cloud scavenging by large-scale precipita-tion assumes that 100% of the aerosol in the soluble accu-mulation and coarse modes is taken up by cloud water inthe cloudy fraction of each 3-D grid box, and is then re-moved at the same rate at which the large-scale cloud wa-ter is converted to rain. (Nucleation, Aitken and insolublemodes are not subject to in-cloud scavenging.) Aerosol is re-moved immediately, and is not returned to the atmospherewhen rain evaporates. Convective rainfall is treated similarly,but assumes a cloud fraction of 30 % and a conversion rateof 99 % over 6 h in all grid-boxes where convective rain isproduced. (This is different to the TOMCAT-based versionof GLOMAP-mode, in which convective scavenging is de-pendent on the rain rate while large-scale scavenging uses afixed removal timescale.) The scavenged aerosol is removedfrom the grid-box mean tracers after the convection schemehas run – i.e. from the post-convection environmental air atthe level where the precipitation formed, rather than the con-vective updraught itself. This allows a greater separation ofthe convection and aerosol schemes, but may limit the abilityof convective scavenging to control vertical transport (as weshow in Sect.6.1).

The model configuration used here is based on a devel-opment version of HadGEM3 (atmosphere-only, climatolog-ical SST, Met. Office Unified Model version 7.3) at N96L38resolution (1.25◦ latitude× 1.875◦ longitude× 38 vertical

levels up to∼ 40km) with UKCA in a standard troposphericchemistry and aerosol configuration as described above, withaerosol feedbacks disabled. In order to capture the meteo-rological conditions at the time of the flight campaign, weuse the technique of nudging (Jeuken et al., 1996). In theHadGEM implementation (Telford et al., 2008, 2013), po-tential temperature and horizontal wind are relaxed towardsfields from the ERA-Interim reanalysis (Dee et al., 2011).The relaxation time constant is the “natural” one of 6 h (thetime spacing of the reanalysis data); this choice is validatedin Telford et al.(2008). The nudging is applied between lev-els 14 (∼ 4 km) and 32 (∼ 21 km) inclusive; levels 13 and33 are nudged at half strength (i.e. with a 12 h time con-stant), and no nudging is performed on levels outside thisrange. Free-running simulations (without nudging) were alsorun for comparison.

For the sensitivity tests, four different simulations werecarried out covering the period of the first three phases of theHIPPO campaign, as shown in Table2. All simulations wererun from September 2008 through to the end of April 2010,allowing four months spin-up before the start of HIPPO-1. No re-tuning of the model was performed for either thenudged or the free-running simulations.

The BASE configuration is derived from the standardUKCA aerosol configuration, which takes its black carbonemissions from the AEROCOM hindcast inventory (Diehlet al., 2012), including emissions from fossil fuel, biofuel andbiomass burning through to the end of 2006. Although theHIPPO campaign began after this, the fossil fuel and biofuelemissions have little interannual variability and so we simplyrepeat those for 2006. Biomass burning, however, has signif-icant interannual variability; since the emissions inventorydoes not cover the required period, we used a monthly cli-matology derived from the “modern” portion of the AERO-COM hindcast inventory (1997 to 2006), which is based onmonthly-mean emission fields of the Global Fire EmissionsDatabase (GFED) version 2 (van der Werf et al., 2006). Other(non-BC) emissions are also taken from year 2006 of theAEROCOM hindcast inventory, or (for additional gas-phaseemissions not included therein but required by the UKCAchemistry scheme) from year 2006 of Representative Con-centration Pathway (RCP) 8.5 (Riahi et al., 2011).

3.2 ECHAM5–HAM2

ECHAM5 (Roeckner et al., 2003) is the fifth-generation cli-mate model developed at the Max Planck Institute for Mete-orology. It has a spectral dynamical core, solving prognosticequations for vorticity, divergence, surface pressure and tem-perature in spherical harmonics with a triangular truncation.A hybrid sigma/pressure vertical coordinate is used. Physi-cal parameterisations are solved on a corresponding Gaus-sian grid. Tracer transport is semi-Lagrangian in grid-pointspace (Lin and Rood, 1996).



Table 2. Configurations and emissions used for model simulations of the HIPPO campaign. The inventory (GFED2 or GFED3.1) usedfor biomass-burning emissions is shown, along with the year for which these emissions are specified. Other emissions are taken from theAEROCOM Hindcast inventory, or (for additional gas phase emissions in UKCA) RCP 8.5.

Model Label Description Biomass-burning emissions

HadGEM3–UKCA BASE Basic configuration, with only diagnosticmodifications for flight-track sampling.

GFED2 1997–2006 clim. (monthly)

G3M As BASE, but with GFED3.1 monthly biomassemissions.

GFED3.1 2008–2010 (monthly)

CVSCAV As BASE, but with in-plume convective scavengingscheme added, as described in the text.


CVSCAV+G3M Combining both in-plume convective scavenging(CVSCAV) and GFED3.1 emissions (G3M).


ECHAM5–HAM2 BASE Basic configuration, with only diagnosticmodifications for flight-track sampling.


G3M As BASE, but with GFED3.1 monthly biomassemissions using modified vertical distribution.


G3H As BASE, but with GFED3.1 3-hourly biomassemissions using modified vertical distribution.

GFED3.1 2008–2010 (3-hourly)

HAM 2.0 (Stier et al., 2005; Zhang et al., 2012) is also atwo-moment modal aerosol scheme based on the M7 frame-work (Vignati, 2004), transporting five components (sul-phate, sea salt, black carbon, particulate organic matter andmineral dust) in seven internally-mixed log-normal modes(four soluble and three insoluble). Unlike in UKCA, mineraldust in ECHAM5–HAM2 is incorporated into the M7 frame-work.

Primary BC emissions use a modified version of the AE-ROCOM recommended size distributions, accounting for thewidth of the M7 modes. Fossil-fuel and biofuel emissionsare added as a surface flux to the boundary-layer verticaldiffusion equations, while biomass-burning emissions usea biome-dependent vertical profile, as specified for AERO-COM Phase I (Dentener et al., 2006). BC aerosol is initiallyinsoluble, but can be “aged” by sulphate through condensa-tion and coagulation to become soluble; in contrast to UKCAonly a single monolayer is required.

Dry deposition of soluble and insoluble particles followsGanzeveld et al.(1998), modified to use the explicit size dis-tribution from the model, and is applied as a surface flux tothe boundary-layer vertical diffusion along with the emis-sions. Below-cloud scavenging is calculated according to therain and snow fluxes, using size-dependent collection effi-ciencies fromSeinfeld and Pandis(1998). In-cloud scaveng-ing assumes that a prescribed fraction of the number andmass of aerosol in each mode from the cloudy part of eachgrid box is susceptible to removal, at the rate at which large-scale cloud water/ice is converted to rain/snow. Scavengingin convective clouds is coupled with the tracer transport inthe mass-flux convection scheme, and proceeds similarly but

removing aerosol from the convective tracer flux accordingto the rate at which water and ice are removed in convec-tive precipitation. Where (a fraction of) the precipitation ina column evaporates before reaching the ground, the samefraction of the aerosol removed from the column is returnedto the atmosphere.

Large-scale cloud follows the two-moment scheme ofLohmann et al.(2007) with modifications byLohmann andHoose(2009) and Abdul-Razzak and Ghan(2000) aerosolactivation, withSundqvist et al.(1989) cloud cover. Convec-tion follows the mass-flux scheme ofTiedtke (1989), withmodifications byNordeng(1994).

The model configuration used here is based on ECHAM5.5 (atmosphere-only, AMIP2 prescribed SST) at T63L31resolution (∼ 1.875◦

× 31 vertical levels up to∼ 10hPa) withHAM 2.0. Once again, the large-scale dynamics are nudgedtowards ERA-Interim (Dee et al., 2011) reanalysis data, fol-lowing Jeuken et al.(1996): temperature, vorticity and di-vergence are surface log-pressure are relaxed towards the re-analysis fields with time constants of 24 h, 6 h, 48 h and 24 h,respectively, on all model levels. The nudging is performedin spectral space, on all but the wavenumber-0 (global-mean)spectral component.

For the sensitivity tests, three different simulations werecarried out for the period covering the first three phasesof the HIPPO campaign, as shown in Table2. As forHadGEM3–UKCA, all simulations were run from Septem-ber 2008 through to the end of April 2010, allowing fourmonths spin-up before the start of HIPPO-1, and no re-tuningwas performed.



In the BASE configuration, emissions are taken from theAEROCOM hindcast inventory (Diehl et al., 2012) for 2006,with biomass-burning emissions using a 1997 to 2006 clima-tology as described for HadGEM3–UKCA.

4 Method

As mentioned in Sect.1, to best compare the simulations withthe aircraft measurements from the HIPPO campaign, wesample the output of the HadGEM3–UKCA and ECHAM5–HAM2 models along the flight track. However, to give a gen-eral indication of how the models’ BC distributions compareto the observations, we first show regional maps of the simu-lated BC column burden with that derived from the SP2 mea-surements over-plotted along the flight track (Fig.1).

4.1 Burdens

From a modelling perspective, column-integrated mass bur-dens are a useful metric by which to measure the distributionof aerosol. However, it is difficult to obtain direct measure-ments of aerosol burden on large scales, as satellite-basedinstruments can only measure integrated optical properties(with passive instruments) or vertically-resolved backscatter(with active lidar). Burdens cannot be inferred from suchmeasurements without additional knowledge of the chem-ical and microphysical properties of the aerosol particles.Ground-based sun-photometers and lidar are similarly lim-ited, while ground-based in-situ measurements are limited toparticles near the surface. The geographical and vertical cov-erage of the HIPPO campaign, however, provides a basis onwhich to evaluate model burdens directly.

We estimate the local BC column burden in the vicinityof each HIPPO ascent or descent profile. Suitable profilesare identified as periods of near-continuous ascent or descentcovering at least the 0.5 km to 7.5 km altitude range. Fromeach profile, the mean BC concentration (mass of BC perunit volume) in each 0.5km altitude interval from 0 to 15kmis calculated. These are then integrated vertically to give anestimate of the column burden (shown on the maps in Fig.1as coloured circles).

Because the HIPPO profiles do not extend all the way tothe surface or our 15 km “lid”, there is some uncertainty inhow we extrapolate the profile when calculating the burden.We calculate a lower estimate by assuming the BC concen-tration is zero outside the altitude range of the observations;for an upper estimate, we assume that the BC concentrationsobserved at the bottom and top of the profile continue to thesurface and 15km, respectively. (This does not give a true up-per bound on the burden, since the concentrations outside theobserved altitude range may be higher then those within, butprovides an estimate of the extrapolation uncertainty.) Be-causeSchwarz et al.(2010) attribute the largest part of the±30% uncertainty in BC MMR to calibration (correlated)

rather than random (uncorrelated) error, we assume that thefull ±30% may apply to the derived burden estimates. Theseranges (including both the extrapolation and measurementuncertainty) are shown as the red bars on the side-plots inFig. 1.

4.2 Point-by-point comparison

For a more detailed point-by-point comparison, we performon-line interpolation of the instantaneous mass mixing ratiofields from each model to the points along the HIPPO flighttrack, following O’Connor et al.(2005) and Telford et al.(2013). The spatial interpolation is linear in log-pressureand both horizontal directions. Temporally, each observa-tion is matched to the following model time-step. Coupledwith nudging to reproduce the observed synoptic conditions(notwithstanding the uncertainty in reanalysis fields in re-mote regions with sparse observations), this allows us tosample the model output consistently with the observationsrather than using a monthly mean or climatology. Althoughneither water vapour nor any cloud variables are nudgeddirectly, Telford et al. (2008) show that large-scale cloudand precipitation patterns are reproduced well in a nudgedmodel, whileRusso et al.(2011) show that for convection anudged model performs as well as an offline chemical trans-port model (CTM) driven directly by meteorological fieldsfrom a reanalysis.

Once this sampling has been done, we can evaluate themodel output pointwise against the actual HIPPO observa-tions both visually and quantitatively. For a visual compar-ison we simply plot the differences in mass mixing ratio ateach point on the flight track (see Figs.3 and4, discussed indetail in Sects.6.1and6.2). For a more quantitative analysis,we look at the mean difference (bias) and correlation coeffi-cient between the logarithms of the real and simulated massmixing ratios, over all the points along the flight track (seeFig. 5, discussed in detail in Sect.6.3). Logarithms are takenas the distribution of observed mixing ratios appears to be ap-proximately log-normal; this results in a distribution whichis more symmetric and closer to a normal distribution, mak-ing standard statistical techniques more meaningful. Withoutlogarithms, the correlation coefficient is distorted by differ-ences in the long upper tail of the distribution.

To estimate the uncertainty in the quantitative analysis, weuse bootstrapping to construct 95% confidence intervals forthe bias and correlation. Because both the observed and mod-elled data series show significant autocorrelation, we use amoving-block bootstrap (Kunsch, 1989) with block length30 (i.e. resampling in approximately half-hour blocks). Thisprovides an estimate of the uncertainty due to random sam-pling variability. To incorporate the uncertainty in the SP2-derived mixing ratios, we extend the error bars on the bias by±30% (to accommodate the worst-case effect on the bias, ofa systematic calibration error). For the correlation, we applyrandom multiplicative Gaussian white noise with a standard



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Fig. 3. Difference of BC mass mixing ratio (kg kg−1) simulated by HadGEM3–UKCA in each configuration (rows) from that observedduring each phase of the HIPPO campaign (columns). The model is nudged and sampled along the HIPPO flight track at 1-min intervals;observed mixing ratio is calculated from HIPPO SP2 data aggregated over 1-min intervals.

deviation of 30 % to each bootstrap sample (to accommodatethe worst case effect on the correlation, of completely uncor-related observation errors).

There is some additional uncertainty in the comparison,due to the limited size range of the SP2 measurements –we adjust the measurements as described in Sect.2 to ac-count for this, but some uncertainty remains as in practicethe fraction of BC which is within the detectable range willbe variable. An alternative approach, of calculating the num-

ber of modelled particles which would contain a detectableamount of BC, is problematic because the models assumeuniform composition, with BC mass spread over all particlesin a given mode. This results in lower BC masses per par-ticle, and many fewer detectable particles, than if the BC isconfined to a subset of particles – whichReddington et al.(2013) show may indeed be the case, at least in the more pol-luted air over continental Europe.



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Fig. 4.Difference of BC mass mixing ratio (kgkg−1) simulated by ECHAM5–HAM2 in each configuration (rows) from that observed duringeach phase of the HIPPO campaign (columns). The model is nudged and sampled along the HIPPO flight track at 1-min intervals; observedmixing ratio is calculated from HIPPO SP2 data aggregated over 1-min intervals.

5 Sensitivity tests

5.1 Biomass-burning emissions

One of the most variable and uncertain sources of BC is frombiomass burning (responsible for approximately half the BCemissions by mass in the models, the remainder coming fromfossil fuel and biofuel burning). The emissions used in theBASE configurations of both models are a monthly clima-tology derived from the AEROCOM hindcast inventory, it-self based on the Global Fire Emissions Database (GFED),version 2 (van der Werf et al., 2006). However, GFED ver-sion 3.1 is now available (van der Werf et al., 2010). Amongstvarious improvements to the emission estimates, there is asubstantial reduction in total carbon emissions from biomassburning, which is reflected in the BC emissions. In addition,GFED3.1 provides daily fractional emission fields (at thesame 0.5◦ resolution as the monthly data, but not resolved bychemical species) which can be applied to each month’s data

to estimate emissions at daily time resolution, and a diurnalprofile for each month (also at 0.5◦ resolution, in 3-h inter-vals), giving estimates at 3-hourly time resolution (Mu et al.,2011). The new dataset now covers the period to the end of2010, sufficient for simulations during the first three phasesof the HIPPO campaign and removing the need to extrapo-late the emission dataset. Switching to GFED3.1 emissionsfor biomass burning gives the G3M (monthly emissions) andG3H (3-hourly emissions) configurations (the latter only im-plemented in ECHAM5–HAM2).

In HadGEM3–UKCA, both BASE and G3M configura-tions distribute the biomass-burning emissions uniformly inheight over levels 2 to 12 (∼ 50 m to 3 km). In ECHAM5–HAM2, the BASE configuration uses a biome-dependent ver-tical profile for the emissions, as in AEROCOM Phase I(Dentener et al., 2006), while the G3M and G3H configura-tions divide the emissions equally between the model levelsdiagnosed to be within the boundary layer.



5.2 Convective scavenging

Convection plays a dominant role in the upward transportof both gaseous and particulate matter in the atmosphere,with wide variation amongst models especially for short-lived species (Hoyle et al., 2011). However, convection (es-pecially the vigorous, deep convection that can transport airparcels from the boundary layer to the upper troposphere) isalso associated with intense precipitation, and thus a signifi-cant amount of material may be removed by wet scavengingbefore it is detrained from the convective updraught.Schwarzet al.(2010) identify the treatment of this process as likely tobe a major factor in the diversity of the AEROCOM modelsand their high bias compared to the HIPPO-1 SP2 observa-tions, particularly in the tropics.

The models used here take two different approaches toconvective scavenging. In the operator-split approach, asused in the BASE configuration of HadGEM3–UKCA, con-vective scavenging removes aerosol from the grid-box meanfield after the convection scheme (including convective tracertransport) has run. In the in-plume approach, as used inECHAM5–HAM2, aerosol is removed directly from thetracer flux in the convective updraught, along with the re-moval of water by convective precipitation. Additional sim-ulations with HadGEM3–UKCA have also been carried outusing an in-plume scheme (CVSCAV and CVSCAV+G3Mconfigurations). This assumes that 100 % of the soluble accu-mulation and coarse modes in the upward convective tracerflux is taken up by the cloud drops and, therefore, removedin proportion to the amount of cloud water which precipi-tates (as in the existing scheme for large-scale cloud); addi-tionally, 50 % (by mass and number) of the soluble Aitkenmode is taken up and thus susceptible to removal, as a cruderepresentation of the fact that smaller particles can be ac-tivated in the faster updraughts found in convective cloud.(The figure of 50% is somewhat arbitrary, and there is cer-tainly scope for refinement – both the large-scale and con-vective schemes should ideally use an appropriate critical ra-dius based on Kohler theory.) It should be noted, however,that this scheme does not yet include resuspension of aerosolwhen rain evaporates (nor does the existing operator-splitscheme, or the large-scale scavenging scheme), unlike thatin ECHAM5–HAM2.

Although in this study we focus on the impact of the cou-pling between convective transport and wet deposition, it isworth noting that the parameterisation of convective trans-port itself (in particular entrainment and detrainment) mayhave a significant impact on the vertical distribution of trac-ers, as demonstrated inHoyle et al.(2011) andCroft et al.(2012).

6 Results

6.1 HadGEM3–UKCA

The BC MMR from HadGEM3–UKCA (in its BASE config-uration), sampled at 1-minute intervals along the flight trackfor the first three phases of the HIPPO campaign, is shownin the second row of Fig.2. Although some features (e.g.the disparity between the hemispheres in the HIPPO-3 data)are well reproduced, the model does not appear to reproduceother large-scale features of the observations very well. Mostnoticeably, for all three phases, the model has a significantexcess of BC in the upper troposphere, especially in the trop-ics.

Figure 3 shows the difference between the HadGEM3–UKCA simulations in each configuration and the actual ob-servations from each phase of the HIPPO campaign.

It is clear from these difference plots that, at least forHIPPO-1 and HIPPO-2, the upper-tropospheric excess seenin the BASE configuration is largely removed when the in-plume convective scavenging scheme is switched on (i.e. inCVSCAV and CVSCAV+G3M), suggesting that the lack ofrealistic convective scavenging may have been responsible.This is supported by the fact that – without adjusting anyother parameters in the model – the improvement is so strong,while introducing very little in the way of new visible er-rors which we might expect to see if the new scheme wascompensating for errors in a different process (which wouldlikely have a different structure). The third row of Fig.2shows the BC mixing ratio from the CVSCAV+G3M simula-tion, which is visibly more realistic with respect to the obser-vations. For HIPPO-3, the improvement is largely confined tothe Southern Hemisphere; in the Northern Hemisphere bothsimulations produce too little aerosol at lower levels and toomuch aloft. This is despite the fact that HIPPO-3 observedmore BC at upper levels in the Northern Hemisphere thanthe earlier phases.

The change in switching to GFED3.1 biomass-burningemissions (i.e. BASE to G3M) is less dramatic. While, forHIPPO-1 and HIPPO-2, the difference plot for the G3M sim-ulation (second row of Fig.3) indicates less of a positivebias than for BASE, the upper-tropospheric excess remainsclear. Applying the emissions change on top of the in-plumeconvective scavenging (i.e. going from CVSCAV to CVS-CAV+G3M) removes what little excess remains in the mid-dle and upper troposphere, but appears to leave an overallnegative bias compared to the observations. For HIPPO-3,the differences from the choice of emissions are even lessclear. Unlike for convective scavenging, we cannot be con-fident that the small improvements seen here are genuinelyattributable to better emissions, rather than compensating forbiases elsewhere in the model.

It thus appears that for HIPPO-1 and HIPPO-2 glob-ally, and for HIPPO-3 in the Southern Hemisphere, the dis-agreement between the BASE model and observations is



dominated by the lack of realistic convective scavenging, andis much improved when an in-plume approach is introduced.For HIPPO-3 in the Northern Hemisphere, however, it ap-pears that the disagreement is dominated by other effectswhich have not yet been identified. This is consistent withHIPPO-3 occurring in Northern Hemisphere spring, whenconvective precipitation in the northern mid-latitude Pacificis relatively weak.

The differences can also be seen in the burdens (top tworows of Fig.1). The BASE simulation over-predicts the BCburden at most of the profile locations, in many cases by anorder of magnitude. CVSCAV+G3M performs much better,with the model burden frequently close to the range esti-mated from the HIPPO observations. For brevity, the sepa-rate plots for CVSCAV and G3M are omitted; however asbefore, most of the improvement is seen in the former and isparticularly pronounced over the tropical warm pool regionwhere strong convective scavenging is expected. The highburdens observed in the Arctic in HIPPO-1 were attributedto a localised biomass-burning plume (Schwarz et al., 2010)as they were dominated by two particular profiles which wereclose together. In HIPPO-2 and HIPPO-3, however, the highArctic burdens are a more systematic feature of the profilesin this region. This suggests that the model is underestimat-ing the transport of BC to the Arctic – either due to errorsin the transport itself, or because it is removed too rapidly(probably by large-scale wet scavenging, since this affectsboth BASE and CVSCAV simulations).

6.2 ECHAM5–HAM2

The BC MMR from ECHAM5–HAM2 (in its BASE con-figuration), sampled at 1-minute intervals along the flighttrack for the first three phases of the HIPPO campaign, isshown in the bottom row of Fig.2. These do not exhibitthe large upper-troposphere excesses seen in the HadGEM3–UKCA BASE simulation, but there are some unexpectedlylarge mixing ratios at even higher altitudes (including intothe lower stratosphere).

Figure 4 shows the difference between the ECHAM5–HAM2 simulations in each configuration and the actual ob-servations from each phase of the HIPPO campaign. Thelower-stratosphere anomalies are clear in all simulations, andfor HIPPO-1 and HIPPO-2 the BASE configuration showspatches of (mostly positive) bias throughout the tropospherethat are not immediately obvious from Fig.2.

Some of the strongest biases are reduced in the G3M sim-ulation: in particular, at lower levels around the equator (forall three phases) and also in the southern mid-latitudes (forHIPPO-1). This suggests that part of the tropospheric error inthe BASE configuration may be attributable to the choice andimplementation of biomass-burning emissions; however as inHadGEM3–UKCA, the improvement is not decisive enoughto exclude the possibility that we are compensating for otherbiases in the model. It is also possible that some of this dif-

ference is due to the different vertical profile of emissionsbetween BASE and G3M. However, an additional simula-tion (not shown here) for HIPPO-1 with the same GFED2emissions as BASE, but the boundary-layer-following ver-tical profile of G3M shows results very similar to BASE.This suggests that it is the updated inventory, rather than thechange in vertical profile, which makes the difference. Sim-ilarly, using a boundary-layer-following emission profile inHadGEM3–UKCA (instead of the default fixed∼ 50 m to3 km profile) makes little difference, indicating that the dif-ferent emission profiles do not contribute significantly to thedifferences between the two models.

As in HadGEM3–UKCA (CVSCAV), HIPPO-3 looksrather different to the earlier phases. There appears to be littlechange between the ECHAM5–HAM2 BASE and G3M sim-ulations, with the tropospheric error in both simulations dom-inated by negative anomalies throughout most of the North-ern Hemisphere.

For all three phases, there is almost no visible differ-ence between the G3M and G3H simulations. This indicatesthat, at least for simulations in remote regions such as thosecovered in the HIPPO campaign, monthly biomass-burningemissions are sufficient as any high-frequency variability atthe source is smoothed out during transport. Higher-time-resolution emissions could provide more benefits for simu-lations closer to source regions, however.

The ECHAM5–HAM2 simulated burdens (third row ofFig. 1) show a similar pattern of overestimating the ob-servations as the HadGEM3–UKCA BASE simulation, de-spite ECHAM5–HAM2 already having an in-plume con-vective scavenging scheme. We cannot determine from thisanalysis what process is responsible for the high burden inECHAM5–HAM2 – a more detailed study of the role ofthe different processes in this model would be required. Itmay still be some aspect of scavenging which is too weak,but equally the problem may be elsewhere. The AERO-COM Phase I (Textor et al., 2006) median model, shown inthe bottom row, shows a similar positive bias.

The presence of high BC burdens in the remote Pacific inthese models, in a way that does not correspond with obser-vations, suggests that the model BC lifetime is too long. InHadGEM3–UKCA this appears to be a structural issue withconvective scavenging; in ECHAM5–HAM2 and other mod-els it may be due to different processes.

6.3 Quantitative evaluation

Figure 5 shows the bias and correlation coefficient oflog(BC MMR) for each simulation against each phase of theHIPPO campaign, along with bootstrap uncertainty estimatesas described in Sect.4.

The improvement in both bias and correlation whenswitching to in-plume convective scavenging in HadGEM3–UKCA can clearly be seen when going from BASE to CVS-CAV, or from G3M to CVSCAV+G3M (with the exception



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Fig. 5. Bias-correlation plots of log(BC mass mixing ratio) between the HadGEM3–UKCA (top row) and ECHAM5–HAM2 (bottom row)simulations and each phase of the HIPPO campaign (columns). The error bars represent a 95% confidence interval based on a moving-blockbootstrap and the±30 % error in the SP2-derived mixing ratios from HIPPO-1. The solid symbols represent nudged simulations, while thehollow symbols (for HadGEM3–UKCA) represent free-running simulations. The “obs.” point on the right-hand side indicates where a modelwhich reproduces the observations perfectly would be located.

of the negative bias against HIPPO-2 and HIPPO-3 in theCVSCAV+G3M simulation). The improvement in correla-tion should perhaps be regarded as more relevant, as the biasis likely to be more susceptible to model tuning/calibration.For all three phases, this increase in correlation (0.22→

0.41, 0.27→ 0.42, 0.51→ 0.65 between BASE and CVS-CAV for HIPPO-1, -2, -3 respectively) is statistically signifi-cant in the sense that the error bars of the nudged BASE andCVSCAV (or G3M and CVSCAV+G3M) simulations do notoverlap. Carrying out the analysis separately for the points inthe two hemispheres (not shown) indicates that the increasein correlation comes largely from the Northern Hemisphere.

For both models, a small improvement in correlation(although not in bias for HadGEM3–UKCA CVSCAV) isseen when going from BASE to G3M (or CVSCAV toCVSCAV+G3M), although the overlapping error bars in-dicate that this is not statistically significant. As with thevisual analysis, there is almost no difference between theECHAM5–HAM2 G3M and G3H simulations. It is proba-bly the case that evaluation closer to source regions would bemore powerful in distinguishing between emissions invento-ries and their time resolution.

The correlation and, in most cases, also the bias are muchimproved in the nudged HadGEM3–UKCA simulations(solid symbols) compared to their free-running counterparts(hollow symbols). The correlation increases for the BASEconfiguration are 0.14→ 0.22, 0.08→ 0.27, 0.44→ 0.51

between free-running and nudged simulations for HIPPO-1, -2, -3, respectively. In addition, the improvement in biasand correlation from changes to the model configuration isenhanced in the nudged simulations. This is particularly sig-nificant for HIPPO-1, where nudging eliminates the overlap-ping error bars on the correlation axis between BASE andCVSCAV. This allows us to conclude that the improvementin CVSCAV is statistically significant, which may not havebeen clear from the free-running simulations alone. (It shouldbe noted in this context that the error bars on the free-runningmodels are an underestimate – ensemble simulations wouldbe needed to quantify the additional uncertainty from thesimulated meteorology.) Thus, not only does nudging help toproduce realistic simulations of aerosol during a given flightcampaign, but it also makes it easier to evaluate the effectof changes to the aerosol scheme by damping errors due todifferences in large-scale dynamics.

6.4 Comparison with profile curves

To compare the point-by-point analysis presented here witha more traditional approach, we have constructed profilecurves from the HIPPO-1 observations for four latitudebands using the (geometric) mean and standard deviationover all the profiles identified for the burden analysis (asdescribed in Sect.4.1) in each latitude band. We have alsoconstructed corresponding curves from the January 2009



10-14 10-13 10-12 10-11 10-10 10-9

BC MMR / kg kg¡102468

101214

altitu

de / k

m

obs.BASEG3MCVSCAVCVSCAV+G3M

60°N–80°N

10-14 10-13 10-12 10-11 10-10 10-9


101214

altitu

de / k

m

20°N–60°N

10-14 10-13 10-12 10-11 10-10 10-9


101214

altitu

de / k

m

20°S–20°N

10-14 10-13 10-12 10-11 10-10 10-9


101214

altitu

de / k

m60°S–20°S

Fig. 6.Vertical profile curves of BC mass mixing ratio for HIPPO-1and horizontally-matched locations in the January 2009 monthly-mean output from the HadGEM3–UKCA simulations. The shadedregion shows the (geometric) standard deviation of the observedMMR values over the profiles in each latitude band, plus the±30 %measurement error.

monthly-mean output from the HadGEM3–UKCA simula-tions, by horizontally interpolating to the location of eachprofile identified in the observations.

The results are shown in Fig.6. Although the construc-tion is similar to that inSchwarz et al.(2010), the curvesare not identical due to the different profile-detection algo-rithm used. While some improvement from both CVSCAVand G3M can be seen in these curves, this is rather less clearthan in the point-by-point analysis (Fig.3). In many cases,the differences are overshadowed either by the variability be-tween profiles within a region (e.g. in the 60◦ N–80◦ N band,where all four model curves lie almost entirely within thespread of the observed profiles) or by overall regional biases(e.g. in the 20◦ S–20◦ N band, where all four model curveslie almost entirely outside the spread of the observed pro-files). It is therefore difficult to ascribe statistical significanceto the model improvements from this analysis, as we did inSect.6.3from Fig.5.

This demonstrates the usefulness of the point-by-pointanalysis presented here in allowing us to evaluate process-level changes to the model with rather more confidence thancan be obtained from a more traditional approach.

7 Conclusions

In this study, we develop methods for evaluating aerosol–climate models against large-scale aircraft campaigns, andapply these to investigate the impact of convective scaveng-ing and biomass-burning emissions on the vertical profile ofblack carbon.

By running two aerosol–climate models in nudged con-figurations and interpolating their output onto the track of aflight campaign, we make a detailed pointwise comparisonbetween model output and in-situ aircraft observations. Us-ing data from a campaign such as HIPPO, which has goodvertical resolution over an extended geographical area, thisgives a powerful tool for evaluating the vertical distributionof aerosol in the models. We also show how these measure-ments can be used to evaluate column-integrated burdens inthe models, which are a more direct product of most mod-els than the optical/radiative properties (e.g. aerosol opticaldepth) which can be evaluated via remote sensing.

We apply this approach to black carbon aerosol inthe HadGEM3–UKCA and ECHAM5–HAM2 models, andshown how each has different areas of disagreement with theHIPPO SP2 observations. Both models significantly over-predict BC burden, especially in the more remote regions,suggesting that the BC lifetime is too long. In the case ofHadGEM3–UKCA, the largest discrepancy (an excess ofaerosol in the tropical upper troposphere) can be eliminatedby switching from the default operator-split convective scav-enging scheme to one which scavenges directly from theconvective plume. This change improves both the verticaldistribution of BC and the simulated burdens against theHIPPO observations, yielding a statistically significant in-crease in the pointwise correlation coefficient for all threephases of the HIPPO campaign (0.22→ 0.41, 0.27→ 0.42,0.51→ 0.65 for HIPPO-1, -2, -3, respectively).

In both models, a somewhat smaller and not statisti-cally significant improvement can be seen when switchingfrom GFED2-based biomass-burning emissions to GFED3.1;however, there is virtually no change in this remote regionwhen the time resolution of these emissions is increased frommonthly to 3-hourly. It seems likely that a similar analysiswith a wider range of flight campaigns, including the majorbiomass-burning regions, might better constrain the choiceof such emissions.

We show for HadGEM3–UKCA that both the correlationbetween the BASE configuration and the observations, andthe increase in correlation due to the new convective scaveng-ing scheme, are enhanced when the simulations are nudgedas opposed to free-running. In this way, nudging can enablestatistically significant improvements in the model to be de-tected where they might not be in a free-running simulation;e.g. the above increase in the correlation of the nudged modelagainst HIPPO-1 (from 0.22 to 0.41) is statistically signifi-cant, while the corresponding increase for the free-runningmodel (from 0.14 to 0.27) is not.



In an analysis of this kind, there is always the potential forcompensating errors in different parts of the model to obscurewhich processes are poorly represented. This is particularlytrue in this case where we make a change in the biomass-burning emissions and compare to observations in remoteregions; an analysis nearer the source regions might bet-ter distinguish emissions effects from other sources of bias.For convective scavenging in HadGEM3–UKCA, however,we see a very clear improvement from a more physically-realistic implementation without adjusting any other modelparameters, with very little new error introduced; thus weconclude that we are not simply compensating for other er-rors, but that it is the convective scavenging itself which mustbe accurately represented in the model to obtain a realisticvertical profile.

It is clear that vertically-resolved in-situ measurementsof aerosol have an important role to play in evaluating theaerosol distributions simulated by aerosol–climate models, inconjunction with satellite remote sensing and ground-basedobservations, and that they can provide particular insight intothe processes governing the vertical transport of aerosol inthe atmosphere, as we have seen with convective scavenging.

Acknowledgements.This work was supported by the NaturalEnvironment Research Council project AEROS [grant numberNE/G006148/1]; and the Met. Office. GM is supported by theNatural Environment Research Council (NERC) through theNational Centre for Atmospheric Science (NCAS). HIPPO flightdata provided by NCAR/EOL under sponsorship of the NationalScience Foundation (http://data.eol.ucar.edu/). ERA-Interim dataprovided by Paul Berrisford and the European Centre for Medium-Range Weather Forecasts (ECMWF). GFED3.1 biomass-burningemissions data provided by Guido van der Werf. The developmentof GLOMAP-mode within HadGEM is part of the UKCA project,which is supported by both NERC and the Joint DECC/DEFRAMet. Office Hadley Centre Climate Programme. AEROCOMmedian model data supplied by the AEROCOM project, fromdata provided by the contributing modellers. Computing resourcesprovided by the Met. Office/NERC Joint Weather & Climate Re-search Programme (JWCRP) and Deutsche Klimarechenzentrum(DKRZ). Thanks to Nick Schutgens for a number of insightfuldiscussions during the course of this work, and to two anonymousreferees for their many helpful comments.

Edited by: N. Riemer

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