Vertical and Spatial Profiling of Arctic BC on the North

Vertical and Spatial Profiling of Arctic BC on the North Slope of Alaska 2015: Comparison of Model and Observations

A.J. Sedlacek, III1, Y. Feng2, S. Biraud3, and S. Springston1 1Brookhaven National Laboratory; 2Argonne National Laboratory; 3Lawrence Berkeley National Laboratory

Abstract One of the major issues confronting aerosol climate simulations of the Arctic and Antarctic Cryospheres is the lack of detailed data on the vertical and spatial distribution of aerosols with which to test these models. This is due, in part, to the inherent difficulty of conducting such measurements in extreme environments. One class of under measured radiative forcing agents in the Polar Region is the absorbing aerosol – black carbon and brown carbon. In particular, vertical profile information of BC is critical in reducing uncertainty in model assessment of aerosol radiative impact at high latitudes. During the summer of 2015, a Single-Particle Soot Photometer (SP2) was deployed aboard the DOE Gultstream-1 (G-1) aircraft to measure refractory BC (rBC) concentrations as part of the DOE-sponsored ACME-V (ARM Airborne Carbon Measurements) campaign. This campaign was conducted from June 1 through September 15 along the North Slope of Alaska. Flights started in Deadhorse (PASC) and was punctuated by vertical profiling over 5 sites (Atquasuk (PATQ), Barrow (NSA), Ivotuk, Oliktok (AMF), and Toolik). In addition to rBC, measurement of CO, CO2 and CH4 were also taken to provide information on the spatial and seasonal differences in GHG sources and how these sources correlate with BC. It is expected that the aerosol and gas phase measurement will provide an important dataset to assess the representativeness of ground sites at regional scales.

FLANNER: BLACK CARBON INFLUENCE ON ARCTIC CLIMATE

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Figure 1. Equilibrium changes in Arctic (60–90°N) atmospheric and surface air temperature with monthand altitude for the first set of fixed AAOD experiments shown in Table 1. The vertical coordinate ishybrid sigma pressure used in CAM4.

sensitivity studies with prescribed absorption at differentaltitudes [Hansen et al., 2005; Ban-Weiss et al., 2011]. Thisstudy does not, however, treat aerosol-cloud indirect effects,which can warm the Arctic surface through longwave fluxchanges [Lubin and Vogelmann, 2006; Garrett and Zhao,2006], cool the Arctic through shortwave flux changes [e.g.,Alterskjær et al., 2010], and either amplify or reduce theglobal TOA flux perturbation from BC [Ming et al., 2010].Arctic climate response to local BC changed relatively little,however, with inclusion of aerosol-cloud indirect effects intwo other studies [Shindell and Faluvegi, 2009; Sand et al.,2013].

3.2. Seasonally Varying Arctic Forcing[14] The seasonally dependent fixed AAOD experiments

(Table 1, bottom), with BC distributed evenly betweenthe surface and about 550 mbar, show that a unit mass ofArctic tropospheric BC tends to exert the largest TOA radia-tive forcing during April–May, rather than June–July whenlocal insolation peaks. This is a consequence of higherunderlying albedo during spring, caused by greater snowand sea-ice coverage and brighter snow and ice albedo dur-ing the pre-melt season [e.g., Perovich et al., 2002; Flanneret al., 2011]. Coverage of low clouds increases during the

spring-summer progression in all model runs, contribut-ing to increased TOA forcing from atmospheric BC [e.g.,Penner et al., 1998; Haywood and Ramaswamy, 1998;Zarzycki and Bond, 2010] that partially offsets the influenceof decreasing cryospheric cover. Surface forcing followsinsolation more closely, becoming most negative duringJune–July. Opposite of TOA forcing, surface forcing fromabsorbing aerosols is weaker over reflective surfaces [e.g.,Cess, 1983]. Arctic forcing from BC in the cryosphere alsopeaks during April–May because of expansive snow andsea-ice coverage, which reach annual minimums in Augustand September, respectively. Arctic annual mean surfacealbedo in the control simulation is 0.46, peaking at 0.66 inMarch and reaching a minimum of 0.27 in August.

[15] Normalized to forcing, Arctic surface air warms themost from BC residing in snow and sea ice during August–September, which drives a very large sensitivity [3.7 ˙1.8 K (Wm–2)–1]. Because of relatively small forcing in thisexperiment (caused by reduced late-summer insolation andminimal snow and sea-ice coverage), however, the vari-ability in sensitivity is large and overlaps (within 1!) withthat of several other experiments (Table 1). This is alsothe sole experiment with Arctic-only BC to increase netmeridional energy transport into the Arctic (Tables 1 and 2),

1844

Aircraft Technical Information Length: 19.4 m

Wingspan: 23.9 m

Height: 7.1 m

Cabin space: 15.3 m2

External probes (PMS cans): 8

Maximum gross weight: 16,330 kg

Maximum Endurance: 9.5 hours

Maximum Range: 4000 km

Endurance with full payload: 4-5 hours

Crew capacity: 7 max, 2 pilots + 3-5 scientists

Cabin payload: 1,900 kg

Research Power: 700A @ 28 VDC (incl. 85A @ 115 VAC, 60 Hz)

Ceiling: 7.6 km

G-1 (BMI owned, ARM base funded, PNNL based and managed, for the science community)

Gultstream-1 (G-1) Platform

Aircraft Technical Information Length: 19.4 m

Wingspan: 23.9 m

Height: 7.1 m

Cabin space: 15.3 m2

External probes (PMS cans): 8

Maximum gross weight: 16,330 kg

Maximum Endurance: 9.5 hours

Maximum Range: 4000 km

Endurance with full payload: 4-5 hours

Crew capacity: 7 max, 2 pilots + 3-5 scientists

Cabin payload: 1,900 kg

Research Power: 700A @ 28 VDC (incl. 85A @ 115 VAC, 60 Hz)

Ceiling: 7.6 km

G-1 (BMI owned, ARM base funded, PNNL based and managed, for the science community)

Instrumentation

614 J.P. SCHWARZ ET AL.

Coating/DenudingApparatus

DMA

Bypass

Controlled Flame

Plenum

Bypass

DMAImpactor

O2N2C2H4

SP2Filter Sampler SMPS CPC CPMA

FIG. 1. Schematic of the flame-generated soot generation and detection scheme with the filter sampler, single particle soot photometer (SP2), scanning mobilityparticle sizer (SMPS), condensation particle counter (CPC), Couette centrifugal particle mass analyzer (CPMA), and differential mobility analyzer (DMA).

2. SP2 INSTRUMENT AND PROCEDURES

2.1. Single Particle Soot Photometer (SP2) ApparatusThe SP2 consists of an intense intra-cavity laser, a flow con-

trol system to confine sample aerosol to the center 1/4 of the laserbeam, four optical detectors focused on the intersection of thesample aerosol with the laser beam, and electronic resourcesnecessary to store the response of the detectors to individualparticles crossing the laser beam (Figure 2).

A solid-state pump laser (808 nm, Unique-Mode AG, Jena,Germany) is fiber-optically coupled to a Nd:YAG crystal thatis coated on one side with a reflective coating at 1064 nm.The coating defines one side of the laser cavity, while a high-reflectivity mirror defines the other side. The laser is tuned suchthat it is in a TEM 0,0 mode, i.e., with a Gaussian intensity

Nd:YAG crystallasing at 1064nmHigh reflectivity

mirror

Pumpdiodelaser

Sampleaerosol

Optical filters

PMT-Photodetectors

Scatteredlight - APDdetector

Blue light

Red light

2-element detector

Cavity-leakagedetector

Exhaust

FIG. 2. Schematic of the Single Particle Soot Photometer (SP2) followingSchwarz et al. (2006). The sample aerosol is confined with filtered sheath flowto the center 1/4 of the laser beam, and then drawn out of the laser cavity throughthe exhaust line. Although drawn in the page for simplicity, the sample line andexhaust lines extend perpendicular to the detection axes (i.e., vertically into andout of the page).

profile. A pinhole aperture (I.D. 2.3 mm) is placed in the cavityto constrain the spatial mode of the laser and reduce pump lightleakage into the cavity. Some laser radiation (at 1064 nm) leaksthrough the mirror. This light is filtered to remove contributionsfrom the pump laser and detected with a “leakage” photodetec-tor, providing a convenient relative measure of laser intensity.Since the amount of leakage depends on the (unknown) qualityof the mirror, the photodetector signal cannot be used to de-rive an absolute measure of the laser intensity within the cavity.With independent calibration, however, the signal can be usedas a relative measure of intensity (as in Section 2.3). Note thatalthough the laser cavity is defined by high-reflectivity mirrors,the cavity finesse, Q, is limited by the Nd:YAG crystal itself toa low value (on order ∼100). Thus the laser beam is expected tobe insensitive to the small perturbation caused by the presenceof a small particle in its path. This insensitivity has been con-firmed through inspection of the leakage light when particlestraversed the laser.

Sample aerosol is continuously introduced to the laser cavitythrough a capillary (typically 0.43 mm ID). The input aerosolstream is confined to the center ∼ 1/4 of the laser (which has a∼1.1 mm 95% intensity width), so individual particles travers-ing the beam experience peak intensities of ∼70–100% of theintensity at its center. The measurement of the width of theaerosol jet was made with PSL particles in the diameter range of200–600 nm. It is possible that diffusion may increase the widthof the jet for small particles (≪200 nm), thereby reducing theefficiency of their detection by the SP2. Here this mechanismis not addressed. The aerosol interacts with the laser at a pres-sure that is typically within 20 hPa of ambient (i.e., sea-levelpressure, 1013 hPa) in this study.

The aerosol flow into the SP2 is measured with a laminarflow element; the pressure difference across the element is pro-portional to the volume flow through the element. The laminarflow element was calibrated several times over the course ofthe intercomparison with a Gilian flow standard (Gilibrator 2,Sensidyne, Clearwater, FL, USA). In general, the sample flowremained constant as long as the upstream plenum pressure was

Downloaded By: [Brookhaven National Lab] At: 20:47 10 January 2011

Scattered Light - APD Detector

Incandescence - Narrowband

Incandescence - Broadband

Single Particle Soot Photometer (SP2)

Probe refractory black carbon (rBC) using laser-induced incandescence •  rBC mass loading; rBC size/mass distribution; rBC mixing state

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Probe CO and N2O using multipass white cell (NIR absorption) •  CO and N2O Calibration against 3-5 grab samples per flight •  CO and N2O Precision: < 1 ppbV with ~10 sec time response

Probe CO2, CH4, and H2O using Cavity Ring-Down Spectroscopy (IR absorption)

•  Calibration against WMO standards •  Precision: < 0.2 ppm for CO2; < 1 ppbv for CH4

CO, H2O, N2O Analyzer

CO2, CH4, H2O Analyzer

Example Data Streams and Selected Calibration plots

Profiling Statistics

Box and whisker plots of the monthly median, means and 10, 25, 75, and 90 percentiles. Dotted line is CAM5 model based on 2011 inventory.

Observation-model agreement varies from very good: PATQ (June/July); Toolik (July); Barrow (July) to very poor [3x difference] Toolik (June & Sept); Barrow (Aug); Oliktok (Aug).

ACME-V provided the opportunity to collect much needed statistics on the rBC vertical profile in the North Slope region.

Biomass Burning Event (2015-06-30)

06/30/2015: Fire activities and AOD at 550nm from MODIS and back trajectory at 3000m

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dependence of ω on MCEFI can be explained by the variation in chemical composition of BB aerosols pro-duced under different combustion conditions. Specifically, because BC is generated by flaming combustionwhile OC is primarily produced by smoldering combustion [McMeeking et al., 2009], the mass fraction of OC(out of the sum of OC and BC) decreases from ~80% to ~20% as MCEFI increases from ~0.85 to ~1 in ourobservations, a trend that is consistent with previous findings [Christian et al., 2003]. The large decrease of OCrelative to BC for MCEFI> 0.85 measurements results in a sharp decrease of ω in the relatively high MCEcombustions. Aerosols emitted from the low MCE combustion (MCEFI< 0.85) are dominated by OC, resultingin high ω (~0.95) with small variability. In addition, the strong dependence of ω on MCEFI explains the largevariation of ω for the repeated burns of the same fuels. For example, the African grass combustion aerosolsexhibit ω532 (ω at 532 nm) of 0.36–0.96 that decreases with MCEFI (Table S1). Moreover, the ω versus MCEFItrend from FLAME-4 compares well with previous laboratory studies [Chand et al., 2005; Chen et al., 2006](Figure 1b) but dramatically increases the coverage of MCE and ω and extends to multiple wavelengths,strengthening the repeatability of the laboratory measurements.

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Hay Juniper Manzanita Millet Peat Ponderosa Pine Rice Straw Sawgrass Sugar Cane

Wheat Straw Wiregrass

Chen et al. 2006 Chand et al. 2005

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Figure 1. Fire-integrated ω as a function of fire-integrated MCE at (a) 405nm, (b) 532nm, and (c) 781nm measured duringFLAME-4. The solid and dashed lines represent the best fit and the uncertainty bounds (calculated using the fitting errors inTable 1), respectively. (d) The ratio of ω781 to ω405 versus MCEFI. The symbols of the points represent fuel types listed in thelegend, in which “CS” indicates cookstove studies. The points are colored by the ratio of OCmass (MOC) to the total mass of OCand BC (MOC+MBC). The grey points indicate experiments with no EC-OC data. The error bars are calculated using 5% and10% relative uncertainty in the babs and bscat measurements, respectively. Measurements from Chen et al. [2006] and Chandet al. [2005] (Figure 1b). Note that the point of Chand et al. representsω540 andMCE range of 0.02–0.56 in their measurements.

Table 1. Fitting Coefficients of ω as a Function of MCEFI ( y ¼ k0 þ k1xk2 ) and Coefficient of Determination (R2) forthe ω405, ω532, and ω781 Fitsa

Wavelength (nm) k0 k1 k2 R2

405 0.965 (±0.046) #0.747 (±0.071) 21.718 (±4.710) 0.60532 1.000 (±0.046) #0.762 (±0.077) 23.003 (±5.090) 0.60781 1.000 (±0.060) #0.978 (±0.095) 22.016 (±4.800) 0.61

aNumbers in the parentheses represent the standard errors of the fitting coefficients.

Geophysical Research Letters 10.1002/2013GL058392

LIU ET AL. ©2013. The Authors. 744

0.9 Liu et al., 2013

BBOP

MCE

MCE in plume: 0.96

0.5 – 0.8

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Modified Combustion Efficiency (MCE) is used as an index for the relative amount of flaming & smoldering combustion during a fire (Ward & Radke 1993)

MCE à 1: more flaming contribution MCE = 0.8 – 0.9: more smoldering contribution

SSAMCE: ~ 0.7 ± 0.06 SSAObs: 0.92 ± 0.04

Discrepancy between SSAobs and SSAMCE likely due to aging

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Examination of rBC microphysical properties

Conclusion

Climate Model Representation of BC profiles

CAM5.3/MAM4 is used to simulate BC distributions and radiative forcing over the Arctic •  Resolution: ~0.9°x1.25° horizontally and 30 vertical layers •  Fresh and aged BC mass mixing ratios •  BC fractions from fossil fuel, biomass burning and biofuel sources

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1:1 line

rBC biomass burning aerosol exhibit size mode of ~120 nm and mass mode of ~180 nm consistent with previous investigations. Smoke plume is characterized by nearly 100% aged rBC particles consistent with the idea of rapid coating following particle generation.

The figure on the upper right shows the vertical profiles of the rBC mass mixing ratio, and CO measured during the ARCPAC campaign (Spackman et al., 2010). The blue traces are for measurements conducted over ice and open leads while the red traces represent the ascent/descent around Fairbanks. The black line is the mean rBC profile for all the flights conducted during ARCPAC. A tendency for rBC concentrations to increase with altitude in the lower atmosphere is found, with some profiles exhibiting a loading maximum at altitudes of 3-4 kms.

Introduction

Light absorbing aerosols are expected contribute to radiative forcing much as they do in the mid-latitudes – through atmospheric warming (direct effect) and alteration of cloud properties (semi-direct effect) collectively causing surface dimming and subsequent surface cooling. Such behavior has recently been predicted in a series of equilibrium climate simulations conducted by Flanner (2013). The figure on the left (Flanner 2013) displays the temperature profiles as a function of month for five different injection heights of BC. As is readily seen, the surface temperature response is seen to be sensitive to the BC vertical distribution.

ACME-V Field Campaign

The DOE-sponsored ACME-V field campaign, conducted along the North Slope of Alaska provides the opportunity to comparison observations with global climate model (CAM5) simulations to examine the ability of the model to capture observed monthly mean vertical profiles of BC and stratified aerosol layers. Additionally, armed with the observational data, radiative forcing calculations can be conducted to evaluate the impact on local energy balance due to uncertainty in simulated BC profiles during summer time over Arctic.

Shown on the right is the ACME-V flight track on conducted on June 7, 2015, where profile flights were conducted over 5 sites along the North Slope of Alaska (courtesy J. Hubbe (PNNL)).

J. R. Spackman et al.: Observations of black carbon mass in the springtime Arctic 9671

Fig. 3. Vertical profiles of BC mass loadings and CO in the lowest 2 km altitude for all the flights during ARCPAC. The BC and CO datapoints are 30 s and 1 s data, respectively. The blue points denote BC data over the ice and open leads north of Alaska and the red pointscorrespond to ascent/descent data at Fairbanks. The black lines with bars represent the mean BC profiles including the Fairbanks data withone standard deviation around the mean in each 200m altitude bin.

between this layer and the free troposphere with widely vary-ing aerosol and chemical compositions during the samplingperiods especially 18–21 April.Since BC aerosol and CO are both byproducts of incom-

plete combustion, they are often well correlated in the tro-posphere (e.g., Spackman et al., 2008). In the springtimeArctic, background values of CO are⇠160 ppb (Brock et al.,2010). As shown in Fig. 3 on the 12 April flight in the agedArctic air mass, background values of CO persisted through-out the lowest 2 km while BC mass increased by a factor of2 to 3 from 100 to 500m altitude. The observed gradient inBC with respect to constant values of CO indicates that thelower BC mass loadings in the ABL cannot be explained byair mass advection in which a cleaner air mass with less BCand CO was mixed into the ABL from the free troposphereat an earlier time. Rather, the simultaneous measurementsare consistent with the physical removal of BC aerosol fromthis air mass through deposition. Size distributions of BCmass in the ABL and free troposphere on 12 April supportthis conclusion (Fig. 4). The modes for both of the mass dis-tributions are ⇠0.160 µm and the geometric standard devi-ations are nearly identical, suggesting the air masses in the

ABL and the free troposphere have similar sources. Thedata from two other aerosol instruments (for details of theNMASS and UHSAS instruments, see Brock et al., 2004)aboard the WP-3D aircraft that measure particle number andsize distributions also showed evidence for particle removalin the ultrafine and fine modes (i.e., 0.004–1 µm). On thewhole, the tracer and aerosol data across the boundary layertransition strongly suggest the air masses in the ABL andfree troposphere are from the same sources. For comparison,the SP2 instruments on the NASA DC-8 and P-3B reportedvery limited BC mass depletion compared to that observedon the NOAAWP-3D. The processes controlling BC aerosolin the ABL will be explored in greater detail using BC–COand O3–BC correlations in Sect. 4.Polluted midlatitude air was advected into the Alaskan

Arctic and was sampled from 15 April through the remainderof the campaign (Brock et al., 2010; Fuelberg et al., 2010).An analysis using chemical signatures and FLEXPART, aLagrangian particle dispersion model, attributes most of thepollution to agricultural and forest fires in Kazakhstan andthe Lake Baikal region of Siberia, respectively (Warnekeet al., 2009). Most of these plumes were observed above

www.atmos-chem-phys.net/10/9667/2010/ Atmos. Chem. Phys., 10, 9667–9680, 2010

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HG Coef ± one std deva = 0.021918 ± 0.0423b = 0.00035534 ± 1.87e-06

LG Coef ± one std deva = -0.025636 ± 0.0495b = 0.0035257 ± 1.72e-05

Acknowledgements: This research was performed under sponsorship of the U.S. DOE under contracts DE-SC00112704 (BNL) and through DOE ARM (Atmospheric Radiation Measurement) program and AAF (ARM Aerial Facility). References: Flanner, M. G. (2013) J. Geophys. Res. Atmos., 118, 1840 Spackman, J. R., et al.,. (2010) ACP., 10, 9667–9680

Seasonal variations in predicted monthly mean BC prof i les are consistent with the observations (<5 km) at the Toolik site: BC concentrations peak in June and then gradually decrease

During the summer, over the Toolik site, BB is expected to be the predominate source of BC, while fossil fuel and biofuel are expected to become larger contributors to BC loadings in September.

Also similar to the observed profiles, the modeled BC is well-mixed in June between surface and 5 km; and becomes more stratified later with aloft layers in the free troposphere

Except for the boundary layer (<1 km) and above 9 km, fresh emitted aerosols are predicted to dominate the BC mass in the free troposphere and are most likely associated with BB sources.

From June to August, BC mainly from biomass burning is expected to induce a large positive radiative forcing of > +0.9 W m-2 over Arctic. It is very important to constrain the BC loadings and vertical distributions

•  Vertical profiles of rBC were collected along the North Slope of Alaska as part of the DOE-ARM sponsored ACME-V campaign.

•  Comparison of observed monthly means with model calculation (CAM5) reveals a range of agreement/disagreement.

•  SSAMCE – SSAobs comparison for a biomass burning (BB) event suggest that BB aerosols were significantly aged by the time the plume reached the Toolik site.

•  rBC data from this campaign will be available from the ARM website (http://www.arm.gov/campaigns/aaf2015abclp).

Several rBC profiles reveal the presence of pronounced aerosol stratification likely due to biomass burning (BB) events. Suggests high injection heights.

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Community Atmospheric Model with 4 aerosol modes (CAM5.3/MAM4)

Courtesy: B. Schmid/PNNL

*

These events will drive the monthly mean rBC vertical distributions (shown above) if these events are frequent.

Day-to-day model-data comparison not shown here since model requires realistic fire emissions.

Vertical location of rBC likely indicate injection heights, and aging time High rBC events may provide a good data set for studying BB aerosol properties over Arctic.

Fire activities and AOD at 550 nm from MODIS and back trajectory at 3000 m.

Documents

Vertical and Spatial Profiling of Arctic BC on the North