acp-12-6799-2012.pdf

Atmos. Chem. Phys., 12, 6799–

6825

, 2012

www.atmos-chem-phys.net/12/6799/2012/

doi:10.5194/acp-12-6799-2012

Atmospheric

Chemistry

and Physics

An analysis of fast photochemistry over high northern latitudes

during spring and summer using in-situ observations from ARCTAS

and TOPSE

J. R. Olson

, J. H. Crawford

, W. Brune

, J. Mao

, X. Ren

, A. Fried

5,*

, B. Anderson

, E. Apel

, M. Beaver

6,**

D. Blake

, G. Chen

, J. Crounse

, J. Dibb

, G. Diskin

, S. R. Hall

, L. G. Huey

, D. Knapp

, D. Richter

, D. Riemer

J. St. Clair

, K. Ullmann

, J. Walega

, P. Weibring

, A. Weinheimer

, P. Wennberg

, and A. Wisthaler

11,***

NASA Langley Research Center, Hampton, VA, USA

Department of Meteorology, Penn State, University Park, PA, USA

Atmospheric and Oceanic Sciences, Department of Geosciences, Princeton University, Princeton, NJ, USA

NOAA Air Resources Laboratory, Silver Spring, MD, USA

NCAR, Boulder, CO, USA

California Institute of Technology, Pasadena, CA, USA

School of Physical Sciences, Department of Chemistry, University of California, Irvine, CA, USA

School of Physical Sciences, Department of Chemistry, University of New Hampshire, Durham, NH, USA

Georgia Institute of Technology, Atlanta, GA, USA

Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA

Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria

now at: Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA

now at: Environmental Protection Agency, Research Triangle Park, NC, USA

***

now at: Norwegian Institute of Air Research, Kjeller, Norway

Correspondence to:

J. R. Olson (jennifer.r.olson@nasa.gov)

Received: 12 March 2012 – Published in Atmos. Chem. Phys. Discuss.: 11 April 2012

Revised: 9 July 2012 – Accepted: 10 July 2012 – Published: 1 August 2012

Abstract.

Observations of chemical constituents and mete-

orological quantities obtained during the two Arctic phases

of the airborne campaign ARCTAS (Arctic Research of the

Composition of the Troposphere from Aircraft and Satellites)

are analyzed using an observationally constrained steady

state box model. Measurements of OH and HO

from the

Penn State ATHOS instrument are compared to model pre-

dictions. Forty percent of OH measurements below 2 km are

at the limit of detection during the spring phase (ARCTAS-

A). While the median observed-to-calculated ratio is near

one, both the scatter of observations and the model uncer-

tainty for OH are at the magnitude of ambient values. Dur-

ing the summer phase (ARCTAS-B), model predictions of

OH are biased low relative to observations and demonstrate

a high sensitivity to the level of uncertainty in NO observa-

tions. Predictions of HO

using observed CH

O and H

as model constraints are up to a factor of two larger than ob-

served. A temperature-dependent terminal loss rate of HO

to aerosol recently proposed in the literature is shown to

be insufficient to reconcile these differences. A compari-

son of ARCTAS-A to the high latitude springtime portion

of the 2000 TOPSE campaign (Tropospheric Ozone Produc-

tion about the Spring Equinox) shows similar meteorologi-

cal and chemical environments with the exception of perox-

ides; observations of H

during ARCTAS-A were 2.5 to

3 times larger than those during TOPSE. The cause of this

difference in peroxides remains unresolved and has impor-

tant implications for the Arctic HO

budget. Unconstrained

model predictions for both phases indicate photochemistry

alone is unable to simultaneously sustain observed levels of

O and H

; however when the model is constrained

with observed CH

O, H

predictions from a range of

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

6800

J. R. Olson et al.: An analysis of fast photochemistry over high northern latitudes

rainout parameterizations bracket its observations. A mech-

anism suitable to explain observed concentrations of CH

is uncertain. Free tropospheric observations of acetaldehyde

(CH

CHO) are 2–3 times larger than its predictions, though

constraint of the model to those observations is sufficient to

account for less than half of the deficit in predicted CH

The box model calculates gross O

formation during spring

to maximize from 1–4 km at 0.8 ppbv d

−

, in agreement

with estimates from TOPSE, and a gross production of 2–

4 ppbv d

−

in the boundary layer and upper troposphere dur-

ing summer. Use of the lower observed levels of HO

place of model predictions decreases the gross production

by 25–50 %. Net O

production is near zero throughout the

ARCTAS-A troposphere, and is 1–2 ppbv in the boundary

layer and upper altitudes during ARCTAS-B.

1 Introduction

The climate of the Arctic environment is changing more

rapidly than any other region. While polar temperature trends

vary, the overall trend is one of substantial warming (Arc-

tic Climate Impact Assessment

http://amap.no/acia/

), and the

September sea ice extent is decreasing at accelerating rates

(Lemke et al., 2007; Comiso et al., 2008). Studies indicate

that regional radiative forcing from aerosols and tropospheric

is likely to be a significant contributor to the Arctic warm-

ing trend during the winter and spring, and that factors con-

trolling these constituents in the Arctic are poorly understood

and simulated (Stohl, 2006; Law and Stohl, 2007; Shindell et

al., 2007; Quinn et al., 2008).

A major mechanism for long-range transport of O

aerosols and other pollutants into high latitudes is the near-

surface transport of pollution from northern Euro-Asian

sources into the wintertime Arctic (the “Arctic haze” phe-

nomenon; e.g., Radke et al., 1984; Brock et al., 1989; Shaw,

1995). Recent studies have expanded upon that understand-

ing to show that transport of middle latitude pollutants into

the Arctic can occur throughout the extent of the Arctic tro-

posphere at various times throughout the year, from source

regions located in Asia, Europe and North America (Stohl,

2006; Shindell et al., 2008; Singh et al., 2010; Fisher et al.,

2010). Additionally, emissions from high latitude boreal for-

est fires over North America and Asia are transported into

the Arctic predominantly during summer, but evidence of

long range transport from fires is found during spring as well

(Scheuer et al., 2003; Singh et al., 2010).

photochemistry at high latitudes is impacted by sev-

eral unique characteristics: extreme cold and dry conditions

and long seasonal periods of darkness followed by long pe-

riods of sunlight at high solar zenith angles. The persis-

tent snow and sea ice surface increases incident radiation

due to a heightened surface albedo, and photochemical reac-

tions within the snowpack itself result in emissions of gases

such as hydrogen peroxide (H

)

, formaldehyde (CH

O),

and HONO, which can impact near-surface HO

chemistry

(Chen et al., 2004; Frey et al., 2009). During spring, en-

hanced concentrations of gaseous bromine radicals (BrO) are

frequently observed over Arctic sea ice upon polar sunrise.

The catalytic cycling of these halogen radicals has been im-

plicated as the cause of local scale near-complete destruc-

tion of surface O

and further, they influence the cycling and

photochemistry of HO

, NO

, and O

. (e.g., Barrie et al.,

1988; Evans et al., 2003; McElroy et al., 1999; von Glas-

gow et al., 2004). Studies stemming from the 2000 TOPSE

aircraft campaign (Tropospheric Ozone Production about the

Spring Equinox) highlight the importance of photochemistry

at high latitudes, suggesting that gross photochemical O

for-

mation is equal to or greater than the source from long range

transport throughout the spring in the free troposphere, and is

greater than transport sources at surface altitudes after March

(Stroud et al., 2004; Emmons et al., 2003).

This study examines fast photochemistry from the per-

spective of in-situ data and model analysis during the high

latitude spring and summer, including radical budgets and

cycling, and O

production and destruction. The data are

from NASA’s 2008 ARCTAS (Arctic Research of the Com-

position of the Troposphere from Aircraft and Satellites)

campaign, and data from springtime high latitudes from por-

tions of the TOPSE campaign are compared to ARCTAS.

2 Data and modeling approach

2.1 Campaign deployments

2.1.1 ARCTAS

The high-latitude portion of ARCTAS was comprised of two

deployment phases during spring and summer of 2008 and

utilized three aircraft: the NASA DC-8, P-3B and B-200.

The DC-8 instrumentation provided a suite of measurements

of species related to aerosol and tropospheric O

chemistry,

pollution sources, and radical chemistry. A more complete

description of the aircraft payload is in Jacob et al. (2010).

The spring deployment phase (ARCTAS-A) was based

out of Fairbanks, Alaska (65

◦

N, 148

◦

W) from 1–19 April,

during which the DC-8 flew 9 sorties across the Arctic be-

tween Alaska, Thule Greenland, and Iqaluit Canada (see

Fig. 1 for DC-8 flight paths during ARCTAS). The summer

phase (ARCTAS-B) was based out of Cold Lake, Edmonton

Canada (54

◦

N, 110

◦

W) from 26 June–14 July, with 9 sor-

ties ranging across Canada and up to Thule and Summit in

Greenland. A third phase (ARCTAS-CARB) took place im-

mediately prior to ARCTAS-B in coordination with the Cal-

ifornia Air Resources Board (CARB), and was focused on

middle latitude pollution off the coast of California to im-

prove state emission inventories for greenhouse gases and

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J. R. Olson et al.: An analysis of fast photochemistry over high northern latitudes

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Fig. 1.

Flight paths for the NASA DC-8 during ARCTAS-A and -B,

and for the NCAR C-130 during TOPSE-sp (subset of TOPSE data

obtained during March and April and north of 50

◦

N).

aerosols. The weight of analysis for this high latitude study

is on ARCTAS-A and -B.

2.1.2 TOPSE

During 2000, NCAR (National Center for Atmospheric Re-

search) sponsored the airborne experiment TOPSE (Tro-

pospheric Ozone Production about the Spring Equinox); it

was conducted using an instrumented C-130 aircraft from

February–May 2000, which brackets the seasonal period

covered by ARCTAS-A. The geographical extent of the cam-

paign included middle and high latitudes over North Amer-

ica (40–85

◦

N) (Atlas et al., 2003). The high latitude data

covered a relatively narrow longitude range over central to

eastern Canada and up to Thule, Greenland, meaning most

data was obtained further to the east over the North Amer-

ican high latitudes and nearly a decade earlier than during

ARCTAS-A (see Fig. 1). Nevertheless, it is reasonable to

compare data obtained within the high latitude air masses

during these two campaigns. Here, the subset of the TOPSE

data obtained during March and April at latitudes north of

◦

N is considered (herein called TOPSE-sp).

2.2 Box model

The NASA Langley Research Center photochemical box

model (LaRC-V08) (Crawford et al., 1999; Olson et al.,

2006) uses a diurnal steady state (DSS) approach with long-

lived species constrained to measurements. In the DSS ap-

proach, each input point of in-situ data (generally taken from

data merges averaged to a 1-min common timeline) is inte-

grated by the model to find an internally self-consistent di-

urnal cycle for all computed species to within a given toler-

ance (

1 %). Predictions are then taken from the computed

diurnal cycle at the same time of day as the data for direct

comparison of radical predictions and measurements. Reac-

tions and rates for basic HO

-NO

-CH

-CO chemistry are

those recommended by Sander et al. (2006) and Atkinson

et al. (2006). Non-methane hydrocarbon (NMHC) chemistry

is originally based on the lumped scheme from Lurmann et

al. (1986), with adjustments as discussed in the appendix of

Crawford et al. (1999). Because halogen chemistry is ex-

pected to impact surface O

chemistry and radical cycling

near the surface and in the lower troposphere the box model

is updated to include bromine photochemistry (see Table 1).

As in previous studies, photolysis rate coefficients are

based on in-situ measurements (Shetter and Muller, 1999).

A DISORT four-stream implementation of the NCAR Tro-

pospheric Ultraviolet Visible (TUV) radiative transfer code

is first used to calculate the diurnal variation of photoly-

sis rates for clear-sky conditions (Madronich and Flocke,

1998). To adjust these clear-sky rates to account for local

cloudiness and surface reflectance, a normalization factor

is applied so that observed rates at the time of observation

are matched. In the event that a given radical precursor is

not constrained to observations, surface deposition and rain-

out for soluble species are parameterized as in Logan et

al. (1981), i.e., for altitudes

1 km, a surface deposition loss

rate of 1

−

is assigned to most species impacted by

surface deposition, with a smaller loss rate of 0

−

for CH

OOH. Above the boundary layer, the rainout param-

eterization assumes a 5 day lifetime for altitudes up to 4 km,

with an exponentially decreasing loss rate above. Aerosol

and cloud uptake for HO

is not directly computed in the

base simulation but potential impacts of heterogeneous loss

in the high latitude environment (e.g., Mao et al., 2010) are

explored.

Model calculations require constraint to observations of

temperature, pressure, H

O, O

, CO, NO, CH

, NMHCs,

ketones (acetone and MEK), and alcohols (methanol and

ethanol). In addition to these minimum requirements, several

additional constraints may be incorporated if desired when

measurements are available; these include CH

O, H

and

OOH, nitric acid (HNO

)

, peroxy acetyl nitrate (PAN),

and BrO. Constraining parameters are held constant through-

out the diurnal cycle with the exception of NO. The total

short-lived nitrogen (NO

HONO

HNO

)

is held constant, with partitioning into the indi-

vidual species calculated by the model throughout the day.

The sum total of short-lived nitrogen is determined when NO

matches the observed value at the time of measurement.

There are occasions when pronounced heterogeneity of

1 Hz NO measurements within the averaged 1 min time span

can result in erroneous model predictions of HO

and re-

lated radical species (Olson et al., 2006). In these cases, the

1 min average has been broken into 60 one-second points

for modeling purposes, using 1 Hz data for NO, O

, CO

and H

O. The high-resolution model predictions are then

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J. R. Olson et al.: An analysis of fast photochemistry over high northern latitudes

Table 1.

Bromine reactions included in the model.

Reaction

Rate Coefficient, cm

molec

−

Reference

Reaction

Number

→

BrO

−

(

−

800

)

Sander et al. (2006)

(R1)

→

HBr

−

(

−

580

Atkinson et al. (2006)

(R2)

CHO

→

HBr

MCO

−

(

−

460

Atkinson et al. (2006)

(R3)

ALD

→

HBr

RCO

−

(

−

610

Atkinson et al. (2006)

(R4)

HBr

−

(

−

310

Sander et al. (2006)

(R5)

BrONO

→

−

(

−

365

)

NIST,Soller et al. (2001)

(R6)

→

HBr

2CO

−

(

440

)

Atkinson et al. (2006)

(R7)

→

BrMCO

Products

−

(

224

)

(

−

3200

)

Atkinson et al. (2006)

(R8)

where

−

[

]

HBr

→

−

(

200

)

Sander et al. (2006)

(R9)

BrO

→

HOBr

−

(

460

)

Sander et al. (2006)

(R10)

BrO

→

−

Atkinson et al. (2006)

(R11)

.25(CH

)

.75(HOBr

)

BrO

→

−

(

260

)

Atkinson et al. (2006)

(R12)

BrO

(

→

BrONO

(

[

]

[

]

∞

)

{

[

log

(

[

]

∞

)]

}

−

Atkinson et al. (2006)

(R13)

−

(

300

)

∞

−

(

300

)

BrONO

→

BrO

(

−

12389

)

Orlando and Tyndall (1996)

(R14)

BrO

→

2Br

−

Atkinson et al. (2006)

(R15)

BrO

→

−

(

840

)

Atkinson et al. (2006)

(R16)

HBr

→

rainout

Logan et al. (1981)

(R17)

HOBr

→

rainout

Logan et al. (1981)

(R18)

BrONO

→

rainout

Logan et al. (1981)

(R19)

Photoloysis Reaction

Reference

Reaction

Number

HOBr

hν

→

Sander et al. (2006)

(R20)

BrONO

hν

→

Sander et al. (2006)

(R21)

.15(Br

)

.85(BrO

)

hν

→

2Br

Sander et al. (2006)

(R22)

BrO

hν

→

Sander et al. (2006)

(R23)

averaged back to the 1 min merge timeline for analysis. The

merged data and selected box model results from ARCTAS

are available on a public archive (

http://www-air.larc.nasa.

gov/cgi-bin/arcstat-c

Sources of uncertainty in model predictions include uncer-

tainties in kinetic and photolytic rate constants, and uncer-

tainties in measurements of constraining observations. Esti-

mates of total model uncertainty are obtained using a Monte

Carlo technique and/or a Sensitivity approach. The method

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J. R. Olson et al.: An analysis of fast photochemistry over high northern latitudes

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Table 2.

Observations used for Model Constraints and/or Predictive Comparisons.

Species

Institution, Instrument

Reference

Physical parameters

(e.g., UTC, Latitude, Longitude,

Pressure-Altitude,

Temperature, Pressure)

Overhead O

Column

Photolysis rates

Species for Model Constraint

O, CH

, CO

, NO

NMHC

Acetone, methanol

MEK, ethanol

BrO, SO

optional constraints

(can also be used for predictive

comparisons):

PAN

HNO

Peroxides (H

, CH

OOH)

Species for predictive

comparisons

OH, HO

, HO

NASA DFRC, REVEAL

NASA, OMI Satellite

NCAR, spectral radiometer

NASA LaRC, TDLAS

NCAR, Chemiluminescence

UCI, WAS-GC

U. Innsbrook, PTR-MS

NCAR, GC-MS (TOGA)

Georgia Tech, CIMS

UNH, mist chamber/IC

NCAR, DFGAS

Cal Tech, CIMS

Penn State, LIF (ATHOS)

NCAR, CIMS

http://toms.gsfc.nasa.gov/

Shetter and Muller (1999)

Diskin et al. (2002), Sachse et al. (1987)

Weinheimer et al. (1994)

Blake et al. (2003)

Wisthaler et al. (2002)

Apel et al. (2003)

Slusher et al. (2004)

Scheuer et al. (2003)

Webring et al. (2007)

Crounse et al. (2006)

Faloona et al. (2004)

Cantrell et al. (2003b)

∗

REVEAL

Research Environment for Vehicle-Embedded Analysis on Linux, OMI

Ozone Monitoring Instrument, TDLAS

Tunable diode

laser absorption spectroscopy, WAS-GC

Whole Air Sampling – Gas Chromatography, PTR-MS

Proton Transfer Reaction – Mass Spectrometry,

GC-MS – Gas Chromatography – Mass Spectrometry, CIMS

Chemical Ionization Mass Spectrometry, IC

Ion Chromatography, LIF

Laser

Induced Fluorescence.

for uncertainty estimation for the LaRC box model is de-

scribed in more detail in Appendix A.

2.3 Data

The data utilized for model constraint and analysis is pre-

sented in Table 2. Key measurements for the high latitude

budget include peroxides (H

in particular) from

the CIT-CIMS (California Institute of Technology – chem-

ical ionization mass spectroscopy) instrument, which has

a 2

uncertainty of 100 pptv

50 % of the measurement

value (Crounse et al., 2006). Formaldehyde is measured

using a difference frequency generation (DFG)/absorption

spectroscopy technique (Weibring et al., 2007) with a sys-

tematic uncertainty at the 2

level of 12.4 %, and a typ-

ical 1 min LOD (2

σ)

of 22 pptv with degradation of the

LOD to

∼

59 pptv during periods of large changes in air-

craft cabin pressure (

http://www-air.larc.nasa.gov/cgi-bin/

arcstat-c

). Water vapor is obtained from the NASA Lang-

ley Research Center DLH (Diode Laser Hygrometer) system

with an uncertainty of 10 % (Diskin et al., 2002). The O

and NO measurements are obtained from the NCAR four-

channel chemiluminescence instrument (Weinheimer et al.,

1994) with an 8 % 2

uncertainty for O

and a 16 pptv 2

uncertainty for the 60 s average of NO when NO is less than

100 pptv.

Measurements of OH and HO

from the Penn State laser

induced fluorescence Airborne Tropospheric Hydrogen Ox-

ide Sensor (ATHOS) are available for comparison to model

predictions. The HO

measurements have an uncertainty for

both species of ±32 %, and a limit of detection (LOD) of

0.01 pptv for OH and 0.1 pptv for HO

(Brune et al., 1999;

Faloona et al., 2004). During ARCTAS, the NCAR CIMS

instruments also provided measurements of OH and HO

(Cantrell et al., 2003b; Mauldin et al., 1999); however due to

some irregularities of the inlet heater, the level of coverage

from the CIMS instrument was reduced. This analysis uses

the ATHOS HO

measurements. Ren et al. (2012) focuses on

a comparison between the two independent measurements of

NMHC data are taken from the UC Irvine Whole Air Sam-

pling – Gas Chromatography instrument (Blake et al., 2003),

and primary data for acetone and methanol are from the Uni-

versity of Innsbrook Proton Transfer Reaction – Mass Spec-

trometry instrument (Wisthaler et al., 2002). To maximize

the total number of points available for modeling, missing

data for acetone, methanol, and NMHCs (with the exception

of isoprene) are interpolated from adjacent measurements

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J. R. Olson et al.: An analysis of fast photochemistry over high northern latitudes

within ±5 min and ±0.5 km. Any remaining missing ace-

tone and methanol are filled where possible using data from

the NCAR Trace Organic Gas Analyzer (TOGA) Gas Chro-

matography – Mass Spectrometry instrument (Apel et al.,

2003); TOGA data are used to fill values 4 % of the time dur-

ing ARCTAS-A and 8.5 % of time during ARCTAS-B. For

the few remaining points with missing acetone or methanol

data (0.5 % and 1.5 % during ARCTAS-A and –B), val-

ues are filled using observed correlations of PTR-MS ace-

tone or methanol data with CO for each phase of ARCTAS.

During ARCTAS-A (spring), the correlations are altitude-

independent, while during ARCTAS-B, separate correlations

are derived for the lower troposphere where pollution from

biomass burning is prevalent (CO

200 pptv) and for cleaner

portions of the troposphere (CO

200 pptv).

Methyl-ethyl-ketone (MEK) and ethanol data are taken

from the TOGA observations. Where data for MEK are miss-

ing (

2 % of the time for ARCTAS-A and 8 % of the time

for ARCTAS-B) values are filled using its observed altitude-

dependent correlation to acetone for each phase. Missing

ethanol (

1 % of the time for both phases)is filled using its

observed altitude-dependent correlation to methanol.

A percentage of the reported measurements of NO are neg-

ative, particularly during the summer phase: 6 % of the NO

is negative during ARCTAS-A and 20 % is negative during

ARCTAS-B (see Fig. 2). For modeling purposes, any 1 min

averaged NO less than a threshold value of 1 pptv, including

negative values, is set equal to 1 pptv. To test the sensitivity

to this choice of minimum threshold, it is varied between 0.1

and 2 pptv. Relative to a 1 pptv threshold, median predictions

of OH for these tests vary less than 8 %, and median predic-

tions of HO

vary less than 2 %, all of which are less than the

relative differences between predictions and measured values

at the low NO points. Note that this is not equivalent to esti-

mating the uncertainty in model predictions due to the total

uncertainty in NO observations.

Data points identified as stratospherically influenced are

removed from this analysis. Points are considered strato-

spherically influenced when O

100 ppbv while either CO

100 ppbv or H

O is

a threshold value (50 ppmv dur-

ing ARCTAS-A and 100 ppmv during ARCTAS-B). Thirteen

percent of the data during ARCTAS-A are identified and re-

moved, and 4 % of the summer data are removed. Because

the altitude range of the C-130 used during TOPSE is lim-

ited to less than 8 km, instances of stratospheric influence are

rare; about 2 % of the TOPSE-sp data are removed, using

the same criteria as for ARCTAS-A. In all cases, the latitude

range of the considered data is limited to north of 50

◦

ARCTAS-A: NO

-20

100

120

NO, pptv

Altitude, km

6.87 %

1.56 %

0.49 %

10.3 %

9.84 %

0.00 %

(a)

ARCTAS-B: NO

-20

100

120

NO, pptv

Altitude, km

8.25 %

12.1 %

19.1 %

26.5 %

28.1 %

19.6 %

(b)

Fig. 2.

Range of NO observations during

(a)

ARCTAS-A and

(b)

ARCTAS-B for 1-min averages of NO. Bars show the inner 50th

percentile range (25–75 %) and vertical lines within bars indicate

the median values. Negative values are shown with black points to

the left of the dashed line that indicates zero. The percentage of

negative values is indicated at each altitude bin.

3 Meteorological and photochemical background at

high latitudes

3.1 Meteorology

During ARCTAS-A, the general location of the Arctic front

was 60

◦

N (Fuelberg et al., 2010), and most of the aircraft

sampling was well north of that. Therefore most of the data

was obtained within the extreme cold and dry air of the Arc-

tic air mass. Springtime values for temperature and moisture

at high latitudes during TOPSE-sp were comparable to those

during ARCTAS-A (Table 3). Alternately, the median sam-

pled latitude during ARCTAS-B was 57

◦

N, while the Arctic

front was located well to the north of the bulk of sampled lat-

itudes during that period, leading to higher temperatures and

moisture relative to the spring campaigns.

Table 3 also shows the median observed photolysis rates

for NO

(J-NO

)

and O

(J-O

D). The near-surface in-

stantaneous observations for J-NO

are higher during the

spring campaigns (ARCTAS-A and TOPSE-sp) than dur-

ing summer (ARCTAS-B), while J-O

D shows the oppo-

site tendency. Neglecting transient influences from cloud

and aerosols, photolysis is affected by three primary fac-

tors: the range of sampled solar zenith angles (SZA), the sur-

face albedo, and the overhead O

column. While the range

of SZA during the spring is limited to higher values (lower

photolysis rates), the persistent snow and ice cover signifi-

cantly enhances the surface albedo and increases photolysis

rates. This albedo-driven increase during spring dominates

the difference in rates for J-NO

. While J-NO

is relatively

unaffected by changes in the overhead O

column, the larger

overhead O

column during spring (396 DU) relative to sum-

mer (316 DU) suppresses J-O

D and offsets its increase due

to the albedo enhancement.

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Table 3.

Median meteorological conditions.

Altitude

Temp (K)

O (ppmv)

Inst. J-O

Inst. J-NO

−

ARCTAS-A

◦

8 km

224

1.04

1.39

6–8 km

229

148

0.68

1.17

4–6 km

242

296

0.84

1.17

2–4 km

251

614

0.46

0.95

0–2 km

257

1270

0.46

0.91

TOPSE-sp (Mar–Apr)

◦

8 km

–

6–8 km

229

161

0.71

1.06

4–6 km

237

225

0.56

1.08

2–4 km

253

869

0.44

0.93

0–2 km

258

1368

0.32

0.79

ARCTAS-B

◦

8 km

231

210

1.78

1.25

6–8 km

246

436

2.73

1.22

4–6 km

260

1783

2.14

1.06

2–4 km

273

6292

0.89

0.78

0–2 km

285

9967

1.05

0.51

3.2 Chemical environment

3.2.1 Seasonal comparison

Figures 3–5 show observed profiles of selected species

during the spring from ARCTAS-A (dark blue) and from

TOPSE-sp (lighter shade of blue). The inner 50th percentile

range (25–75 %) within each altitude bin is shown by the col-

ored bars, and the median value is indicated by the vertical

lines within the bars. For seasonal comparison, profiles ob-

tained during the summer (ARCTAS-B) are also shown (red).

During ARCTAS-B, there were a number of episodic en-

counters with fresh biomass burning plumes, and the in-

fluence of biomass burning is prevalent to various extents

throughout the region. Data points that are clearly within

fresh pollution or biomass burning plumes are identified

based on at least one of several thresholds being exceeded:

300 ppbv, NO

500 pptv excluding the upper tropo-

sphere, CH

300 pptv, or benzene

150 pptv. Below

2 km, 37 % of the data are identified as fresh plumes (9 %

of the data above 2 km). The remaining points represent the

“background” shown by the bars in Figs. 3–5. The dashed red

line in the figures shows the median profile obtained when

all data for ARCTAS-B is used, including that from fresh

plumes.

Figure 3a and b reflect the build-up of longer-lived species

(e.g., CO and C

)

during winter and early spring. The life-

times of CO and the NMHC species (Table 4) are dominated

by reaction with OH and are 3–4 times longer during spring

than summer due to the seasonal variation of OH. Long range

transport from middle to high latitudes can be important for

Table 4.

Diurnally averaged lifetimes (days) based on model calcu-

lations.

Altitude

OOH

ARCTAS-A

4 km

220

3.8

0.28

3.9

2.4

0–4 km

213

3.9

0.38

4.8

3.1

ARCTAS-B

4 km

1.3

0.16

1.8

1.0

0–4 km

1.4

0.32

3.0

1.3

these species with lifetimes greater than a few weeks. Fisher

et al. (2010) concluded that long-range transport during this

time is predominantly from Europe at altitudes near the sur-

face and from Asia throughout the remaining troposphere.

Alternately, the dominant impact of local biomass burning

emissions during summer (ARCTAS-B) is clearly shown to

dominate profiles of the shorter-lived C

(Fig. 3c). While

middle tropospheric median concentrations of C

are sim-

ilarly low during the two seasons, there is a noticeable in-

crease in the range of values from 2–6 km during ARCTAS-

A, and

8 km during ARCTAS-B. The increased middle tro-

pospheric variability during ARCTAS-A is associated with

plumes containing increased CH

CN, indicating influence

from transport of biomass burning emissions, primarily from

Asian source regions (Singh et al., 2010; Fisher et al., 2010).

During summer, the highest C

concentrations at upper al-

titudes are also associated with higher CH

CN and CO, con-

sistent with biomass burning pollution. Instances of fast con-

vection of fresh pollution during ARCTAS-B have been iden-

tified (e.g., Apel et al., 2012), suggesting that the increased

range of values for ethene at upper altitudes during summer

is impacted by convection of local biomass burning pollu-

tion. O

profiles are similar throughout the troposphere dur-

ing both seasons, with surface values near 30 ppbv increasing

to 70 ppbv in the upper troposphere (Fig. 3d). The relatively

broad 50th percentile range near the surface during the spring

is the result of sampling instances of near-surface halogen-

driven O

depletion events.

The larger background concentrations and range of NO

at low altitudes during ARCTAS-B reflect the pervasive in-

fluence from local biomass burning (Fig. 4a). Middle tro-

pospheric concentrations of NO are generally less than 10–

20 pptv during both seasons. Browne et al. (2011) determined

that both of the NO

measurements available during ARC-

TAS are likely to be contaminated by CH

at low

temperatures. Therefore, the NO / NO

ratio shown in Fig. 4b

is computed using observed NO and model predictions of

. The lower summertime ratio near the surface is im-

pacted equally by the lower near surface J-NO

during sum-

mer relative to spring (Sect. 3.1) and by the larger summer-

time concentrations of peroxy radicals, which convert NO to

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Fig. 3.

Profiles of measurements of

(a)

CO,

(b)

(c)

and

(d)

during ARCTAS and during TOPSE-sp. Bar plots are

as described in Fig. 2. Data for ARCTAS-B shown by the bar plots

exclude values identified as within fresh plumes. The dashed lines

show the median profile for ARCTAS-B when all data are consid-

ered (including fresh plumes).

. Measurements of gas phase HNO

using the Univer-

sity of New Hampshire mist chamber instrument are shown

in Fig. 4c, and indicate similar and consistent concentrations

in the upper troposphere of less than 100 pptv. The data show

flat altitude profiles during spring, and an increase in concen-

tration near the surface during summer.

Figure 5 shows profiles for HO

precursor reservoirs

, CH

OOH, H

/ CH

OOH and CH

O. The rela-

tively short lifetimes for these species shown in Table 4 sug-

gest that long-range transport is unlikely to have a dom-

inant direct impact on concentrations. Concentrations are

larger during ARCTAS-B throughout the full extent of the

troposphere, reflecting both increased local emission from

biomass burning sources and more vigorous photochemical

formation in the background atmosphere during the summer.

In comparing the “background” profiles during ARCTAS-

B with those using all data, including fresh pollution plumes

(dashed red line), it is clear that median values increase in

the lowest few km for CO, NMHCs, NO, CH

OOH and

O as a result of including pollution plumes. HNO

and

concentrations, however, are relatively unaffected. This

Fig. 4.

Profiles of

(a)

NO,

(b)

NO / NO

and

(c)

HNO

during ARC-

TAS and during TOPSE-sp. Bar plots and dashed lines are as de-

scribed in Fig. 3.

implies there is little impact on net O

production within

the biomass burning plumes, consistent with the analysis of

Singh et al. (2010).

3.2.2 TOPSE-sp versus ARCTAS-A

The data in Table 3 and Figs. 3–5 indicate that the air masses

sampled during ARCTAS-A and TOPSE-sp were highly

similar in meteorological and photochemical characteristics,

with the exception of peroxides. Median observations for

during ARCTAS-A were 300–450 pptv throughout the

free troposphere, which are 2.5 to 3 times higher than those

during TOPSE-sp (100–200 pptv). Measurements from the

Atmospheric Chemistry Experiment satellite (ACE; Rins-

land et al., 2007) suggest a rapid seasonal change of per-

oxides in the upper Arctic troposphere between March and

May, which would create the possibility of a timing bias be-

tween ARCTAS-A and TOPSE; however both of the March

and the April profiles from TOPSE remain distinctly lower

than that from ARCTAS-A (Fig. 6) and the difference be-

tween March and April TOPSE data above 4 km is minimal.

If the differences in H

between the campaigns are due

to a significant difference in rainout history between TOPSE

and ARCTAS-A, these differences should also be reflected in

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Fig. 5.

Profiles of

(a)

(b)

OOH,

(c)

/ CH

OOH,

and

(d)

O during ARCTAS-A and -B and during TOPSE-sp.

Bar plots and dashed lines are as described in Fig. 3.

other soluble species, notably HNO

and to a lesser extent,

OOH. Figure 4c shows slightly lower median values of

HNO

during TOPSE relative to ARCTAS-A; however, the

ambient variability for both campaigns indicated by the 50th

percentile ranges is heavily overlapping, while there is a clear

separation between peroxide ranges of ambient variability

between the two campaigns. Further, a reverse bias is seen

for CH

OOH, with observations during ARCTAS-A being

50–75 % lower than those during TOPSE-sp, which is not

supportive of an increased role for rainout during TOPSE.

As a result of the different biases for the two peroxides, the

/ CH

OOH ratios in the free troposphere are dramati-

cally different for the two spring campaigns, with values of

3–4 during ARCTAS-A, and values near one during TOPSE-

sp. The similarity in CO, NO, and NMHC observations dur-

ing the two spring campaigns precludes a significant differ-

ence due to transport source regions. Given the similarities

in physical conditions (H

O, temperature, radiation) and in

other species important in the photochemical budget of per-

oxides (O

, CO, CH

O), it is difficult to identify a physi-

cal reason that peroxides would show such significant differ-

ences between the two campaigns.

measurements during ARCTAS were obtained from

both the single-quad and triple-quad implementations of the

Fig. 6.

Median observations of H

during ARCTAS-A and dur-

ing TOPSE-sp, using combined March/April data versus March

only and April only.

CIMS instruments, which use independent calibrations for

determinations (Crounse et al., 2006). The two mea-

surements show a high correlation to each other, with no sig-

nificant bias. The measurements of peroxides during TOPSE-

sp were obtained using the University of Rhode Island’s

instrument utilizing High Performance Liquid Chromatog-

raphy (URI-HPLC) (Lee et al., 1995; Snow et al., 2003).

Both the URI-HPLC instrument and the CIT-CIMS instru-

ments were onboard the DC-8 during NASA’s 2004 INTEX-

NA campaign (Singh et al., 2006; Snow et al., 2007), dur-

ing which the two instruments compared well. The median

value of H

at altitudes

6 km during INTEX-A was

337 pptv, within the range of concentrations measured during

ARCTAS-A. At these lower-concentrations, the fit of CIT-

CIMS data to the URI-HPLC data gives a slope of 0.87 and

an intercept of 38 pptv. The median ratio between the two

peroxide measurements is 0.98, and the

is 0.85, suggest-

ing no bias between the two instruments during INTEX-NA.

The cause of the difference in H

of several hundred pptv

in the free troposphere during ARCTAS-A and TOPSE-sp is

unresolved, and, as will be shown, has important implications

for conclusions related to the Arctic HO

budget.

4 Assessment of HO

photochemistry

For the purpose of a comparison of HO

observations during

ARCTAS to model predictions, the box model is run with full

constraints, including CH

O. The analysis is limited to the

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subset of data points that include measurements of the ma-

jor reservoir HO

precursors (CH

O and H

, discussed in

Sect. 4.1). The other constraints (CH

OOH, PAN and HNO

)

are used when measurements are available. With these re-

strictions, there are a total of 1928 ATHOS HO

measure-

ments available for analysis during ARCTAS-A and 2101

during ARCTAS-B.

4.1 HO

sources

Because of the rapid cycling between OH and HO

, it

is useful to examine the source of the combined radi-

cals (HO

)

. Primary sources of HO

include

photolysis in the presence of water vapor, the photol-

ysis of ketones (particularly acetone), and ozonolysis of

alkenes. Additionally, HO

reservoir species such as H

OOH, CH

O and HO

formed during HO

cycling

are a source of HO

upon photolysis. The source of HO

originating from multiple species flows through CH

O (e.g.,

NMHC, and branches from CH

OOH and acetone). The con-

straint of CH

O to observations in these model simulations,

rather than allowing the model to predict concentrations of

O, limits the ability to segregate HO

sources from

O into the individual initiating species. Figure 7 shows

median profiles of the following instantaneous HO

sources

calculated from observations: the primary source stemming

from O

photolysis, the radical channel of CH

O photoly-

sis, the branch of CH

OOH photolysis that does not flow

through CH

O (i.e., that which is not already included im-

plicitly in the calculated source from constrained CH

O), and

the photolysis of H

and HO

. The source from ke-

tones and ozonolysis of alkenes are minor contributors and

are not shown.

The total HO

source during ARCTAS-B is nearly an

order of magnitude larger than during ARCTAS-A in the

boundary layer, and is larger by a factor of 4 in the free

troposphere. Dry conditions and low J-O

D photolysis rates

during spring result in a suppressed primary source for HO

from O(

O in the free troposphere, amplifying the

importance of the reservoir sources. The flow through CH

is the second largest HO

source in the free and upper tro-

posphere, at about 25 % of the total during both seasons.

The source from H

is the largest component of the HO

source in the middle and upper troposphere, at 45 % dur-

ing spring and 30 % during summer. This emphasizes the

importance of differences in H

measurements during

ARCTAS-A and TOPSE, as it has potential consequences for

the total HO

source.

4.2 Model versus observations of HO

Observations of OH and HO

are shown by the bar plots

in Fig. 8a and b. During ARCTAS-A, concentrations of OH

are markedly low. Twenty percent of all OH observations are

at LOD (

≤

0.01 pptv), and nearly 40 % of the OH measure-

ARCTAS-A

Sources

source molec cm

-3

-1

Altitude, km

(a)

ARCTAS-B

Sources

source molec cm

-3

-1

Altitude, km

(b)

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

+hv -> 2OH

O+hv -> 2HO

D)+H

O -> 2OH

OOH+hv-> OH

photolysis and thermal decomposition

Total

Fig. 7.

Gross HO

production during

(a)

ARCTAS-A and

(b)

ARCTAS-B.

ments below 2 km are at LOD. OH concentrations generally

increase with altitude. During ARCTAS-B, OH maximizes in

the middle troposphere at concentrations more than 4 times

larger than those during spring. While springtime values of

are uniformly low with no vertical gradient (median val-

ues are 4 pptv or less), summertime values are largest near the

surface (16.7 pptv) and decrease to 7–8 pptv above 8 km.

Median model predictions of OH and HO

from the fully

constrained model are shown by the bars in Fig. 8c and d.

The thin solid lines in the figures reproduce the median ob-

served profiles shown in the upper panels for direct com-

parison. Both the median concentrations and altitude gra-

dient of OH are well represented by the box model during

ARCTAS-A, with a median R-Obs/Calc (OH) of 0.96. Alter-

nately, predictions of OH during ARCTAS-B show a gradual

increase with altitude, and are generally biased lower than

observations. Predictions of HO

are significantly and con-

sistently larger than observations throughout the full mid-

dle and upper troposphere during both phases. The overes-

timate of HO

persists down to the surface during ARCTAS-

A. For further comparison, Fig. 9 shows scatterplots of ob-

served and modeled values for OH and for HO

during the

two phases. Tables 5 and 6 quantify statistical quantities (me-

dian R-Obs/Calc and

)

, calculated using the full set of

model/measurement pairs within various altitude bins. The

number of OH observations at LOD during ARCTAS-A (of-

ten reported as negative values) makes interpretation of R-

Obs/Calc problematic. In the cases where both the OH mea-

surement and the model calculation are less than the in-

strument LOD of 0.01 pptv the two values are assumed to

agree and a value of “1” is assigned for R-Obs/Calc. The

correlation between measurements and model predictions

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