Constraints from ozone (O3) observations over oceans
are needed in addition to those from terrestrial regions to fully understand
global tropospheric chemistry and its impact on the climate. Here, we
provide a large data set of ozone and carbon monoxide (CO) levels observed
(for 11 666 and 10 681 h, respectively) over oceans. The data set is derived
from observations made during 24 research cruise legs of R/V Mirai during 2012 to
2017, in the Southern, Indian, Pacific, and Arctic oceans, covering the
region from 67∘ S to 75∘ N. The data are suitable for
critical evaluation of the over-ocean distribution of ozone derived from
global atmospheric chemistry models. We first give an overview of the
statistics in the data set and highlight key features in terms of
geographical distribution and air mass type. We then use the data set to
evaluate ozone mixing ratio fields from the tropospheric chemistry
reanalysis version 2 (TCR-2), produced by assimilating a suite of satellite
observations of multiple species into a global atmospheric chemistry model,
namely CHASER. For long-range transport of polluted air masses from
continents to the oceans, during which the effects of forest fires and
fossil fuel combustion were recognized, TCR-2 gave an excellent performance
in reproducing the observed temporal variations and photochemical buildup of
O3 when assessed from ΔO3/ΔCO ratios. For clean
marine conditions with low and stable CO mixing ratios, two focused analyses
were performed. The first was in the Arctic (> 70∘ N)
in September every year from 2013 to 2016; TCR-2 underpredicted O3
levels by 6.7 ppbv (21 %) on average. The observed vertical profiles from
O3 soundings from R/V Mirai during September 2014 had less steep vertical
gradients at low altitudes (> 850 hPa) than those obtained by
TCR-2. This suggests the possibility of a more efficient descent of the
O3-rich air from above than assumed in the models. For TCR-2 (CHASER),
dry deposition on the Arctic ocean surface might also have been
overestimated. In the second analysis, over the western Pacific equatorial
region (125–165∘ E, 10∘ S to 25∘ N), the
observed O3 level more frequently decreased to less than 10 ppbv in
comparison to that obtained with TCR-2 and also those obtained in most of
the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP)
model runs for the decade from 2000. These results imply loss processes that
are unaccounted for in the models. We found that the model's positive bias
positively correlated with the daytime residence times of air masses over a
particular grid, namely 165–180∘ E and 15–30∘ N; an
additional loss rate of 0.25 ppbv h-1 in the grid best explained the
gap. Halogen chemistry, which is commonly omitted from currently used
models, might be active in this region and could have contributed to
additional losses. Our open data set covering wide ocean regions is
complementary to the Tropospheric Ozone Assessment Report data set, which
basically comprises ground-based observations and enables a fully global
study of the behavior of O3.
Introduction
The global burden and distribution of tropospheric ozone (O3) have
changed from preindustrial times to the present, and have induced a radiative
forcing of +0.4±0.2 W m-2 (IPCC, 2013) by interactions with
the Earth's radiative field. The distribution of O3 is critical for
determining concentration fields of hydroxyl radicals, which control the
lifetimes of many important chemical species, including methane. Changes in
atmospheric chemistry and their impacts on the climate are often
investigated by using O3 distributions derived from global atmospheric
chemistry model simulations. Therefore the model performance determines the
accuracy of the assessment, requiring model evaluation against field
observations of levels of O3 and its precursors. For example, the
models included in the Atmospheric Chemistry Climate Model Intercomparison
Project (ACCMIP; Shindell et al., 2011; Lamarque et al., 2013) and the
Chemistry–Climate Model Initiative (CCMI; Morgenstein et al., 2017) were
carefully evaluated against field observations (e.g., Tilmes et al., 2016).
Recently, an unprecedented comprehensive data set of O3 measurements
was systematically compiled under a community-wide activity, i.e., the
Tropospheric Ozone Assessment Report (TOAR) (Cooper et al., 2014; Schultz et
al., 2017; Gaudel et al., 2018), and this provided additional constraints
for model simulations.
However, even with the data in the TOAR, observation data coverage over
oceans is still poor; Schultz et al. (2017) reported that the “true oceanic
sites” covered by TOAR were American Samoa (14.25∘ S,
170.56∘ W), Sable Island (43.93∘ N, 59.90∘ W),
the Ieodo Ocean Research Station (32.12∘ N, 125.18∘ E),
Ogasawara (27.08∘ N, 142.22∘ E), and Minamitori
Island
(24.28∘ N, 153.98∘ E), while Cape Grim (40.68∘ S, 144.69∘ E), Amsterdam Island (37.80∘ S,
77.54∘ E), and Mace Head (53.33∘ N, 9.90∘ W)
were also included in other categories. For assessment of the Arctic region
(AMAP, 2015), observations obtained truly over the Arctic Ocean were largely
unavailable; only observations at coastal or inland sites such as Alert
(82.45∘ N, 62.51∘ W), Barrow (71.32∘ N,
156.61∘ W), Zeppelin (78.90∘ N, 11.88∘ E),
Pallas–Sodankyla (67.97∘ N, 24.12∘ E), Summit
(72.58∘ N, 38.46∘ W) and Thule (76.5∘ N,
68.8∘ W) were used to test simulations. Measurements from
individual cruises (e.g., Dickerson et al., 1999;
Kobayashi et al., 2008; Boylan et al., 2015; Prados-Roman et al., 2015), a large collection from
multiple cruises (e.g., Lelieveld et al., 2004), O3 soundings from the
SHADOZ network (e.g., Oltmans et al., 2001; Thompson et al., 2017), and
those from various campaigns (e.g., Kley et al., 1996; Takashima et al.,
2008; Rex et al., 2014) have provided important O3 data over oceanic
regions. However, their spatiotemporal coverage over open oceans is too
sparse to complete the global picture. Aircraft observations (e.g., HIAPER
Pole-to-Pole Observations (HIPPO); Wofsy et al., 2011) also sampled marine
boundary layer but the measurement frequency was not necessarily high. One
mature example of such global observations that include the over-ocean
atmosphere is that for CO2. For example, observational data at a large
number of remote islands are available from the World Data Centre for
Greenhouse Gases (WDCGG) database of the World Meteorological Organization
(WMO)/GAW. Atmospheric CO2 measurements are now accepted by SOCAT
Version 4 (https://www.socat.info/, last access: 27 May 2019), which is a database of ship-based
observations, e.g., from research vessels and voluntary ships. Similarly
dense O3 observations over oceans are needed.
An understanding of the processes behind the O3 mixing ratio
distribution is important. Young et al. (2018) reported that the rates of
chemical processes (production and loss) and
deposition/stratosphere–troposphere exchange could differ among models by
factors of 2–3. More observational constraints for characterizing
photochemical buildup and long-range transport events using tracers such as
carbon monoxide (CO) are required. The chemical loss term under clean marine
conditions should also be examined; the loss rate caused by halogen
chemistry and the regions where such chemistry is important need to be
evaluated.
Since 2010, we have conducted ship-borne observations on R/V Mirai of the
atmospheric composition, including the O3 and CO levels, for more than
10 000 h. The geographical coverage was wide, covering the Arctic, Pacific,
Indian, and Southern oceans. Such observations, together with currently
available data sets, will enable critical testing of model simulations that
cover the entire globe. In this paper, we present our observational data set
for O3 and CO for the first time. The data were separately analyzed for
cases affected by long-range transport and those under clean marine
conditions. For each case, the observations were compared with independent
reanalysis data from Tropospheric Chemistry Reanalysis version 2 (TCR-2).
The aims are to interpret the observations and to evaluate the reanalysis
data over the oceans. The reanalysis data were produced by assimilating a
suite of satellite data for O3 and precursors into a global atmospheric
chemistry model, namely CHASER. The precursor emissions were simultaneously
optimized. This has advantages over forward model simulations incorporating
a bottom-up emission inventory because realistic emissions are taken into
account, even those for recent years, for which a bottom-up emission
inventory is not yet ready. TCR-2 was updated from the previous version,
TCR-1 (Miyazaki et al., 2015): the spatial resolution has been improved and
newer satellite products are used for assimilation. For TCR-1, evaluation
against surface, sonde, and aircraft observations was successful (Miyazaki
et al., 2015; Miyazaki and Bowman, 2017). For TCR-2, the performance has
been evaluated using the KORUS-AQ aircraft campaign measurements over east Asia (Miyazaki et al., 2019). However, insufficient evaluation against data
over remote oceans has been performed and our motivation in this study was
to attempt this.
In Sect. 2, field observations and the assimilation model are outlined. In
Sect. 3, geographical and statistical overviews of the observational data
are presented and then compared with the data from TCR-2. CO data and
backward trajectories are used to classify O3 data into cases that are
influenced by long-range transport of pollution from continents and other
cases, namely clean remote air masses. For the former cases, the
reproducibility of the O3 mixing ratio levels and whether the chemical
buildup is well reproduced by the reanalysis were tested for more than 20
events. For the latter cases, a particular focus was placed on
underestimation for the Arctic Ocean and overestimation for the western
Pacific equatorial region by TCR-2. Similar trends were observed with the
ensemble median of model runs of ACCMIP. Possible explanations for these
discrepancies were investigated.
MethodologyObservations on R/V Mirai
Atmospheric composition observations were conducted on R/V Mirai (8706 gross
tons) of the Japan Agency for Marine–Earth Science and Technology (JAMSTEC)
from 2010. The O3 and CO levels were determined by UV and IR absorption
methods (Models 49C and 48C, Thermo Scientific, Waltham, MA, USA),
respectively. The instruments were located in an observational room on the
top floor. Two Teflon tubes (6.35 mm o.d.) of length ∼20 m
were used to sample air near the bow to best avoid contamination from the
ship's exhaust. The exhaust effect was clearly discerned in the 1 min
O3 data record as high concentrations of NO in the exhaust titrated
O3. Minute data exceeding 3σ of the standard deviation in an hour
were eliminated before producing hourly averages. The CO data for the same
minutes were removed. The CO instrument alternately measured the ambient
(for 40 min) and zero (for 20 min) levels. For the zero-level observations,
CO was removed from the ambient air by using a zero-air generator equipped
with a heated Pt catalyst (Model 96, Nippon Thermo, Uji, Japan). The O3
instrument was calibrated twice per year in the laboratory, before and after
deployment, using a primary standard O3 generator (Model 49PS, Thermo
Scientific, Waltham, MA, USA). The CO instrument was calibrated on board
twice per year, on embarking and disembarking of the instrument, using a
premixed standard gas (CO/N2, 1.02 ppm, Taiyo-Nissan, Tokyo, Japan).
The reproducibility of the calibration was to within 1 % for O3 and
3 % for CO.
The 24 cruise legs during which the two instruments were operated are listed
in Table 1. During MR12-02 Legs 1 and 2, the CO instrument did not work well
and only the O3 data were used for analysis. The cruise regions ranged
widely, from the Arctic, the north, the Equator, South Pacific Ocean, and the
eastern part of the Indian Ocean to the Southern Ocean. The Arctic cruises
took place every year during the period 2013 to 2016 (specifically during
the MR13-06, 14-05, 15-03, and 16-06 cruises). Other cruises aimed to study
geology, meteorology, and oceanography and took place in the Pacific,
Indian, and Southern oceans. The western Pacific equatorial region and
Indian oceans were also frequently visited for operation of the TRITON buoy.
Regions near Japan were frequently observed because the departure and arrival
ports were often in that country. Our data basically did not include
observations made while the vessel was anchored in ports; exceptions were
the inclusion of short on-port data when the ports were visited during
cruise legs (see the far-right-hand column in Table 1). During many cruises,
other instruments, i.e., for performing black carbon and fluorescent aerosol
measurements, and multi-axis differential optical absorption spectroscopy
(MAX-DOAS), were operated together (e.g., Taketani et al., 2016; Takashima
et al., 2016). These data will be reported in future publications.
Overview of cruise legs of R/V Mirai used in this study.
CruiseStudy areaDepartureArrivalRemarkMR12-02 Leg1Western North Pacific00:00 UTC 4 Jun 2012Mutsu08:00 UTC 24 Jun 2012Onahama (out of port)MR12-02 Leg2Western North Pacific08:00 UTC 24 Jun 2012 Onahama (out ofport)00:00 UTC 12 Jul 2012MutsuHachinohe Port from 00:30 to08:00 UTC 11 Jul (no data)MR13-04Western North Pacific23:50 UTC 9 Jul 2013Yokohama00:00 UTC 29 Jul 2013MutsuMR13-05Bering Sea23:50 UTC 12 Aug 2013Mutsu17:40 UTC 26 Aug 2013Dutch HarborMR13-06 Leg1Arctic Ocean, BeringSea18:00 UTC 28 Aug 2013DutchHarbor18:40 UTC 7 Oct 2013Dutch HarborMR13-06 Leg2Bering Sea, NorthPacific17:40 UTC 9 Oct 2013DutchHarbor23:50 UTC 20 Oct 2013MutsuMR14-01East Indian Ocean,equatorial region23:00 UTC 8 Jan 2014Mutsu00:00 UTC 13 Feb 2014PalauMR14-02Western Pacific,equatorial region00:00 UTC 15 Feb 2014Koror, Palau00:00 UTC 23 Mar 2014MutsuHachinohe Port, 04:00–09:00 UTC 21 Mar 2014 (no data)MR14-04 Leg1Western North Pacific22:10 UTC 8 Jul 2014Yokohama04:00 UTC 15 Jul 2014KushiroMR14-04 Leg2North Pacific01:00 UTC 17 Jul 2014Kushiro17:50 UTC 29 Aug 2014Dutch HarborMR14-05Arctic Ocean, BeringSea, North Pacific18:10 UTC 31 Aug 2014Dutch Harbor00:20 UTC 10 Oct 2014YokohamaMR14-06 Leg1Western Pacific,equatorial region06:10 UTC 4 Nov 2014Mutsu23:20 UTC 17 Dec 2014ChuukYokohama Port, from 23:10 UTC 5 Nov to 07:00 UTC 7 Nov (withdata)MR14-06 Leg2Western Pacificequatorial region00:07 UTC 20 Dec 2014Chuuk00:10 UTC 19 Jan 2015PalauMR14-06 Leg3Western Pacific, EastIndian Oceanequatorial region00:00 UTC 22 Jan 2015Palau00:00 UTC 25 Feb 2015MutsuHachinohe Port, from 23:30 UTC23 Feb to 07:00 UTC 24 Feb (with data)MR15-03 leg 1North Pacific, BeringSea, Arctic Ocean22:50 UTC 23 Aug 2015Mutsu18:50 UTC 6 Oct 2015Dutch HarborMR15-03 leg 2Bering Sea, NorthPacific18:10 UTC 9 Oct 2015DutchHarbor23:50 UTC 21 Oct 2015MutsuHachinohe Port, from 23:00 UTC20 Oct to 06:50 UTC 21 Oct (with data)MR15-04Western Pacific, EastIndian Ocean equatorial region06:00 UTC 5 Nov 2015Mutsu02:20 UTC 20 Dec 2015JakartaHachinohe Port, from 22:50 UTC5 Nov to 06:20 UTC 6 Nov (withdata)MR15-05 leg 1East Indian Ocean03:10 UTC 23 Dec 2015Jakarta00:50 UTC 11 Jan 2016BaliMR15-05 leg 2East Indian Ocean,Western North Pacific01:00 UTC 13 Jan 2016Benoa, Bali23:50 UTC 24 Jan 2016YokohamaMR16-06Arctic Ocean, BeringSea, North Pacific00:00 UTC 22 Aug 2016Hachinohe00:00 UTC 5 Oct 2016MutsuNome port, 16:00–20:10 UTC23 Sep (with data); HachinohePort from 22:30 UTC 3 Oct to07:20 UTC 4 Oct (with data)MR16-08Western Pacificequatorial region07:00 UTC 27 Nov 2016Shimizu21:00 UTC 23 Dec 2016SuvaMR16-09 leg 1South Pacific17:10 UTC 26 Dec 2016Suva11:00 UTC 17 Jan 2017Puerto MontMR16-09 leg 3Southern Ocean13:10 UTC 8 Feb 2017PuntaArenas21:00 UTC 4 Mar 2017AucklandMR16-09 leg 4Western Pacific21:20 UTC 7 Mar 2017Auckland00:00 UTC 28 Mar 2017MutsuHachinohe Port, from 22:40 UTC26 Mar to 06:50 UTC 27 Mar (withdata)Reanalysis and ACCMIP models
The first version of tropospheric chemistry reanalysis from JAMSTEC, i.e.,
TCR-1, which used an ensemble Kalman filter (EnKF) approach with a global
atmospheric chemistry model (CHASER) as a base forward model, has been
previously described (Miyazaki et al., 2015). Here, the updated version,
TCR-2, was used (Miyazaki et al., 2019,
https://ebcrpa.jamstec.go.jp/tcr2/about_data.html, last acces: 27 May 2019). The
detailed description of the basic data assimilation framework and the
evaluation results using the KORUS-AQ aircraft measurements over east Asia
are available in Miyazaki et al. (2019), while detailed global evaluations
are ongoing. The major aspects of the update of TCR-1 were a finer
horizontal resolution (1.1∘, compared with 2.8∘ in
TCR-1), assimilation of newer satellite data products – OMI NO2
(QA4ECV), GOME-2 NO2 (TM4NO2A v2.3), TES O3 (v6), MOPITT CO (v7
NIR), and MLS O3, HNO3 (v4.2) – and extension of the period to 2017
(from 2005). A priori emissions were obtained from EDGAR v4.2 for
anthropogenic sources, GFED v3.1 for open-fire emissions, and GEIA for
biogenic sources. In addition to concentration fields of chemical species,
NOx emissions (surface and lightning, separately) and CO were included
in the state vector and were simultaneously optimized. An advantage was that
analysis for recent years (e.g., 2017) was possible before development of a
bottom-up emission inventory. The base forward model CHASER V4.0 used for
TCR-2 has been described by Sekiya et al. (2018). Briefly, 93 species and
263 reactions (including heterogeneous reactions) represent
Ox-NOx-HOx-CH4-CO photochemistry and oxidation of
nonmethane volatile organic compounds. Tropospheric halogen chemistry is
not included. The dry deposition velocity (vd) of O3 was computed
as (ra+rb+rs)-1, where ra, rb, and
rs are the aerodynamic resistance, the surface canopy (quasi-laminar)
layer resistance, and the surface resistance, respectively (Wesely, 1989).
1/rs over ocean surface was assumed to be 0.075 cm s-1 globally,
irrespective of region (Sudo et al., 2002). As a result, vd was
∼0.04 cm s-1 over the Arctic open ocean in September,
for instance. This will be a subject of discussion in Sect. 3.3.2.
Because the number of assimilated TES O3 retrievals decreased
substantially after 2010, the data assimilation performance became worse
after 2010 in the previous version TCR-1 (Miyazaki et al., 2015), and it can
be expected that TCR-2 has similar increases in O3 analysis errors
after 2010. Nevertheless, the multi-constituent data assimilation framework
provides comprehensive constraints on the chemical system and entire
tropospheric O3 profiles through corrections made to precursors'
emissions and stratospheric concentrations, as demonstrated by Miyazaki et
al. (2015, 2019).
We also used the monthly ACCMIP ensemble simulations for the present day
(Shindell et al., 2011), which were represented as simulation results for a
decade from 2000 (Stevenson et al., 2013; Young et al., 2013). The monthly
mean O3 field from an ensemble member, MIROC-Chem, with a modeling
framework similar to that of TCR-2, provided corresponding climatological
mixing ratio levels at a given location. These values were used to evaluate
the performance of TCR-2, which uses meteorological data and estimated
emissions in those particular years. Seven other ACCMIP ensemble members
that provide hourly O3 mixing ratios at the Earth's surface
(CESM-CAM-superfast, CMAM, GEOSCCM, GFDL-AM3, GISS-E2-R, MOCAGE, and
UM-CAM) were also used for comparative analysis in terms of frequency
distribution of O3 mixing ratios in the Arctic region (domain 1,
72.5–77.5∘ N, 190–205∘ E) and in a narrow western
Pacific equatorial region (domain 3, 0–15∘ N, 150–165∘ E). The ACCMIP results were chosen for the latest intercomparison exercises
because the data were publicly available.
Backward trajectory
Five-day backward trajectories from an altitude of 500 m above sea level
(a.s.l.) were calculated every hour from the position of R/V Mirai by using
NOAA's Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT)
model (Draxler and Rolph, 2013) to trace the origin areas of the observed
air masses. GDAS1 three-dimensional meteorological field data with a
resolution of 1.0∘ were used. Cases that did not involve traveling
over land regions (at altitudes lower than 2500 m a.s.l.) during the 5
days were extracted as marine air mass cases. Here, the land mask data from
NASA (https://ldas.gsfc.nasa.gov/gldas/data/0.25deg/landmask_mod44w_025.asc, last access: 27 May 2019) at a resolution of 0.25∘ were used
for making judgments.
Results and discussionOverview of geographical distributions of O3 and CO
Figure 1a shows the entire O3 data set from observations on R/V Mirai from
2012 to 2017. The covered latitudinal range was wide, from 67.00∘ S (at 06:00 UTC on 16 February 2017 during MR16-09 Leg 3) to
75.12∘ N (at 06:00 UTC on 6 September 2014 during MR14-05). The
highest hourly mixing ratio, i.e. 66.6 ppbv, was recorded twice: first at
35.89∘ N, 141.95∘ E, about 70 km east off the coast of
the Kanto area, Japan, at 04:00 UTC on 6 July 2012, and second at
32.29∘ N, 146.04∘ E, about 500 km southeast of the Kanto
area at 14:00 UTC on 18 March 2014. During 57 h, the hourly values exceeded
60 ppbv (which corresponds to the environmental standard in Japan) in a
similar region off the coast of Japan, under the influence of Asian regional
air pollution. The lowest hourly mixing ratio, i.e. 4.3 ppbv, was recorded
twice on the western Pacific equatorial region: first at 5.86∘ N,
156.00∘ E and secondly at 7.96∘ N, 156.02∘ E
at 09:00 and 21:00 UTC, respectively, on 11 March 2014. Values lower than 10 ppbv were recorded for a total of 800 h; this will be discussed in the
following section.
Geographical distributions from (a) observed hourly
O3 mixing ratios (N= 11 666) on R/V Mirai during 24 research cruise legs
in 2012–2017 and (b) those from reanalysis (TCR-2) of data along R/V Mirai cruise track; (c) reanalysis/observation ratios (TCR-2/obs) for O3
mixing ratios. In (a) stationary points of TOAR data set are plotted
(magenta dots). In (a) and (c), three green rectangles show focused domains
in the Arctic (domain 1, 72.5–77.5∘ N, 190–205∘ E) and two
in the western Pacific equatorial region (domain 2, 10∘ S to
25∘ N, 125–165∘ E; domain 3, 0–15∘ N,
150–165∘ E).
Figure 2a shows the entire CO data set. The highest mixing ratio was 556 ppbv, which was recorded at 58.00∘ N, 179.23∘ E, at
01:00 UTC on 26 September 2016 during the MR16-06 cruise, where a dense
plume from severe forest fires in Russia reached as far as the Bering Sea.
All 8 h in which the mixing ratio exceeded 500 ppbv were recorded on the
same day. There were 59 other hours when the CO mixing ratio exceeded 300 ppbv, when anthropogenic emissions from east Asia and/or biomass burning
were important.
Geographical distributions of (a) observed CO mixing
ratios and (b) those from TCR-2. In (a) magenta boxes indicate 23 regions
where ΔO3/ΔCO ratios were analyzed (see Fig. 8 and
Table 2).
Figure 3 shows an example of O3 variations for data from MR14-06 Leg 1
with backward trajectories. The vessel started from Mutsu port
(41.37∘ N, 141.24∘ E), stayed for about 32 h at Yokohama
port (35.45∘ N, 139.66∘ E), and then headed to the
western Pacific equatorial region. A latitudinal gradient was apparent:
north of 27∘ N was dominated by air masses originating from the
Asian continent, as shown by violet trajectory lines. During some hours,
high levels, i.e., greater than 50 ppbv of O3, were recorded (points
with red trajectory lines). In contrast, marine air masses from the east
were dominant south of 27∘ N and the mixing ratio levels decreased
to less than 30 ppbv. South of 15∘ N, even lower levels were
dominant, i.e., < 15 ppbv, particularly in equatorial regions where
levels less than 10 ppbv of O3 were frequently observed. The
latitudinal gradient, air mass exchange, and transport of photochemically
produced O3 are the three important factors determining distributions.
Figure S1 in the Supplement shows the spatial distributions of O3 and backward
trajectories for each cruise leg, and their time series. All three factors
listed above for the MR14-06 Leg 1 had major effects on the variations. The
classification of air masses as marine and other cases was satisfactory for
identifying cases of long-range transport of pollutants from continents,
although some events with continental effects with traveling times longer
than 120 h were probably wrongly categorized. Because longer trajectories
may not be reliable, we will use this criterion (i.e., 120 h) in the
following sections with a notice of such limitations. Stratospheric
influences were not identified: we did not find any events with reasonable
O3-level enhancement accompanied by descent of air masses from 8 km or
higher altitudes within 72 h prior to observations.
Observed O3 mixing ratios during MR14-06 Leg 1 and
backward trajectories (120 h). Magenta lines indicate cases in which
trajectory entered regions over land (at altitudes < 2500 m a.s.l.).
Red lines indicate cases in which observed O3 mixing ratios exceeded 50 ppbv.
Comparisons between observations and TCR-2 data: Global features
Figures 1b and 2b show the geographical distributions of O3 and CO
obtained from TCR-2 along the cruise track. The nearest grid and time (2 h
resolution) at the lowest layer were sampled to achieve the best possible
comparison with the observations shown in Figs. 1a and 2a. Most features
were in agreement with the observations, in terms of areas with high mixing
ratios and latitudinal gradients. This comparison indicates a reasonably
high quality of reanalysis data over the oceans. This is partly because the
NOx and CO emissions rates were optimally estimated during the data
assimilation cycle and were reflected in the mixing ratio field. The
emission changes provide substantial influences on ozone forecasts over many
regions. Assimilation of the TES ozone measurements has limited impacts on
near-surface ozone owing to its weak sensitivity to the lower troposphere.
In Fig. S1, the comparisons are shown as time-series plots. Excellent
matches in the evolution with time of O3 and CO mixing ratios were
found for the latter period of MR14-02, i.e., during the period 12–22 March
2014, in the time series (Fig. S1g). The blue lines show plots of monthly
climatological mean mixing ratios, which were obtained from MIROC-Chem.
Although the monthly means sometimes followed the observed baseline trend
for O3, the performance of TCR-2 was superior, particularly for
reproducing detailed peak patterns for CO. This case will be analyzed in
Sect. 3.3.1 on photochemical buildup. A similarly excellent performance was
obtained for MR15-05 Leg 2 (Fig. S1r) and MR16-09 Leg 4 (Fig. S1w), when the
ship returned to Japan from the south.
Clear gaps were also sometimes observed. These are probably related to
limited reproducibility in the meteorological field and errors in the
emission estimation and other physicochemical processes that were taken into
account in the model system. A large gap was observed during the latter half
of the MR14-01 cruise (Fig. S1f); the predictions produced by TCR-2 for CO
and O3 in the eastern Indian Ocean were too high. The climatological
mean from MIROC-Chem reproduced the observed level better, which suggests
that false information was introduced from satellite observations to the
surface mixing ratios, for instance, associated with the use of total column
measurements and errors in vertical transport, or that the position of ITCZ
was too far south in the simulation. During other cruises, smaller gaps in
the peak occurrence times and peak mixing ratio levels were found. For
example, during MR14-04 Leg 2 (Fig. S1i), the peak times for O3 and CO
during the period 27 July to 6 August 2014 were out of phase by about 1 to
2 days. This was probably caused by a small displacement of pollution air
masses after long-range transport. High O3 mixing ratios, i.e.,
> 70 ppbv (off the scale in the time-series plot) for 23 h were
predicted by TCR-2, which was an overestimation compared with the
observations. Except for 2 h on 9 October 2014, these were always in July
(in 2012, 2013, and 2014). All were found within 200 km east of Japan's main
islands, suggesting overestimation of photochemical O3 production
during long-range transport in TCR-2.
With such case-dependent reproducibility in mind, Fig. 1c summarizes the
overall geographical distribution of the reanalysis/observation ratios for
O3 mixing ratios. For clarity, the data were limited to cases of marine
origins, where the differences between the TCR-predicted and -observed CO
mixing ratios were less than 50 ppbv (N=5662), in contrast to Fig. 1a, b, where all data (N= 11 666) were included. The model's underestimation
in the Arctic Ocean and overestimation over the oceans south of Japan
(western Pacific equatorial or subtropical regions) are clearly shown. These
features will be discussed in Sect. 3.3.
Figure 4 shows the latitudinal distribution of the observed and TCR-2
O3 mixing ratios and their ratios. In Fig. 4a, the data were again
limited to cases of marine origins, where the differences between the
model-derived and observed CO mixing ratios were less than 50 ppbv (N=5662). The variability shown here reflects seasonality and cases of
long-range transport from continents over 120 h. The range of
TCR-2/observation ratios (shown in red in Fig. 4b) narrowed when TCR-2
successfully captured the variability. Data selection (N=5662) also
contributed to narrowing of the range (grey points indicate without data
selection). The ratios binned by latitudes indicate that TCR-2 tended to
give underestimations at high northern latitudes and overestimations at low
latitudes. The 10th, 50th (median), and 90th percentiles (range shown as
bars) of the ratios at high latitudes (> 70∘ N) were
0.62, 0.81, and 0.94 (N=876); the range was off unity. The medians for
low latitudes (0–10, 10–20, and
20–30∘ N) were 1.12 (N=1061), 1.28 (N=268), and 1.28 (N=195), respectively.
(a) Latitudinal distribution of O3 mixing ratios
from observation (black) and TCR-2 (red). Data are limited to cases of
marine origins where difference between predicted and observed CO mixing
ratios was less than 50 ppbv (N=5647). (b) Latitudinal distribution of
TCR-2/observation ratio for O3 (light-red dots) and binned medians
(ranges are from 10th to 90th percentile) over 10∘. Gray dots
represent all cases without data selection.
Figure 5 shows a scatterplot of the comparison. The data set was re-expanded
by removing the criteria of marine air mass selection and CO differences
(i.e., N= 11 666) to show the overall correlation between the observed and
TCR-2 data over the full range. The square of the correlation coefficient
(R2) was 0.59; this increased to 0.62 when clear outliers (latter half
of MR14-01 and cases in which TCR-2 exceeded 70 ppbv) were omitted (red, N= 11 305).
The excellent performance of TCR-2 was assured by a resolution of 2 h
in reproducing O3 mixing ratio variations. When the data were further
binned to a resolution of 1 day (with observations for 6 h or more),
R2 improved to 0.66; this suggests that the original variability was
partly derived from small shifts in the peak times by less than a day. In
the past, reanalysis data were not used for such detailed diagnoses but
were normally compared with observations after monthly averaging or binning
to coarse latitudinal bands. Recently, Akritidis et al. (2018) evaluated
stratosphere-to-troposphere transport processes represented by the
Copernicus Atmosphere Monitoring Service (CAMS) system, but evaluation of
transport and transformation processes for tropospheric O3 over oceans
has not been reported with reanalysis. Here, a successful comparison was
made for the first time at the daily or finer timescale. The regression
line for the data set, i.e., [TCR-2, ppbv] =(6.90±0.15)+(0.71±0.01)× [obs, ppbv], suggests a statistically significant
positive intercept on the y axis (i.e., TCR-2) and a slope of less than
unity. The result was unchanged even when MR14-01 and cases with
> 70 ppbv in TCR-2 were included. The green circles in Fig. 5
represent data from the Arctic region (> 70∘ N) and
clearly suggest an underestimation by TCR-2. In contrast, the blue circles
from the western Pacific equatorial region (125–165∘ E,
10∘ S to 25∘ N) fell on the opposite side, with respect
to the 1:1 line, indicating an overestimation by TCR-2. These two aspects
will be discussed in detail in the following sections.
Scatterplot of observed and reanalysis O3 mixing
ratios. Gray circles include all data (N= 11 666), red circles are cases
in which data from MR14-01 and extreme cases in which TCR-2 exceeded 70 ppbv were
removed. Green circles indicate data from Arctic region (> 70∘ N) and blue circles indicate data from domain 2
(125–165∘ E, 10∘ S to 25∘ N).
Specific analysis with event or regional focuses: Implications for
processes
In this section, more detailed comparisons between observations and
simulations are made to shed light on the underlying mechanisms determining
O3 mixing ratio levels and distributions.
Long-range transport events with photochemical production
We selected events with simultaneous increases in CO and O3 levels or
those with at least evident CO peaks. We then assessed the reproducibility
of the peak mixing ratios and ΔO3/ΔCO ratios in TCR-2;
the ΔO3/ΔCO ratio is an index of the efficiency of
photochemical production of O3 from CO as a precursor. In total, 23
cases were selected (Table 2 and magenta rectangles in Fig. 2a). The two
cases discussed below were studied in depth.
Cases of long-range transport events used for examination
of photochemistry (unit for ΔO3/ΔCO is ppbv/ppbv).
CaseStartEndLatitudeLongitudeRΔO3/ΔCORΔO3/ΔCOCOmaxCOmaxO3maxO3maxtimetime(∘ N)(∘ E)(obs)(obs)(TCR-2)(TCR-2)(obs)(TCR-2)(obs)(TCR-2)A22:00 UTC 15 Aug 201322:00 UTC 17 Aug 201341.32–45.22151.81–162.710.810.42±0.040.680.96±0.15101.687.538.240.0B12:00 UTC 23 Aug 201312:00 UTC 25 Aug 201354.47–56.59182.07–193.250.790.28±0.03-0.30-0.12±0.06101.9102.733.126.0C00:00 UTC 05 Oct 201322:00 UTC 06 Oct 201356.95–65.12191.36–192.820.800.54±0.060.850.86±0.08116.4117.243.138.7D23:00 UTC 16 Oct 201323:00 UTC 18 Oct 201340.08–40.88143.72–154.340.750.32±0.040.580.15±0.03137.4129.251.140.9E15:00 UTC 12 Jan 201415:00 UTC 14 Jan 201425.21–31.33126.85–136.280.660.03±0.010.290.01±0.01331.2269.752.845.8F00:00 UTC 13 Mar 201400:00 UTC 15 Mar 201411.51–18.95151.64–154.580.830.34±0.030.930.51±0.03137.4125.335.437.3G00:00 UTC 17 Mar 201400:00 UTC 19 Mar 201425.08–34.12145.21–149.150.860.11±0.010.750.06±0.01299.1219.066.656.3H13:00 UTC 24 Jul 201410:00 UTC 26 Jul 201442.67–45.32152.68–157.020.570.38±0.090.920.23±0.01132.2208.048.247.9I00:00 UTC 02 Aug 201400:00 UTC 04 Aug 201446.99–47.01171.62–177.970.900.11±0.010.930.14±0.01249.4220.038.037.2J00:00 UTC 25 Aug 201400:00 UTC 27 Aug 201451.15–53.38208.00–221.140.870.53±0.040.700.15±0.02119.0116.135.431.5K00:00 UTC 08 Nov 201400:00 UTC 10 Nov 201426.61–32.60141.81–149.900.920.44±0.030.670.23±0.04154.4145.155.646.4
Continued.
CaseStartEndLatitudeLongitudeRΔO3/ΔCORΔO3/ΔCOCOmaxCOmaxO3maxO3maxtimetime(∘ N)(∘ E)(obs)(obs)(TCR-2)(TCR-2)(obs)(TCR-2)(obs)(TCR-2)L02:00 UTC 07 Feb 201521:00 UTC 08 Feb 2015-1.60–2.1689.95–90.190.950.21±0.010.960.60±0.03181.1105.731.824.4M00:00 UTC 18 Feb 201500:00 UTC 20 Feb 201524.42–30.05123.50–131.790.860.05±0.0040.190.02±0.02390.7222.158.951.5N17:00 UTC 21 Feb 201517:00 UTC 23 Feb 201534.29–40.27139.89–142.33-0.27-0.03±0.02-0.20-0.04±0.03478.6220.653.652.9O00:00 UTC 05 Oct 201517:00 UTC 06 Oct 201553.98–60.57192.01–193.510.760.38±0.050.380.09±0.03127.3126.246.136.1P14:00 UTC 16 Nov 201512:00 UTC 18 Nov 2015-9.21–0.19114.13–119.130.820.29±0.030.830.37±0.04148.8185.028.948.7Q18:00 UTC 16 Dec 201515:00 UTC 18 Dec 2015-6.14–4.04101.01–102.270.390.09±0.04-0.75-0.03±0.00594.7214.418.817.5R04:00 UTC 27 Dec 201504:00 UTC 29 Dec 2015-24.78–21.16110.59–112.770.900.33±0.030.890.44±0.0382.793.128.337.3S08:00 UTC 23 Jan 201620:00 UTC 24 Jan 201631.97–35.03138.91–139.94-0.40-0.02±0.01-0.63-0.21±0.05357.8168.546.446.3T01:00 UTC 25 Sep 201601:00 UTC 27 Sep 201654.78–61.49173.23–184.47-0.74-0.03±0.004-0.74-0.16±0.02555.7161.447.945.3U12:00 UTC 27 Sep 201612:00 UTC 29 Sep 201647.42–53.85161.78–171.570.000.00±0.01-0.78-0.10±0.01317.9139.643.636.1V12:00 UTC 03 Dec 201610:00 UTC 05 Dec 20167.66–12.46136.43–137.000.670.24±0.040.970.60±0.0392.799.125.134.8W00:00 UTC 20 Mar 201700:00 UTC 22 Mar 201719.86–27.70147.83–152.620.810.25±0.030.830.13±0.02214.8214.663.059.0
Temporal evolution of surface CO and
O3 distribution from TCR-2 (colored open squares) and
from observations on R/V Mirai (all data,
position of vessel is black circle) during MR14-02 (from 16 to 19 March 2014). Black circles in
the bottom-left CO panel indicate locations with high anthropogenic CO emissions
(EDGAR).
Figure 6 shows the time evolution of the O3 and CO mixing ratios from
observations and TCR-2 during the period 16–19 March 2014, when the vessel
returned to Japan from the equatorial Pacific during MR14-02. The
observational data along the entire track are shown, with the exact position
of R/V Mirai at that time marked by a black circle. This part of the cruise has
already been briefly mentioned in Sect. 3.2; here, the origins of pollution
and the photochemical states for three episodes with increased mixing ratios
are discussed. First, at 09:00 UTC on 16 March 2014, at 23.69∘ N,
149.72∘ E, the CO mixing ratio peaked at around 284 ppbv. The
geographical distribution of surface O3 and CO in TCR-2 implied that
this was a tongue-shaped pollution event originating from mainland southeast Asia (formerly the Indochina
Peninsula) and extending to the east; however, it was not. Instead, as
inferred from the backward trajectories (Fig. S1g), weather charts, and time
evolution of CO distributions from TCR-2 (Fig. 6), it should be interpreted
as a belt of pollution originating from east Asia, present at the south edge
of a high-pressure system moving toward the southeast. An O3 peak of
54.2 ppbv occurred 10 h later, which was also captured by TCR-2. A second CO
peak occurred the next day, at 19:00 UTC on 17 March 2014, at
28.56∘ N, 147.68∘ E; in this case an O3 peak of
66.2 ppbv occurred just 1 h before. TCR-2 suggested that the plume
originated from east Asia and was transported to the east from the continent
and then to the south, under the influence of another high-pressure system
traveling over Japan and a low-pressure system that followed (see the second
row panel in Fig. 6 for 01:00 UTC on 17 March 2014). The timing and location
of the third peak were also well predicted; a CO peak of 273 ppbv occurred
at 17:00 UTC on 18 March 2014, at 32.88∘ N, 145.77∘ E, 3 h after the O3 peak of 66.6 ppbv (the highest in the data set). In this
case, the air mass traveled directly from central east China, with a large
precursor emission (see the distribution of grids with high CO emissions in
the panel for 01:00 UTC on 19 March 2014) via a quick westerly flow under
the influence of passage of a cold front. The ΔO3/ΔCO
ratio, calculated as the slope of the regression line in a scatterplot of
hourly mixing ratios of O3 and CO between 00:00 UTC on 17 March 2014
and 00:00 UTC 19 on March 2014, was 0.11±0.01 ppbv/ppbv (R=0.83)
for observations and 0.06±0.01 ppbv/ppbv (R=0.75) for TCR-2
(Table 2, case G). The underestimation of O3 peaks by TCR-2 (maximum
56.3 ppbv) is attributable to a combination of less efficient production of
O3 and underestimation of CO peaks by TCR-2 (maximum 219 ppbv).
Figure 7 shows a similar time evolution during MR14-04 Leg 2, when a plume
reached the central Pacific from the Asian continent about 5000 km away. The
ΔO3/ΔCO ratio from 00:00 UTC on 2 August 2014 to 00:00 UTC on 4 August 2014 was 0.11±0.01 ppbv/ppbv (R=0.90) for
observations and 0.14±0.01 ppbv/ppbv (R=0.93) for TCR-2. The
maximum CO and O3 mixing ratios were 249 and 38.0 ppbv, respectively,
for observations, and 220 and 37.2 ppbv, respectively, for TCR-2 (Table 2,
case I). The pollution source was probably forest fires in the far east of
Russia (shown by plus signs in the upper-left panel of Fig. 7) with a
smaller contribution from anthropogenic emissions from east Asia.
Specifically, on 26 July 2014, the head of a plume extending from the forest
fires arrived in northern Japan (see Zhu et al., 2019 and references therein
for details) and affected our ship observations. The main body of the plume
arrived from the west and traveled with a migrating high-pressure system
until 6 August 2014. The observed CO and O3 mixing ratios decreased
during the period 29 July to 1 August 2014, when the vessel was located at
the center of a low-pressure system traveling to the east. After the
low-pressure system had weakened, the plume was still present in its
southern part and was pushed northeast under the influence of the
high-pressure system in the south, which again affected the ship
observations during 2 and 3 August 2014, in the middle of the North Pacific.
Temporal evolution of surface CO and
O3 distribution from TCR-2 (colored open squares) and
from observations on R/V Mirai (all data;
position of vessel is shown by black circle) during MR14-04 Leg 2 (from 26 July to 3 August 2014). Plus
signs in the top-left panel show points with high carbon emissions from
wildfires (GFED).
Table 2 summarizes the results from all 23 cases, including the two events
discussed above (cases F and G from MR14-02 and cases H and I from MR14-04
Leg 2). The span of the study area was from the Indian Ocean in the Southern
Hemisphere, and the central equatorial Pacific Ocean to the Bering Sea (see
magenta rectangles in Fig. 2a). In most cases, the observed ΔO3/ΔCO slopes were positive, suggesting photochemical buildup
of O3 with emissions of CO. The ranges were well reproduced by TCR-2.
It is interesting to note that negative slopes for ΔO3/ΔCO were found for at least three cases and that these were also well
captured by TCR-2. These were the cases where very high CO levels were
recorded (479, 358, and 556 ppbv for cases N, S, and T, respectively). The
strong negative correlation for case N was influenced by the low O3
mixing ratios (11.2 ppbv) recorded with high CO levels. When such irregular
points were removed, the observed slope was 0.01±0.01 ppbv/ppbv. For
case S, a strong CO peak occurred in the south of Japan (∼200 km off the coast) on 23 January 2016, without an O3 increase (Fig. S1r).
For case T, a plume from a Russian forest fire affected the
observations over the Bering Sea (Fig. S1s). Relatively fresh air pollution
from nearby ships or weak UV in January and February for cases N and S, and
weak co-emission of NOx from forest fires for case T, could cause the
observed negative slopes. Processes other than daytime photochemistry (e.g.,
nocturnal chemistry) might also have been important.
Figure 8 shows the correlation between the observed and reanalysis ΔO3/ΔCO ratios. The reasonable correlation, with a slope of
1.15±0.29 and R2=0.42, suggests that TCR-2 reproduced the
state of O3 production fairly well for various cases in different
geographical domains in different seasons. Case B is an outlier in terms of
correlation: observations and TCR-2 yielded ΔO3/ΔCO
ratios of 0.28±0.03 and -0.12±0.06 ppbv/ppbv,
respectively. This was recorded at the center of the Bering Sea, where TCR-2
failed to reproduce the coinciding increases in the observed O3 and CO
mixing ratios.
Correlations between ΔO3/ΔCO ratios from
observations and TCR-2.
The observed and simulated ranges of the ΔO3/ΔCO ratios
are in accordance with previously reported data. On the Azores in the
central Atlantic (38.73∘ N) in spring and on Sable Island
(43.93∘ N) in Atlantic Canada in summer, the ΔO3/ΔCO ratios were uniform, at 0.3–0.4 ppbv/ppbv (Parrish et
al., 1998). For TRACE-P aircraft measurements, Hsu et al. (2004) reported
smaller values, i.e., 0.08 ppbv/ppbv in the tropics and 0.03 ppbv/ppbv in
the extratropics, with CO > 200 ppbv. Weiss-Penzias et al. (2006)
reported 0.22 ppbv/ppbv in April and May 2004 for two long-range transport
events that reached a mountain on the west coast of the United States from
Asia. Tanimoto et al. (2008) summarized the range as being from slightly
negative to ∼0.4 ppbv/ppbv when observing Siberian fire
plumes at Rishiri Island. Zhang et al. (2018) documented the ΔO3/ΔCO ratios at Pico Mountain in the Atlantic Ocean and found
that the ratios for anthropogenic pollution were higher (0.45–0.71 ppbv/ppbv) than those for observations affected by wildfires
(0.12–0.71 ppbv/ppbv). They suggested that the low ratios from wildfires could be the
result of lower NOx/CO emission ratios compared with those for
anthropogenic sources.
Arctic processes
In the Arctic Ocean (> 70∘ N), the CO mixing ratios
were regularly close to the background and stable (101±10 ppbv, Fig. S1d, j, n, and s), suggesting that the measurements were not affected by
strong pollution events. The average observed O3 mixing ratio was 31.3 ppbv (N=1804)
and the reanalysis significantly underestimated this (24.6 ppbv) based on Welch's t test (p<0.001). The magnitude of the
relationship was common for all 4 years of measurements (Fig. 9). A low
bias with TCR-2 was also clear for cumulative frequency distributions of
hourly O3 mixing ratios from observations (black, N=1031) and TCR-2
(red) in domain 1 (72.5–77.5∘ N, 190–205∘ E) in September (Fig. 10). The frequency distributions
for eight model members of ACCMIP and their ensemble median (magenta) are
also included in Fig. 10. The median was close to that for TCR-2 and
similarly significantly underestimated, although three members (GEOSCCM,
GFDL-AM3, and GISS-E2-R) showed better agreement with observations. These
analyses suggest that, in the simulations, the sources were too weak or the
losses were too strong. The average diurnal variations were generally almost
flat (the variability was within 5 % of the average) for observations and
TCR-2 (not shown), suggesting that the missing processes did not show
significant diurnal variability. At high latitudes, the assimilated
measurements have either low quality or low sensitivity in the troposphere,
while the optimization of precursors emissions generally has limited impacts
on ozone. The reanalysis ozone over the Arctic Ocean can be similar to the
model predictions, except when poleward transports are strong enough to
propagate observational information from low latitudes and midlatitudes.
Repeated underestimations of O3
mixing ratios ranges by TCR-2 relative to observed values in the Arctic region
(> 70∘ N). Boxes and horizontal bars indicate 75 %,
50 % (median), and 25 %, and whiskers indicate 90 % and 10 %.
Circles are averages.
Cumulative relative frequency distributions of
O3 mixing ratios from observations (black), TCR-2
(red), and members and ensemble median of ACCMIP in the Arctic grid 1
(72.5–77.5∘ N, 190–205∘ E)
in September.
During the MR14-05 cruise, 10 O3 sondes were launched every other day
at 22:00 UTC during the period 6–24 September 2014 (Inoue et al., 2018).
The average vertical profile was compared with that from TCR-2 at the
nearest grid (Fig. 11) to investigate the altitude to which this low bias
of TCR-2 continued. At the lowest altitude near the surface, the
underestimation by TCR-2 against the O3 sonde observations was evident.
However, the gap only continued to about 850 hPa, at which the two mixing
ratios crossed over, and TCR-2 gave higher mixing ratios at higher
altitudes. This suggests that the missing process was only important for the
boundary layer, not for the entire troposphere. Inoue et al. (2018) compared
the O3 sonde profiles with ERA-Interim (ERA-I) products, with a focus
on troposphere–stratosphere exchange. They found that in the upper-troposphere ERA-I had a high bias against observations, similarly to the
case for TCR-2; this confirms that the underestimation by the model for the
surface did not continue into the tropopause. One possibility is that the
model's vertical (downward) mixing near the top of the boundary layer was
too weak: this mixing would otherwise have effectively carried the
O3-rich air mass from higher altitudes. Another possibility is that dry
deposition on the surface of the Arctic Ocean by the model was too fast. The
vd, ∼0.04 cm s-1 over the Arctic open ocean in
September for CHASER (TCR-2), is on the high side of ∼0.01–0.05 cm s-1, a range adopted into global atmospheric chemistry
models (see Fig. 4 of Hardacre et al., 2015). Ganzeveld et al. (2009)
discussed a sensitivity model run, shifting their standard vd of 0.05 to 0.01 cm s-1, which substantially increased surface ozone
concentrations by up to 60% in high-latitude regions.
Average profile from O3 soundings
(blue) and that from TCR-2 (red). Average from surface observations on R/V Mirai is shown as black circle.
At Barrow (71.32∘ N, 156.6∘ W), the nearest ground-based
site, the monthly average O3 mixing ratios in September were 29.8 and
29.1 ppbv, in 2013 and 2014, respectively (McClure-Begley et al., 2014).
These values are close to our observations over oceans, and the model
ensemble tended to underestimate mixing ratios in September (AMAP, 2015),
similarly to our case.
O3 levels below 10 ppbv at low latitudes
Figures 1c, 4, and 5 show that TCR-2 tended to overestimate O3 mixing
ratios over the western Pacific equatorial region. For the whole global data
set, TCR-2 predicted O3 levels below 10 ppbv O3 during 262 h,
which is much less than the observed duration, i.e., 800 h. The occurrence
frequencies in the region 125–165∘ E, 10∘ S to
25∘ N (defined as domain 2, N=2258) were large, namely 295 and
205 h for observations and TCR-2 predictions, respectively. When the studied
region was further narrowed to 150–165∘ E and 0–15∘ N
(defined as domain 3, N=657), the discrepancy increased again, i.e., 199
and 80 h for observations and predictions, respectively. In March and
December, observations were frequently conducted in domain 3 (N=211 and
321, respectively). Figure 12 shows the cumulative relative frequency
distributions for observations, and the TCR-2 and ACCMIP models for these
two months. In March (Fig. 12a), the observed O3 mixing ratios in the
low ranges (< 30th percentiles) were as low as 5 ppbv; these were
overestimated by TCR-2. The shape of the distribution was in better
agreement for higher ranges (30th to 100th percentiles). The ensemble
medians for the ACCMIP runs were higher than the observations for any of the
percentiles, whereas CESM-CAM-superfast, GISS-E2-R, GEOSCCM, and MOCAGE
better captured features of the observed low mixing ratios (at higher
percentiles GEOSCCM and MOCAGE gave mixing ratios that were too high). For December
(Fig. 12b), the performances of the models were poorer: although
CESM-CAM-superfast and GISS-E2-R again captured the observed distributions
in low ranges, all others, including the ACCMIP ensemble median and TCR-2,
overestimated the mixing ratios for any of the percentiles. The large
variations among the model results may reflect the impact of large
variations in transport, particularly in March. Because of the lack of
direct constraints over the remote oceans on near-surface mixing ratios in
the current satellite observing systems, the systematic mismatches imply
requirements for exploring model error sources to improve the reanalysis
quality.
Cumulative relative frequency distributions of
O3 mixing ratios from observations (black), TCR-2
(red), and members and ensemble median of ACCMIP for domain 3
(0–15∘ N and 150–165∘ E) in (a) March and
(b) December.
The average relative diurnal variations normalized to the maximum mixing
ratios during these 2 months (Fig. 13, x axis is UTC+11 h, adjusted to
local time for the selected region) showed a pattern of daytime decreases,
suggesting photochemical destruction. However, the observed decrease was
stronger (15 %) and earlier than those simulated in the TCR-2 and ACCMIP
runs, in which the HOx cycle was primarily responsible for
photochemical destruction. This feature may indicate O3 loss via a
different process, e.g., halogen chemistry, which is not included in any of
the model simulations considered in this study.
Diurnal variation patterns relative to maximum mixing
ratios from observations (black), TCR-2 (red), and members and ensemble
median of ACCMIP for domain 3 (0–15∘ N and 150–165∘ E). Data from March and December are merged.
To obtain further insights into the regions in which this additional loss
could be important, the differences between the O3 levels predicted by
TCR-2 and the observed levels were calculated and the correlations with the
residence times (in the daytime) of back trajectories in 15∘× 15∘ grid regions around domain 3 were examined. Use of
the residence time during the day can be justified because the destruction
probably occurred in the daytime (Fig. 13). Figure S2 shows the results for
17 regions, covering 120–195∘ E and 15∘ S to
45∘ N. We found that the residence time in the grid region
165–180∘ E and 15–30∘ N, located northeast of domain
3, had the largest positive correlation coefficient (Fig. 14). This suggests
that this region is a possible hotspot for additional loss. It is worth
noting that data from all five cruises contributed to the positive
correlation, which suggests that the relationship is reproducible. The slope
of the regression line was 0.25 ppbv h-1. This corresponds to the
possible loss rate in the grid that best explains the discrepancy between
observations and TCR-2 predictions. If the rate is attributed to dry
deposition on the sea surface, a deposition velocity as high as 0.33 cm s-1 is required, assuming a boundary layer height of 1000 m and an
average O3 mixing ratio of 17.4 ppbv. This high velocity is not
supported by previous studies (e.g., Ganzeveld et al., 2009; Hardacre et
al., 2015). Such a loss rate can be more easily explained by bromine and/or
iodine chemistry in the atmosphere (e.g., Saiz-Lopez et al., 2012, 2014). We
observed elevated iodine monoxide (IO) radical concentrations with a MAX-DOAS instrument
aboard R/V Mirai during the cruises included in the analysis in Fig. 14. Further
analysis will be reported in a future publication.
High biases in TCR-2 with respect to observations in
domain 2 (10∘ S to 25∘ N, 125–165∘ E) show positive
correlations with daytime residence times in grid 15–30∘ N and
165–180∘ E.
Substantially reduced O3 levels (< 10 ppbv) have been reported
in the marine boundary layer over the equatorial Pacific. Johnson et al. (1990) found near-zero (3 ppbv or less) O3 mixing ratios in the central
equatorial Pacific during April and May. Kley et al. (1996) observed similar
events in March 1993, with mixing ratios occasionally reaching 3–5 ppbv,
from O3 soundings in a region between the Solomon Islands
(9.4∘ S, 160.1∘ E) and Christmas Island (2∘ N,
157.5∘ W). Takashima et al. (2008) reported substantially reduced
O3 events throughout the year on Christmas Island. Rex et al. (2014)
used a combination of O3 sounding data from the TransBrom cruise of R/V Sonne in October 2009, atmospheric chemistry models, and satellite observations
to identify a region with strong O3 depletion in the marine boundary
layer at 0–10∘ N along the north–south cruise track (at around
150∘ E). This is in good agreement with our observations. Hu et
al. (2010) reported that average O3 mixing ratios at Kwajalein Island
(8.72∘ N, 167.73∘ E), Republic of the Marshall Islands,
located near our hotspot grid, during July, August, and September 1999,
were lower than 10 ppbv throughout 24 h, with an afternoon decrease of about
1 ppbv. Gómez Martín et al. (2016) reported year-round continuous
observations of surface O3 at San Cristóbal (0.90∘ S,
89.61∘ W) and at Puerto Villamil, Isabela Island (0.96∘ S, 90.97∘ W), in the Galápagos Islands in the equatorial
eastern Pacific. Daily averages as low as 5–6 ppbv were observed during the
period February to May. During the Malaspina circumnavigation in 2010,
O3 mixing ratios as low as 3.4 ppbv were detected around the central
Pacific equatorial region (Prados-Roman et al., 2015). During PEM-West A in
October 1991, Singh et al. (1996) reported O3 levels as low as 8–9 ppbv from aircraft measurements at altitudes of 0.3–0.5 km in the region
0–20∘ N in the western and central Pacific. It is worth noting
that the O3 levels over the Atlantic were typically higher, e.g.,
30–35 ppbv (Read et al., 2008), even when halogen-mediated destruction was
considered. In these studies, detailed statistical comparisons of the
observed O3 levels with chemistry–climate model simulations or
reanalysis data as focused here have not been achieved. Conducting
continuous observations of O3 during a large number of cruise legs was
useful for obtaining a large data set for performing detailed statistical
analysis.
The influence of halogen (bromine and/or iodine) chemistry on O3 levels
has been studied using model simulations at assumed (e.g., Davis et al.,
1996; Hu et al., 2010) and observed levels of halogen compounds (Mahajan et
al., 2012; Dix et al., 2013; Großmann et al., 2013; Prados-Roman et al.,
2015; Koenig et al., 2017) over the Pacific. For example, for bromine
chemistry, Hu et al. (2010) estimated a photochemical loss rate of up to
0.12 ppbv h-1. A loss of about 0.4 ppbv d-1 can be attributed to
halogen chemistry in the marine boundary layer south of Hawaii (Dix et al.,
2013). From CAM-chem-based global model simulations, Saiz-Lopez et al. (2012) estimated an annually integrated rate of surface O3 loss through
halogen chemistry as ∼0.15 ppbv h-1 in the daytime, over
the region 20∘ S to 20∘ N. On the basis of GEOS-Chem,
Sherwen et al. (2016) suggested that halogen chemistry would reduce surface
O3 levels by ∼3–5 ppbv over our domain 3. These loss
rates caused by halogen chemistry are in fair agreement with the required
additional loss rate, i.e., ∼0.25 ppbv h-1, estimated in
this study.
Our observational O3 data set with a large geographical coverage will
be useful for evaluating up-to-date model simulations with inclusion of
halogen chemistry. Analysis at midlatitude regions over the Pacific are
also of interest because the importance of halogen chemistry in this region
has been indicated (e.g., Nagao et al., 1999; Galbally et al., 2000; Watanabe
et al., 2005).
Summary and outlook
We compiled a large data set of shipborne in situ observations of O3
and CO levels with a 1 h resolution, which were recorded on R/V Mirai over the
Arctic, Bering, Pacific, Indian, and Southern oceans from 67∘ S to
75∘ N, during the period 2012 to 2017. We used the data set to
evaluate tropospheric chemistry reanalysis data from TCR-2 and ACCMIP model
simulations. TCR-2 captured the basic features of the observations,
including the latitudinal gradient and air mass exchange, and therefore
enabled interpretation of observations regarding transport and pollution
sources. Correlations with observations were sufficient (R2 up to 0.62
for hourly data and 0.67 for daily data). This suggests an excellent
performance by TCR-2 in representing the temporal and geographical
distributions of surface O3. For over 23 long-range transport events
with CO and/or O3 buildup, variations in the ΔO3/ΔCO ratios were well reproduced by TCR-2. This suggests that the nature of
photochemical evolution during transport of pollution plumes was also well
captured. However, two major discrepancies were identified: in the Arctic
(> 70∘ N) in September, TCR-2 and the ensemble median
of model runs of ACCMIP tended to underestimate O3 levels. From
analysis of O3 sonde measurements, we concluded that the gap was
related to processes relevant to the boundary layer: downward mixing from
the free troposphere, with a higher O3 abundance, might have been too
weak in the models. For TCR-2, dry deposition on the Arctic Ocean surface
might have been too fast. Conversely, in the western Pacific equatorial
region, TCR-2 and ACCMIP simulations significantly overestimated the
observed O3 levels, which were often less than 10 ppbv. The minimum in
the observed diurnal pattern occurred earlier than those in the models. The
gap of mixing ratios correlated with the residence time of trajectories over
a particular grid, i.e., 15–30∘ N, 165–180∘ E.
These analyses indicate the importance of halogen chemistry, which is not
accounted for in the models, and the region in which it is active. The data
set from our observations (which will continue) is open and complements the
TOAR data collection and will be useful for critically evaluating
global-scale atmospheric chemistry model simulations, including those from
CCMI, AerChemMIP (Collins et al., 2017), and future intermodel comparisons.
Data availability
The observational data set for O3 and CO obtained on R/V Mirai for the study
period (2012–2017) is collectively available from https://ebcrpa.jamstec.go.jp/atmoscomp/obsdata/ (last access: 23 May 2019) (Atmospheric Composition Research Group, 2019) as a single text file and
from http://www.godac.jamstec.go.jp/darwin/e (last access: 23 May 2019) (DARWIN, 2019) for individual
cruise legs. TCR-2 reanalysis data are available from http://ebcrpa.jamstec.go.jp/tcr2/download.html (last access: 23 May 2019) (JAMSTEC, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-7233-2019-supplement.
Author contributions
YuKa designed the study, conducted analyses, wrote the manuscript,
managed the instruments and created the observational data set. KM, TS, and
KeS created TCR-2 data. FT, TM, HT, YuKo, XP, and SK
substantially contributed to shipborne observations and preparation. HT and
SK also provided supporting data from MAX-DOAS observations. JI, KaS,
and KO conducted shipborne ozone soundings and provided the data. All
co-authors provided professional comments to improve the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We gratefully acknowledge assistance from the Principal Investigators of all
cruises and support from Global Ocean Development Inc. and Nippon Marine
Enterprise, Ltd. Part of
the research was carried out at the Jet Propulsion Laboratory, California
Institute of Technology, under a contract with the National Aeronautics and
Space Administration. We thank Helen McPherson, PhD, from Edanz Group
(https://www.edanzediting.com, last access: 27 May 2019) for editing a draft of this
manuscript.
Financial support
This research has been supported by the Ministry of
Education, Culture, Sports, Science and Technology (MEXT) (Coordination Funds for Promoting AeroSpace Utilization and the Arctic
Challenge for Sustainability (ArCS) project grants), the MEXT/JSPS KAKENHI (grant
nos. 24241009, 18H04143, and 18H01285), and the Ministry of the
Environment, Japan (Environment Research and Technology Development
Fund, grant no. 2-1803).
Review statement
This paper was edited by Neil Harris and reviewed by two anonymous referees.
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