ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-12519-2015Diurnal, seasonal and long-term variations of global formaldehyde
columns inferred from combined OMI and GOME-2 observationsDe SmedtI.isabelle.desmedt@aeronomie.behttps://orcid.org/0000-0002-3541-7725StavrakouT.HendrickF.DanckaertT.VlemmixT.https://orcid.org/0000-0003-2584-3402PinardiG.https://orcid.org/0000-0001-5428-916XTheysN.LerotC.GielenC.VigourouxC.HermansC.FaytC.VeefkindP.MüllerJ.-F.Van RoozendaelM.Belgian Institute for Space Aeronomy (BIRA-IASB),
Brussels, BelgiumRoyal Netherlands Meteorological Institute (KNMI), De
Bilt, the NetherlandsI. De Smedt (isabelle.desmedt@aeronomie.be)10November20151521125191254526February201523April201521October201523October2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/12519/2015/acp-15-12519-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/12519/2015/acp-15-12519-2015.pdf
We present the new version (v14) of the BIRA-IASB algorithm for the retrieval
of formaldehyde (H2CO) columns from spaceborne UV–visible sensors.
Applied to OMI measurements from Aura and to GOME-2 measurements from MetOp-A
and MetOp-B, this algorithm is used to produce global distributions of
H2CO representative of mid-morning and early afternoon conditions. Its
main features include (1) a new iterative DOAS scheme involving three fitting
intervals to better account for the O2–O2 absorption, (2) the use
of earthshine radiances averaged in the equatorial Pacific as reference
spectra, and (3) a destriping correction and background normalisation
resolved in the across-swath position. For the air mass factor calculation, a
priori vertical profiles calculated by the IMAGES chemistry transport model
at 09:30 and 13:30 LT are used. Although the
resulting GOME-2 and OMI H2CO vertical columns are found to be highly
correlated, some systematic differences are observed. Afternoon columns are
generally larger than morning ones, especially in mid-latitude regions. In
contrast, over tropical rainforests, morning H2CO columns significantly
exceed those observed in the afternoon. These differences are discussed in
terms of the H2CO column variation between mid-morning and early
afternoon, using ground-based MAX-DOAS measurements available from seven
stations in Europe, China and Africa. Validation results confirm the capacity
of the combined satellite measurements to resolve diurnal variations in
H2CO columns. Furthermore, vertical profiles derived from MAX-DOAS
measurements in the Beijing area and in Bujumbura are used for a more
detailed validation exercise. In both regions, we find an agreement better
than 15 % when MAX-DOAS profiles are used as a priori for the satellite
retrievals. Finally, regional trends in H2CO columns are estimated for
the 2004–2014 period using SCIAMACHY and GOME-2 data for morning conditions,
and OMI for early afternoon conditions. Consistent features are observed,
such as an increase of the columns in India and central–eastern China, and a
decrease in the eastern US and Europe. We find that the higher horizontal
resolution of OMI combined with a better sampling and a more favourable
illumination at midday allow for more significant trend estimates, especially
over Europe and North America. Importantly, in some parts of the Amazonian
forest, we observe with both time series a significant downward trend in
H2CO columns, spatially correlated with areas affected by deforestation.
Introduction
Atmospheric formaldehyde (H2CO) is an intermediate product common to the
degradation of many volatile organic compounds (VOCs). While the global
formaldehyde background is due to methane oxidation, emissions of non-methane
volatile organic compounds (NMVOCs) from biogenic, biomass burning and
anthropogenic continental sources result in important and localised
production of H2CO. The global sink of H2CO is due to photolysis
and oxidation by OH, resulting in a photochemical lifetime of only a few
hours. Elevated concentrations of H2CO can therefore be related to the
emission of reactive NMVOCs. Monitoring the spatial and temporal variability
of NMVOC emissions is essential for a better understanding of the processes
that not only control the production and the evolution of tropospheric ozone,
a key actor in air quality and climate change, but also of the hydroxyl
radical OH and secondary organic aerosols. For these reasons, H2CO
satellite observations have been increasingly used in combination with
tropospheric chemistry transport models to constrain NMVOC emissions (i.a.
Palmer et al., 2006; Fu et al., 2007; Millet et al., 2008; Stavrakou et al.,
2009a, b; 2014; Curci et al., 2010; Barkley et al., 2013; Fortems-Cheiney et
al., 2012; Marais et al., 2012, Zhu et al., 2014).
For more than 15 years, mid-morning formaldehyde tropospheric columns have
been retrieved from the successive nadir-scanning spectrometers GOME on ERS-2
(1996–2003) (Chance et al., 2000; Palmer et al., 2001; De Smedt et al.,
2008), SCIAMACHY on ENVISAT (2002–2011) (Wittrock et al., 2006; De Smedt et
al., 2008) and GOME-2 on MetOp-A and MetOp-B (2006– and 2012–) (De Smedt et
al., 2012; Hewson et al., 2013). Since 2004, complementary early afternoon
H2CO columns have also been available from the OMI imaging spectrometer
on Aura (Kurosu, 2008; Millet et al., 2008; González Abad et al., 2015a),
and since 2011 from OMPS on SUOMI-NPP (Li et al., 2015; González Abad et
al., 2015b). In addition to formaldehyde, glyoxal – another short-lived
NMVOC – has also successfully been retrieved from SCIAMACHY (Wittrock et
al., 2006), GOME-2 (Vrekoussis et al., 2010; Lerot et al., 2010) and OMI
(Alvarado et al., 2014; Chan Miller et al., 2014).
From 2017 onward, the morning observations will be continued with a third
GOME-2 instrument to be launched on MetOp-C (Callies et al., 2000), while the
afternoon observations will be extended with the TROPOMI instrument (Veefkind
et al., 2012), to be launched in 2016 as part of the Copernicus Sentinel 5
Precursor (S-5P) mission, and later with the Sentinel-5 mission to be
operated on the MetOp Second Generation platform (Ingmann et al., 2012). Also
at the 2020 horizon, the Sentinel-4 instrument on the geostationary Meteosat
Third Generation (MTG) platform will allow for hourly observations of
H2CO over Europe, while TEMPO (NASA) and GEMS (KARI) will provide
geostationary measurements for North America and Asia respectively. To
realise the full potential of these missions, it is crucial to develop
high-quality and consistent retrieval algorithms applicable to the different
satellite sensors, taking into account their differences in horizontal
resolution and sampling. Likewise, it is essential to understand the diurnal
variations of the sources and sinks of formaldehyde, in order to exploit the
synergy between the morning and afternoon satellite observations. To our
knowledge, so far, the use of combined morning and afternoon H2CO
satellite observations has only been reported over Amazonia, using SCIAMACHY
and OMI measurements (Barkley et al., 2011, 2013).
Ground-based measurements are essential to quantitatively assess the seasonal
and diurnal variations of the tropospheric H2CO columns. However, up to
now, very few validation studies have been reported for satellite H2CO
observations (Wittrock et al., 2006; Vigouroux et al., 2009) because of the
general lack of suitable ground-based measurements, in particular for
tropical regions where H2CO columns are among the highest worldwide
(Stavrakou et al., 2009b; Marais et al., 2012; Barkley et al., 2013). Also,
very little attention has been paid to the diurnal variations of the
H2CO columns and to their local dependencies, which are a complex blend
of local NMVOC emission variations, H2CO production and loss via
oxidation and photolysis depending on local chemical regimes and season. In
this regard, the latest generation of MAX-DOAS instruments and retrieval
algorithms offer new perspectives for the validation of tropospheric trace
gas concentrations and aerosol optical densities (Clémer et al., 2010;
Pinardi et al., 2013; Vlemmix et al., 2014; Wang et al., 2014).
This study focuses on tropospheric formaldehyde retrievals from OMI, using an
algorithm historically developed within the TEMIS (Tropospheric Emission
Monitoring Internet Service) framework and applied to morning observations
from the GOME, SCIAMACHY and GOME-2 sensors (http://h2co.aeronomy.be).
We present several adaptations that have been implemented to handle
observations from the OMI imaging spectrometer, as well as a number of more
general improvements to the algorithm, giving rise to a new version of the
BIRA-IASB H2CO retrieval algorithm (version 14). This version has been
applied to the complete time series of OMI measurements, as well as to the
GOME-2 measurements from MetOp-A and MetOp-B platforms. For the first time,
differences between morning and afternoon H2CO columns are estimated at
the global scale and discussed in terms of H2CO diurnal variations,
horizontal resolution effects and retrieval uncertainties. Moreover,
ground-based measurements at seven stations, covering mid-latitude and
tropical locations, are used to validate the observed H2CO columns and
their diurnal changes as derived from the combined satellite data sets.
The paper is structured as follows: Sect. 2
introduces the main characteristics of the OMI and GOME-2 instruments.
Section 3 describes the new version (v14) of the
H2CO retrieval algorithm. The H2CO tropospheric columns obtained
from GOME-2 and OMI measurements are presented and compared in Sect. 4, and the main results of our validation studies
are outlined in Sect. 5. Finally, the long-term
variations of the H2CO columns over the last decade are discussed in
Sect. 6.
Satellite instrumentsOMI on Aura
The Aura satellite was launched in July 2004, in a Sun-synchronous polar
orbit crossing the Equator around 13:30 LT (in ascending mode). It is the
third major component of the NASA Earth Observing System (EOS) following
Terra (launched 1999) and Aqua (launched 2002). OMI (Ozone Monitoring
Instrument) is a nadir-viewing imaging spectrometer that measures the solar
radiation backscattered by the Earth's atmosphere and surface over the
wavelength range from 270 to 500 nm with a spectral resolution of about
0.5 nm (Levelt et al., 2006). The light entering the telescope is
depolarised using a scrambler and then split into two channels: a UV channel
(wavelength range 270–380 nm) and a VIS (visible) channel (wavelength range
350–500 nm). The 114∘ viewing angle of the telescope corresponds to
a 2600 km wide swath on the Earth's surface, which enables nearly daily
global coverage. In the nominal global operation mode, the OMI ground pixel
size varies from 13×24 km2 at nadir to 28×150 km2 at
the edges of the swath. For this work, we have used the OMI Level 1B UV
Global Radiances Data Product (OML1BRUG – version 003) provided on the NASA
website
(http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/oml1brug_v003.shtml).
GOME-2 on MetOp-A and MetOp-B
The MetOp-A and MetOp-B satellites were respectively launched in October 2006
and September 2012, in Sun-synchronous polar orbits with Equator-crossing
times of 09:30 and 09:00 LT (in descending node). Both satellites carry the
same types of GOME-2 (Global Ozone Monitoring Experiment) instruments.
Hereinafter, we will refer to them as GOME-2A and GOME-2B (or G2A and G2B).
GOME-2 (Callies et al., 2000; Munro et al., 2006) is an improved version of
the GOME instrument which flew on the ERS-2 satellite. It is a nadir-viewing
scanning spectrometer with four main optical channels, covering the spectral
range between 240 and 790 nm with a spectral resolution between 0.26 and
0.51 nm. Additionally, two polarisation components are measured with
polarisation measurement devices (PMDs) at 30 broad-band channels covering
the full spectral range. A direct Sun spectrum is also measured via a
diffuser plate once per day. The default swath width of the GOME-2 scan is
1920 km, allowing for global Earth coverage within 1.5–3 days at the
Equator. The nominal ground pixel size is 80 × 40 km2. For
this work, we have used the EUMETSAT GOME-2A and GOME-2B level 1B data
version 5.3.0 from the beginning of their time series up to mid-June 2014,
and version 6.0.0 afterwards.
Formaldehyde retrievals
We use a DOAS (differential optical absorption spectroscopy) algorithm,
including three main steps, further detailed in Sects. 3.1, 3.2 and 3.3:
(1) the fit of absorption cross-section databases to the log ratio of
measured Earth reflectance to retrieve H2CO slant columns
(Ns), (2) a background normalisation procedure to eliminate
remaining unphysical dependencies, and (3) the calculation of tropospheric
air mass (M) factors using radiative transfer calculations and modelled a
priori profiles. The tropospheric H2CO vertical column (Nv)
is related to intermediate quantities by the equation
Nv=ΔNsM+Nv,0,CTM,
where ΔNs is the background-corrected slant column density, and
Nv,0,CTM is the model background column in the reference sector. More
detailed equations can be found in our previous publications (e.g. De Smedt
et al., 2011, 2014).
Level-2 formaldehyde products developed at BIRA-IASB are provided via the
TEMIS website. The algorithms used to generate these products are designed to
be as consistent as possible, in order to optimise the overall coherency of
the resulting time series. Over the years, scientific developments have led
to step-by-step improvements in the quality of the data products. This
entails regular reprocessing of the data sets. The retrieval settings
presented here for the GOME-2 and OMI measurements are based on the BIRA
algorithm developed for GOME-2 (De Smedt et al., 2012), but also include a
number of adaptations allowing for the efficient processing of imaging
instruments and additional improvements as further detailed in this section.
The main differences compared to version 12 are (1) the use of daily
radiances as DOAS reference spectra, (2) the inclusion of O2–O2 in
the BrO and H2CO retrieval intervals, and (3) the pre-fit of
O2–O2 in a dedicated retrieval interval. For the reference sector
correction, which so far has been resolved in latitude and time, an
additional dimension (viewing zenith angle or detector row) is now introduced
as a destriping procedure. An updated version of the IMAGES model a priori
profiles is sampled at the respective satellite overpass times. Quality flags
have been defined, in order to better select the valid satellite
observations. Finally, the format of the level-2 data files has been changed
to HDF-5. As for the previous algorithm versions, level-2 and level-3 data
products of version 14 are openly available on TEMIS
(http://h2co.aeronomie.be). Comparisons between the BIRA-IASB OMI
H2CO product and the OMI operational product from SAO can be found in
González Abad et al. (2015b), while comparisons with the NASA OMPS PCA
product can be found in Li et al. (2015).
Slant columns
Typical log ratio of the measured Earth reflectance and fitted
low-order polynomial (black and red lines in the first panel), and optical
densities of O2–O2 (O4), BrO, H2CO, O3, NO2
and the Ring effect (solar lines and molecular) in the near UV. The slant
columns have been taken as 0.4×1042 molec2 cm-5 for
O4, 1014 molec cm-2 for BrO, 1016 molec cm-2 for
H2CO, 1020 molec cm-2 for O3 and 1×1016 molec cm-2 for NO2. The different wavelength intervals
used in the retrieval are indicated as w1, w2 and w3.
The formaldehyde slant columns are retrieved in the interval 328.5–346 nm.
Retrieval settings are summarised in Table 1. We use the QDOAS software
developed at BIRA-IASB for the DOAS retrieval of trace gases from many common
satellite and ground-based instruments. QDOAS is distributed under the GNU
GPL license version 2.0 (Danckaert et al., 2014,
http://uv-vis.aeronomie.be/software/QDOAS).
In QDOAS, the wavelength registration of the reference spectrum is fine-tuned
by means of a calibration procedure making use of the solar Fraunhofer lines.
To this end, a highly accurate reference solar atlas (Chance and Kurucz,
2010) is degraded at the resolution of the instrument, through convolution by
the instrumental slit function. The absorption cross sections of the
different trace gases are also convolved with the instrumental slit function.
In the case of GOME-2, the slit function shape is fitted during the
calibration procedure, in order to take into account its changes with time
(Dikty and Richter, 2011; De Smedt et al., 2012). In the case of OMI,
pre-flight measured slit functions (Dobber et al., 2006) are used, and the
calibration is performed for each binned spectrum of the detector array (60
rows). Except for the reported row anomaly that has dynamically evolved over
the years
(http://www.knmi.nl/omi/research/product/rowanomaly-background.php),
the performance of the OMI instrument has proven to be very stable in time
(Dobber et al., 2008). In contrast to previous work, we now moved to the
systematic use of daily radiance spectra averaged in the equatorial Pacific
(15∘ S–15∘ N, 180–240∘ E) as a reference for the
DOAS retrieval. Different reference spectra are selected daily for each OMI
row and their wavelength registration is optimised as described above. This
serves as a first correction for the OMI stripe effect. Consequently, all
retrieved slant columns are differential columns relative to the mean
reference spectra.
A fifth-order polynomial is used to fit the low-frequency variations of the
spectra, as well as a linear offset term. The H2CO Meller and Moortgat
(2000) laboratory measurements are fitted to the differential absorption
features. The absorption cross sections of O3 at 228 and 243 K,
NO2 and BrO are included. To take into account the Ring effect, two
cross sections are used (Vountas et al., 1998). They have been calculated in
an ozone-containing atmosphere for low and high SZA (solar zenith angle)
using LIDORT RRS (Spurr et al., 2008). Two additional terms (called O3L
and O3O3 in Table 1), resulting from the Taylor expansion of the
O3 absorption as a function of the wavelength, are included in order to
better cope with strong O3 absorption effects (Puķīte et al;
2010; De Smedt et al., 2012). Introduced in the previous version 12, a second
(larger) retrieval interval is used to pre-fit the BrO slant columns
(Fig. 1). The benefit of this procedure is to decorrelate the H2CO and
BrO absorption features, resulting in a reduction of the noise on the BrO and
H2CO slant columns. However, the O2–O2 (O4) absorption
had not been taken into account in version 12, neither in the larger nor in
the shorter interval. While the weak and smooth O4 signature appears to
be well fitted by the fifth-order polynomial used in the shorter interval
(Hewson et al., 2013), not including the stronger O4 term in the larger
interval is clearly a shortcoming. Nevertheless, this solution has often been
selected in past studies, because experience has shown that including O4
tends to destabilise the BrO fit (Kurosu, 2008; Begoin et al., 2010; Theys et
al., 2011), hence leading to increased noise on the H2CO slant columns
retrieved in the shorter interval. In version 14, we propose the addition of
a third fitting interval (339–364 nm) covering the entire O4
absorption band around 360 nm (Fig. 1). We use the recently published
O4 absorption cross sections by Thalman and Volkamer (2013), and our
H2CO retrieval scheme therefore now includes three fitting intervals: w1
for the pre-fit of O4, w2 for the pre-fit of BrO, and w3 for the fit of
the H2CO slant columns, in which the O4 and BrO slant columns are
fixed to values determined in the other intervals.
It should be noted that for the GOME and SCIAMACHY H2CO retrievals,
only one retrieval interval is used (328.5–346 nm) because the quality of
the recorded spectra has been found to be insufficient in the 360 nm region
(De Smedt et al., 2008). O4 absorption effects are therefore
significantly reduced.
Summary of retrieval settings used in version 14 of the BIRA H2CO
retrieval algorithm, applicable to GOME-2 and OMI measurements.
Settings for the DOAS equation parameters CalibrationAccurate solar atlas (Chance and Kurucz, 2010) Slit functionOMI: one slit function per binned spectrum as a function of wavelength (Dobber et al., 2006)GOME-2: asymmetric Gaussian slit function fitted during calibration (De Smedt et al., 2012)Reference spectrumDaily average of radiances selected in the equatorial Pacific PolynomialFifth order Intensity offsetLinear offset Hot pixels treatmentIterative spike removal algorithm (Richter et al., 2011; De Smedt et al., 2014) Absorption cross-section data sets H2COMeller and Moortgat (2000) O3Brion et al. (1998), Daumont et al. (1992), Malicet et al. (1995) BrOFleischmann et al. (2004) NO2Vandaele et al. (2002) O2–O2 (O4)Thalman and Volkamer (2013) Ring effectTwo Ring cross sections calculated in an ozone-containing atmosphere Ring effectfor low and high SZA, using LIDORT RRS (Spurr et al., 2008). Non-linear O3 absorption effectTwo pseudo cross sections from the Taylor expansion of the wavelength and the O3 optical depth (Puķīte et al., 2010). Fitting interval w1: O4339–364 nm Included cross sectionsO4 (293 K), O3 (228 K), BrO (223 K), H2CO (298 K), NO2 (220 K), Ring1, Ring2 Fitting interval w2: BrO328.5–359 nm Included cross sectionsBrO (223 K), H2CO (298 K), O3 (228 and 243 K), NO2 (220 K), Included cross sectionsO4 (293 K, not fitted), Ring1, Ring2, O3L, O3O3Fitting interval w3: H2CO328.5–346 nm Included cross sectionsH2CO (298 K), O3 (228 and 243 K), BrO (223 K, not fitted), NO2 (220 K), InclO4 (293 K, not fitted), Ring1, Ring2, O3L, O3O3
Figure 2 illustrates the correlation effects occurring between the O4,
BrO and H2CO absorptions, for one OMI orbit on 1 July 2005. The upper
panel shows the O4 differential slant columns retrieved in w1
(339–364 nm, light blue) or in w2 (328.5–359 nm, dark green) as a
function of the latitude. The improved quality of the O4 slant columns
in w1 is observed, as expected from Fig. 1. The second and third panels show
the differences observed in the BrO and H2CO slant columns, depending on
whether O4 is included in the fits or not (v14–v12), as a function of
the O4 slant columns. Those differences are shown for two cases: O4
slant columns from w1 (light blue, v14) or from w2 (dark green). The pre-fit
of O4 in w1 reduces the noise on the O4, BrO and H2CO slant
columns in the three intervals. The correlation between the three molecules
is not reduced by the introduction of this third interval (the slope of the
differences remains the same), but the slant columns are limited to more
realistic values for each molecule, allowing for an effective reduction of
the noise. It is interesting to note that the observed slopes of the
differences in BrO and H2CO columns are exactly the same for GOME-2 and
OMI retrievals and for different periods of the years (not shown), pointing
to a fundamental spectral effect rather than an instrumental feature.
Interdependencies between O4, BrO and H2CO slant columns
shown for one OMI orbit of 1 July 2005 (UT 0548). The first panel shows the
differential O4 slant columns retrieved in w1 (339–364 nm, light blue)
and in w2 (328.5–359 nm, dark green) as a function of the latitude. The
second and third panels show the differences obtained in BrO and H2CO
slant columns as a function of the O4 slant columns, when including or
not including O4 in the fits. Differences are shown for two cases:
O4 slant columns retrieved in w1 (light blue) or in w2 (dark green). The
limits of the wavelength intervals are indicated in Fig. 1.
From Fig. 2, one can conclude that the net effect of including O4 in the
fits is a positive correlation of the H2CO slant columns with the
O4 differential columns, with H2CO column deviations of less than
1016 molec cm-2 but sometimes reaching 2×1016 molec cm-2
for extreme O4 values. Figure 3 presents O4 differential slant
columns retrieved in w1 from OMI measurements in February and August 2007,
giving an idea of the spatial distribution of the expected differences.
Monthly averaged O2–O2(O4) differential slant
columns retrieved in the interval 339–364 nm. The slant columns are
differential since radiance spectra over the equatorial Pacific region
(delimited by the black box) are used as reference spectra.
Over the Pacific Ocean, the O4 differential slant columns are generally
positive and increase with the solar zenith angle. Figure 4 presents the
averaged zonal variation of the H2CO, BrO and O4 differential slant
columns for January, April, July and October 2007, as a function of the
latitude (a) when O4 is not included in the fit (v12), and (b) when
O4 is pre-fitted in w1 and included in the fit (v14). Dashed lines show
the slant columns before the background normalisation procedure. The decrease
of the H2CO slant columns with latitude is greatly reduced when O4
is considered, with a minimum impact on the standard deviations of the
columns, owing to the pre-fit of O4. However, a positive artificial
dependency remains that is related to ozone absorption interferences and
still needs to be corrected (see Sect. 3.2). The O4 effect is exactly
the opposite for BrO, for which a decrease of the columns is observed for
large O4 slant columns (about -20 %). This can have a significant
impact for BrO studies in polar regions (Salawitch et al., 2010; Choi et al.,
2012), but it is beyond the scope of this paper.
Averaged zonal variation of the H2CO, BrO and O4
differential slant columns for 4 months of OMI observations in 2005 (January,
April, July, October), over the Pacific Ocean, as a function of the latitude
(a) when O4 is not included in the fit (v12), and (b)
when O4 is included and pre-fitted in w1 (v14). Dashed lines show the
slant columns before the background normalisation procedure, while the plain
lines show the normalised H2CO slant columns.
Impact of O4 on the absolute OMI and GOME-2A H2CO columns
(respectively in red and green) and on their differences (in black). Dashed
lines show results when O4 is not included in the fits, while solid
lines show v14 results.
Over the continents, where H2CO column enhancements are expected, the
differential O4 slant columns are almost always negative (i.e. lower
than over oceans), due to the combined effect of higher altitude and lower
surface reflectivity. We observe therefore a decrease of the H2CO
columns over continental emission regions, by 0 down to -25 %. This
reduction is the same for GOME-2 and OMI; that is, it has no impact on the
observed diurnal variations. As an example, Fig. 5 presents the time series
of GOME-2 and OMI H2CO columns over India and equatorial Africa,
representative for mid-latitudes and tropical emission regions. Results
without (v12) and with (v14) O4 are plotted, as well as the differences
between OMI and GOME-2 for the two versions. A reduction of the columns is
observed when including O4 in the fits, but the OMI-GOME-2 differences
are equivalent for v12 and v14.
Across-track and zonal reference sector correction
The use of daily radiance spectra as a reference for the DOAS retrievals
results in differential slant columns close to zero in the equatorial
Pacific. However, latitude-dependent biases in the H2CO columns, due to
unresolved spectral interferences, remain a limiting factor for the DOAS
retrieval of weak absorbers such as H2CO. Furthermore, in the case of a
2-D detector array such as OMI, across-track striping arises, due to
imperfect calibration and different dead/hot pixel masks for the detectors.
Such instrumental effects also affect scanning spectrometers like GOME-2, but
since these instruments have one single detector, these errors do not appear
as stripes, but rather as constant offsets (Boersma et al., 2011). These
different retrieval artefacts can be compensated for to a certain extent,
using normalisation approaches such as the reference sector correction
(Kurosu, 2008; De Smedt et al., 2008). As for the DOAS reference radiance
selection, the reference sector is chosen in the Pacific Ocean, where the
only significant source of H2CO is the CH4 oxidation. The H2CO
background is replaced by model simulations in the same region. The reference
sector correction is also meant to handle possible time-dependent
instrumental degradation effects, for example the evolution of the OMI stripe
artefacts, or the GOME-2 signal degradation (De Smedt et al., 2012). Note
that our analysis shows that the most efficient method to reduce across-track
stripes in OMI H2CO retrievals is to use row-dependent mean radiances as
a reference spectrum in the DOAS equation (see Sect. 3.1).
We apply a two-step normalisation of the H2CO slant columns. In a first
step, a row-dependent median H2CO value is determined in the equatorial
Pacific (15∘ S–15∘ N, 180–240∘ E) and subtracted
from all the columns (in the case of GOME-2, we use a viewing angle-dependent
correction). The aim is to reduce possible remaining offsets between rows,
resulting from the different detectors. In addition to the destriping
procedure, those OMI rows presenting a level of noise and fitting residuals
significantly higher than the average of the other rows for a particular day,
are assigned a bad-quality flag and not further used in our applications.
This criterion removes a few rows from the analysis in 2005 and, more
importantly, all the rows affected by the row anomaly which started in
June 2007, and further developed over the years
(http://www.knmi.nl/omi/research/product/rowanomaly-background.php).
As illustrated by the first line of Fig. 6, the affected rows can be
identified using the fitting residuals. This filtering procedure is less
systematic than the use of flags provided in the level-1 files, and is aimed
to keep as many observations as possible in the analysis, which is of
fundamental importance to mitigate the noise on formaldehyde observations. In
a second step, the latitudinal dependency of the offset-corrected H2CO
slant columns is modelled by a polynomial in the entire reference sector
(90∘ S–90∘ N, 180–240∘ E). These two corrections
are sequentially subtracted from the global slant columns and replaced by the
latitudinal dependency of the modelled columns in the same region. The result
of the across-track and zonal reference sector correction is illustrated in
Fig. 4 as a function of the latitude and in the second line of Fig. 6,
showing daily maps of normalised OMI H2CO slant columns in 2005 and
2014. The loss of coverage due to the OMI row anomaly and to our filtering
scheme is clearly visible when comparing the 2005 and 2014 maps.
Intermediate retrieval quantities of the H2CO retrieval
algorithm illustrated with OMI on 1 April 2005 (first column) and
1 April 2014 (second column). The first line shows the fit residuals, while
lines 2 to 4 show respectively the H2CO reference sector corrected slant
columns, the air mass factors and the vertical columns.
Air mass factors
In the troposphere, scattering by air molecules, clouds and aerosols leads to
complex altitude-dependent air mass factors. Multiple scattering calculations
are required for the determination of the air mass factors, and the vertical
distribution of the absorber has to be assumed a priori. In the case of
optically thin absorbers, the formulation of Palmer et al. (2001) is used and
has been described for the TEMIS H2CO retrievals in De Smedt et
al. (2012). It decouples the vertical sensitivity of the measurements (the
scattering weighting functions, derived with radiative transfer model
calculations) from the vertical profile shape of the species of interest
(vertical shape factors, taken from an atmospheric chemistry transport model
or from some other prior knowledge of the vertical distribution of the
absorber). Details on these two calculation steps are given below. The
decoupling of the AMF calculation allows one to address separately the
radiative transfer effects, including clouds, and the atmospheric composition
of optically thin absorbers like H2CO. Vertical columns might be
improved for particular locations by using more accurate a priori profiles,
for example based on input from regional models, ground-based or aircraft
measurements. Furthermore, using shape factors from an atmospheric chemistry
model ensures consistency for subsequent evaluation of the model with the
retrieved vertical columns (Barkley et al., 2013). For these reasons, the
averaging kernels and the a priori profiles are provided in the level-2 data
files for each individual measurement. The third line of Fig. 6 presents
global daily maps of AMF in April 2005 and 2014. These maps have been
filtered for effective cloud fractions larger than 0.4. Clouds and a priori
profile shapes are further discussed below and in the validation Sect. 5.2.
Scattering weighting functions
Scattering weighting functions calculated at 340 nm using the LIDORT v3.3
radiative transfer model (Spurr, 2008) are tabulated according to their
dependencies with solar, viewing and relative azimuth angles, surface
altitude and surface reflectivity. We use the surface reflectivity database
derived from OMI by Kleipool et al. (2008), in both GOME-2 and OMI H2CO
retrievals. Radiative cloud effects are corrected using the independent pixel
approximation (IPA, Martin et al., 2002) and the respective cloud products of
the instruments provided on the TEMIS website, namely the GOME-2 O2
A-band Frescov6 product (Wang et al., 2008) and the OMI O4 cloud product
(Stammes et al., 2008). While the cloud fractions are in general good
agreement between OMI and GOME-2, we observe larger discrepancies for the
cloud altitudes. The differences between OMI and GOME-2 cloud-free AMFs range
from 0 to -10 % (observation geometry effects), but the differences
between IPA cloud-corrected AMFs can reach -20 % where and when the cloud
lies in the lower troposphere. This implies an uncertainty of about 10 % in
the final product, since it is not clear whether those cloud-related
differences reflect real differences in cloud properties or differences in
the cloud retrieval algorithms. This stresses the need for a multi-instrument
homogenised cloud product, for example based on the O4 absorption band
that can be measured by all sensors. No explicit correction is applied for
aerosols, but the cloud correction scheme accounts for a large part of their
scattering effect (Boersma et al., 2011). The uncertainty related to aerosol
effects is estimated to be lower than 15 % in average (Leitão et al.,
2008; Castellanos et al., 2015; Theys et al., 2015).
Vertical shape factors: IMAGESv2
The a priori profile shapes are extracted from daily simulations performed
with the IMAGES model, at 09:30 for GOME-2 and 13:30 for OMI. The IMAGESv2
CTM (chemistry transport model) calculates the global distributions of 90
long-lived and 41 short-lived trace gases at a resolution of 2∘
(latitude) × 2.5∘ (longitude) and on 40 vertical levels from
the surface to the lower stratosphere. The current model version is
thoroughly described in Stavrakou et al. (2013). The model time step is set
to 4 h. Diurnal changes in the photolysis and kinetic rates, meteorological
fields, and the emissions are taken into account through correction factors
calculated from a simulation with a 20 min time step (Stavrakou et al.,
2009a) and applied to model runs using longer time steps. Simulations have
been performed for all years between 2005 and 2013, spun up by a period of 4
months.
Anthropogenic VOC emissions are obtained from the RETRO 2000 global database
(Schultz et al., 2008) and are kept constant throughout the years. Over
Asia, RETRO is overwritten by the REASv2 inventory (Kurokawa et al., 2013)
until 2008, whereas 2008 values are used for more recent years.
Anthropogenic VOC emissions are equal to 147 and 150 Tg VOC in 2005 and
2006, respectively, and to 156 Tg VOC for the following years. Emissions of
isoprene from vegetation are taken from the MEGAN-MOHYCAN-v2 inventory
(Stavrakou et al., 2014). The global annual fluxes range between 323 and
363 Tg isoprene, the lowest and the highest values corresponding to 2008 and
to 2010, respectively. Open biomass burning emissions are taken from the
GFEDv3 inventory (van der Werf et al., 2010) until 2011, whereas a
climatological mean based on 1997–2011 GFEDv3 emissions is used for 2012 and
2013. Global annual fire emissions range between 70 Tg VOC (in 2009) and 105 Tg VOC (in 2010).
The photochemical production of H2CO is estimated at ca. 1600 Tg
annually. The main formaldehyde sinks are the oxidation by OH (Sander et
al., 2011) which leads to CO production and conversion of OH to HO2,
and two photolysis reactions which produce CO and HO2 radicals. Based
on IMAGESv2 model calculations, photolysis is by far the dominant removal
process, estimated at 71 % of the global sink, whereas the OH sink is less
efficient (26 %). Dry and wet deposition account for the remainder
(< 3 %). The global photochemical H2CO lifetime is estimated
at 4.5 h.
Quality criteria of the H2CO vertical columns
The H2CO level-2 files of version 14 are provided in HDF-5 format. They
include all the intermediate quantities of the H2CO retrieval from slant
columns to vertical columns, air mass factors (cloud free or including the
IPA cloud correction) and averaging kernels. A detailed error budget is also
provided. Error contributions resulting from each step of the retrieval to
the final vertical column error are provided separately, including their
random and systematic parts (for details, see De Smedt et al., 2014). This
allows for an estimation of the total error on the column averages. A quality
flag is also provided for each observation. The following criteria are
considered for assigning a bad-quality (< 0) flag:
per orbit: fit residuals larger than 3 times the averaged fit residual
per day: unsuccessful across-track and zonal reference sector correction (see Sect. 3.2)
per orbit: corrected slant columns lower than the mean corrected column minus 3 times the column standard deviations
per pixel: effective cloud fractions larger than 0.4
per pixel: snow or ice flag in the cloud product
per pixel: solar zenith angles larger than 70∘
per pixel: individual vertical column errors larger than 3 times the column
GOME-2 and OMI H2CO vertical columnsBackground values and precision
Time series of H2CO retrieval statistics for OMI and GOME-2 on
METOP-A and MetOp-B in the remote equatorial Pacific region ([-15,
15]∘ lat., [180, 240]∘ long.). The first panel presents the
H2CO vertical columns, before and after background correction (dotted
and plain lines). The second, third and fourth panels present respectively
the fit residuals, the column standard deviation and the number of valid
observations. See text for details on the DOAS retrieval settings.
Figure 7 presents time series of monthly averaged GOME-2A, GOME-2B and OMI
H2CO vertical columns in the reference sector (first panel) together
with the retrieval residuals, standard deviations and number of observations
(second to fourth panels). In the first panel, uncorrected columns are
displayed with dotted lines, while background-corrected columns are
represented with plain lines. From this figure, it can be concluded that even
the uncorrected OMI H2CO columns are remarkably stable in time (yet
across-track and zonal corrections are needed). In the case of GOME-2A
retrievals, however, the use of radiances as DOAS reference spectra does not
completely compensate for a weakening of the signal over the years, although
the loss of signal is reduced by a factor of 2 compared to the use of solar
irradiances, as was the case in version 12 (De Smedt et al., 2012).
Regarding the fit residuals and the standard deviations of the monthly
averaged H2CO columns, the OMI retrievals also show a very good
stability in time, the random errors having increased by less than 4 % in
10 years, while the GOME-2A random errors have increased at a rate of about
10 % per year due to degradation effects. Accordingly, the OMI H2CO
individual pixel precision is about 7×1015 molec cm-2, while
the GOME-2A precision degraded from 5×1015 to
8×1015 molec cm-2. The GOME-2B performances are found to be
equivalent to those of GOME-2A in its early lifetime; however, a degradation
rate similar to the one of GOME-2A is expected.
As shown in the fourth panel, the number of OMI observations passing our
quality criteria has decreased by 40 % since the beginning of the mission
due to the row anomaly issue. The resulting change of sampling has an impact
on the calculated monthly means that should be taken into account in trend
analyses (see Sect. 6).
Continental emission regions
H2CO vertical columns retrieved from GOME-2/MetOp-A (first
panel, EUMETSAT level-1 data), OMI/Aura (second panel, NASA level-1 data),
and their absolute differences (third panel) between 2007 and 2013.
H2CO vertical columns retrieved from GOME-2/MetOp-A (first row)
and OMI/Aura (second row) over Europe (from April to September) and Asia
(from February to November), between 2007 and 2013. Note that different
colour scales have been used for GOME-2 and OMI.
Figure 8 presents multi-year maps of H2CO vertical columns retrieved
from GOME-2A and OMI between 2007 and 2013, and their averaged absolute
differences over the same period. For these maps (and further below), the
quality flags as defined in Sect. 3.4 have been used to filter out invalid
satellite observations. We observe consistent H2CO distributions, with
the highest columns over tropical regions, India, China, south-eastern China
and the south-eastern US. We also observe noticeable differences, OMI showing
more elevated columns at mid-latitudes and over regions with moderate
H2CO concentrations, but lower columns in the tropics, where the
H2CO columns are highest. To better visualise the regional structures,
Fig. 9 presents H2CO vertical columns derived from both sensors over
Europe (from April to September) and Asia (from March to November), averaged
between 2007 and 2013.
Mean seasonal and diurnal variations of the H2CO columns as
observed by MAX-DOAS or FTIR instruments (in black) and by GOME-2 and OMI (in
red). Values are shown for five regions where BIRA-IASB operates ground-based
measurements: Cabauw and Brussels (northern Europe), OHP (southern France),
Beijing and Xianghe (north-eastern China), Bujumbura (central Africa) and
Reunion (southern Africa). Satellite measurements have been averaged within
100 km around each location, and filtered using quality criteria as defined
in Sect. 3.4. Details of the ground-based measurements are summarised in
Table 3.
Different effects can be identified.
The better horizontal resolution of OMI allows for a better identification
of strong hotspots over localised H2CO sources, as for example over
cities like Mexico City, Pretoria, Hong Kong/Guangdong, Beijing, Cairo,
Tehran and Mumbai. This resolution effect can also be identified along
coastal areas (Cape Town (South Africa), the Algerian coast, the Turkish
coasts, Kerala (India) or California (US)) and along mountain chains (north
of India, Pyrenees, Alps).
The increase in H2CO columns between 09:30 and 13:30 appears to be largest
over southern Europe, southern Australia, north-eastern China or central
Siberia, while an inverse diurnal variation, of equivalent magnitude, is
observed over the equatorial forests of the Amazon, Africa and Indonesia. The
same effect has been reported for glyoxal columns, another important
indicator of NMVOC emissions (Alvarado et al., 2014). The observed diurnal
variations will be further discussed in Sect. 5, using ground-based
observations.
Differences in retrieval sensitivity appear when comparing GOME-2 and OMI. We
note that over oceans, poleward of 30∘, OMI columns are found to be
higher than GOME-2. These differences cannot be explained only by the diurnal
variation of the CH4 oxidation, but they also result from a relative
lack of sensitivity of GOME-2 to lower tropospheric H2CO in comparison
to OMI. This is due to the morning overpass time of GOME-2, which leads to
larger SZA over middle and high latitudes (and therefore to lower sensitivity
to the lowest atmospheric layers) and to lower H2CO concentrations (and
therefore to lower absorption).
Table 2 provides a list of regions where the highest annual H2CO columns
are observed. The annual means are provided with the standard deviations of
the monthly averaged columns, as an indication of the amplitude of the
seasonal variations. The same regions stand out in both the GOME-2 and OMI
observations, with equivalent seasonal variations. However, as shown on the
maps, the GOME-2 observations are maximal in the tropics (Africa and South
America), while the OMI observations maximise over megacities like Hong
Kong/Guandong or Delhi. As can be deduced from the seasonal deviations
provided in Table 2, the largest seasonal variations are found in
Rondônia (Brazil) and Tianjin (China), for both instruments.
Interestingly, the OMI H2CO columns exhibit large seasonal variations
along the coasts of India, which are less pronounced in GOME-2 observations
(Goa, Kerala, Orissa). This could be explained by resolution effects or by
diurnal variations in the H2CO production and loss processes during the
monsoon season. In Sect. 6, the complete time series of monthly averaged
H2CO vertical columns in China, India, Africa, South America, North
America and Europe are presented.
GOME-2A and OMI largest annual mean H2CO columns between 2007 and
2013 (1015 molec cm-2).
ContinentCountryProvince/stateGOME-2Aseas.dev.OMIseas.dev.AfricaCongo9.371.717.741.27AfricaGhana8.972.798.472.6AfricaSierra Leone10.263.769.213.95AfricaTogo8.962.778.492.62AsiaBangladesh9.761.4810.52.3AsiaCambodia8.633.398.743.71AsiaChinaAnhui7.962.859.213.18AsiaChinaGuangdong8.591.449.821.34AsiaChinaGuangxi8.61.889.041.63AsiaChinaHong Kong9.052.2511.863.53AsiaChinaJiangsu7.813.249.393.64AsiaChinaTianjin7.84.759.863.84AsiaIndiaBihar9.671.4410.52.01AsiaIndiaDadra and Nagar Haveli7.932.329.53.92AsiaIndiaDaman and Diu8.352.59.274.31AsiaIndiaDelhi10.22.8512.22.91AsiaIndiaGoa7.733.089.414.61AsiaIndiaHaryana9.24210.952.16AsiaIndiaKerala7.262.769.344.10AsiaIndiaOrissa9.062.389.923.01AsiaIndiaPunjab9.091.9710.972.26AsiaIndiaTripura8.761.689.792.47AsiaIndiaUttar Pradesh8.791.5810.21.69AsiaIndiaWest Bengal9.851.6110.792.34AsiaSingapore9.142.798.93.62AsiaThailand8.53.178.793.58South AmericaBrazilMato Grosso11.194.238.723.82South AmericaBrazilRondônia11.045.298.84.71South AmericaBrazilTocantins10.552.668.622.57South AmericaBolivia8.912.966.192.46Validation using MAX-DOAS measurements
We validate the observed satellite column variations using ground-based
measurements operated by BIRA-IASB at seven stations: Cabauw, Brussels, the
Haute-Provence Observatory, Beijing, Xianghe, Bujumbura and Reunion. Details
of the ground-based measurements, and related publications, are summarised in
Table 3. In Reunion, measurements have been performed with an FTIR instrument
(Vigouroux et al., 2009), while at all other stations, multi-axis DOAS
(MAX-DOAS) instruments have been used. Although installed at the end of 2013,
the Bujumbura instrument could only deliver 6 months of data due to a failure
of the UV channel in April 2014.
The MAX-DOAS measurement technique has been developed to retrieve
tropospheric trace gas total columns and profiles. The most recent generation
of MAX-DOAS instruments allows for measurement of aerosols and a number of
tropospheric pollutants, such as NO2, H2CO, SO2, HONO, O4
and CHOCHO (see e.g. Heckel et al., 2005; Clémer et al., 2010; Irie et
al., 2011; Ma et al., 2013; Hendrick et al., 2014; Vlemmix et al., 2014; Wang
et al., 2014). H2CO slant columns have been retrieved from the six
MAX-DOAS instruments using consolidated settings published in Pinardi et
al. (2013). While the scientific-grade instruments installed in China and in
Bujumbura allow for vertical profile retrievals by optimal estimation
(Clémer et al., 2010; Hendrick et al., 2014), only vertical columns could
be retrieved from the less sensitive instruments operated at the other
stations. We therefore use the ground-based total columns to validate the
seasonal and diurnal variations in all stations, while a more quantitative
validation making use of the profile information is performed in
Beijing/Xianghe and Bujumbura.
Seasonal and diurnal variations
Figure 10 presents the mean diurnal variations of the H2CO columns,
averaged by season over the complete period of measurements, as observed from
the ground (in black) and from space (in red). For this figure, measurements
in Cabauw and Brussels have been combined, as well as those in Beijing and
Xianghe. We therefore consider five validation sites, representative for
different emission sources and illumination conditions. The satellite
observations are averaged within 100 km around each station, and filtered as
described in Sect. 3.4. Although larger than the typical length of air masses
sampled by a MAX-DOAS spectrometer, which is less than a few tens of
kilometres (Gomez et al., 2014), this radius allows inclusion of enough
satellite pixels to ensure significant analysis. MAX-DOAS observations are
filtered using similar thresholds on total errors (retrieval errors larger
than 3 times the columns) and solar zenith angles (70∘). Cloudy
observations are excluded using the multiple-scattering cloud filter
described in Gielen et al. (2014) since such sky conditions can potentially
degrade the quality of the MAX-DOAS retrievals. The error bars shown in
Fig. 10 include random and systematic error contributions. The random errors
are taken as the standard deviations of the daily averaged observations
divided by the square root of the number of days considered in the seasonal
means. Wintertime satellite columns in Europe and China are not shown,
because of their reduced quality resulting from a combination of low
H2CO values and lower satellite sensitivity close to the surface.
Furthermore, Table 4 provides quantitative differences between the afternoon
and morning observations for the five locations, in each season. Both for
satellite and ground-based measurements, the correlations between 09:30 and
13:30 columns have been considered to estimate the error in the differences,
using a standard uncertainty propagation formulation.
The first conclusion that can be drawn from Fig. 10 is the general
underestimation of the satellite columns by 0 up to 50 % compared to the
MAX-DOAS observations, especially during the seasons of maximum
concentration. In comparison, the agreement is found to be better with
tropical background FTIR measurements (Vigouroux et al., 2009). The second
conclusion is the qualitative agreement between the satellite and
ground-based measurements regarding the H2CO column variations. The
seasonal variations are in very good agreement, with a maximum in summertime
in mid-latitude regions, especially in the south of France and in China.
Regarding diurnal variations, the differences between the OMI and GOME-2
columns should reflect the change in H2CO between 09:30 and 13:30.
However, uncertainties in the satellite H2CO columns are large (De Smedt
et al., 2012), and so are the errors in their differences, as reflected in
Table 4. Nevertheless, the sign of the differences between GOME-2 and OMI
agrees well with both MAX-DOAS and FTIR measurements. Early afternoon values
are almost always equal to or larger than mid-morning values, except in
Bujumbura, where morning columns are larger. The amplitude of the diurnal
variation inferred from ground-based and satellite data is also in relatively
good agreement, as can be seen by comparing the slopes between 09:30 and
13:30, and from Table 4. This is less true for summertime columns in
Brussels/Cabauw, or for springtime columns in OHP and in Beijing/Xianghe,
where the differences in OMI-GOME-2 are larger than the corresponding
MAX-DOAS variations, possibly pointing to an underestimation of the GOME-2
retrievals in those regions/periods.
Summary of ground-based measurements available at BIRA-IASB.
Station/country (lat, long)InstrumentPeriodRetrieved quantityReferenceCabauw/the Netherlands (52∘ N, 5∘ E)MAX-DOAS18 June 2009–21 July 2009VCDPinardi et al. (2013)Brussels/Belgium (50.78∘ N, 4.35∘ E)Mini-MAX-DOAS1 May 2011–23 April 2012VCDGielen et al. (2014)OHP/France (43.94∘ N, 5.71∘ E)MAX-DOAS26 July 2007–20 March 2013VCDValks et al. (2011)Beijing/China (39.98∘ N, 116.38∘ E)MAX-DOAS3 July 2008–17 April 2009VCD + profileVlemmix et al. (2014)Xianghe/China (39.75∘ N, 116.96∘ E)MAX-DOAS7 March 2010–26 December 2013 VCD + profileVlemmix et al. (2014)Bujumbura/Burundi (3∘ S, 29∘ E)MAX-DOAS25 November 2013–22 April 2014VCD + profile–Reunion/France (20.9∘ S, 55.5∘ E)FTIR1 August 2004–25 October 2004 21 May 2007–15 October 2007 2 June 2009–28 December 2009 11 January 2010–16 December 2010VCDVigouroux et al. (2009)
Mean diurnal variations of the H2CO columns as observed with
ground-based and satellite instruments. Values are given for five regions
where BIRA-IASB operates ground-based measurements: Cabauw and Brussels, OHP,
Beijing and Xianghe, and Bujumbura and Reunion. Details of the ground-based
measurements are summarised in Table 3.
Validation of GOME-2 and OMI retrievals in Beijing and Xianghe,
using MAX-DOAS retrievals (represented by black squares). Upper panel:
mid-morning observations (GOME-2 and MAX-DOAS averaged over 8–11 h). Lower
panel: early afternoon observations (OMI and MAX-DOAS averaged over
12–15 h). Observations have been averaged per month, over the period
2008–2013, selecting correlative days between GOME-2/OMI and the MAX-DOAS
instrument. Satellite measurements have been averaged within 100 km around
each location, and filtered using quality criteria as defined in Sect. 3.4.
Three satellite VCs are presented: IMAGES a.p. profile/no cloud correction,
IMAGES a.p. profile/IPA cloud correction, and MAX-DOAS a.p. profile/IPA cloud
correction. Correlation plots are shown for the two latter cases,
respectively, in the left and right panels.
Validation of GOME-2 and OMI retrievals in Bujumbura, using MAX-DOAS
retrievals (represented by black squares). Upper panel: mid-morning
observations (GOME-2 AandB and MAX-DOAS averaged over 8–11 h). Lower panel:
early afternoon observations (OMI and MAX-DOAS averaged over 12–15 h).
Observations have been averaged per month, over the period 2013–2014,
selecting correlative days between GOME-2/OMI and the MAX-DOAS instrument.
Satellite measurements have been averaged within 100 km around each
location, and filtered using quality criteria as defined in Sect. 3.4. Three
GOME-2B and OMI VCs are presented: IMAGES a.p. profile/no cloud correction,
IMAGES a.p. profile/IPA cloud correction, and MAX-DOAS a.p. profile/IPA cloud
correction.
Monthly averaged columns retrieved from GOME-2, OMI and MAX-DOAS measurements
are shown in Figs. 11 and 12, respectively, for the stations of
Beijing/Xianghe (averaged over 2008–2013) and Bujumbura (averaged over
2013–2014). As the instrument in Bujumbura was installed at the end of 2013,
we use the more recent H2CO columns from GOME-2B, but the GOME-2A
columns are shown up to the end of 2013 (light green). The monthly means have
been calculated using days in common between, respectively, GOME-2 and
morning MAX-DOAS data (08:00–11:00), and OMI and afternoon MAX-DOAS data
(12:00–15:00). Again, the satellite observations are averaged within 100 km
around each station and filtered as described in Sect. 3.4. In China, the
resulting number of correlative days amounts to 711 for GOME-2 and 807 for
OMI. In Burundi, we obtain respectively 58 and 90 days over the 5 months of
measurements. Quantitative comparisons between GOME-2, OMI and MAX-DOAS
columns are provided in Table 5. Three sets of monthly averages are used for
the satellites, in order to evaluate uncertainties related to the AMF
calculation: (1) the vertical columns calculated using the IMAGES a priori
profile shapes and no cloud correction (plain dots), (2) same but applying
the IPA to correct for cloud radiative effects (empty dots), and (3) the
vertical columns calculated using as a priori the H2CO vertical profile
shapes retrieved from the MAX-DOAS measurements and the IPA cloud correction
(diamonds). In Fig. 11, the lower panels display scatter plots between
monthly averaged observations for the satellite retrievals versions (2) and
(3). In Beijing/Xianghe, the correlation coefficients are similarly high
between the different AMF versions, and slightly better for OMI than GOME-2
(about 0.8 for GOME-2 and 0.9 for OMI). However, the slopes and offests of
the regression lines between the satellite and ground-based observations show
larger dependency on the AMF calculation settings. We find slopes of
respectively 0.8, 0.6 and 0.9 for GOME-2 and 0.6, 0.6 and 1 for OMI. In
Bujumbura, the number of ground-based measurements is unfortunately not large
enough to draw conclusions about the correlations, and we rather provide the
mean differences between the satellite and MAX-DOAS columns. The mean
differences are found to be respectively -2.8, -2.6 and 1.1×1015 molec cm-2 for GOME-2 and -3.3, -2.6 and -0.9×1015 molec cm-2 for OMI. Results in China and in Burundi
suggest that the cloud correction has little systematic influence, and
therefore a limited effect on the monthly averaged columns, as indicated by
the mean difference variations between (1) and (2) (less than 10 %). On the
contrary, the a priori vertical profile has a larger systematic effect on the
vertical columns, as indicated by the different results between (2) and (3).
Both for GOME-2 and OMI, switching from modelled to measured profile shapes
increases the H2CO columns by 20 to 50 %, bringing the satellites and
ground-based observations to a satisfactory agreement within 15 %. We note
that the OMI columns in Beijing/Xianghe present a positive offset of about
3×1015 molec cm-2. The large effect of the a priori profile
shape is explained by the vertical sensitivity of the satellite measurements,
decreasing strongly in the lowest atmospheric layers, and by the shape of the
H2CO vertical distribution, peaking near the surface. This is
illustrated in Fig. 13, where the H2CO profile shapes modelled by IMAGES
and those retrieved from the MAX-DOAS measurements are plotted next to the
satellite vertical sensitivity for June 2010 in Xianghe. It must be noted
that the retrieved MAX-DOAS profiles also have their own uncertainties
(Vlemmix et al., 2014); however, using them to re-calculate the satellite
AMFs allows one to remove from the comparison the error associated with the a
priori profile shapes (Eskes and Boersma, 2003). Indeed, only the shapes of
the a priori profiles impact the satellite AMFs, not their total columns
(Palmer et al., 2001). The satellite averaging kernels (AKs) are much closer
in shape to the FTIR AKs than to the MAX-DOAS retrievals, which may explain
the better agreement of the columns (Vigouroux et al., 2009).
IMAGES CTM and MAX-DOAS retrieved H2CO profile shapes
(concentration profiles divided by their total columns) averaged in June 2010
in Xianghe. IMAGES prim1 includes an additional primary source, as reported
by Chen et al. (2014). The corresponding mean satellite scattering weighting
functions are also displayed.
Results of the comparisons between GOME2, OMI and MAX-DOAS columns,
shown in Fig. 11 for Beijing/Xianghe and in Figure 12 for Bujumbura. Three
satellite VCs are used: IMAGES a.p. (a priori) profile/no cloud correction,
IMAGES a.p. profile/IPA cloud correction, and MAX-DOAS a.p. profile/IPA cloud
correction. Mean differences (satellite–MAX-DOAS) are given in both
stations. In Beijing/Xianghe, the slopes and offsets of a linear regression
between MAX-DOAS and satellite columns are provided.
GOME-2 OMI IMAGES a.p. profilesIMAGES a.p. profilesMAXDOAS a.p. profilesIMAGES a.p. profilesIMAGES a.p. profilesMAXDOAS a.p. profilesNo cloud correctionCloud correctionCloud correctionNo cloud correctionCloud correctionCloud correctionBeijing/Xianghe No. of common days711 807 Mean difference 1015 molec cm-2-4.4-5.4-1.4-2.8-3.42.1(%)(-33 %)(-41 %)(-11 %)(-19 %)(-24 %)(15 %)Correlation coefficient0.820.850.840.880.870.88Slope0.800.640.910.610.620.97Offset-1.60.60.23.02.33.21015 molec cm-2Bujumbura No. of common days58 90 Mean difference 1015 molec cm-2-2.8-2.61.1-3.3-2.6-0.9(%)(-29 %)(-27 %)(11 %)(-39 %)(-31 %)(-9 %)
The effect of the rather coarse resolution of the global CTM on the modelled
profiles (here 2∘× 2.5∘) needs to be further
investigated, particularly for anthropogenic sources, as well as other
possible effects of vertical transport and chemical processes. For example, a
recent analysis of in situ measurements in Beijing (Chen et al., 2014)
indicated that primary sources of H2CO are responsible for as much as
about 32 % of the total H2CO source in the area, suggesting a strong
underestimation of this direct source in current inventories. In the standard
IMAGES simulation using the REASv2 inventory, this primary source contributes
less than 1 % of the total H2CO source during the summer. Adjusting
the primary source in the model to match the direct fraction estimated by
Chen et al. (2014) results in the IMAGES profile shapes also shown in Fig. 13
(IMAGES prim1), found to agree much better with MAX-DOAS profiles. It is not
clear whether this large apparent contribution of direct emissions is real or
reflects e.g. a fast chemical production from highly reactive VOCs currently
not well represented in inventories and models. Whatever the reasons might be
for the improved model profiles in comparison with MAX-DOAS data,
generalisation to other areas would appear premature. It is worth noting that
the IMAGES model was found to reproduce very well observed vertical profiles
of formaldehyde measured during aircraft campaigns over the United States and
over oceans (Stavrakou et al., 2009c). More studies and measurements are
required to refine our understanding of factors governing the formaldehyde
vertical profiles.
Long-term variations
Formaldehyde columns are mainly formed by oxidation of NMVOCs from biogenic
biomass burning and anthropogenic sources. Column inter-annual variabilities
are mainly driven by fire events and temperature changes (Millet et al.,
2008; Barkley et al., 2009; Stavrakou et al., 2014). However, over
industrialised regions, changes in anthropogenic emissions have also been
identified as drivers of observed H2CO column trends (De Smedt et al.,
2010; Zhu et al., 2014; Khokhar et al., 2015; Mahajan et al., 2015; Stroud et
al., 2015).
The TEMIS time series of morning H2CO columns, consisting of GOME,
SCIAMACHY and GOME-2A and GOME-2B data, spans over 15 years of observations,
while the afternoon time series derived from OMI covers 10 years of
observations. Meaningful trend studies are therefore possible. We used a
linear model with a seasonal component to fit the monthly averaged columns
separately for the morning and afternoon time series. The error and
statistical significance of the inferred trends are also estimated, as
described in De Smedt et al. (2010). It is understood that the accuracy of
such a trend analysis is limited in the case of H2CO because, like for
the amplitude of the diurnal variation (between 1 and 4×1015 molec cm-2), the amplitude of the trends to be detected
(about 1–2×1015 molec cm-2 in 10 years) is 1 order of
magnitude smaller than the H2CO columns, their errors and their seasonal
variations. However, statistically significant trends are detected in several
regions independently with both data sets, which gives confidence in our
H2CO column long-term variation estimates.
Monthly averaged H2CO vertical columns as observed from
satellite instruments in India and China. Mid-morning columns (in green)
consist of SCIAMACHY (2003–2011) and GOME-2A (2007–2013) and GOME-2B
(2013– ) measurements. If statistically significant, results of the trend
analysis are displayed (De Smedt et al., 2010).
Monthly averaged H2CO vertical columns as observed from
satellite instruments in South America and Africa. Mid-morning columns (in
green) consist of SCIAMACHY (2003–2011), GOME-2A (2007–2013) and GOME-2B
(2013– ) measurements, while early afternoon columns (in red) are derived
from OMI measurements. If statistically significant, results of the trend
analysis are displayed (De Smedt et al., 2010).
Monthly averaged H2CO vertical columns as observed from
satellite instruments in US and Europe. Mid-morning columns (in green)
consist of SCIAMACHY (2003–2011) and GOME-2A (2007–2013) and GOME-2B
(2013– ) measurements, while early afternoon columns (in red) are derived
from OMI measurements. If statistically significant, results of the trend
analysis are displayed (De Smedt et al., 2010).
Figures 14 to 16 present the time series of monthly averaged H2CO
vertical columns in India, China, South America, Africa, North America and
Europe. In those figures, the mid-morning time series (in green) combine
SCIAMACHY (2003–2011), GOME-2A (2007–2013) and GOME-2B (from 2013)
measurements, while early afternoon columns (in red) are derived from OMI
measurements. The good agreement between the different morning observations
can be noted (see also De Smedt et al., 2012), as well as the previously
described differences and similarities between afternoon and morning time
series. In particular, the higher morning columns over tropical forest are
also observed in the SCIAMACHY time series. The results of our trend analysis
are displayed whenever they have been found statistically significant, i.e.
if the absolute value of the calculated trend is larger than twice the
associated error. Furthermore, Fig. 17 presents a global map of the most
significant trends found in the OMI H2CO columns between November 2004
and August 2014. The analysis has been performed at country level on the
global scale, at province or state level in the largest countries, and in a
radius of 100 km around the main urban areas.
Annual absolute trends observed in the OMI H2CO columns between
November 2004 and August 2014. The change in the OMI spatial sampling over
the years has been taken into account in this trend analysis.
For these calculations, “sampling-corrected” OMI columns are used. Indeed,
as shown in Fig. 6, the OMI daily spatial sampling has been reduced over the
years because of the growing row anomaly. This has an impact on the
H2CO monthly averaged columns, which tend to decrease in time if this
effect is not taken into account. This decrease could be explained by the
fact that a large number of central rows (rows 27–44 since January 2009),
which have the finest spatial resolution, are affected by the anomaly and
need to be filtered out. For this reason, we have calculated special OMI
monthly averages, selecting only the rows that were still valid at the end
of 2013. The net effect is a slight decrease of the columns at the beginning
of the time series, almost negligible when looking at the absolute values,
but significant when considering trends.
In India and China (Fig. 14), we observe increasing H2CO columns. The
spatial distribution of the observed increases, and their values, is similar
in the SCIAMACHY-GOME-2 and OMI time series. No trend is observed in the
largest Chinese cities like Beijing and in the Pearl River delta. However,
large positive trends are detected in the surrounding provinces, located in
the centre of the country (e.g. Henan and Hunan provinces). This spatial
shift of the Chinese trends outside the largest cities was already reported
in De Smedt et al. (2010), using GOME and SCIAMACHY observations between 1997
and 2009. This might be related to a larger influence of newer, cleaner
technologies in these mega-cities compared to the Chinese heartland (Zhang et
al., 2009). In India, we observe positive trends almost everywhere, with an
(absolute and relative) amplitude increasing from the south to north of the
country. Values obtained from morning and afternoon observations are found to
be consistent, and similar to the ones reported for the period 1997–2009.
In the eastern US and western Europe (Fig. 16), we observe decreasing trends
more clearly detected in the OMI retrievals. The morning H2CO
observations have larger errors and the wintertime observations cannot be
used. Negative trends have previously been reported, but with less
statistical significance (De Smedt et al., 2010). Here, the correlation
between the SCIAMACHY/GOME-2 and OMI observations is found to be good even in
these retrieval-sensitive regions, giving some new confidence in the morning
time series over northern Europe and North America. The expected negative
trends due to emission controls are better captured by the early afternoon
observations of OMI, allowing the detection of significant decreases in
Germany and France. In southern European countries like Spain, negative
trends can be detected in both time series, which show remarkable
correlation, with for example very low values in spring 2008 that could be
related to power plant emission reductions as reported for NO2 by Curier
et al. (2014).
(a) Monthly averaged H2CO vertical columns from
SCIAMACHY/GOME2 (in green) and from OMI (in red) (first panel) and
(b) MODIS fire count (second panel) over the Rondônia Brazilian
state. Inset values are the correlation coefficient between the satellite
H2CO columns and the fire counts. (c) Reported yearly
deforestation rates in selected Brazilian states, relative to their
respective surfaces (third panel; source: Brazil INPE). Inset values are the
total surface of the state and the total rate of deforestation between 1988
and 2013.
Over the African continent (right panels of Fig. 15), almost no change is
detected, with the exception of Madagascar, and its capital Antananarivo,
where a large positive trend is found in the OMI time series. The origin of
this trend is not understood for the moment. No similar increase has been
reported in satellite NO2 observations over this region (see for example
Hilboll et al., 2013). In South America (left panels of Fig. 15), we observe
very significant negative trends over the Brazilian state of Rondônia, of
-2×1014 molec cm-2 yr-1, in both morning and
afternoon time series. Such negative trends are also present, with lower
amplitudes, in the surrounding Brazilian states covered by the Amazon forest.
The largest H2CO columns worldwide are observed in those regions, with
very large variations between the dry and the wet season (see Table 2).
Figure 18 shows the H2CO columns in Rondônia between 2003 and 2014,
the MODIS (on Terra and Aqua satellites) Collection 5 Active Fire Product
(ftp://fuoco.geog.umd.edu/modis/C5/cmg,
Giglio, 2013), and the
yearly deforestation rates reported by the Brazilian INPE
(http://www.obt.inpe.br/prodes/index.php),
in selected Amazonian states. Deforestation in Rondônia amounted to
23 % of its surface area between 1988 and 2013. This is the highest surface
ratio among all Brazilian states. The years showing the highest deforestation
rates are 1995 and 2004. In Rondônia, a strong decrease of the
deforestation rate has been observed between 2005 and 2010, and a slight
increase is again observed since 2011. As illustrated by the middle panel of
Fig. 18, we find high correlation coefficients between the SCIAMACHY/GOME-2
and OMI H2CO columns and the MODIS fire product, of respectively 0.8 and
0.9 (see also Barkley et al., 2013). We have also compared the H2CO
vertical columns with GISS surface temperature anomalies (Gridded Monthly
Maps of Temperature Anomaly Data,
http://data.giss.nasa.gov/gistemp/;
Hansen et al., 2010), but no correlation was found in this area. Vegetation
burning related to deforestation appears to have strongly decreased in
Rondônia, while it is not yet the case in the surrounding areas. It
should be noted that the strong enhancement of natural fire emissions during
very dry years (such as 2005 and 2010) somehow reduces the observed downward
trend in the fires and H2CO columns, and therefore the correlation with
the reported deforestation rates.
Besides these direct effects of biomass burning activity changes on the
H2CO columns, more studies are needed in order to assess the impact of
deforestation and land use changes, and possibly related meteorological
changes, on biogenic NMVOC emissions (Stavrakou et al., 2014). It is worth
noting that BIRA-IASB is currently installing a FTIR instrument in Porto
Velho, in the Rondônia state. This will bring new information on both the
diurnal cycle of H2CO columns and its chemistry, and more generally, on
the carbon cycle molecules (CO2, CH4, CO and NMVOCs).
Conclusions
This paper presents a new version of the BIRA-IASB formaldehyde retrieval
algorithm that has been applied to the complete time series of OMI and GOME-2
measurements and delivered for public use on the TEMIS website. Our focus is
the continuity and the consistency of the H2CO data set, as well as a
good characterisation of the satellite observations. The spectral fits have
been improved by means of a better treatment of the interference between
O4, BrO and H2CO differential structures, resulting in H2CO
columns of higher accuracy and precision. Daily remote radiance spectra are
used as DOAS reference, and a destriping procedure is included in the
background sector correction, reducing the impact of the OMI row anomaly, but
also the GOME-2 across-track variability. Daily morning and afternoon a
priori profiles are provided by a state-of-the-art version of the IMAGES
global CTM.
The GOME-2 and OMI H2CO data sets agree very well qualitatively, in
terms of both long-term variations and seasonal variations. Vertical columns
also agree reasonably well, although systematic differences are observed,
depending on the geographical location. The morning H2CO observations
are higher than the afternoon observations over tropical rainforests of the
Amazon basin, Africa, and Indonesia. The OMI observations are larger than the
GOME-2 columns over urban areas (pointing to a horizontal resolution effect)
and more generally over all mid-latitude regions (pointing to a combination
of actual diurnal variation effects and differences in retrieval
sensitivities between morning and afternoon observations).
A detailed validation study has been performed using correlative ground-based
MAX-DOAS measurements in Belgium/Netherlands, southern France, north-eastern
China, Burundi and FTIR measurements in Reunion. We show that the differences
observed between the GOME-2 and OMI H2CO columns are mainly consistent
with the diurnal variations observed from the ground, within the error bars
of the satellite and ground-based observations. In Beijing/Xianghe and
Bujumbura, MAX-DOAS vertical profiles have been used to re-calculate the
satellite air mass factors, allowing one to eliminate from the comparison the
error coming from the a priori profiles. By doing so, the satellite and
MAX-DOAS columns are found to agree to within 15 % or better.
To conclude, while the precision is driven by the signal-to-noise ratio of
the recorded spectra, the accuracy is limited by our current knowledge of the
external parameters needed for the retrieval, mainly the a priori profile
shapes and their diurnal variation and the cloud and aerosol properties. To
fully exploit the potential of satellite data, scientific studies relying on
tropospheric H2CO observations require consistently retrieved long-term
time series, provided with well-characterised errors and averaging kernels.
In the framework of prototype algorithm developments for the future TROPOMI
instrument to be flown on the ESA Copernicus Sentinel-5 Precursor mission, we
are currently investigating the impact of using global CTM profiles on a
finer horizontal resolution. Verification and validation studies are ongoing
with the aim of further improving the retrieval algorithms. Furthermore, in
the context of the EU QA4ECV project (www.qa4ecv.eu/), a H2CO
climate data record (CDR) using all the satellite instruments based on a
jointly optimised European algorithm is currently under development.
Acknowledgements
The H2CO data products from GOME-2 were generated at BIRA using level-1
data developed by EUMETSAT. Level-2 and level-3 H2CO scientific products
from GOME-2 have been jointly supported by Belgian PRODEX (A3C and
TRACE-S5P), ESA (PROMOTE) and EU (AMFIC). BIRA is also involved in the O3MSAF
(CDOP-2 project), where it supports the development and validation of the
GOME-2 H2CO operational product generated at DLR. The H2CO data
products from OMI were generated at BIRA using level-1 data developed at
NASA/KNMI. Level-2 and level-3 OMI H2CO developments are supported as
part of the Sentinel-5 precursor TROPOMI level-2 project, funded by ESA and
Belgian PRODEX (TRACE-S5P project). Multi-sensor H2CO developments at
BIRA are currently supported by EU FP7 (QA4ECV project), in cooperation with
KNMI, the University of Bremen and MPIC-Mainz. Modelling at BIRA was funded
by the Belgian PRODEX projects A3C and ACROSAT. MAX-DOAS measurements were
funded by Belgian Federal Science Policy Office, Brussels (AGACC-II project),
the EU 7th Framework Programme projects NORS and ACTRIS, and the ESA CEOS
Intercalibration project. Acknowledgements are addressed to the
Université de La Réunion and CNRS (LACy-UMR8105 and UMS3365) for
their support of the OPAR station and the OSU-R
activities.Edited by: T. von Clarmann
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