Introduction
In 2010 the European Space Agency (ESA) set up the Climate Change Initiative (CCI) program,
which aims to realize the full potential of long-term Earth observation data records for a
number of Essential Climate Variables (ECVs) from the atmospheric, oceanic and terrestrial domains
. These data records are essential to assess the state and future evolution
of climate, as observations from space provide unique information and global coverage. However
they are often limited by a lack of homogeneity and continuity. Therefore the aim of the
ESA-CCI program is to provide stable and long-term Climate Data Records (CDRs) derived from multiple
satellite data sets which are then suitable for both monitoring and modelling of climate and
which meet the target requirements defined within the Global Climate Observing System .
European total ozone satellite instrument
characteristics.
Parameter
GOME
SCIAMACHY
GOME-2A
Data availability
06/1995–07/2011a
08/2002–04/2012
01/2007–today
Organization
ESA
ESA
EUMETSATb
Satellite
ERS-2
ENVISAT
MetOp-A
Spectral coverage
240–790 nm
240–2380 nm
240–790 nm
Spectral resolution
0.2–0.4 nm
0.2–1.5 nm
0.2–0.4 nm
PMDc coverage
3 p-PMD
6 p-PMD
15 p-PMD and
300–800 nm
320–2380 nm
15 s-PMD
310–790 nm
Viewing geometries
nadir
nadir, limb, occultation
nadir
Ground pixel size
320×40 km2
60×30 km2
40×80 km2
Swath width
960 km
960 km
1920 km
Altitude
785 km
800 km
817 km
Equator crossing
10:30 a.m. LTd
10:00 a.m. LT
09:30 a.m. LT
Global coverage
3 days
6 days
almost dailye
Reference
a No global coverage since June 2003;
b EUropean Organisation for the Exploitation of METeorological
SATellites; c PMD: polarization measurement device detecting
polarized light perpendicular (p-) or parallel (s-) to the optical plane;
d LT: local time; e until June 2013.
In this paper we focus on measurements of the ozone layer which protects life on Earth from harmful
ultraviolet solar radiation and which plays an important role in the radiation budget of the atmosphere.
As a consequence of the 1987 Montreal Protocol and subsequent phasing-out
of the emissions of the ozone-depleting substances (ODSs) the stratospheric ozone layer is
expected to recover within the next decades . However, significant uncertainty
remains as to the timing of this recovery, because of complex interaction with climate change and
continuously increasing emissions of greenhouse gases.
Within the phase I of the ESA's Ozone CCI (Ozone_cci) project, total ozone and ozone profile
data records from nadir ultra-violet (UV) backscatter sensors, as well as ozone profiles
from limb and occultation sensors , have been created. In this paper we
introduce the multi-sensor total ozone data record which covers the period 1996–2011. The record
is based on observations from three European instruments – all mounted on sun-synchronous
low earth orbit platforms – namely the Global Ozone Monitoring Experiment (GOME) onboard
the second European Remote Sensing satellite (ERS-2), the SCanning Imaging Absorption
SpectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard the ENVIronmental SATellite
(ENVISAT), and GOME-2 (referred to as GOME-2A in the following) onboard the first of a series
of three Meteorological Operational satellites (MetOp-A). Detailed descriptions of the instruments
are given in , , and , respectively. A brief
overview of the main platform and sensor characteristics is presented in Table .
GOME data are available for July 1995 to June 2011, but their global coverage ended in June 2003
due to the permanent loss of the ERS-2 onboard data storage capability. As a consequence, the data
coverage has been initially limited to the European and North Atlantic sector since only data
within reach of an ERS-2 receiving station were transmitted to ground. Subsequently additional
ground stations have been brought online and the data coverage has been incrementally increased.
On 4 July 2011 the ERS-2 science mission ended. SCIAMACHY/ENVISAT was launched in March 2002
and provided data from August 2002 to April 2012, which marks the end of the ENVISAT mission due
to the unexpected loss of contact with the satellite.
As part of the Ozone_cci project, the total ozone data sets have been recently reprocessed
with the retrieval algorithm GOME-type Direct FITting version 3 (GODFIT_V3) for the entire
time series of the GOME, SCIAMACHY, and GOME-2A observations . The GODFIT_V3
algorithm leads to high-quality retrievals in all conditions, including high solar zenith
angles and large optical depths. Since the deployment of the GODFIT version used in the
GOME Data Processor (GDP) operational ground segment version 5 ,
a number of new developments in GODFIT have made the algorithm even more robust.
These improvements are related to the tropospheric ozone content, a semi-empirical Ring correction
and a correction for atmospheric polarization, as well as enhanced computational performance.
Although common retrieval settings are used for all three sensors,
significant differences may appear when individual data sets are compared.
These differences are largely due to calibration issues in the level 1 data.
To improve inter-sensor consistency, a soft-calibration scheme for measured
reflectances has been developed by , which relies on a
statistical comparison of the level 1 sun-normalized radiances with simulated
spectra at a few reference sites (viz., European stations equipped with
Brewer spectrophotometers). The identification and correction of any
artificial offset or spectral structures in the measured reflectances greatly
improves the agreement between individual level 2 total ozone data sets. On
the other hand, this procedure introduces a dependency of the satellite data
on the observations from the Brewer instruments themselves, and this has to be kept in mind for the
assessment of the geophysical validation results.
Together, these level 2 data sets based on the GODFIT_V3 retrieval algorithm
span the time period 1996–2012. They have been recently validated, using
ground-based measurements with Brewer and Dobson spectrophotometers as well
as UV–visible DOAS/SAOZ (Differential Optical Absorption
Spectroscopy/Système d'Analyse par Observation Zénithale) instruments
as a reference. The main findings were that the three
sensors are characterized by similar patterns (such as seasonality and solar
zenith angle dependence) against the reference data sets. No trends or
unexplained jumps were detected. Furthermore, a marked improvement in quality with
respect to the operational products was identified, along with an enhanced inter-sensor consistency.
Following the papers by describing the retrieval algorithm
itself, and by presenting the geophysical validation of the level 2 data, this paper is the
third article on the ESA-CCI total ozone ECV. It describes the construction
and validation of a cohesive merged level 3 data product. The aim is to show
that the combination of the three individual homogenized total ozone data
sets forms a consistent long-term time series, which meets the GCOS
requirements and is therefore suitable for climate applications.
The paper is organized as follows: Sect. contains a detailed description of the
generation of the GTO-ECV CCI total ozone data record. Section is dedicated
to the validation of the level 3 merged product using ground-based measurement systems,
and Sect. shows the results of comparisons with two comparable satellite-based
data records. Section contains the summary and outlook.
Construction of GTO-ECV data record
In this section we describe the construction of the level 3 data set and the
inter-satellite calibration approach, which has been developed and applied to
combine the individual observations into a homogeneous long-term product.
An analysis of issues related to spatial and temporal sampling is presented in
Sect. . Section contains a short description of
the final output NetCDF (Network Common Data Form) files.
Level 3 algorithm description and merging approach
The level 3 algorithm is designed to map the level 2 measurements, processed
with the GODFIT_V3 retrieval algorithm, onto a daily fixed global grid of
1∘×1∘ in longitude and latitude. This spatial
resolution has been selected according to the user requirements defined for
the ESA-CCI total ozone ECV product which specify a horizontal
resolution of 20–100 km. These requirements are based on the ozone requirements of GCOS,
CMUG (Climate Modelling User Group), IGACO (Integrated Global Atmospheric
Chemistry Observations), and the World Meteorological Organization (WMO).
Each grid cell contains an average of all level 2 data from the same GMT
(Greenwich Mean Time) day, that
overlap with the level 3 cell. Cell values are computed as weighted averages
in which the fractional area of overlap of the satellite ground pixel with
the given grid cell is used as the weight. Level 2 data can be mapped onto more than one grid cell. The
gridding algorithm is applied separately to GOME, SCIAMACHY, and GOME-2A
measurements.
The next step is to merge the individual level 3 data sets from the three
sensors into one homogeneous record using an inter-instrument calibration
approach. Predecessors of this algorithm are described in
and . We apply an external adjustment to SCIAMACHY and
GOME-2A results with respect to the GOME results in order to account for
inter-sensor differences, which possibly remain from the GODFIT_V3 level 2
algorithm, albeit these differences are small and the inter-sensor
consistency is high . Furthermore, all three individual data
records exhibit good temporal stability, well within the GCOS
target requirement of 1–3 % per decade . We selected
the GOME data record to serve as the reference data base because it has the
longest overlap periods with the other two sensors and, furthermore, it was
found to be the most stable instrument over its lifetime before the application of the soft-calibration
correction .
The calculation of the correction factors is based on a comparison of 1∘ zonal monthly means,
which are computed at first for GOME and SCIAMACHY. These zonal monthly means are based on common daily
gridded data only in order to minimize the differences in spatial and temporal sampling.
In particular this becomes important after June 2003, when GOME lost its global coverage.
We did not consider diurnal changes of ozone in the merging approach, since all three instruments
provide measurements within 1 h of each other (see Table ). However, the peak-to-peak
difference in total ozone may reach 1 % over the course of a day .
The correction factors for SCIAMACHY with respect to GOME are derived using
the ratios of these zonal monthly means. The correction factors comprise two
parts: (1) a “basic” correction for each month of the year (averaged over
all years from 2002 to 2011) in terms of third-order polynomials as a
function of latitude, and (2) an offset for each individual month, which is
added to the “basic” correction. This offset does not depend on latitude,
but it accounts for the time-dependence (i.e. short-term fluctuations) in the
differences between SCIAMACHY and GOME from 2002 to 2011. The correction
factors are then applied to the SCIAMACHY daily gridded data by linear
interpolation in time. They are shown in the top panel of
Fig. as a function of latitude and time. The correction is
well below 2 % without obvious trends. It is between -0.5 and 1.0 %
in the tropical region and increases slightly toward higher latitudes.
Correction factors for SCIAMACHY 2002–2011 (top panel) and GOME-2A
2007–2011 (bottom panel)
as a function of latitude.
In preparation for the GOME-2A adjustment, an intermediate product of
averaged GOME and corrected SCIAMACHY daily gridded data is generated for the
overlap period with GOME-2A from January 2007 to June 2011. This is referred
to as GS_MERGED in the following. 1∘ zonal monthly means are
computed for GS_MERGED and GOME-2A based on common daily gridded data only.
The correction factors for GOME-2A with respect to GS_MERGED are derived
similarly to those for SCIAMACHY: fourth-order polynomials as a function of
latitude and month plus a time-dependent offset. They are applied to GOME-2A
daily gridded data by linear interpolation in time. The bottom panel of
Fig. shows the GOME-2A correction, which is also well
below 2 %, as for SCIAMACHY, and without trends. It is between 0.0 and
1.0 % in the tropics and decreases towards higher latitudes. In September
2009 the behaviour of the GOME-2A instrument changed owing to the second
throughput test . The soft-calibration scheme applied within
the GODFIT_V3 retrieval algorithm mitigates the long-term impact of this
test, so that only an insignificant increase in the correction factors can be
identified. Furthermore, no unexpected jumps with respect to ground-based
data were found during the geophysical validation exercise of the level 2
data . Only a small increase in the correction factors for
a limited period in time is visible, which is caused by the low time
resolution of 1 year for the soft-calibration scheme.
Latitudinal and monthly constraints imposed on the GTO-ECV CCI
monthly mean calculation.
Month
Latitude range
January
60.0∘ N–90.0∘ S
February
70.0∘ N–90.0∘ S
March
80.0∘ N–80.0∘ S
April
90.0∘ N–65.0∘ S
May
90.0∘ N–60.0∘ S
June
90.0∘ N–57.5∘ S
July
90.0∘ N–57.5∘ S
August
90.0∘ N–62.5∘ S
September
82.5∘ N–72.5∘ S
October
72.5∘ N–85.0∘ S
November
65.0∘ N–90.0∘ S
December
60.0∘ N–90.0∘ S
Once SCIAMACHY and GOME-2A data have been adjusted, 1∘×1∘ monthly
mean gridded data are computed for each instrument. In order to provide representative
monthly means that contain a sufficient number of measurements equally distributed over
time, cut-off values for latitude as a function of the month have been defined
(see Table ). Thereby we avoid calculating monthly averages based on
a small number of measurements at the beginning or end of a month which appear close to the polar night.
Nonetheless, differences in monthly means among the instruments may occur due to regular
differences in spatial and temporal sampling (see Table ). This will be
discussed in the next subsection.
Flow chart of the GTO-ECV CCI level 3 algorithm and merging
approach. Red-shaded boxes denote data records
which are part of the official ESA Ozone CCI Climate Research Data Package (CRDP). Blue-shaded boxes denote
intermediate data sets needed to create the merged final product, and green shading denotes the steps of
the merging approach.
GTO-ECV CCI total ozone column data record 1996–2011 as a function
of latitude. Vertical black dashed
lines indicate the change-over from GOME to SCIAMACHY in April 2003 and from SCIAMACHY to GOME-2A
in April 2007.
Subsequently, the three data sets are combined into one single record as
follows: only one instrument is used at a time, i.e. the merged GTO-ECV total
ozone time series contains GOME measurements from March 1996 to March 2003,
adjusted SCIAMACHY measurements from April 2003 to March 2007, and adjusted
GOME-2A measurements from April 2007 to June 2011. We decided not to include
GOME data after the onboard tape recorder failure because of the very limited
spatial coverage. Furthermore we omit SCIAMACHY data after the
start of the GOME-2A record since a significant increase in data coverage
and, hence, a reduction in sampling uncertainty is not expected. The whole
procedure is summarized in Fig. . Red-shaded boxes denote
data records which are part of the official ESA Ozone CCI Climate Research
Data Package (CRDP). Blue-shaded boxes denote intermediate data sets needed
to create the merged final product and green shading denotes the three steps
of the merging approach.
The complete data record with typical total ozone features is shown in
Fig. . Highest ozone values occur in northern hemispheric
springtime, whereas monthly mean values are below 200 DU from September to
November south of 70∘ S. Extreme events such as the anomalous
Antarctic ozone hole in 2002 and the severe ozone loss in 1997 and 2011 in
the Arctic are visible. Instrument switches from GOME to SCIAMACHY in
April 2003 and from SCIAMACHY to GOME-2A in April 2007 are indicated with the
black vertical bars.
Sampling patterns of GOME (left column), SCIAMACHY (middle column),
and GOME-2A (right column) exemplified for April 1997, 2005, and 2008,
respectively. Top row: total number of measurements per month and grid cell,
middle row: number of days for which measurements are available, and bottom
row: effective mean day deff representing the monthly mean
according to Eq. ().
Illustration of sampling issues
As already noted in the previous section, inhomogeneous or incomplete
sampling – intrinsic to these types of satellite sensors – may have
systematic effects and may therefore lead to erroneous average estimates
e.g.. Since the platforms are in polar orbits, for each
day there are coverage gaps in the tropics (even for GOME-2A, which has the
largest swath width) as well as repeated views of the summertime poles,
leading to non-uniform undersampling or oversampling of ozone. This can result in
inaccurate monthly average estimates, in particular when natural variability
is strongest, i.e. in spring months in the Northern Hemisphere or under ozone
hole conditions. The problem is exacerbated when the satellites sample only a
few days at the beginning or end of the month owing to the beginning or end
of the polar night.
Figure exemplifies the diverse sampling patterns of
GOME (left column), SCIAMACHY (middle column), and GOME-2A (right column) for
April 1997, 2005, and 2008, respectively. The total number of measurements
per month and grid cell, i.e. mapped level 2 data according to the level 3
algorithm described above, are shown in the top row, the number of days for
which measurements are available is indicated in the middle row, and the
effective mean day deff representing the monthly mean is found in
the bottom row. The latter has been calculated using
deff=∑d=1Dd⋅nd/∑d=1Dnd.
D is the maximum number of days in the month, i.e. 31 in January, 30 in
April, etc., and nd is the number of measurements per day and grid
cell.
GOME-2A has the densest and most uniform sampling, i.e. the highest number of measurements
(top right panel). The effective day is close to the middle of the month (between day 14
and 16, bottom right panel), although some longitudinal structures are visible in particular
in the tropics. The GOME sampling is less dense and the effective mean day shows a larger spread
around the middle of the month as well as pronounced longitudinal structures in low and middle
latitudes (bottom left panel). The sampling pattern of SCIAMACHY strongly reflects the
alternation of the nadir and limb measurement modes for this instrument, leading to extreme longitudinal as
well as latitudinal structures (middle panels).
Toward the north polar regions (in April) the number of measurements increases due to
overlapping orbits and hence multiple views per day. Toward the south polar regions the
number of measurements increases up to about 65∘ S and then rapidly decreases due
to the beginning polar night. The effective mean day (bottom row) indicates that
only the first half of April is sampled. We decided to exclude these regions close to the
polar night from the level 3 data record. Therefore, we defined cut-off latitudes
(see Table ) for each month in order to avoid using data covering only
a limited part of the month.
Percentage differences between SCIAMACHY and GOME-2A monthly mean
total ozone for April 2008.
Figure illustrates the impact of the diverse sampling
patterns on the monthly averages. It shows the percentage differences between
SCIAMACHY (sparse sampling) and GOME-2A (dense and most uniform sampling)
monthly mean total ozone for April 2008. Biases of ±5 % reflect the
differences in the sampling patterns, in particular in the middle latitudes,
where natural variability is strong in this month. It is less pronounced in
the tropics, where variability is low, and in the north polar region, where
the SCIAMACHY sampling is enhanced due to overlapping orbits and, thus,
multiple views per day. These sampling issues will be addressed in more
detail in the second phase of the Ozone_cci project.
GTO-ECV data files
The final GTO-ECV CCI total ozone monthly mean output data are stored in
NetCDF files (one file per month), which are publicly available via
www.esa-ozone-cci.org. All files follow the NetCDF Climate and Forecast
(CF) metadata convention version 1.5. Table gives an
overview of the content of the individual files. The reported grid of the
data record is 1∘×1∘ in longitude and latitude, i.e.
the dimensions are 360×180 and the centre of the first grid cell is
located at latitude 89.5∘ N and longitude 0.5∘ E. Besides
the mean total ozone column, the corresponding standard deviation (SD), the
standard error, and the number of measurements per month are provided. The
sample standard deviation is the standard deviation of the monthly mean
obtained from the daily gridded values. It characterizes the scatter of the
measured data encompassing the natural variability, the measurement error as
well as the sampling uncertainty. The standard deviations of the GTO-ECV
product are compared with those from another satellite-based data record in
Sect. . The standard error (SE), however, quantifies the
spatial-temporal sampling errors inherent to the satellite measurements.
These errors have been estimated using the aforementioned standard deviation
(SD) and the number of available measurements per grid cell (Nmeas)
according to
SE=SDNmeasr.
Description and dimensions of all variables contained in the level 3
monthly mean total ozone
NetCDF files. Nlat=180 and Nlon=360.
Description of variable
Unit
Dimension
Latitude of grid center
degree
Nlat
Longitude of grid centre
degree
Nlon
Mean total ozone column
DU
Nlat×Nlon
Standard deviation of mean
DU
Nlat×Nlon
total ozone column
Standard error of mean
DU
Nlat×Nlon
total ozone column
Number of measurements used to derive
–
Nlat×Nlon
the monthly mean total ozone column
The factor r has been obtained using an Observing System Simulation
Experiment (OSSE) for which high-resolution ECMWF (European Centre for
Medium-Range Weather Forecasts) data were taken as the reference data set.
Then, three sets of daily observations were simulated from the reference
using the sampling patterns appropriate to GOME, SCIAMACHY, and GOME-2A,
respectively. Finally, the average monthly simulations are compared with the
corresponding monthly reference in order to estimate the sampling errors
corresponding to the total ozone monthly averages. The standard error is
shown in Fig. for April 1997 (GOME, top panel), 2005 (SCIAMACHY,
middle panel), and 2008 (GOME-2A, bottom panel). The errors increase from the
tropics to higher latitudes following the increasing ozone variability. GOME
errors are larger than those for SCIAMACHY and GOME-2A due to the much larger
ground-pixel size (see Table ). The SCIAMACHY errors reflect the
sampling pattern seen in Fig. , middle column, with
latitudinal and longitudinal variance. GOME-2A errors are quite small and do
not have noticeable structures.
Standard error associated with the product for GOME (top), SCIAMACHY
(middle), and GOME-2A (bottom) for April 1997, 2005, and 2008, respectively.
Ground-based validation
The validation of level 2 satellite total ozone columns using independent ground-based observations
has been a substantial part of retrieval algorithm development for many decades. A well-established
procedure exists in assessing the level 2 total ozone products using global ground-based
Brewer, Dobson, and UV–visible SAOZ spectrophotometer measurements e.g.and references therein. Taking into account that the long-term
climate study of the total ozone atmospheric content is based on using level 3 gridded products, one must
ensure that the transition from level 2 to level 3 does not introduce artifacts.
These might be induced by the level 3 algorithm itself, mainly through sampling
issues which could lead to inaccurate average estimates, or by the merging approach through
improper inter-sensor calibration. The aim of the following section is to compare the
current level 2 validation of the individual satellite GOME, SCIAMACHY, and GOME-2A GODFIT_V3
products with the new level 3 GTO-ECV CCI integrated long-term record of total ozone on a global
scale.
Representativeness of the ground-based network
The representativeness of the ground-based reference network used to
validate a product with global coverage determines both the validation
approach, and the representativeness of the validation results. While
validation results (and the level 3 data themselves) are often shown and
used as zonal averages, e.g. plotted against time and latitude as in
Fig. , Fig. illustrates the significant
spatial representativeness error when comparing zonal means of global gridded
data with zonal means based on the limited geospatial coverage of the
ground-based network.
Simulated differences between zonal means based either on data
coincident (in geolocation) with ground-based reference instruments, or on
full global gridded data. These differences constitute the so-called spatial
representativeness error. Data used for this graph are 6-hourly modelled
fields calculated with IFS-MOZART for MACC . The green solid
and dashed lines correspond to 75 and 80∘ solar zenith angles at
noon, respectively.
For this figure, IFS-MOZART (Integrated Forecasting System – Model for OZone
And Related chemical Tracers) modelled fields were averaged
to zonal monthly means, either using all data or using only data coincident
(in geolocation) with the Dobson, Brewer, and SAOZ instruments. The relative
difference between these two simulated zonal means yields estimated spatial
representativeness errors. As these errors exceed the expected performance of
the level 3 product, the validation work presented here is based solely on
level 3 grid-cells co-located with the ground stations, and on zonal
statistics derived from those co-locations. Besides avoiding the spatial
representativeness error, this approach allows for a more direct comparison
with the validation results of the level 2 data sets. However, it must be kept
in mind that this validation strategy is blind to the product quality outside
of the ground network. This issue is tackled by comparing the product with
other satellite data sets in Sect. . Temporal
representativeness errors, due to limited numbers of measurements within each
month at a given station, are minimized in the following by requiring at
least 10 measurements per month for an accepted co-location. In view of the
temporal sampling issues known to be present in the level 3 data set (see
Sect. ), no attempt was made here to further characterize the
errors due to limitations in temporal sampling of the reference measurements.
Comparison with Dobson and Brewer measurements
The Brewer and Dobson spectrophotometer measurements, as extracted from the
World Ozone and UV radiation Data Center (WOUDC, http://www.woudc.org)
have already been used numerous times in the last 2 decades for the
validation of various satellite-based global total ozone records
e.g.and references therein. A
comprehensive description of the individual station selection criteria has
been presented in . Station selection updates may
be found in more recent papers listed above. The measurements involved in
this current study are the same as those used and discussed in the companion
level 2 validation paper by in which all level 2
comparisons shown in the following are discussed.
For comparison consistency, the Dobson–Brewer WOUDC ground-based data set was
transformed into a monthly level 3 field in order to match the 1∘×1∘ grid of the GTO-ECV CCI data. Measurements from all
stations were gridded in the same latitude–longitude boxes with some specific
considerations. First, only the direct sun observations were used. Even
though in some cases, as is shown in the subsequent figures, this severely
decreases the number of measurements, after rigorous testing it was found
that the usage of direct sun ground-based observations ensures an optimal
level 3 ground-based product. Secondly the threshold on the number of
measurements available before the computation of the associated monthly mean
was investigated. As a compromise between obtaining the highest global
coverage possible and the most representative monthly means, especially at
high latitudes, a lower limit of 10 measurements per month and grid box was
imposed.
The validation of the GTO-ECV CCI level 3 product against the Dobson and
Brewer network is presented here as a series of comparative figures: in each
plot, four lines are presented, namely the level 3 comparison (in dark blue)
and three level 2 comparisons for GOME (in light blue), SCIAMACHY (in green),
and GOME-2A (in red), respectively. In order to compare as closely as
possible the same validation results for level 3 and level 2, a time
constraint was imposed on the level 2 comparisons according to the time
periods for each instrument in the merged data record (see
Sect. ). Furthermore, the same latitudinal constraints for the
monthly means were imposed (see Table ).
Figure shows the latitudinal dependency of the percentage
differences for both Brewer (left) and Dobson (right) instrument types. The
three satellite instruments reveal a remarkable inter-sensor consistency for
all latitudes and an excellent agreement with the ground data. The level 3
comparison (blue) closely follows that for level 2. The slight positive
deviation of about 0.5 % of level 3 data (compared to level 2) for the
40–60∘ N belt (right panel) will be discussed in the next section.
Percentage difference between satellite data records and
ground-based data as a function of latitude. Left: Brewer comparisons and
right: Dobson comparisons. Level 3 comparison in dark blue, GOME level 2
comparison in light blue, SCIAMACHY in green, and GOME-2A in red. The
1-σ standard deviation of the average is only given for the level 3
lines.
Percentage difference between satellite data records and
ground-based data as a function time for the Northern Hemisphere. Left:
Brewer comparisons and right: Dobson comparisons. Level 3 comparison in dark
blue, GOME level 2 comparison in light blue, SCIAMACHY in green, and GOME-2A
in red.
Northern Hemisphere statistics
The Northern Hemisphere time series comparisons are shown separately for the
Brewer and Dobson instrument types in Fig. . The Brewer
comparisons (left panel) show very good agreement between level 3 and individual level 2
lines, well within the ±1 % difference level for most of the 15-year
data record and with negligible bias. The two outliers during the GOME period
and the two during the SCIAMACHY period are discussed below. The Dobson
analysis (right panel) shows equally good comparisons, falling within the
1.5 % difference level with a bias of ∼1 %, due to the known
differences in the treatment of the stratospheric temperature dependence of
the ozone absorption cross sections and how this issue is dealt with by the
ground-based algorithm and references
therein. their Table IV
have shown that no long-term drift in the individual level 2 data sets was
found for both Dobson and Brewer comparisons. For the corresponding level 3
comparisons in the Northern Hemisphere, the drift (per decade) of the
differences with respect to ground-based data is also negligible, i.e.
-0.12±0.12 and 0.17±0.11 %, respectively. This makes the GTO-ECV
CCI data record exceedingly useful for longer-term analysis of the ozone
layer such as decadal trend studies e.g..
Figure shows the percentage differences as a function of
time for six zonal belts 0–10, 30–40, 40–50, 50–60, 60–70, and
70–90∘ N (from top to bottom) for the Brewer
comparisons (left) and the Dobson comparisons (right). The patterns of the level 3 comparison are nearly
identical to those from the individual level 2 comparisons. The agreement for
both types of instruments is excellent up to high latitudes, except for a
small number of outliers in the 60–70∘ N belt for the Brewer
comparisons and in the 50–60∘ N belt for the Dobson comparisons.
For this latter belt, some strong disagreement up to 5–10 % between
level 3 and level 2 coincidences is shown for the SCIAMACHY period. These
outliers, during years 2004 and 2005, were basically due to sampling issues.
A different set of days was considered for creating the monthly mean
differences for the level 2 data set and the level 3 data set, due to the
6-day SCIAMACHY global coverage and the scarcity of ground-based stations in
those latitudes. We have to keep in mind that the level 2 comparisons are
based on coincident measurements with respect to geolocation (150 km radius)
and time (same day), whereas the level 3 comparisons are based on coincident
measurements with respect to geolocation (same 1∘×1∘
grid box) only. Therefore, a different set of days might form the basis for
the level 3 monthly averages from ground-based and satellite-based data,
respectively. Consequently, these larger differences do not necessarily
indicate poorer quality of the level 3 data record. A similar reason explains
the outliers noted in the 60–70∘ N belt for the Brewer comparisons.
Furthermore, for the high latitude belts it is possible that we include
comparisons with one ground-based station alone. Overall, considering the
excellent agreement for the remainder of the belts, the consistency between
the level 2 and level 3 validation results is very satisfactory. As for the
entire Northern Hemisphere statistics (see Fig. ) no
long-term drift in the differences is found for the individual latitude belt
statistics.
Percentage difference between satellite data records and
ground-based data as a function of time for the Northern Hemisphere for six
zonal belts 0–10, 30–40, 40–50, 50–60, 60–70, and 70–90∘ N
from top to bottom. Left column: Brewer comparisons and right column: Dobson
comparisons. Level 3 comparison in dark blue, GOME level 2 comparison in light
blue, SCIAMACHY in green, and GOME-2A in red.
Percentage difference between satellite data records and Dobson
ground-based data as a function of time for the Southern Hemisphere for seven
10∘ zonal belts from 0–70∘ S and one belt from
70–90∘ S. Level 3 comparison in dark blue, GOME level 2 comparison
in light blue, SCIAMACHY in green, and GOME-2A in red.
Southern Hemisphere statistics
In the Southern Hemisphere, the validation is restricted to Dobson
measurements. Figure shows the percentage differences
between satellite and ground-based data as a function of time for seven
10∘ belts from 0–70∘ S and one belt from 70–90∘ S
(top to bottom). As for the Northern Hemisphere, the level 3 comparisons show
a near-perfect agreement with the level 2 comparisons up to 50∘ S.
The outliers in higher latitudes are mostly due to differences in sampling as
explained in the previous section. The mean bias between GTO-ECV CCI level 3
data and the Dobson ground-based network in the Southern Hemisphere is
0.66±1.63 % and the drift per decade is 0.77±0.12 %.
Seasonal and latitudinal dependence
The seasonal variability of the GTO-ECV CCI data compared to the Dobson
network is shown in Fig. as a contour plot of latitude
vs. month of year. Very small seasonal features are observed with a slight
oscillation of ±1 %. For the very high southern latitudes some
underestimations are seen for the summer months (around -2 to -2.5 %)
and overestimations for the winter months (around +3 to +4 %). This
seasonality probably originates from the Dobson sensitivity to atmospheric
effective temperature, which leads to positive differences between Dobson and
satellite observations for high effective temperatures in local summer
(negative differences in winter). For the Brewer stations no significant
features are observed in the contour comparison; the seasonal variation of
biases is 0.16±0.30 % and the latitudinal variation of biases is
0.30±0.41 %.
Summary of the Brewer and Dobson comparisons
In conclusion, the GTO-ECV CCI level 3 validation results were found to be very consistent
with the separate GOME, SCIAMACHY, and GOME-2A level 2 validation comparisons.
In particular, on a monthly mean basis, for the Dobson comparisons, both the Northern
and Southern Hemisphere time series are in very close agreement. Similarly, for the Brewer
comparisons (Northern Hemisphere), an excellent agreement is found apart from a handful of
outliers. On a seasonal basis, both the Brewer and the Dobson level 3 comparisons show
close agreement with the level 2 comparisons.
The statistics following the Figures presented in
Sect. for the Northern Hemisphere.
GTO-ECV CCI
Dobson
Brewer
Mean bias
1.00±0.75 %
0.16±0.66 %
Monthly mean variability
±2.35 %
±2.20 %
Drift per decade
-0.12±0.12 %
0.17±0.11 %
Seasonal variation of biases
0.80±0.21 %
0.16±0.30 %
Latitudinal variation of biases
0.72±0.96 %
0.30±0.41 %
According to Table 5 of the Ozone_cci User Requirement Document
it is stated that the decadal stability of the total ozone
column provided by the three instruments must fall within 1–3 %, the
long-term accuracy of each product at 2 % and short term accuracy at
3 %. The seasonal cycle and inter-annual variability must also fall
within the 3 % level. In Table , the statistics extracted
from the Dobson and Brewer comparisons for the Northern Hemisphere are
summarized. Under the header “mean bias” we refer to the mean bias and
standard deviation (1-σ) of the time series (see
Fig. ). It is 1±0.75 % for the Dobson
comparisons and 0.16±0.66 % for the Brewer comparisons, respectively. The header “monthly mean variability”
refers to the standard deviation of the standard deviations of the monthly
mean values in the Northern Hemisphere time series. The header “drift per
decade” refers to the decadal drift and drift error calculated from the
Northern Hemisphere time series (Fig. ); the header
“seasonal variation of biases” indicates the mean difference from the
seasonal plots (see Fig. ) and the amplitude of the
seasonal variability. The header “latitudinal variation of biases” refers
to the mean bias and standard deviation as calculated by the latitudinal
variability plots on a global scale.
It is evident that the product easily meets the User requirement levels listed above.
Hence, we can conclude that the current GTO-ECV CCI level 3 total ozone product is of the
same high quality as the constituent level 2 total ozone products. As the relative drift
compared to the ground-based reference is less than 1 % per decade, the GTO-ECV data record
will be useful for studies of long-term total ozone trends.
Comparison with SAOZ UV–visible instruments
The NDACC (Network for the Detection of Atmospheric Composition Change,
http://www.ndacc.org) UV–visible working group operates about 35
certified SAOZ zenith-sky UV–visible absorption spectrometers
distributed from the Arctic to the Antarctic. Most of
the instruments perform twice-daily measurements of the total ozone column
during twilight between 86 and 91∘ solar zenith angle at all
latitudes and seasons. The retrieval is based on the DOAS approach in the
visible Chappuis band of ozone between 470 and 540 nm.
Seasonal variability of the GTO-ECV CCI data compared to the Dobson
network as a contour plot of latitude vs. month of the year.
Figure shows time series of monthly mean differences between
GTO-ECV CCI level 3 data and the UV–visible network grouped by latitude zones
of 30∘. Red dots correspond to comparisons for single stations and
the white-faced red circles represent the mean of those differences over all
stations within a given latitude zone. For belts 0–30∘ N (bottom
left panel) and 30–60∘ S (middle panel on the right) only one
station contributes data for the better part of the time series, and the zonal
mean therefore coincides with the station's difference.
Time series of monthly mean relative differences for NDACC
UV–visible instruments for six 30∘ latitude zones; Northern
Hemisphere in the left panels (from top to bottom: high, middle, and low
latitudes), and Southern Hemisphere in the right panels (from top to bottom: low,
middle, and high latitudes). Red dots correspond to individual stations,
black dots correspond to the zonal means. If only one station contributes,
the single-station differences are coincident with the zonal mean.
These comparisons with UV–visible instruments in general confirm the
validation results based on Dobson and Brewer comparisons. Large discrepancies are evident in the southernmost
bin, in particular during Antarctic ozone hole conditions. These are in large
part due to co-location space–time mismatches and differences in horizontal
smoothing of the large gradients occurring at the border of the polar vortex
. The positive bias observed in the northernmost bin,
which is not seen in the comparisons with Brewer observations, is noteworthy.
While the GODFIT_V3 retrieval uses more recent ozone cross sections than
those used in the default Brewer data processing, the good agreement between
the GTO-ECV CCI total ozone column level 3 product and the Brewer
observations should be interpreted with care as the GODFIT_V3 uses a soft-calibration scheme based on total
ozone measurements obtained with Brewer measurements at a set of northern
mid-latitude reference sites . As such, the accuracy of the
GTO-ECV CCI level 3 product somehow depends on that of the Brewer network. On
the other hand, as this positive bias between the GTO-ECV product and the
SAOZ instruments only appears at high latitudes, errors in the SAOZ AMFs
cannot be ruled out either.
Comparison with other satellite data
In this section the GTO-ECV CCI level 3 monthly mean total ozone product is
compared with two other satellite-based data records: (1) its predecessor
product GTO-ECV GDP and (2) the SBUV version 8.6 merged ozone data
record.
GTO-ECV GDP
The preceding GTO-ECV GDP data record is based on
GOME, SCIAMACHY, and GOME-2A total ozone columns obtained with the GDP 4.X
retrieval algorithm . The
first version of GTO-ECV GDP covered the period from 1995 to 2008, but this
has now been extended to June 2013. In addition to the retrieval algorithm,
the level 3 gridding method and the merging algorithm differ from the
approach used for GTO-ECV CCI. Regarding the level 3 generation, only one
measurement per day and grid cell is used for the GTO-ECV GDP product and the
daily grid cells have a size of 0.33∘×0.33∘.
Regarding the merging approach, all available satellites are averaged instead
of using only one at a time. GTO-ECV GDP was incorporated already in the
preceding WMO scientific assessment of ozone depletion .
Moreover, it has been used for chemistry–climate model evaluation
as well as the investigation of decadal ozone trends and
variability . Both GTO-ECV CCI and GDP data
records agree very well regarding the long-term trends, emphasizing their
excellent decadal stability.
Percentage differences between GTO-ECV CCI and GDP 1∘×1∘ monthly means 1996–2011 binned into 5∘ latitude
belts (black dots). The grey shading denotes the 1-, 2-, and 3-σ
standard deviations, respectively.
Figure presents the percentage differences between
GTO-ECV CCI and GDP 1∘×1∘ monthly means binned into
5∘ latitude belts (black dots). The grey shading denotes the 1-, 2-,
and 3-σ standard deviations, respectively. Both data records show a
remarkable inter-consistency; the overall mean difference is
0.3%±1.7%. The deviations are slightly positive in low and
middle latitudes, and negative in high latitudes. This latitudinal structure
of the differences is mainly due to the usage of different level 2 retrieval
algorithms. The application of different level 3 gridding methods leads to
differences of up to ±4 % in regions where two or more orbits per day
overlap each other.
SBUV version 8.6 merged ozone data record
Within the framework of the NASA (National Aeronautics and Space
Administration) program MEaSUREs (Making Earth System data records for Use in
Research Environments) data from a series of nine BUV, SBUV, and SBUV/2
instruments have been reprocessed using the version 8.6 ozone retrieval
algorithm . From these data records a coherent
long-term 5∘ zonal monthly mean ozone time series covering the
periods 1970–1972 and 1979–2014 has been created which contains both
profile and total ozone column information .
compared this merged data set (referred to as SBUV-MOD in the following) with
GTO-ECV CCI and ground-based total ozone columns for the 16-year overlap
period from March 1996 to June 2011. They found very good agreement in terms
of monthly zonal mean total ozone and monthly zonal mean anomalies (their
Figs. 6 and 8). The mean difference between both data sets is
0.3±1.1 %.
Figure shows the percentage difference between GTO-ECV
CCI and SBUV-MOD 5∘ zonal mean ozone columns as a function of
latitude. The black curve denotes the annual mean difference and its standard
deviation (grey shaded area), and the blue, red, yellow, and green lines
denote the seasonal differences. On average, the differences are positive in
middle and low latitudes, and negative in high latitudes, where largest
deviations occur in the summer months. Largest scatter is found in the
Southern Hemisphere poleward of 50∘ S. The amplitude of the seasonal
cycle in the differences is about 1 %.
In addition to total ozone columns we compare the standard deviations of the
5∘ zonal monthly means. Figure indicates that
the latitudinal and temporal structures of the standard deviations agree very
well. The absolute differences (shown in the bottom panel) are small in low
and middle latitudes, and reveal larger spread in the months and latitudes
close to the polar night terminator.
Percentage differences between GTO-ECV CCI and SBUV-MOD 5∘
monthly zonal mean ozone columns 1996–2011 as a function of latitude. Black:
annual mean difference and its standard deviation (grey shaded area). Blue,
red, yellow, and green lines denote the differences for northern hemispheric
winter, autumn, summer, and spring, respectively.
Standard deviation of 5∘ monthly mean ozone columns as a
function of latitude and time: GTO-ECV CCI (top panel)and SBUV-MOD (middle
panel). The bottom panel shows the absolute difference between GTO-ECV CCI
and SBUV-MOD standard deviations.
Summary and outlook
In this paper, which is the third in a series of three on the ESA Ozone_cci
total ozone products, we have described the new GTO-ECV CCI level 3 global
monthly mean data record spanning the 15-year time period 1996–2011. The
data record is composed of total ozone measurements from three European nadir
UV backscatter sensors GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/MetOp-A. It
is publicly available at http://www.esa-ozone-cci.org. The companion
papers by and introduced the ozone
retrieval algorithm GODFIT_V3 and presented the validation of the level 2
total ozone products, which form the basis for the GTO-ECV CCI merged level 3
product described herein.
The merging approach relies on an inter-sensor calibration procedure using
GOME as the reference. Small corrections have been applied to SCIAMACHY and
GOME-2A in order to reduce the differences among the instruments. Special
emphasis was placed on the analysis of sampling issues intrinsic to the
satellite data and their impact on the final GTO-ECV CCI product.
We presented level 3 product geophysical validation results using as
reference ground-based measurements with Brewer, Dobson, and UV–visible SAOZ
instruments. The validation of the GTO-ECV CCI level 3 data record was found
to be very consistent with the equivalent separate GOME, SCIAMACHY, and
GOME-2A level 2 validation . In particular, on a monthly
mean basis, for the Dobson comparisons, both the Northern and Southern
Hemisphere time series are in strong agreement. Similarly, for the Brewer
comparisons (Northern Hemisphere), an excellent agreement is found apart from
a handful of outliers. On a seasonal basis, both the Brewer and the Dobson
level 3 comparisons show close agreement with the level 2 comparisons. We
conclude that the current 15-year GTO-ECV CCI level 3 total ozone data
product is of the same high quality as the equivalent individual level 2 data
products that constitute it. This is due to a very high level of consistency
among the level 2 products themselves and a robust merging approach. Both
absolute agreement and long-term stability are excellent for almost all
latitudes apart from a few outliers which are mostly due to sampling
differences between the level 2 and level 3 data that cannot be completely
eradicated.
This study demonstrates that the current GTO-ECV CCI data record is suitable
for a variety of applications. In particular it is useful for the long-term
monitoring of the past evolution of the ozone layer. Due to its excellent
decadal stability – the relative drift compared to the ground-based
reference is less than 1 % per decade – it is valuable for long-term
trend analysis of the ozone field. The high spatial resolution of the level 3
data record of 1∘×1∘ enables us to investigate ozone
changes on global as well as regional scales as recently demonstrated by
.
Furthermore, global long-term data records such as GTO-ECV CCI can be
compared with chemistry–climate model simulations. One of the main purposes
of these models is to identify and quantify relevant processes and forcings
affecting the ozone layer and to project their future evolution. In
particular, the simulations are analysed to assess the returning of ozone to
historical levels and the complete recovery from ODSs as a consequence of the
1987 Montreal Protocol . The satellite-based data records
enable us to evaluate these model projections and to calibrate the efficacy
of the model system .
Regarding total ozone, the second phase of ESA-CCI is dedicated to an
improvement of the sampling errors (see Sects. and
) using spatio-temporal statistical tools and an extension of
the GTO-ECV CCI data record. The GOME-2A sensor used in this study is the
first of a series of three identical instruments. GOME-2 on MetOp-B was
launched in September 2012 and the data will be included in the new version
of GTO-ECV. In addition measurements performed with the Ozone Monitoring
Instrument (OMI) onboard the NASA Aura satellite (2004–present) – which
have been recently reprocessed with an adapted version of the GODFIT_V3
retrieval algorithm – and data from the Ozone Mapping and Profiler Suite
(OMPS) onboard the NASA Suomi National Polar-orbiting Partnership satellite
(2011–present) will be included. Thereby we can take advantage of OMI's
excellent long-term stability over the 10 plus years of operation. The
GOME-2 on MetOp-C is planned to be launched in 2018, and together with the
Sentinel-5 Precursor (to be launched in 2016) and the Sentinel-4 and
Sentinel-5 sensors (to be launched by the end of this decade), these future
instruments will contribute to the extension of this reference data set.