ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-9277-2017An assessment of ozone mini-hole representation in reanalyses over the Northern HemisphereMillánLuis F.luis.f.millan@jpl.nasa.govManneyGloria L.Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USANorthWest Research Associates, Socorro, New Mexico, USANew Mexico Institute of Mining and Technology, Socorro, New Mexico, USALuis F. Millán (luis.f.millan@jpl.nasa.gov)4August201717159277928912April20172May20178July201710July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/9277/2017/acp-17-9277-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/9277/2017/acp-17-9277-2017.pdf
An ozone mini-hole is a
synoptic-scale region with strongly decreased total column ozone resulting
from dynamical processes. Using total column measurements from the Ozone
Monitoring Instrument and ozone profile measurements from the Microwave Limb
Sounder, we evaluate the accuracy of mini-hole representation in five
reanalyses. This study provides a metric of the reanalyses' ability to
capture dynamically driven ozone variability. The reanalyses and the
measurements show similar seasonal variability and geographical distributions
of mini-holes; however, all of the reanalyses underestimate the number of
mini-holes and their area, and in many reanalyses their location displays an
eastward bias. The reanalyses' underestimation of mini-hole number ranges
from about 34 to about 83 %. The mini-hole vertical representation in the
reanalyses agrees well with that in the MLS measurements and, furthermore, is
consistent with previously reported mechanisms for mini-hole formation. The
skill of the reanalyses is not closely tied to the ozone fields assimilated,
suggesting that the dynamics of the reanalysis models are more important than
the assimilated ozone fields to reproducing ozone mini-holes.
Introduction
Since early ozone measurements , it has been known that the
total column ozone is characterized by day-to-day local fluctuations that are
correlated with the passing of synoptic weather systems.
found a significant correlation between the total column ozone and
stratospheric temperature and potential temperature, as well as the density
and height of the tropopause. studied the relative
importance of horizontal advection and vertical motion to producing such
fluctuations and the manner in which those two mechanisms combine to produce
the ozone–weather relationships.
Events with very low total ozone columns were named ozone mini-holes by
because of their rapid ozone decline and their synoptic
scale, which was in contrast to the slow ozone decline and planetary scale of
the Antarctic ozone hole. These events are found mainly throughout
midlatitudes in both hemispheres e.g.,. Unlike the well-known Antarctic ozone hole, mini-holes are mainly
driven by dynamical atmospheric processes rather than photochemical
processes. As pointed out by , they result from a combination
of uplift of air and horizontal advection. Assuming net divergence, local
uplift of the air increases the amount of the column occupied by ozone-poor
tropospheric air e.g., while horizontal advection brings ozone-poor air into
the column through poleward transport around the tropopause, through
equatorward transport of polar air around the middle of the stratosphere, or
through
a combination of the two e.g.,.
As an example, Fig. shows the mini-hole event observed
over the UK on 19 January 2006. On this day, a record low total ozone column
of 177 DU was observed at Reading (where the long-term mean is about 310 DU
at this time of year) as well as low total ozone columns over other northwest
European stations . Figure also shows
the same region a few days before and after the event to illustrate the
transient nature of this phenomenon.
OMI total ozone column for 16–21 January 2006. The data over the
pole are missing due to the lack of UV backscattering. Red/purple indicate
relatively high/low values of OMI total ozone column.
Reanalyses from data assimilation systems provide globally gridded high-resolution meteorological fields based on an optimized combination of general
circulation models and observational data. Reanalyses from the latest
generation also provide fields of assimilated ozone but use different ozone
inputs and assimilation methods e.g.,. In this paper,
the accuracy of mini-hole representation is used as a metric to assess the
reanalyses' ability to capture dynamically driven ozone variability. We used
five of the most recent high-resolution reanalyses: the European Centre for
Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis, the
National Centers for Environmental Prediction Climate Forecast System
Reanalysis (CFSR), the Japanese 55-year Reanalysis (JRA-55), the Modern-Era
Retrospective Analysis for Research and Applications (MERRA), and its
successor MERRA-2. We evaluate the mini-hole representation comparing the
mini-hole events' area, distance, and orientation with respect to the events
found in the Ozone Monitoring Instrument (OMI) data .
Further, we use vertical profiles of ozone and temperature from the Aura
Microwave Limb Sounder (MLS) , as well as derived
meteorological products (DMPs) and detailed tropopause
information from the JEt and Tropopause Products for Analysis and
Characterization (JETPAC) package , to study the vertical
structure and relationships to the tropopause of the mini-holes in reanalyses
and satellite data. Dynamically induced ozone mini-holes can produce extreme
ozone deficits that result in significant local increases in surface UV; thus
our ability to predict and characterize these events is important to human
health. Because of the combination of dynamical and transport processes that
produce mini-holes, they are a stringent test of the representation of upper troposphere–lower
stratosphere (UTLS)
dynamics in the reanalyses. Understanding of the reanalyses' ability to
reproduce these events can thus be used to guide improvements in the models
and data assimilation systems, and hence in our ability to forecast such
events. This paper is organized as follows: Sect. gives an
overview of the satellite data and the reanalyses used in this study.
Section describes the mini-hole definition used.
Sections and show comparisons with OMI and MLS,
respectively. Lastly, Sect. provides a summary.
Data
In this section a description is given of the observational data as well as
the reanalyses used. We use observations from the NASA's Earth Observing
System (EOS) Aura satellite, launched on July 2004 into a polar sun
synchronous orbit. In particular, we use OMI and MLS data. As mentioned
before, we use the following meteorological reanalyses: MERRA, MERRA-2
ERA-Interim, JRA-55, and CFSR.
Aura OMI
Aura OMI is a nadir-viewing push-broom spectrometer
designed to monitor ozone and other trace gases, as well as aerosols, cloud
top heights, and UV irradiance at the surface. It measures ultraviolet/visible
solar backscatter radiation with a high spectral resolution over the entire
wavelength range from 270 to 500 nm. Total column ozone is derived using two
distinct algorithms: the Total Ozone Mapping Spectrometer (TOMS) algorithm
and the differential optical absorption spectroscopy (DOAS) algorithm.
The OMI-TOMS algorithm is described by . This algorithm is
based on the TOMS version 8 algorithm that has been used to estimate total
ozone columns from four TOMS instruments since 1978. The OMI-DOAS algorithm
is described by . Both datasets have been extensively
validated , indicating
agreement within 2 % with ground-based and airborne measurements. In this
study we use the OMI-TOMS algorithm. In particular, we use the level 3 files
(OMTO3d, V003) with a 1∘ by 1∘ resolution.
Aura MLS
Aura MLS is a small radio telescope whose mission
objective is studying ozone, air quality, and climate
. It vertically scans the Earth's limb from the surface
to ∼ 95 km, measuring thermal microwave emission with a spectral range
varying from 118 to 2500 GHz. These radiances are inverted using a
tomographic optimal estimation retrieval algorithm ,
producing ∼ 3500 vertical profiles per day of temperature, cloud ice,
and 16 atmospheric trace gases. In this study we use version 4.2 temperature
and ozone data filtered as described in the MLS data quality document
. This dataset provides ozone profiles from 261 to
0.02 hPa with vertical resolution of around 3 km in the upper troposphere
and stratosphere, precision varying from 0.03 ppmv at 261 hPa to
0.2 ppmv at 1 hPa, and stratospheric accuracy better than 10 %.
Temperature is provided from 261 to 0.001 hPa with vertical resolution
varying from around 4.5 km in the upper troposphere to 3.6 km in the middle
stratosphere, precision around 1 K at these levels, and accuracy better
than 2.5 K.
Basic specifications of the reanalysis forecast models.
Timelines of ozone observations (vertical profiles and total column
ozone – TCO) assimilated during the MLS/OMI period by the ERA-Interim
(blue), JRA-55 (purple), MERRA (dark red), MERRA-2 (light red), and CFSR
(green) reanalysis systems. Based on Fig. 9 from . Gray
vertical line indicates the start of the OMI and MLS measurements. Note that
JRA-55 does not assimilate OMI TCO directly; first, ozone concentrations are
estimated using a chemistry transport model and then nudged to the TCO
observations .
Ozone version 2.2 was extensively validated , indicating agreement at the 5 to 10 % level with satellite,
balloon, aircraft, and ground-based ozone data. Above the tropopause, version
4.2 is very similar to version 2.2, so past validation results still hold. In
the upper troposphere, version 4.2 has reduced spurious vertical oscillations
found in previous versions, particularly at midlatitudes. Also, version 4.2
has reduced the ozone retrieval sensitivity to thick clouds through changes
in the forward model representation of cloud impacts on the MLS radiances
. assessed the long-term stability,
overall bias, and short-term variability of several satellite ozone records
using ground-based data and found MLS version 3.3 to be stable in the entire
stratosphere (to within 1.5 % decade-1 in the middle stratosphere).
Version 2.2 temperature data were extensively validated ,
indicating agreement at the 2.5 K level with satellite and radiosonde data,
as well as with reanalysis fields. Version 4.2 is similar to version 2.2 so,
again, the validation still holds .
Reanalyses
The reanalyses used in this study are MERRA and MERRA-2 , ERA-Interim , CFSR
, and JRA-55 . A detailed overview of
these reanalyses is given by . Briefly, MERRA, MERRA-2,
and CFSR use a 3D-FGAT (“first guess at the appropriate time”;
) assimilation scheme, while ERA-Interim and JRA-55 use
an incremental 4D-Var approach. Overall, all reanalyses
use the same conventional data (e.g., surface records, radiosonde profiles,
and aircraft measurements); there are, however, many differences in the
satellite data usage. In particular, the ozone inputs vary widely (see
Fig. ): only MERRA-2 and ERA-Interim assimilate OMI and MLS
ozone data. Only MERRA-2 assimilates MLS temperature retrievals at pressures
less than or equal to 5 hPa. There are also differences in the horizontal
and vertical grids, lid heights, and models' ozone treatment among the
reanalyses. Table summarizes these specifications. An assessment
of the upper tropospheric and stratospheric reanalysis ozone fields can be
found at . Note that JRA-55 does not assimilate measurements
directly; first, ozone concentrations are estimated using a chemistry
transport model and then nudged to the total column ozone observations
.
To ease the comparison of the reanalysis fields against the OMI data, the
reanalysis data were first interpolated to the OMI measurement times and then
interpolated onto the OMI latitude–longitude grid, that is to say, a
1∘ latitude by a 1∘ longitude spacing. In addition, gaps in
the OMI data (for example, polar winter periods) were identified and masked
out in the interpolated reanalysis fields to ensure that the same regions
were compared day by day.
To ease comparison with MLS, we use the MLS DMPs . These
DMPs are meteorological and derived meteorological fields interpolated to the
MLS measurement locations (in time and space) computed within the JETPAC
package , which also characterizes UTLS jets and multiple
tropopauses. In particular, for this study we use ozone, temperature,
equivalent latitude (EqL), and the JETPAC tropopause characterization. EqL is
a quasi-Lagrangian coordinate widely used in stratospheric studies
e.g.,. Simply put, EqL is the latitude that would
enclose the same area as the corresponding potential vorticity contours.
Thermal tropopauses are determined from the reanalysis temperature profiles
using the World Meteorological Organization (WMO) definition
e.g.,, that is to say, where the temperature lapse
rate falls below 2 K km-1 for at least 2 km. Similarly, additional
tropopauses are identified above the primary tropopause
e.g.,.
Ozone mini-holes: definition and analysis
Several mini-hole definitions can be found in the literature:
used a constant threshold of 215 DU, while
used 220 DU. used thresholds computed
by subtracting 70 DU from zonally and meridionally averaged monthly means,
while chose to subtract 80 DU. defined a
threshold based on monthly mean values minus 1 standard deviation, while
used monthly mean values minus 2 times the standard
deviation. In addition to these thresholds, other constraints have been
applied: only considered as mini-holes those events with
area greater than 500 000 km2 in the 40–65∘ latitude regions,
while only considered events found over at least a
5.533∘ latitude by 5.625∘ longitude region, that is, covering
an area equivalent to the ones found by .
Figure shows the geographical distribution of mini-hole
events found in the OMI data during 2005 using different mini-hole
definitions: Fig. a shows the mini-hole geographical
distribution found using a constant threshold of 220 DU;
Fig. b shows the events found using thresholds computed by
subtracting 70 DU from the monthly mean; Fig. c shows the
events found using thresholds computed by subtracting 2 times the standard
deviation from the monthly mean; Fig. d shows the events
found when the total column ozone value is less than 25 % below the
monthly mean. In each case, we use a flood filling algorithm – an algorithm
that determines pixels meeting a threshold value in a 2-D array – in the
region of the total column ozone anomaly to find the adjacent pixels that
were below the chosen threshold. Note than no additional constraints (size of
the event or geographical position) were applied. Because of this, there are
a disproportionate number of events in the Southern Hemisphere compared to
the Northern Hemisphere in panels (a), (b), and (d): most of these events are
related to the Antarctic ozone hole; that is to say, they are due to
heterogeneous chemistry and not driven by dynamics. Hence, we will not
analyze them in this paper.
As shown in Fig. , the occurrence frequency of mini-holes, as
well as their geographical distribution, depends strongly on the definition
used. In this study, we chose to define mini-hole events as regions where the
total column ozone value is less than 25 % below the monthly mean.
Monthly means were used as opposed to climatological monthly means to avoid
biasing the number of events by any long-term trend in ozone or by
interannual variability in the planetary-scale “background” ozone values.
We choose this definition because a constant – below 220 DU – threshold
would identify more events in those months when the background ozone levels
are naturally low. For example, OMI midlatitude mean total column ozone
varied from ∼ 390 DU around March to ∼ 290 DU around
mid-October. Hence, using a constant 220 DU threshold would identify more
events during fall than in spring. An analogous argument applies to a fixed
difference threshold, e.g., 70 DU less than the monthly mean. In a similar
manner, the standard deviation threshold identifies many events in the
tropics where ozone levels and variability are naturally low.
In addition to identifying the mini-holes in OMI and the reanalysis fields,
the algorithm matches the events found in the reanalyses with the ones found
in OMI. Within each day's events, the algorithm checks whether the events
found in OMI and the reanalysis overlap; if they do not, the algorithm finds
the closest one within a distance of 2000 km. Although this is a simple
algorithm, visual inspection of many days showed it to be appropriate.
Geographical distribution of mini-hole events for OMI data in 2005.
Four different mini-hole definitions are compared: (a) using a
constant threshold of 220 DU, (b) using as thresholds the monthly
mean minus 70 DU, (c) using as thresholds the monthly mean minus 2
times its standard deviation, and (d) the percentage threshold
discussed in the text. Red/blue indicate relatively high/low number of events
counts.
Comparison with OMI
Figure compares the mini-hole events per month found in
OMI and the reanalysis fields during 2005 to 2014. Figure a
shows a time series of all the events found regardless of their area, while
Fig. b shows only events with area greater than
200 000 km2. Clearly, the reanalyses are underestimating the frequency
of the smaller events. Historically, the term mini-hole refers to
synoptic-scale events, and hence these subsynoptic events have been regarded as
hindrance by and filtered out either by interpolating to
synoptic-scale grids or by only analyzing events greater than a particular
area . In this study, we will only analyze
events with areas greater than 200 000 km2 to avoid these subsynoptic-scale events. We note an increase in the number of subsynoptic-scale events
after 2010; however, a detailed study of these events is beyond the scope of
this paper.
Mini-hole events per month during 2005–2014 in the Northern Hemisphere as found in OMI data and reanalysis fields (black, green, blue,
red, pink, and purple lines represent OMI, CFSR, ERA-Interim, MERRA, MERRA-2, and
JRA-55, respectively). Panel (a) shows all the events
while (b) displays only the events with area greater than
200 000 km2. Dashed vertical lines indicate the beginning of each
January; dotted vertical lines show the beginning of each July.
Panel (c) shows the mean number of mini-hole events in a given month
(during 2005–2014) for events with area greater than 200 000 km2.
Figure c shows that the mini-hole events' seasonal
variations found in reanalyses and in the observations are similar. In OMI
and the reanalysis fields, mini-hole events are most frequent during winter
when the atmosphere is more dynamically active. Synoptic-scale storms are
strongest and most common during midwinter, resulting in powerful storm
tracks that uplift the air – which, assuming net divergence, increases the
amount of the column occupied by ozone-poor tropospheric air – which is one
of the mechanisms responsible for mini-hole genesis . Despite the similarities between the representation of mini-holes
in reanalyses and OMI data, differences exist among their seasonal
variations: the most noticeable is that all reanalyses underestimate the
number of mini-hole events, with the underestimation ranging from 34 %
less for ERA-Interim up to 83 % less for JRA-55. Further, the events
found in OMI display a mildly positively skewed distribution (the increase in
number of events between September and January is rapid while the decay
between January and March is slow), as opposed to the events found in the
reanalyses (except for ERA-Interim), which display a distinctly negatively
skewed distribution (the increase in number of events between September and
March is slow, followed by a rapid decay in spring).
The geographical count of mini-hole events is shown in
Fig. . Although the reanalyses underestimate the number of
mini-hole events, the mini-hole count morphologies are similar, with
mini-holes occurring most frequently over the North Atlantic storm tracks.
This region of maximum activity has been identified before by
and . This, in addition to the increase in
mini-hole activity during winter, suggests that all reanalyses simulate the
storm track influence upon mini-hole genesis to some degree.
Geographical distribution of mini-hole events during 2005–2014 as
found in OMI and reanalysis fields. Red/blue indicate relatively high/low
number of events counts.
Using the matching algorithm described above, it is possible to compute the
distance between the matching events as well as to study their areas.
Figure compares the distance between the events found in
the reanalysis fields and OMI data, as well as their area fractions. Ideally,
one would like to have a delta function at zero when comparing the distance
between events. The closest reanalysis to display this behavior is MERRA-2,
which shows a narrower distribution centered around 75 km; the other
reanalyses display positively skewed distributions with the majority of
values lying between 75 and 300 km. With respect to their area fractions,
ideally one would like to see a delta function at one; again, only MERRA-2
displays a narrow distribution, in this case near 0.8. Overall, the other
reanalyses usually underestimate the area of the mini-hole events. The
slightly better performance in MERRA-2 may be related to the fact that this
is not an independent comparison: MERRA-2 assimilates OMI total column ozone
data throughout the comparison period. Note that ERA-Interim assimilates OMI
data after 2008. However, CFSR only assimilates SBUV/2 ozone and performs
similarly to ERA-Interim, suggesting that the dynamics produced by the
reanalyses are more important than the assimilated ozone fields for
reproducing mini-holes.
(a) Histograms of the distance between the mini-hole events
found in the reanalysis fields and the ones found in OMI data (black, green,
blue, red, pink, and purple lines represent OMI, CFSR, ERA-Interim, MERRA,
MERRA-2, and JRA-55, respectively). Also shown is the total number of events
as well as the number of matches between the events found in OMI and in the
reanalyses. (b) Histograms of the area fraction of mini-hole
events.
Figure 6 also shows the number of events found in each dataset, the number of
matching events, and their relative score (number of matches divided by their
total number of events). Despite having a similar number of matches, MERRA-2,
CFSR, and ERA-Interim have different relative scores: 0.72, 0.75, and 0.55,
respectively. ERA-Interim's low relative score indicates that half of its
mini-hole events were not found in OMI in contrast to around a third of those
in MERRA-2 or CFSR. This indicates that although ERA-Interim displays the smallest
underestimation of the number of events, many of the events in ERA-Interim are not found in the OMI data. Note that the
relatively high scores of JRA-55 and MERRA result from those reanalyses
detecting mostly the strongest mini-hole events that are in OMI, whereas the
low number of events indicates that those reanalyses do poorly at detecting
the smaller events seen in OMI data.
Wind rose plots showing the direction in which events found in the
reanalysis fields would have to move to match the events found in OMI data, as
well as the mean angular distance to be moved in a particular direction. Gray
dashed circles show angular distance. Red/blue indicate relatively high/low
normalized number of matches.
In addition to computing the distance between matching events and their area
fraction, we also computed the direction that the events found in the
reanalysis fields would have to move to match the position (the
latitude–longitude center) of the events found in the OMI data.
Figure summarizes the overall direction in which the
mini-holes found in the reanalyses would have to move. The position of each
pie slice indicates the direction in a polar coordinate system, its length
represents the mean angular distance to be moved, while its color represents
the percentage of mini-hole matches in a particular direction. As can be
seen, the mini-holes found in the CFSR, MERRA, MERRA-2, and ERA-Interim
reanalyses display an eastward bias. That is, most of the time, these
mini-holes would have to move westward to match the OMI event's positions.
This suggests the possibility that the reanalyses could have westerlies that
are too strong, which would shift the reanalysis events eastward. However,
investigating this in detail would require extensive study that is beyond the
scope of this paper. Note that JRA-55 does not show a particular bias
direction; that is, individual JRA-55 mini-holes have to move in different
directions to match the OMI events' positions, which is more likely related to
their crude treatment of ozone.
Comparison with MLS
Comparisons with MLS allow us to study the vertical distribution of ozone and
temperature during the events. Note that to increase the number of MLS
co-locations with the mini-hole events we use day and night data. As a case
study, Fig. a shows the ozone vertical distribution for
MLS during the mini-hole event shown in Fig. .
Figure a also shows a reference profile constructed using
profiles over the mini-hole region from 15 days prior to 15 days after the
event, excluding profiles under mini-hole event conditions. The ozone
reduction occurs between 200 and 20 hPa. Following , the
profiles were split into two vertical regimes, an UTLS region (from 300 to 65 hPa) and a mid-stratospheric (MS)
region (from 65 to 1 hPa). In each of these layers we computed the ozone
decrease with respect to the total column ozone. In the MLS data, about two
thirds (67 %) of the reduction occurs in the UTLS region while around one
third (33 %) originates in the mid-stratosphere. Using ozone sondes,
found similar values (UTLS: 66 %; MS: 34 %).
(a) MLS mean ozone vertical profile (red), reference ozone
profile (black), and reference standard deviation (gray envelope) during the
mini-hole event observed over the UK on 19 January 2006. The reference
profile was constructed using profiles over the mini-hole region from 15 days
prior to 15 days after the event, excluding profiles under mini-hole event
conditions. The percentage ozone reduction with respect to the total column
ozone in the UTLS and in the mid-stratosphere is shown in dark and light
blue, respectively. (b) As in (a) but for temperature.
(c)) As in (a) but for equivalent latitude (EqL) derived
from the MERRA-2 reanalysis sampled at the MLS measurement locations.
(d) MERRA-2 trajectories launched at the MLS measurement locations
in the mini-hole event region at 100 and at 30 hPa.
Composite of the difference between the events and the reference
values for all mini-hole events found between 2005 and 2014. The first row
shows the ozone vertical profile differences. The ozone composite event
difference profile is shown in red, its 1 standard deviation envelope is
shown by the thin light red lines, and the gray envelope is the reference
composite standard deviation. In the reanalysis panels, the blue dashed lines
show the ozone composite event difference when the MLS averaging kernels were
applied. The percentage ozone reduction with respect to the total column of
ozone in the UTLS and in the mid-stratosphere is shown in dark and light
blue, respectively. Similarly, the second row displays the temperature
profile differences and the third row displays the EqL vertical profile
differences. Lastly, the fourth row shows the percentage difference of the
total ozone column, the tropopause altitude difference, and the DT fraction
deviations (dark gray, green, blue, red, pink, and purple bars represent the
OMI, CFSR, ERA-Interim, MERRA, MERRA-2, and JRA-55 differences,
respectively). In this column, light gray bars show the composite reference
standard deviation, color bars show the corresponding composite event
difference, and the black lines display the composite event difference
standard deviation.
In a similar manner, Fig. b shows the vertical temperature
distribution during the 19 January 2006 event. In this case, the
mid-stratospheric temperatures and to some extent UTLS temperatures are lower
than normal. Low temperatures in the troposphere are usually associated with
anticyclonic disturbances, which lead to local uplift of the air
. Using the tropopauses calculated by JETPAC, we computed
the mean tropopause altitude during the event as well as during the reference
period. The tropopause altitude found in MERRA-2 was 13.4 km ± 3.3
during the event as opposed to 11.3 ± 1.6 km during the reference
period, 2.1 km higher than normal. As indicated in Sect. ,
assuming divergence of air, raising of the tropopause
leads to the replacement of relatively ozone-rich air in the column with
tropospheric ozone-poor air. To verify that there was net divergence, we
estimated the pressure between the isentropes 330 and 500 K; we found
168.4 hPa during the event versus 170.9 hPa during the reference period. As
pointed out by , the uplift of air results in adiabatic
cooling of the mid-stratosphere. For example, analyzed an
ensemble of 71 extreme mini-holes (in this case, using a constant 215 DU
threshold) and found a nearly linear relationship between the total column
ozone and the 30 hPa temperature deviations.
Near the polar vortex edge, low temperatures in the mid-stratosphere are
associated with planetary wave disturbances that are responsible for
large-scale ozone redistribution e.g.,. As an example,
Fig. c shows the EqL vertical profile derived using
MERRA-2 potential vorticity fields. For this, we use the DMPs that, as
mentioned before, have been interpolated to the MLS measurement locations.
This EqL vertical distribution suggests that in the mid-stratosphere the air
parcels originated at polar latitudes. To corroborate this,
Fig. d shows trajectories launched at 30 hPa from the MLS
measurement locations during the mini-hole event. These trajectories were
taken from the MLS Lagrangian Trajectory Diagnostic dataset
, which is a set of 15-day forward and 15-day reverse
trajectories launched from a curtain of points along the MLS track. These
calculations are based on wind fields and diabatic heating rates taken from
the MERRA-2 reanalysis and the advection calculations are based on the
algorithm used by . As expected, the majority of the air
parcels in the mid-stratosphere originate near the polar vortex.
computed back trajectories for the same event using the Met
Office NAME III model and also found that the air in the mid-stratosphere was
transported from the polar vortex, where it may have undergone ozone
destruction (due to photochemical processes) before reaching the mini-hole
event region.
Figure c, as well as the trajectories launched at 100 hPa
shown in panel (d), indicates that the air parcels in the UTLS originated at
low latitudes. Studies of the characteristics of poleward advection of upper-tropospheric air have shown that such intrusions are associated with Rossby
wave breaking in the upper troposphere . In turn, poleward
Rossby wave breaking has been associated with the presence of double
tropopauses (DTs) . Further, climatological studies have found that DT
occurrence in the Northern Hemisphere coincides with zones of storm track
cyclogenesis e.g., and their occurrence
frequency shows a strong seasonal variation peaking during winter
, both characteristics displayed by the
mini-hole events. Using the JETPAC tropopause information, we computed the DT
fraction (the area with DTs divided by the total area) during
the event as well as during the reference period. During the event, the DT
fraction found in MERRA-2 was 0.8 ± 0.4, as opposed to
0.61 ± 0.49 during the reference period.
Figure displays a composite view of ozone, temperature,
and EqL vertical distributions, as well as their total ozone column,
tropopause altitude, and DT deviations, for all the events, i.e., not only
the matches, found between 2005 and 2014 in the Northern Hemisphere. The
number of events is shown in Fig. . For ozone and
temperature, two composites are shown, one smoothed with the MLS averaging
kernels and one without; note that no significant differences were found
between the two. The observations and reanalysis fields show a picture that
is generally consistent with the one shown in Fig. :
The total ozone column decrease is considerably larger than the natural
variability of total ozone column; that is, it is considerably larger than
the reference standard deviation of total ozone column.
On average, around two thirds of the reduction originates in the UTLS
and the rest in the mid-stratosphere. MERRA-2 and ERA-Interim show very close
agreement with the MLS estimates, presumably because both assimilate, in some
capacity, the MLS O3 profiles; however, the other reanalyses also show
good agreement.
Air parcels in the UT originate from low latitudes, while in the
mid-stratosphere they arrive from high latitudes.
Reanalyses show an elevated tropopause during the events. This is
consistent with anticyclonic disturbances associated with poleward Rossby
wave breaking in the upper troposphere, that is, poleward advection of upper
tropospheric air. Note that, overall, net divergence of air was present
during the events, with a pressure difference between the 330 and 500K
isentropes varying from 6.5 to 8.9 hPa less than that in the reference
period, depending on the data source analyzed.
The local uplift of air adiabatically cools the MS, resulting in lower than normal 30 hPa temperatures.
An increase in DT fraction is seen during the events. However, this increase
is considerably smaller than the DT natural variability. This may be because
even though DTs may be dynamically coupled with Rossby wave breaking events,
DTs occur most frequently above strong cyclonic circulation systems
. That is, mini-hole events are associated with
anticyclonic Rossby wave breaking and thus, while some DTs are favorable for
mini-hole genesis, many others are not.
As shown in Fig. , ozone, temperature, and EqL vertical
distributions, as well as the tropopause altitude, during the events are
close to, or sometimes inside, the limits of their natural variability. This
suggests that mini-hole events are only produced when both the UTLS and
mid-stratosphere processes are favorable for reduction of ozone by dynamical
processes. To verify this, we investigated whether the UTLS or the MS part of the
reduction was enough to produce a mini-hole event, that is a 25 %
reduction below the monthly mean. We found that neither in the MLS data nor
in the reanalysis fields was the UTLS or the MS reduction enough to produce a
single mini-hole event.
Summary
Dynamical redistribution of ozone can produce large transient and localized
ozone reductions, also known as mini-holes. In this study we analyze the
representation of mini-hole events in the Northern Hemisphere from several
reanalyses (ERA-Interim, MERRA, MERRA-2, CFSR, and JRA-55) using data from OMI
and MLS. OMI data allow us to compare their geographical representation while
MLS data allow us to study their vertical representation.
Several definitions of mini-holes exist in the literature. The results
presented here show that the mini-hole frequency as well as their
geographical distribution differs vastly depending on their definition. Here,
we define mini-hole events as regions where the total column ozone value is
less than 25 % below the monthly mean. Further, we only consider as
mini-hole events those ozone fluctuations with an area larger than
200 000 km2.
The main findings can be summarized as follows:
OMI and the reanalysis fields display the same mini-hole seasonal variability,
with more mini-hole events during winter when the atmosphere is more dynamically active.
OMI and the reanalysis fields display similar mini-hole geographical distributions
with mini-holes occurring more frequently over the North Atlantic storm
tracks.
All reanalyses underestimate the number of mini-hole events, with the
underestimation ranging from 34 % less for ERA-Interim up to 83 %
less for JRA-55. Further, reanalyses typically underestimate the area of the
mini-hole events and most of the time are between 75 and 300 km away from
the events found in OMI.
Mini-holes found in CFSR, MERRA, MERRA-2, and ERA-Interim reanalyses
display an eastward bias with respect to the events found in OMI data. JRA-55
does not show a clear bias direction most likely related to their crude
treatment of ozone.
The composite view of the vertical representation of the events agrees
with previously reported mechanisms for dynamical mini-hole formation:
anticyclonic poleward Rossby wave activity breaking into the UTLS and local
uplift of air brings ozone-poor air into the column and is accompanied by
equatorward advection of polar air in the mid-stratosphere.
On average, in the events found in both MLS and the reanalyses, around
two thirds of the ozone reduction originates in the UTLS and the rest in the
mid-stratosphere.
Although mini-hole regions typically show more DTs than in surrounding air,
the association is not strong because DTs occur most frequently above strong
cyclonic circulation systems while mini-holes occur above anticyclonic
systems.
In general, MERRA-2 seems to represent mini-holes marginally better than the
other reanalyses (see Figs. and ),
likely because MERRA-2 assimilates OMI and MLS ozone throughout the
comparison period. CFSR assimilates only SBUV/2 ozone and performs similarly
well to ERA-Interim, which assimilates OMI and MLS ozone during 2008 and
after mid-2009. This suggests that the dynamics produced by the reanalyses
are more important than the assimilated ozone fields in reproducing
mini-holes.
Due to the mismatch between the mini-holes found in OMI and the mini-holes
found in the reanalysis fields, careful attention needs to be paid to ensure
that the regions used to study them coincide with regions where the
reanalysis fields display mini-hole conditions. That is to say, it is
insufficient to identify the mini-hole position in the data and then see what
the reanalysis fields do at those exact locations. Rather, one must find the
mini-holes in the reanalysis fields, compare if the events are of similar
magnitudes, and, if they are, study the meteorological conditions there. This
study exemplifies the importance of assessing the reanalyses – for which the
satellite data are paramount to cover large areas – before studying
atmospheric processes and their variability.
All the data and reanalysis fields used in this study are
publicly available. Reanalysis fields can be found at NASA GMAO, ECMWF, JMA,
and NCEP websites. MLS and OMI data are available from the NASA Goddard Space
Flight Center Earth Sciences (GES) Data and Information Services Center
(http://disc.sci.gsfc.nasa.gov/).
The authors declare that they have no conflict of
interest.
This article is part of the special issue “The SPARC Reanalysis
Intercomparison Project (S-RIP) (ACP/ESSD inter-journal SI)”. It is not
associated with a conference.
Acknowledgements
We thank the JPL MLS team, especially Brian Knosp and Ryan Fuller, as well as
Zachary Lawrence for help in obtaining, managing, and processing the
reanalysis datasets; Nathaniel Livesey for supplying the MLS Lagrangian
Trajectory Diagnostic dataset; NASA's GMAO, ECMWF, JMA, and NCEP for
providing their reanalysis data; and Michaela Hegglin for helpful
discussions. Work at the Jet Propulsion Laboratory, California Institute of
Technology, was done under contract with the National Aeronautics and Space
Administration. Edited by: William
Lahoz Reviewed by: two anonymous referees
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