We introduce a new method to detect and monitor sudden
stratospheric warming (SSW) events using Global Navigation Satellite System
(GNSS) radio occultation (RO) data at high northern latitudes and
demonstrate it for the well-known January–February 2009 event. We first construct RO
temperature, density, and bending angle anomaly profiles and estimate
vertical-mean anomalies in selected altitude layers. These mean anomalies
are then averaged into a daily updated 5
Sudden stratospheric warming events (SSWs) are strong and highly dynamic
phenomena that often occur in the northern polar stratosphere (McInturff et
al., 1978; Butler et al., 2015, Butler et al., 2018). Such events are
characterized by a rapid increase of temperature (
The warming will propagate gradually downward and cause an anomalous widespread warming that persists for several weeks (Baldwin and Dunkerton, 2001; Hitchcock and Shepherd, 2013; Dhaka et al., 2015; Newman et al., 2018). Following the initial warming, a cold anomaly forms in the upper stratosphere that also causes an elevated stratopause (Siskind et al., 2007; Manney et al., 2008; Hitchcock and Shepherd, 2013). The tropical atmosphere is found to be influenced as well (Kodera et al., 2011; Yoshida and Yamazaki, 2011; Dhaka et al., 2015). Cooling can be observed in the tropical stratosphere, and also the tropopause is found to be altered (Yoshida and Yamazaki, 2011; Dhaka et al., 2015). Furthermore, gravity wave activity, cirrus cloud formation, and the electron density of the ionosphere are all found to be affected by SSWs (Eguchi and Kodera, 2010; Yue et al., 2010; Sathishkumar and Sridharan, 2011; Kohma and Sato, 2014). Due to such strong impacts and far-reaching teleconnections of SSWs, it is hence important to detect and monitor SSW events in a robust and reliable way.
The observation and detection of SSWs require evenly distributed and accurate height-resolved observations of the stratosphere at high latitudes. However, robust techniques providing high-quality observations in these remote regions are notoriously sparse. Past research studies mainly used radiosonde, rocketsonde, conventional satellite, or reanalysis data to study SSWs (McInturff et al., 1978; Charlton and Polvani, 2007; Manney et al., 2008, 2009; Hitchcock and Shepherd, 2013). However, both radiosondes and rocketsondes cannot provide evenly distributed observations due to their mostly land-limited properties. Furthermore, since they are vulnerable to radiation biases and constrained by elevation limits, few radiosondes can provide data above 30 km (Butler et al., 2015).
With the advent of the satellite era, it became possible to put passive sounding instruments, such as microwave limb sounders and infrared radiometers, on satellites to observe the atmosphere (e.g., Manney et al., 2008, 2009). Due to the movements of the satellites, observations are globally distributed, in principle. However, satellite passive sounding data come in the form of radiances, and no unique solution then exists, in terms of the radiative transfer equation, to accurately convert radiances to height-resolved temperature or winds, which are key variables for SSW monitoring (McInturff et al., 1978; Manney et al., 2008). Therefore, the fitness for purpose of measurements from these instruments is limited.
With the development of atmospheric data assimilation systems, reanalysis data have become quite a reliable data source for long-term atmospheric analysis, due to their advantages of regularly distributed data in space and time and their capability to provide data up into the mesosphere (Charlton and Polvani, 2007; Yoshida and Yamazaki, 2011; Butler et al., 2018). However, reanalysis data may have inhomogeneities and irregularities in the long term, due to observation system updates and varying analysis biases in sparsely observed domains, which may limit their long-term stability in monitoring SSWs and possible changes in their characteristics due to climate change and interannual variability (Butler et al., 2015).
As a consequence of the limitations of classical observations and
reanalysis data, there is currently no standard definition of SSWs. Early
definitions were usually based on temperature increases and wind reversals.
An often used early definition was provided by McInturff in 1978, presented
in one of the reports of World Meteorological Organization (WMO) Commission
for Atmospheric Sciences (CAS). (1) A stratospheric warming can be called
minor if a significant temperature increase is observed of at least
25 K in a week or less at any stratospheric level in any
area of the wintertime hemisphere and if criteria for major warmings are not
met. (2) A stratospheric warming can be said to be major if at 10 mb or below
the latitudinal mean temperature increase poleward from 60
With the development of observation techniques, several new definitions for
characterizing SSWs have been proposed. Butler et al. (2015) conducted a detailed
literature review on the definitions of SSW and discussed as many as nine often
used definitions of SSWs, such as zonal-mean zonal winds at 10 hPa and
60
One of the most commonly used SSW definitions in recent studies is the one
based on zonal-mean zonal wind at 60
From the above, we can find that it would be impossible to find a single definition to serve every purpose to describe every event perfectly. However, it is still important to find a standard definition for the purposes of statistical assessments, based on historical data and future climate simulations. Butler et al. (2015) suggest that with the development of observation techniques, it is time again to propose a standard definition of SSWs. The new definition should be proposed primarily for the purpose of describing polar winter variability. Secondly, it should be easily calculated and applicable to reanalysis and model outputs, both in post-processing and in real time. Finally, the new definition should not be highly sensitive to details, such as an exact latitude, background climatology, threshold wind speed, spatial extent, or pressure level.
Since the early 2000s, Global Navigation Satellite System (GNSS) radio occultation (RO) has become a new and reliable data source for weather and climate studies (e.g., Kursinski et al., 1997; Steiner et al., 2001; Hajj et al., 2002; Anthes, 2011; Steiner et al., 2011). The RO technique uses GNSS receiver instruments on low Earth orbit satellites to receive GNSS signals for active atmospheric limb sounding in occultation geometry. As the signals propagate through the atmosphere, they are phase-delayed and bent in their path, due to vertical refractivity gradients determined by density and temperature changes. Building on these properties, accurate bending angle profiles can be retrieved from RO signal phase delays, which are highly stable during the measurement time of vertically scanning from the mesopause into the troposphere (setting events) or from the troposphere into the mesopause (rising events) of just about 1 min, called an RO event. The bending angle profile is then converted to a refractivity profile (via an Abel transform), which is directly proportional to the density profile in the stratosphere (refractivity equation), from which then the pressure profile (via hydrostatic integration) and finally the temperature profile (via equation of state) are derived.
The vertical resolution of RO in the stratosphere is about 1 km, supporting height-resolved studies, and validation results against radiosonde and (re)analysis data suggest that RO data are of small discrepancy to these in the upper troposphere and lower stratosphere (Scherllin-Pirscher et al., 2011a, b; Ladstädter et al., 2015). Finally, RO data can be combined without the need of intercalibration, which makes them very suitable for climate-related studies (Foelsche et al., 2011; Steiner et al., 2011, 2013, 2020). Due to these distinctive advantages, RO data have been successfully used in many weather and climate studies and are hence a promising data source also for detecting and monitoring SSWs. Since continuous multi-satellite RO data started in 2006 (see Sect. 2 below), the geographic data coverage is sufficiently dense for monitoring and analyzing regional-scale phenomena such as SSWs. Complementary to reanalysis datasets, which also offer dense coverage, RO reprocessing datasets hence feature an accurate and long-term stable observational data record of climate benchmark quality (Steiner et al., 2020), allowing for stable conditions for SSW monitoring over decades. Therefore, given the high complementarity of these observations to reanalysis (Bosilovich et al., 2013; Parker, 2016; Simmons et al., 2020), RO data fulfill the requirements presented by Butler et al. (2015) well.
A couple of studies have used RO data to analyze SSW already. For example, Wang and Alexander (2009) have used RO to study SSW influences on gravity waves during events in 2007–2008. Yue et al. (2010) and Lin et al. (2012) have used RO data to study ionospheric variations related to the 2009 SSW event. Klingler (2014) has used RO data to examine the temperature changes during the 2009 SSW event and compared the results to data from the European Centre for Medium-Range Weather Forecasts (ECMWF), while Dhaka et al. (2015) have used them to study the dynamical coupling between polar and tropical regions during this event.
In this study, we use RO data to introduce a new method to detect and monitor SSW events. As a demonstration case, the January–February 2009 SSW event was used, since this is well known from other studies (such as the ones just cited above), and therefore context knowledge is good. As a cross-check and for evaluation of robustness, ECMWF analysis data are also used, and the results are compared to those with RO data. The paper is arranged as follows. Section 2 introduces the data and methodology. Section 3 introduces the detection and monitoring results. Section 4 provides our conclusions.
Continuous RO data started in 2001 with the Challenging Mini-satellite Payload mission (CHAMP; Wickert et al., 2001), followed by the Gravity Recovery and Climate Experiment (GRACE; Wickert et al., 2005), the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC; Schreiner et al., 2007), the European Meteorological Operational satellites (MetOp; Luntama et al., 2008), the Chinese FengYun-3C operational satellite (Sun et al., 2018), and others. These missions, especially the launch of the COSMIC mission in 2006, which was a constellation of six satellites, have ensured as of 2006 a sufficient coverage with RO event observations for regional-scale studies such as of SSWs.
In this study, we use the atmospheric RO profile data from the Wegener
Center for Climate and Global Change (WEGC), processed by its latest
Occultation Processing System version 5.6 (denoted as OPSv5.6 hereafter).
Several studies that introduced, validated, and evaluated these OPSv5.6 data
(e.g., Ladstädter et al., 2015; Schwärz et al., 2016; Angerer et
al., 2017; Scherllin-Pirscher et al., 2017) as well as intercomparison to
other RO center datasets (Steiner et al., 2020) show that the OPSv5.6
stratospheric profiling data of interest in this study are of high quality
for the purpose. For a detailed discussion of quality aspects of the OPSv5.6
data, we refer to Angerer et al. (2017). We use the high-quality-flagged
temperature, density, and bending angle profiles over January–February 2009, the time
period of our demonstration study, in the northern high latitude study
domain of 50–90
Figure 1 illustrates the distribution of RO events on 23 January 2009 and the
number of RO events we used per day over January–February 2009. Figure 1a
shows the distribution of RO observations in the study domain from
50
Illustrative distribution of RO event locations on 23 January 2009
(black dots), overplotted on the middle-stratosphere temperature anomaly of
the day
As mentioned in Sect. 1, a robust SSW definition should not only be applied
to observation data, but also be readily applicable to (re)analysis and
model outputs with their regular-gridded datasets. Therefore, we also use
operational analysis data from the ECMWF over the same study period for a
cross-check and demonstration of the applicability of our new approach also
to such gridded datasets. The ECMWF analysis fields used are based on T42L91
resolution (sampled at 2.5
ECMWF data are used for a cross-check in two variants. The first variant is to use the RO-collocated analysis profiles, extracted by interpolation from the analysis fields to the RO event locations, together with the OPSv5.6 RO profiles. We apply the approach in the same way to these collocated analysis profiles as to the RO profiles. We note that while the density and temperature profiles derive directly from analysis field interpolations, the bending angle profiles are obtained from forward modeling (abelian transform from refractivity profiles) in the OPSv5.6 system.
The second variant is that we directly use the ECMWF analysis data at their
regularly gridded resolution of 2.5
Table 1 illustrates the methodology of our SSW detection. The first step is
to generate RO temperature, density, and bending angle anomaly profiles using individual RO profiles minus collocated climatological profiles, with
the latter extracted from long-term gridded RO climatology fields
interpolated to RO locations as described in (1), (2), and (3) of Table 1.
Temperature anomalies are calculated as absolute values, while density and
bending angle anomalies are calculated as relative (percentage) values, by
dividing the absolute-value anomaly profiles by the collocated
climatological profiles. In order to avoid the impacts of background model
on bending angles, we used the non-optimized bending angle in our study.
Anomalies of various atmospheric parameters have been successfully used in
lots of research studies for SSW detection, cloud-top altitude detection and
atmospheric blocking (Hitchcock and Shepherd, 2013; Biondi et al., 2015,
2017; Brunner et al., 2016). The long-term climatology was constructed
monthly using RO data of the same months over 2007 to 2017. It is based on a
2.5
Basic parameters and methodology of the new SSW monitoring approach (all parameters (4)–(18) updated daily; the boldfaced font in (4)–(16) shows the key parameters for the monitoring as also shown in Figs. 6 and 7).
Figure 2 shows RO profiles and their anomaly profiles of two exemplary RO events as indicated in Fig. 1. Figure 2a, c, e show that RO profiles of Event 1, which is located in the area with the most warming, deviate more from climatological profiles than that of Event 2, located in an area with less warming. Anomaly profiles shown in Fig. 2b, d, f illustrate consistent larger anomalies of Event 1.
Event 1 and Event 2 temperature
The next steps are to generate five basic daily updated thresholds
exceedance areas (TEAs) as described in (4)–(8) of Table 1. The TEA is the geographic area wherein RO gridded mean anomalies of the day exceed
predefined thresholds such as 40 K or 40 %. The first step of
calculating the TEA is to calculate vertical mean anomaly values of selected
stratospheric altitude ranges. The vertical mean anomalies are then averaged
into geographic bins on a 5
In order to allow for more RO events coming in, for a reliable statistical
averaging, we use overlapping bin areas on the 5
To examine various atmospheric layers, five basic TEAs are calculated, i.e., MSTA-TEA (middle stratosphere temperature anomaly TEA); LMBA-TEA, (lower mesosphere bending angle anomaly TEA); LSTA-TEA (lower stratosphere temperature anomaly TEA); USDA-TEA (upper stratosphere density anomaly TEA); and USTA-TEA (upper stratosphere temperature anomaly TEA). The altitude ranges for calculating these TEAs are selected according to the response altitude ranges of the three anomalies and also the utilities of the TEAs in formulating the metrics. Response altitude ranges are regarded as the altitude ranges where anomalies show distinct increases and decreases to reflect with good sensitivity the thermodynamic changes caused by an SSW event.
Based on our inspections of small ensembles of individual RO anomaly profiles and also results of Sect. 3.1 on polar-mean anomaly profiles, the response altitude ranges for calculating the five TEAs are carefully selected according to their utilities in measuring SSW. MSTA-TEA and LMBA-TEA are used to capture the sudden warming and are therefore calculated using temperature anomalies of 30–35 km and bending angle anomalies of 50–55 km. LSTA-TEA and USDA-TEA are used to examine the downward-propagated warming, and therefore they are calculated using temperature and density anomalies in lower response altitude ranges, i.e., 20–25 km for LSTA-TEA and 40–45 km for USDA-TEA. Finally, USTA-TEA is used to capture the upper stratospheric cooling in the SSW trailing phase and is calculated using temperature anomalies of 40–45 km.
As the thresholds for calculating these five TEAs, we use those defined in Table 1, (4)–(8); for example, the thresholds for MSTA are 30, 40, and 50 K as seen therein. The selection of these thresholds was mainly guided by results on the polar-mean and regional-mean anomalies shown in Sect. 3.1 and 3.2. We examined the temporal variations of the magnitudes of warming and cooling of the five TEAs by sensitivity checks and finally chose suitable thresholds as summarized in Table 1 for the analysis of this 2009 event. Figure 3 illustrates our selection of height ranges of anomaly profiles for calculating the five TEAs based on representative example profiles. The short vertical lines represent vertical mean values in corresponding altitude ranges. For this RO event, temperature vertical mean anomalies at the 40–45, 30–35, and 20–25 km ranges are about 30, 60, and 15 K, respectively. The density vertical mean anomaly at 40–45 km is near 50 %, and the bending angle vertical mean anomaly at 50–55 km is near 70 %.
Temperature (blue), density (green) and bending angle (red) anomaly profiles of Event 1 (same as in Figs. 1 and 2), with the horizontal gray lines delineating the altitude layers chosen for calculating the five basic TEAs (Table 1, (4)–(8)) and the colored vertical thick lines indicating the vertical mean anomaly values in corresponding altitude layers.
Based on the five TEAs, we formulated our SSW metrics as defined in Table 1,
(9)–(13), where (9)–(11) are the preferred metrics, and (12)–(13) are
fallback metrics for (9)–(10), requiring only temperature as a variable. First
is the SSW primary-phase metric SSW-PP-TEA (9), used to express the main and
primary sudden stratospheric warming anomaly strength. It is calculated by
averaging the exceedance areas MSTA-TEA
The preferred primary- and secondary-phase metrics (9) and (10) are constructed as a two-variable estimate (combining temperature and bending angle/density TEAs), since we find them more robust for characterizing the main phase of the SSW than single-variable metrics. However, users who prefer a simplified approach, or who only have stratospheric temperature profiles or fields available (within 20 to 45 km), can use the temperature-only metrics (12)–(13) instead, which do not include the averaging with the TEAs co-estimated from the bending angle (9) or density (10).
Based on the three metrics, either (9)–(11) or (12)–(13) and (11), we can
finally detect a SSW event and monitor the strength of the event. We
introduce three SSW indicators for this purpose as defined in Table 1,
(14)–(16). The first is the main-phase duration, SSW-MPD, which indicates the
duration of the SSW warming anomaly based on the primary- and
secondary-phase metrics. This indicator is estimated by counting the number
of days, with either the SSW-PP-TEA or the SSW-SP-TEA being larger than a
minimum exceedance area TEA
In a follow-on work using long-term RO and reanalysis datasets, these indicators will be used to detect SSW events, for example by requiring a minimum main-phase duration of 7 d or so to qualify as an SSW and to record the strength of the events. However, the specific thresholds for our metrics and indicators for SSW detection, monitoring, and classification can only be determined after the new approach is applied to longer term data containing multiple events.
Below we demonstrate the utility to do so, both for profile-based RO and gridded analysis data, for the January–February 2009 SSW event. In addition to demonstrating the detection and monitoring approach, we also demonstrate the parallel possibility intrinsic in our TEA-based approach to dynamically track the geographic movements of any event of interest. For this purpose we introduce the parameters anomaly maximum/minimum (AM) value, and the location of these AM values, which can be used to locate the warming/cooling centers and their geographic track for the five basic TEAs. For convenience, Table 1, (17)–(18), lists and also briefly explains these auxiliary parameters.
Section 3.1 presents temporal evolution of polar-cap mean RO anomaly profiles to have a general understanding of the characteristics of RO anomalies. Section 3.2 shows the distribution of RO gridded mean anomalies on several selected days for providing insight on the basic space–time dynamics tracked by the approach. Section 3.3 introduces our detection results of the January–February 2009 SSW demonstration event in terms of the five basic TEAs at selected thresholds and also discusses the SSW metrics of the event.
Figure 4 shows the temporal evolution of polar-cap (60–90
Temporal evolution of polar-cap (60–90
Before the sudden and rapid warming, negative anomalies are found for all the three parameters, indicating a moderate precursor cooling of the stratosphere. The cooling signal is imprinted more strongly in the density and bending angle anomalies in the upper stratosphere and lower mesosphere than observed in temperature over the lower and middle stratosphere. After the sudden warming, negative (cooling) anomalies are again found at higher altitude levels than altitude levels, showing the sudden and rapid warming, with a particular strong imprint in upper stratosphere temperature, where its fingerprint lasts many weeks, while the altitude of maximum cooling exhibits a slow downward propagation. Related to the chosen altitude layers for computing the five TEAs, we can see that they are defined so that they can capture the SSW evolution from the initial phase to the trailing phase well.
Comparing the RO-profile-based anomalies with the ECMWF-analysis-based
anomalies, we find that both the magnitudes and dynamical variations of the
anomalies from the two datasets are generally consistent below about 45 km.
The differences are found above 45 km, where RO data show larger positive
density and bending angle anomalies during the sudden warming and smaller
negative temperature anomalies compared to ECMWF data, from early February
after the sudden warming. These increased differences are attributable to
both datasets for the following reasons: (1) ECMWF data are of sparse
vertical resolution and with limited constraint from assimilated data above
50 km (e.g., Untch et al., 2006; Simmons et al., 2020), degrading their
accuracy; (2) RO data accuracy reaches a somewhat higher level in bending angle and
density profiles (errors
Figure 5 shows distributions of MSTA, USDA, and USTA anomalies over
50–90
Middle stratosphere temperature anomaly (MSTA; left column), upper stratosphere density anomaly (USDA; middle column), and upper stratosphere temperature anomaly (USTA; right column) on the 4 exemplary days of 16, 23, and 30 January and 13 February 2009, illustrating the space–time dynamics of the SSW event in these three anomaly quantities.
Looking at MSTA results, temperature anomalies are generally negative in
most of the regions on 16 January, with values up to near
On 23 January, positive MSTA values dominate the whole polar-cap region across
the Atlantic sector, from over North America to over Europe. The warmest
region is found centered on Greenland, with anomalies exceeding 50 K. Results
in this section (and in Sect. 3.3 below) indicate that 23 January 2009 is the
warmest day of this SSW event. With the further progression of time,
positive anomalies decrease, indicating a decrease of the strength of the
warming. On 30 January, smaller anomalies of up to 20 K are found. On 13 February, which
is 3 weeks after the warmest day, negative anomalies of up to
USDA results, which are well suited to capturing the downward-propagated
positive anomalies, show largest anomalies at the end of January. The
warmest region is found from over eastern Greenland to oceanic regions north
of Russia. On 13 February, large positive anomalies still occupy most of the
polar region, indicating a long-lasting warming effect caused by the SSW. The
USTA results show positive (warming) anomalies on the initial 2 d
illustrated. However, on 30 January, cooling anomalies are found to occupy most
of the polar region. On 13 February, the magnitude of the cooling anomalies
increases to more than
Figure 6 shows the temporal evolution of the MSTA-TEA, LMBA-TEA, LSTA-TEA,
USDA-TEA, and USTA-TEA results that instructively exhibit the threshold
exceedance area changes during the SSW event. The geographic tracking of
maximum (positive and negative) anomaly (AM) values is also shown. MSTA-TEA
and LMBA-TEA results (first two rows) are generally of similar
characteristics, with positive anomalies emerging from 17/18 January, which then
quickly increase to maximum values on 22/23 January. MSTA-TEAs are found to be
largest on 22 January, amounting for threshold exceedance areas over 30, 40, and
50 K to 20, 10, and
Time evolution of the daily MSTA, LMBA, LSTA, USDA, and USTA (from
top to bottom) threshold exceedance areas (TEAs) during the SSW event, using
thresholds according to Table 1, (4)–(8) (left column). For complementary
space–time dynamics information, geographic tracks and magnitude classes (color
scheme of left panels, numbering by day of year) of maximum
positive/negative anomaly values are shown for days with TEAs with smallest
thresholds
After the maximum value day, both MSTA-TEA and LMBA-TEA quickly decrease to
zero. Such quick increase and decrease of the two metrics further reflect
the sudden and rapid warming character of the SSW. Before the sudden
warming, LMBA exhibits negative (cooling) anomalies as a precursor signal,
with the TEA exceeding
LSTA-TEA and USDA-TEA results are generally consistent in their evolution
pattern as well, with most warming days found near the end of January and
early February. Compared to the sharp increase and decrease of positive
anomalies of MSTA-TEA and LMBA-TEA, the increase and decrease of LSTA-TEA
and USDA-TEA are smoother, with maximum warming days somewhat delayed. The
numbers of days showing positive (warming) anomalies are more than for
MSTA-TEA and LMBA-TEA, indicating a longer lasting warming at the lower
stratospheric altitude levels. The locations of AM values of the warming
anomalies are centered over northern Russia. Negative (cooling) anomalies
are found from early to middle January and are strongest over the oceanic
part northeast of Russia. The USTA-TEA results, finally, show strong cooling
anomalies from early February throughout the month until the end of February
(end of this demonstration study analysis period). From the middle to the end of
February, the TEAs that exceed a cooling of
Figure 7 depicts the overall results for our SSW metrics that we suggest
practically using for the detection and monitoring of SSW events. Geographic
tracks of the metric-relevant temperature anomalies are shown as well. The
first day on which the primary-phase metric SSW-PP-TEA exceeds
The number of days of this
main phase, our defined main-phase duration SSW-MPD, is found to be 19 d for
this January–February 2009 demonstration event. The mean TEA over the main-phase
duration, our defined main-phase area SSW-MPA, is
Time evolution of the daily primary-phase (heavy red),
secondary-phase (heavy yellow), trailing-phase (light blue), primary-phase
temperature-only (light red), and secondary-phase temperature-only (light
yellow) metrics, respectively
Summarizing relevant definitions, the first day of the main phase is defined
as the start day of the detected event, and the end of the main phase is
defined as its final day. The center day is defined as the day with a maximum
TEA value of the primary metric, i.e., 23 January of this demonstration
event. The trailing metric SSW-TP-TEA (blue in Fig. 7) is an auxiliary
metric to capture the long-lasting upper stratospheric cooling in the wake
of the event. For this January–February 2009 event, the SSW-TP-TEA exceeds
As introduced in Sect. 2.3, a simplified fallback of the approach is to use temperature as the only variable for the metric estimation. Hence we also illustrate in Fig. 7 the results for which the primary- and secondary-phase metrics are computed from temperature only (the trailing-phase metric is temperature-only anyway). These two simplified metrics are generally seen to be consistent with the preferred dual variable-based metrics, but it is visible that they appear somewhat more “volatile” and less robust in the sense that they exhibit more short-scale time variation. Follow-on work for a longer term data record with a range of SSW events will analyze these characteristics in more detail.
Figure 7b shows that the main warming tracked by SSW-PPT-TEA (red) emerges from near Iceland and extends to Greenland and moves towards higher latitudinal regions. The lower stratosphere warming (yellow/orange), tracked by SSW-SPT-TEA, is found to be emerging at the high latitudinal regions of Greenland and moving towards the northern part of Russia. The upper stratospheric cooling (blue) tracked by SSW-SPT-TEA is found to be mainly at the high latitudinal oceanic region north of Russia.
These detection and monitoring results have been cross-tested using RO-collocated profiles from ECMWF analysis and also the regularly sampled ECMWF analysis fields as alternative data sources for these datasets. The results from both datasets (not separately shown) are found to be generally consistent for this demonstration event with the detection results using RO data. This indicates that, on an individual SSW event basis, RO observational data are of comparable utility to ECMWF (re)analysis data to monitor the event, and the influence of sampling uncertainty is small. This verifies that the new approach can be readily applied to both observational and (re)analysis data (and also model output data). As discussed in the Introduction (Sect. 1), and along with the analysis data description (Sect. 2.2), follow-on work on long-term records next needs to show how the possible advantages in long-term stability and accuracy of the RO data play out or not in SSW detection and monitoring in comparison to reanalysis data.
To summarize, the metrics proposed in this study for monitoring the SSW events can satisfy the conditions well that Butler et al. (2015) suggest for proposing a standard definition (see Sect. 1). Firstly, our approach captures the sudden warming of the main phase and also its downward propagation into the lower stratosphere well, as well as the cooling occurring after the warming phase in the upper stratosphere. Secondly, the approach can be used for both RO and other suitable profile data, and likewise for reanalysis data, and can be applied for both post-processing and in real time. Finally, the new approach uses anomalies over several height layers, and TEAs over a larger area, and hence the detection and monitoring results are not sensitive to details such as exact latitude or pressure level.
Potential further refinements of the thresholds for our metrics will be determined from recently started follow-on work on multiple SSW events, using longer term data over recent decades, both from RO and reanalysis. This refinement work and testing based on a whole ensemble of SSW events with different characteristics will also complete the assessment of meeting those requirements noted by Butler et al. (2015) that relate to identifying the independence of closely timed events, the classification type (like split-type or displacement-type), and distinction of various strengths (minor, major, etc.).
In this study, we introduced a new approach to detect and monitor SSW events
based on RO temperature, density, and bending angle anomaly profiles over
50–90
Based on constructed anomaly profiles for the three variables temperature,
density, and bending angle, we employed the concept of threshold exceedance
area (TEA), which is the geographic area wherein absolute or relative
anomaly values exceed predefined threshold values, as the basis for
formulating SSW metrics. Computing TEAs based on anomalies in selected
stratospheric altitude layers, and using adequate threshold values (mainly
40 K
The primary-phase metric is to examine the initial main-phase of warming
caused by SSW events. The secondary-phase metric is to examine the further
main phase of downward-propagated warming effects during the SSW. The
trailing-phase metric is an auxiliary metric to co-examine the upper
stratospheric cooling in the wake of an SSW. Based on the two main-phase
metrics, we introduced three key indicators for SSW detection and
monitoring. The first is the main-phase duration, recording the number of
days of SSW warming that exceed a defined minimum TEA (initially set to
For complementary space–time dynamics information, the approach also enables, for the selected anomaly variables, daily tracking of the maximum anomaly values and of the related geographic center location of the event. In combination with the daily TEA estimates, this also quantifies the approximate effective radius of the SSW-induced anomalies around the center location.
Applying the new approach for demonstration to the January–February 2009 SSW event,
the detection and monitoring results find, where it is comparable, similar
characteristics to previous studies using other approaches and datasets. We
found that the SSW emerged from about 20 January and reached a maximum on
23 January and then faded by 31 January. In terms of our three indicators, the
duration of the main phase of this SSW was 19 d, with an average
main-phase area of
Based on the encouraging demonstration in this study, follow-on work will apply the method to long-term RO and reanalysis datasets (RO overlapping 2006–2020 with reanalyses over 1979–2020) and assess its utility for long-term SSW monitoring. In this way, the most suitable settings to use for the duration, area, and overall strengths indicators for robust SSW detection, monitoring, and classification can be determined. In addition, we will be able to learn how the possible advantages in long-term stability and accuracy of the RO data play out or not in SSW monitoring in comparison to reanalysis data, including for different variants of RO processing and reanalysis. Overall, we expect the approach to be valuable for monitoring how SSW characteristics unfold event by event but also, and in particular, how they possibly vary under transient climate change and how they are in teleconnection to lower latitude regions.
The code used to produce the results of this study is available from the corresponding author upon qualified request.
The (numeric) data underlying the results of this study are available from the corresponding author upon qualified request.
YL implemented the new method, performed the analysis, produced the figures, and wrote the initial draft of the manuscript. GK served as primary coauthor, providing advice and guidance on all aspects of the design, analysis, and figure production, and significantly contributed to the writing of the manuscript. MS supported the setup and advancements of the OPSv5.6 analysis system and advised on data and algorithms. FL supported RO climatology provision and use and advised on data and the algorithm, as well as on the results' interpretation. YY advised on analysis and algorithm comparison. All authors commented on the final submitted paper.
The authors declare that they have no conflict of interest.
We acknowledge ECMWF (Reading, UK) for providing access to their analysis and forecast data. We also thank the WEGC RO processing team for the contribution to the provision of the OPSv5.6 profile data for the study.
The research at APM (Wuhan, China) was supported by the National Key Research and Development Program of China “Collaborative Precision Positioning Project” (grant no. 2016YFB0501900) and the National Natural Science Foundation of China (grant nos. 41874040, 41604033). At the WEGC (Graz, Austria) the work was supported by the Aeronautics and Space Agency of the Austrian Research Promotion Agency (FFG-ALR) under the Austrian Space Applications Programme (ASAP) project ATROMSAF1 (project no. 859771) funded by the Ministry for Transport, Innovation, and Technology (BMVIT).
This paper was edited by Roeland Van Malderen and reviewed by three anonymous referees.