Major, sudden midwinter stratospheric warmings (SSWs) are large and rapid
temperature increases in the winter polar stratosphere are associated with a
complete reversal of the climatological westerly winds (i.e., the polar
vortex). These extreme events can have substantial impacts on winter surface
climate, including increased frequency of cold air outbreaks over North
America and Eurasia and anomalous warming over Greenland and eastern Canada.
Here we present a SSW Compendium (SSWC), a new database that documents the
evolution of the stratosphere, troposphere, and surface conditions 60 days
prior to and after SSWs for the period 1958–2014. The SSWC comprises data
from six different reanalysis products: MERRA2 (1980–2014), JRA-55
(1958–2014), ERA-interim (1979–2014), ERA-40 (1958–2002), NOAA20CRv2c
(1958–2011), and NCEP-NCAR I (1958–2014). Global gridded daily anomaly
fields, full fields, and derived products are provided for each SSW event.
The compendium will allow users to examine the structure and evolution of
individual SSWs, and the variability among events and among reanalysis
products. The SSWC is archived and maintained by NOAA's National Centers for
Environmental Information (NCEI,
The winter polar stratosphere is highly dynamic. In the Northern Hemisphere (NH), breaking planetary-scale waves propagating up from the troposphere or the excitation of resonant modes can lead to the disruption and deceleration of the climatological westerly circulation of the polar vortex (see Schoeberl, 1978 for a historical review). Associated with this wind deceleration is a dramatic warming, sometimes increasing the temperature of the polar stratosphere by as much as 30–40 K in a few days. In the most extreme cases, the stratospheric polar vortex can reverse direction completely in an event called a major sudden stratospheric warming (SSW). SSWs in the NH occur roughly six times per decade (Charlton and Polvani, 2007). SSWs can also occur in the Southern Hemisphere (SH), as in a remarkable case in September 2002 (Kruger et al., 2005), but are rare due to smaller planetary wave amplitudes in the SH (van Loon et al., 1973).
Large perturbations in the stratospheric circulation can drive changes in surface climate for days to weeks (Kidston et al., 2015). In particular, SSWs are often followed by an equatorward shift of the North Atlantic tropospheric storm track, projecting onto the spatial pattern of the negative phase of the North Atlantic Oscillation (NAO). On average, this pattern results in warm anomalies over Greenland, eastern Canada, and subtropical Africa and Asia and cold anomalies over northern Eurasia and the eastern United States. However, the impacts of individual SSWs vary widely, depending on the evolution of the vortex breakdown, the strength of the stratospheric–tropospheric coupling, and the state of the tropospheric climate.
Because of the impact of SSWs on winter surface climate and midlatitude cold air outbreaks, as well as their potential influence on ozone and chemical transport (e.g., Manney et al., 2009; Schoeberl and Hartmann, 1991), tropical convection and dynamics (e.g., Gómez-Escolar et al., 2014; Kodera, 2006), and mesospheric processes (e.g., Hoffmann et al., 2007), a research-ready database of these events would be useful. Daily three-dimensional gridded variables are needed to examine the full evolution and impacts of SSWs. Therefore, reanalysis products, which assimilate observations to constrain a global climate model, are often used. However, the calculation of daily anomalies or additional derived products using reanalysis data can be computationally expensive and storage intensive. In addition, different reanalyses also differ in time spans, assimilated observations, assimilation scheme, parameterizations, and model physics. This makes intercomparison of multiple reanalysis products useful for assessing what features of SSWs and their associated climate variability are robust.
Here we describe a SSW Compendium (SSWC), which provides a detailed historical dataset of major SSWs, allowing users to consider the development, evolution, and impacts of individual SSWs and to provide a basis for model evaluation and improvement. A compendium is a concise compilation of comprehensive information on a specific subject, and therefore is an appropriate term to describe this dataset. The SSWC includes data from six established reanalysis products and includes anomaly fields and additional derived products to highlight the dynamics and effects of SSW events. We present an overview of the reanalysis source data and the methodology for SSW event selection and data processing in Sect. 2. Section 3 discusses potential applications of this database, and Sect. 4 highlights the availability of the database at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) archives and at the NOAA Earth Systems Research Laboratory (ESRL).
The SSWC comprises data from six different reanalyses (Table 1): the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective-analysis for Research and Applications version 2 (MERRA2), Japanese 55-year Reanalysis (JRA-55), European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40), ECMWF Interim Reanalysis (ERA-interim), NOAA 20th Century Reanalysis version 2c (NOAA20CRv2c), and NOAA's National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP-NCAR I) reanalysis.
The reanalyses included in the SSW Compendium.
Reanalyses are derived from observations from multiple sources (including surface observations, aircraft, radiosondes, rocketsondes, and satellites) that are assimilated by global coupled land–atmosphere–ocean models to create spatially and temporally complete observational records. There are advantages and disadvantages of using reanalysis products for this database, as opposed to individual measurement sources or various stratospheric analyses. These analyses include that from the Freie Universitat Berlin, which produces a database of continuous daily gridded synoptic-scale analyses based largely on radiosonde measurements, but only for three stratospheric levels for a 35-year period (Labitzke and Collaborators, 2002), and from the NOAA Climate Prediction Center (CPC), which offers analyzed stratospheric temperatures at eight stratospheric levels based on satellite retrievals of the advanced microwave sounding unit (AMSU). The major advantage of reanalysis is that it allows consideration of the evolution of SSWs and their impacts throughout the entire atmosphere with a spatial and temporal extent that is not feasible using individual measurements or stratospheric analyses alone. A major disadvantage of using reanalysis is that due to sparse observations, particularly in the pre-satellite era, stratospheric reanalysis is poorly constrained, especially above 10 hPa (Manney et al., 2003), and tropospheric reanalysis may be poorly constrained over oceans and remote regions (e.g., Bosilovich et al., 2008). Reanalyses can also suffer from upper-boundary effects and discontinuities due to model streams or changes in the observations being assimilated (Fujiwara et al., 2016; Labitzke and Kunze, 2005). These issues should not have a strong effect on the daily-to-seasonal timescales documented in the SSWC, but should be kept in mind, especially for data above 10 hPa where the discontinuities are conspicuous.
Some biases and uncertainties in individual reanalysis products have been documented (see references in Table 1), and an evaluation of their stratospheric processes is currently the focus of an international effort by the Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP; Fujiwara et al., 2016). While initial studies have shown that stratospheric dynamics and variability of and coupling to the surface are reasonably simulated in reanalyses (Martineau and Son, 2010), particularly in the latest generation products (Martineau et al., 2016), the SSWC enables quick comparison between reanalyses of sudden stratospheric warming events and their evolution on daily timescales. This capability is important when considering the substantial volume of data needed to calculate the daily climatology and anomalies for each grid point and pressure level in each reanalysis.
Certain reanalysis output provided in the SSWC should be used with caution. For example, we provide the reanalysis ozone mass mixing ratio and total column ozone output (where available) since there are interesting changes in ozone following a SSW event (e.g., Fig. 3). However, users should be aware that most reanalysis ozone fields are based on assimilated satellite measurements that utilize backscattered sunlight and cannot measure ozone during polar night. Reanalysis systems thus rely heavily on the model, which typically parameterizes heterogeneous chemistry, to simulate ozone at high latitudes, leading to potentially high errors (Dethof and Hólm, 2004; Dragani, 2011).
In addition, the evolution of SSW events prior to 1964, when concentrated efforts to observe the upper atmosphere using radiosondes and rocketsondes were begun in association with the International Years of the Quiet Sun (IQSY), should be viewed with skepticism. Even radiosonde measurements of the stratosphere were very limited during that time period, and so reanalysis fields may be almost entirely model-driven.
The NOAA20CRv2c is unique among the reanalyses, because it assimilates only surface pressure observations. Thus, the stratosphere is not constrained by any stratospheric observations, and the reanalysis winds are not realistic (Compo et al., 2011). However, because surface pressure observations do a reasonable job of constraining the model throughout the northern hemispheric troposphere (Compo et al., 2011), we include the NOAA20CRv2c to examine the tropospheric impacts of SSWs, using SSW event dates given by the JRA-55 reanalysis (Table 2). The NOAA20CRv2c reanalysis provides the unique opportunity to examine tropospheric and stratospheric interaction prior to and following SSWs, when only the surface is constrained by observations.
The central dates of NH SSWs detected in each reanalysis
product
The primary goal of the SSWC is to provide data for major SSWs, which have
been found to have the largest surface impacts
(Palmeiro et al., 2015). We recognize that
any criteria we use may also select marginal events or miss events that
perhaps should be considered major in terms of surface influences. We employ
the following simple, commonly used definition for major warmings
(Charlton and Polvani 2007; hereafter CP07): the
Temperature anomalies at 10 hPa (shading, (K)) and the potential
vorticity at 550 K (contours shown for 75, 100, and 125 PV units) during
(left) an inactive (or strong) phase of the polar vortex
(
There are two main types of SSW:
While almost all SSWs occur in the NH, we did examine their occurrence in
the SH in the reanalyses (Table 3). The relevant dates for
zonal-mean zonal wind reversals at 10 hPa and 60
The production flowchart for the SSWC is shown in Fig. 2. We
obtained the native horizontal and vertical pressure-level data for each
reanalysis from various research data archives: NOAA20CRv2c and NCEP/NCAR I
from the NOAA Earth System Research Laboratory, Physical Sciences Division
(
We extracted the following fields (when available): vertically integrated
total column ozone; zonal winds, meridional winds, temperatures,
geopotential heights, Ertel's potential vorticity (PV), and ozone mixing
ratio, on provided pressure levels; and at the surface, mean daily
temperature, minimum daily temperature, maximum daily temperature, mean
sea level pressure, surface pressure, total precipitation liquid water
equivalent, and total snowfall liquid water equivalent. Most raw reanalysis
output is available every 6 h (for pressure-level fields) and sometimes
up to every 3 h (for surface-level fields), but we computed daily means
of all fields for the SSWC. We interpolated pressure-level fields onto a
2.5
There are two types of output provided by the SSWC: climatological statistics and event-based data. Climatological statistic files include the mean and standard deviations of all output fields and percentiles from the climatological distribution for a selection of surface fields: minimum and maximum surface temperature and precipitation. The climatological statistics are defined at each spatial point for 366 days spanning 1 July–30 June. The climatological mean is based on the entire time period of each reanalysis (Table 1). To calculate the climatological mean, we first calculate the mean of each day of the year over the full record. Then we calculate the Fourier transform of this daily mean climatology and retain the first four harmonics of the Fourier series (e.g., Wilks, 2006). This methodology smooths out the raw daily climatology while preserving low-frequency variability. The standard deviation is then calculated by taking the square root of the squared deviations in the raw daily data from this smoothed climatological mean. Percentiles are calculated following a method described in Zhang et al. (2005; see Eq. 1). Chosen percentiles are 5, 10, 90, and 95 %. These statistics are calculated using the entire data record.
The central dates of the SH SSW detected in each reanalysis product.
Flowchart showing how the SSWC can be used as is or the different steps to produce the dataset.
Event-based files contain full field, anomaly, and derived fields for the
60 days prior to and following each SSW event in Tables 2 and 3. Anomalies
are calculated using the smoothed climatology for each field, using the
entire data record for each reanalysis. We caution that, while the
climatologies for different time periods are generally quite similar, using
different periods for the climatology for each reanalysis means that
differences in reanalysis anomaly fields may partially be a result of the
climatology chosen. In addition to full fields and anomalies, we derive a
number of useful diagnostics for understanding dynamic processes and surface
climate surrounding SSW events, as described below:
We provide the maximum and minimum daily temperatures. NCEP-NCAR I provides this
output; we calculate these values for the other reanalyses. Note that no
interpolation is used – just the minimum and maximum values of the 3 or 6
hourly data – so these values may underestimate the true maximum and
minimum daily temperatures. We provide standardized geopotential height anomalies. The geopotential heights are
standardized by subtracting the mean and dividing by the standard deviation
for the particular day of year and grid point. We provide absolute vorticity ( We provide filtered absolute vorticity at 10 hPa. Here the absolute vorticity has
been subject to a spherical smoothing procedure, in which the absolute
vorticity is transformed into spherical harmonic space and subsequently
transformed back while retaining only the first 11 harmonic coefficients.
This filtering is part of CP07's event-type determination algorithm. We provide zonal-mean eddy meridional heat flux ( We provide zonal-mean eddy meridional momentum flux We provide the Northern Annular Mode (NAM) and the Southern Annular Mode (SAM)
indices. The NAM or SAM patterns are calculated as the first empirical orthogonal
function (EOF) of daily-mean zonal-mean geopotential height anomalies from 20
to 90 We provide extreme events. For each grid space, either a 0 or 1 is given if the
daily precipitation, minimum temperature, or maximum temperature anomaly
exceeds a certain threshold. For precipitation, the anomaly must exceed the
95th percentile. Temperature anomalies must either be less than the
5th or 10th percentile or greater than the 90th or 95th
percentile. We provide time series of the location of maximum stratospheric warming within the
region of 30–90 We provide time from the SSW event at which the zonal-mean zonal wind becomes
easterly, as a function of pressure and latitude. We provide pressure level at which the zonal-mean zonal wind becomes easterly, as
a function of time and latitude. measures of the phase of the El Niño–Southern Oscillation (ENSO).
These indices allow the user to assess the state of the tropical Pacific,
which has important winter effects on midlatitude climate. SSWs have
been found to occur in 80 % of El Niño winters (Butler and
Polvani, 2011) and may modify the El Niño teleconnections when they
occur (Butler et al.,
2014; Richter et al., 2015). The Multivariate ENSO Index (MEI) is calculated
as the first principal component of six different observed variables
combined. The MEI data are from NOAA Physical Sciences Division (PSD):
the outgoing long-wave radiation Madden–Julian Oscillation (MJO) Index
(OMI) amplitude and phase. SSWs may be related to the anomalous convection
generated by the MJO during certain phases (e.g.,
Garfinkel et al., 2014). The OMI daily data are from NOAA PSD:
the equatorial zonal winds measured by radiosondes near the equator,
provided at 10, 30, 50, and 70 hPa, as a measure of the Quasi-Biennial
Oscillation (QBO). The QBO is thought to modulate the frequency of SSWs via
changes in wave propagation (Baldwin et al., 2001;
Dunkerton et al., 1988), perhaps in relation to the solar cycle
(Labitzke et al., 2006). The QBO data
are provided by Freie Universitat of Berlin:
Finally, a number of climate indices based on independent observations (not
reanalysis data) have been included to provide a sense of other sources of
climate variability that may be contributing to both the forcing of
individual SSWs and the surface climate impacts. These include
We acknowledge that other variables and indices may be useful for examining
SSW dynamics, such as the Eliassen–Palm flux vector components or
transformed Eulerian-mean diagnostics. Some of these diagnostics could be
calculated using the provided daily data on pressure levels, though this may
be imprecise relative to calculations on native model levels. Model-level
data are often used for analyzing transport and processes near the
tropopause, where vertical resolution on provided pressure levels may be
inadequate or may introduce interpolation errors. Regardless, the SSWC is
useful for a wide range of applications, as featured in the next section.
Here we highlight three types of potential applications of the SSWC: (i) composite analysis, (ii) individual event analysis, and (iii) reanalysis intercomparison.
Assessing the composite response to SSWs is useful for separating the
signals from internal noise and identifying where the signal is robust.
Figure 3 shows, as a function of pressure level and time before and
after the event, (a) zonal-mean zonal winds at 60
Composites of the 60 days before and after historical SSWs in the
JRA-55 reanalysis for
These two figures illustrate several important and well-known features of
SSWs and their impacts on circulation and surface climate
(e.g., Baldwin and Dunkerton, 2001). In the
stratosphere, the zonal-mean zonal winds change from westerly to easterly at
10 hPa and 60
At the surface, the composite response in mean sea level pressure anomalies comprises an anomalous high over the polar cap and Greenland and an anomalous low over the North Atlantic, a pattern that projects well onto the negative phase of the NAO, the regional equivalent of the NAM (Fig. 4a). The associated surface temperature anomalies include significant warming over western Greenland and eastern Canada and strong cold air outbreaks over much of northern Europe, Asia, and the eastern United States (Fig. 4b). Conditions are also anomalously wet over western and central Europe and dry over Scandinavia (Fig. 4c).
Composites of the 60 days after historical SSWs in the JRA-55
reanalysis for
Composite analysis could also be used to consider differences in SSW
evolution and impacts in relation to other factors, such as the differences
between split- and displacement- type events, the differences between events
that occur in El Niño or La Niña winters, or the different phases of
the MJO. Figure 5 highlights the differences in the evolution of the
500 hPa geopotential height anomalies prior to and after a SSW during La
Niña versus El Niño winters. Here we use the
December–January–February ONI index to classify El Niño and La Niña
years, with winters with ONI exceeding
Composites of the 500 hPa geopotential height anomalies (m) in
JRA-55 reanalysis for
While compositing is useful for highlighting robust features of SSWs, the dynamic evolution and surface climate anomalies before and after each individual SSW can vary widely. The SSWC can be used to demonstrate this range of variability. Figure 6 illustrates the differences in the tropospheric climate following two similar split-type SSWs, one in January 1985 and the other in January 2009. In both events, the polar vortex split into two lobes: the one associated with the greatest warming anomalies centered over Canada and the other centered over northern Europe and Asia (Fig. 6a, b). The 2009 split SSW had a larger lobe that extended over most of Eurasia, but otherwise the stratospheric evolution was quite similar.
Comparison of two split-type SSW events,
However, the subsequent surface and tropospheric responses in the weeks
following the events differed in several ways. The 500 hPa height anomaly
pattern following the 1985 event projects strongly onto the negative NAO
pattern (Fig. 6c), with positive height anomalies over Greenland and
negative height anomalies over the North Atlantic. This pattern is associated
with much lower surface temperature anomalies over much of Europe and Asia.
However, the height anomalies in the 2 months following the 2009 split-type
event do not look like the negative NAO phase, though there are weakly
positive height anomalies over the Arctic and two centers of low height
anomalies over Europe and Asia (Fig. 6d). Temperature advection associated
with these anomalous low-pressure centers may explain the regional cold air
experienced over Asia and central Europe. Comparison of these two events
shows how different modes of climate variability can impact the tropospheric
climate during the period after a substantial SSW event. While 1985 and 2009
were both (essentially) La Niña winters (2009 misses official La Niña
classification by the NOAA Climate Prediction Center by 0.1
Time series for the 30 days prior to and after the event date of
major SSWs in the JRA-55 reanalysis of
The SSWC allows easy evaluation of the spread among individual events for
different features of SSWs. Figure 7 shows time series of the (a) amplitude and (b) latitude of the maximum temperature anomaly (that occurs
within the range of 30–90
Comparison of three different reanalysis products for the 7 January 2013 SSW event:
Finally, the SSWC includes data from six different reanalyses, both to aid in
reanalysis intercomparison projects such as S-RIP and to allow users the
ability to assess the robustness of SSW features in different products.
Figure 8 demonstrates how these differences manifest during the January 2013 SSW event for (a) a modern reanalysis product (MERRA2), (b) an older
reanalysis product with low model top (NCEP1), and (c) a reanalysis that
only assimilates observations at the surface and has a strong bias in the
stratosphere (NOAA20CR). In MERRA2, there is strong weakening of the zonal
wind anomalies at 60
The surface temperature anomalies and the 200 hPa geopotential height anomalies for days 30–60 after the 2013 SSW are shown in the right-hand panels of Fig. 8. In the SSWC, surface variables are provided at their native horizontal resolution, which is reflected in these panels in the surface temperature anomalies. MERRA2 has the highest horizontal resolution, making more regional structure and detail apparent. The cold anomalies over Asia and parts of the Arctic, and the tropospheric circulation anomalies at 200 hPa (particularly in regions impacted by stratosphere–troposphere coupling, such as the North Atlantic), are weaker in the NOAA20CR relative to MERRA2 and NCEP1. Regional differences between all three reanalyses can be seen, particularly in the polar cap region where observations may not be available to constrain the reanalysis system.
The SSWC is designed to be a public domain product that allows the user either to use the data as packaged or to step into the production process and regenerate parts of the database with customized configurations. A flowchart of these options is shown in Fig. 2. For example, if the user would like to use a different set of event dates or a different climatology, they may use the provided code and documentation to extract full fields from their reanalysis product of choice and to generate new anomaly and derived fields. Nonetheless, one major advantage of the SSWC is that both the full fields and the anomalies are provided (as well as the climatology), so that users can avoid downloading the terabytes of data needed to calculate the daily climatology and anomaly fields.
The SSW Compendium has been archived at NOAA's NCEI (
A user's guide to the SSWC dataset is provided to describe the included
variables and the file format. A production guide and source code in
Interactive Data Language format are provided in case a user would like to
recreate their own version of the SSWC. We anticipate future updates to the
Compendium for those reanalysis products that proceed operationally in the
future when new SSWs occur. When the Compendium is updated with a new SSW
event, the climatologies and anomalies for all events will be updated, based
on the full period of the new record. When publishing results based on the
SSWC, users should clearly state what version and/or climatology is being
used in order to allow reproducible results. A subset of the SSWC can be
plotted or animated at
The ability to readily perform (i) composite analysis, (ii) individual event analysis, and (iii) reanalysis intercomparison is one of the main goals of the SSW Compendium. The SSWC will hopefully allow users to highlight the role of stratosphere–troposphere processes and the importance of major SSW events in winter climate and provide a comprehensive database to compare with and improve model simulation of these events.
The SSWC database provides a simple and computationally inexpensive way to generate, download, and plot information on historical SSW events and their evolution and impacts on daily timescales. The database is designed to be used as is, but the end user also has the ability to use the source code to customize the database to meet their specific needs. The inclusion of six different reanalysis products and a set of full, anomaly, and derived fields for every major SSW in the historical record allows several different applications of the SSWC. The ability to readily perform (i) composite analysis, (ii) individual event analysis, and (iii) reanalysis intercomparison for projects such as S-RIP will hopefully allow users to highlight the role of stratosphere–troposphere processes and the importance of major SSW events in winter climate and provide a comprehensive database to compare with and improve model simulation of these events.
The authors declare that they have no conflict of interest.
This work was funded by the NOAA Climate Program Office.Edited by: G. König-Langlo Reviewed by: W. Seviour and one anonymous referee