Numerical weather prediction (NWP) models are known to possess a distinct moist bias in the mid-latitude lower stratosphere, which is expected to affect the ability to accurately predict weather and climate. This paper investigates the vertical structure of the moist bias in the European Centre for Medium-Range Weather Forecasts (ECMWF) latest global reanalysis ERA5 using a unique multi-campaign data set of highly resolved water vapour profiles observed with a differential absorption lidar (DIAL) on board the High Altitude and LOng range research aircraft (HALO). In total, 41 flights in the mid-latitudes from six field campaigns provide roughly 33 000 profiles with humidity varying by 4 orders of magnitude. The observations cover different synoptic situations and seasons and thus are suitable to characterize the strong vertical gradients of moisture in the upper troposphere and lower stratosphere (UTLS). The comparison to ERA5 indicates high positive and negative deviations in the UT, which on average lead to a slightly positive bias (15 %–20 %). In the LS, the moist bias rapidly increases up to a maximum of 55 % at 1.3 km altitude above the thermal tropopause (tTP) and decreases again to 15 %–20 % at 4 km altitude. Such a vertical structure is frequently observed, although the magnitude varies from flight to flight. The layer depth of increased moist bias is smaller at high tropopause altitudes and larger when the tropopause is low. Our results also suggest a seasonality of the moist bias, with the maximum in summer exceeding autumn by up to a factor of 3. During one field campaign, collocated ozone and water vapour profile observations enable a classification of tropospheric, stratospheric, and mixed air using water vapour–ozone correlations. It is revealed that the moist bias is high in the mixed air while being small in tropospheric and stratospheric air, which highlights that excessive transport of moisture into the LS plays a decisive role for the formation of the moist bias. Our results suggest that a better representation of mixing processes in NWP models could lead to a reduced LS moist bias that, in turn, may lead to more accurate weather and climate forecasts. The lower-stratospheric moist bias should be borne in mind for climatological studies using reanalysis data.
Water vapour is one of the most important greenhouse gases in the atmosphere
and plays a key role for accurately predicting the Earth's weather (Gray et
al., 2014; Shepherd et al., 2018) and climate (Forster and Shine, 2002;
Riese et al., 2012). In the upper troposphere and lower stratosphere (UTLS),
defined as a layer located
In the extratropical UTLS, the distribution of water vapour is driven by transport and mixing processes related to baroclinic waves and associated synoptic- and meso-scale weather systems, which are interacting with chemical processes (e.g. Gettelman et al., 2011; Schäfler et al., 2022). The increased static stability above the tropopause (Birner et al., 2002) impedes water vapour from being vertically transported. Correspondingly, the sharpest decline of water vapour is found just above the tropopause. Exchange processes affect the water concentration around the tropopause (Holton et al., 1995; Stohl et al., 2003) and create the extratropical transition layer (ExTL; Pan et al., 2004; Hoor et al., 2010) with influences of the troposphere and the stratosphere. In particular quasi-isentropic exchange near the polar and subtropical jet streams (Haynes and Shuckburgh, 2000) and cross-isentropic mixing, for instance, through overshooting convection (e.g. Dessler and Sherwood, 2004; Homeyer et al., 2014), are major contributors to increased humidity above the tropopause. Furthermore, tropopause folds are related to mass exchange between the UT and the LS (Shapiro, 1980). Above the ExTL, the concentration of water vapour approaches a low and vertically constant background value (e.g. Hintsa et al., 1994), which is determined by the stratospheric transport from tropics (Fueglistaler et al., 2009) within the Brewer–Dobson circulation (e.g. Dobson et al., 1946; Brewer, 1949) on timescales from months to years (Birner and Bönisch, 2011). The complexity of transport and mixing processes is mirrored in the high water vapour variability in the extratropical UTLS on synoptic and seasonal timescales (e.g. Pan et al., 2000; Randel and Wu, 2010; Zahn et al., 2014; Dyroff et al., 2015; Bland et al., 2021; Schäfler et al., 2022).
The sharp vertical gradients of trace species, PV, wind, and temperature at the extratropical tropopause are challenging to resolve for state-of-the-art NWP models (e.g. Stenke et al., 2008; Schäfler et al., 2020). Current NWP analyses and forecasts are known to possess a distinct moist bias in the extratropical LS (e.g. Kaufmann et al., 2018), which is causing a collocated cold bias at the same altitudes (Stenke et al., 2008; Diamantakis and Flemming, 2014; Shepherd et al., 2018). Recently, Bland et al. (2021) used radiosonde observations of a 2-month period in autumn and confirmed the earlier documented moist bias (about 70 % in the LS) in current operational analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and the Met Office's Unified Model (METUM). They also showed that radiative effects related to the moist bias cause a collocated cold bias in the LS that grows with forecast lead time. For a comprehensive overview of the studies that quantified the LS moist bias in different NWP systems, the interested reader is referred to Table 1 in Bland et al. (2021). The vertical structure of the moist bias is characterized by a small positive bias below the thermal tropopause, followed by a vertical increase in the LS to a maximum at 1–2 km above the tropopause (e.g. Dyroff et al., 2015; Bland et al., 2021). However, different shapes of the LS moist bias above its maximum have been reported. Bland et al. (2021) show an opposing vertical structure of the moist bias beyond 2 km above the tTP using two different radiosonde types. Woiwode et al. (2020) compare humidity cross sections of an airborne passive infrared imager and present a case with vertically increasing moist bias, one with constant bias, and two cases with vertically decreasing moist bias in IFS analysis and forecast data.
The origin of the wet model bias is still under debate: one hypothesis is that it is caused by misrepresented dynamical transport and mixing processes (Kunz et al., 2014; Shepherd et al., 2018), for example, overshooting convection leading to excessive water vapour injection into the LS. Another potential source of overestimated transport of moisture into the LS is numerical diffusion and insufficient model resolution in the semi-Lagrangian advection scheme used in the ECMWF model, leading to an excessive diffusive transport of moisture across strong gradients from high to low mixing ratios (Stenke et al., 2008; Kunz et al., 2014; Dyroff et al., 2015; Shepherd et al., 2018). However, a LS moist bias of similar order is also found for “Eulerian”-formulated models (Jiang et al., 2015; Davis et al., 2017). Moreover, Woiwode et al. (2020) confirm that the bias is already present in the initial conditions and demonstrate a low response of the moist bias to variable vertical or temporal resolutions.
The above-mentioned studies used a variety of observation techniques to
quantify the moist bias. Radiosonde or dropsonde humidity observations
provide temporally continuous series of profiles at the same location, but
their reliability is limited
The goal of this paper is to evaluate the LS moist bias in the ECMWF's most
recent global reanalysis ERA5. The model analyses are compared against a
comprehensive data set of water vapour profiles observed by the airborne
DIAL WALES in the mid-latitude UTLS. Collocated water vapour and ozone
profiles are used to identify tropospheric, stratospheric, and mixed air and
to individually assess the moist bias as we suspect that mixing processes
affect the vertical structure of the moist bias. The following three
specific questions are addressed:
Can the multi-campaign DIAL data set robustly quantify the LS moisture bias
in ERA5? What is the vertical structure of the LS moist bias in ERA5, particularly at
high altitudes? Is the moist bias correlated to the distribution of mixed air masses in the
UTLS?
This paper is outlined as follows: Sect. 2 provides an overview of the
water vapour DIAL observations (Sect. 2.1), the ERA5 reanalysis (Sect. 2.2),
and the methods utilized to compare the observational and model data (Sect. 2.3). In Sect. 3.1 an example cross section of specific humidity and the
bias are illustrated for a mid-latitude jet stream crossing, which is followed
by a statistical tropopause-relative evaluation of the vertical structure of
the bias and its variability in Sect. 3.2. The relationship between the
vertical structure of the moist bias and the distribution tropospheric,
stratospheric, and mixed air is presented in Sect. 3. Thereafter, Sect. 4
provides a discussion of the results. The key conclusions are summarized in
Sect. 5.
The DIAL WALES (Wirth et al., 2009) was developed at the German Aerospace Center (DLR) and has been operated on board the German research aircraft HALO since 2010. The instrument design is based on two identical laser systems that generate four wavelengths in the near-infrared (NIR) absorption band of water vapour between 935 and 936 nm, allowing for water vapour observations from the planetary boundary layer up to the stratosphere. WALES furthermore operates two polarization-sensitive channels at 1064 nm and at 532 nm. The latter channel comprises a high spectral resolution lidar (HSRL; Esselborn et al., 2008), enabling extinction coefficient observations and thus aerosol characterization (Groß et al., 2013). WALES and its underlying DIAL technique is briefly introduced in the following, and a more detailed description can be found in Wirth et al. (2009).
Map of HALO flight sections with WALES DIAL water vapour
observations during the research campaigns NARVAL, NARVAL2, NAWDEX, WISE,
EUREC
Overview of all considered campaigns with DIAL observations. The number of DIAL profiles refers to all profiles that were sampled during 41 flights. The number of DIAL profiles in the LS corresponds to all profiles with measurements in the LS.
The four NIR wavelengths are separated into three online channels (strongly absorbed by water vapour) and one offline channel (weakly absorbed). The number concentration of water vapour in the probed volume is derived from the ratio of the backscattered light of the on- and offline wavelengths and then converted to specific humidity. The online channels are sensitive to different trace gas concentrations and in turn to different altitude levels. The exact wavelengths are selected such that they are optimally aligned to the moist boundary layer, the UT, and the dry LS. Note that the WALES humidity profiles are only available in cloud-free regions or regions with optically thin clouds. In optically thick clouds the extinction by cloud particles is so strong that no water vapour information can be retrieved within or below the cloud.
Due to the photon statistics of the backscattered light as well as detector
and background light noise, the retrieved water vapour profiles undergo
statistical variations, which are effectively reduced by temporal (i.e.
horizontal) and vertical averaging. Thus, the retrieved DIAL water vapour
profiles are averaged over 12 s or approximately 3 km in the horizontal. In
the vertical, data are available every 15 m, although the effective vertical
resolution is 300 m according to the full width of half maximum of the
averaging kernel. It should be stressed that the averaging kernel of the
WALES DIAL is exactly zero outside of about
In this study, we use DIAL observations from six campaigns from 2013–2021
that provide almost 33 000 water vapour profiles obtained during 41 research
flights. The profiles were sampled along the flight track and extend from
the surface up to about 14 km altitude, corresponding to the maximum flight
level of the HALO aircraft (Krautstrunk and Giez, 2012). As the focus of
this study is the mid-latitude UTLS, we only consider flights that provide a
significant amount of data across the tropopause. The majority (25) of these
flights took place in the northern hemispheric autumn season during the North
Atlantic Waveguide Downstream impact EXperiment (NAWDEX; Schäfler et
al., 2018) and the Wave-driven ISentropic Exchange campaign (WISE; Kunkel et
al., 2019). As part of the campaigns ElUcidating the RolE of
Cloud-Circulation Coupling in ClimAte (EUREC
During the WISE campaign, WALES was operated in a different setup to measure
both water vapour and ozone, concurrently. For this purpose, two of the
935 nm NIR water vapour channels were replaced by two ultraviolet (UV)
channels covering the 300–305 nm ozone absorption line (Fix et al., 2019).
The use of two instead of four channels per trace gas leads to a reduced
vertical coverage, which was optimized so that the selected NIR wavelengths
cover the tropopause region. Increased statistical noise required averaging
over a period of 24 s (
The number of observations with respect to latitude (Fig. 2a) illustrates
the high data availability in the mid-latitudes, which is the region of
interest in this study. This data set that covers humidity observations in a
broad spectrum of synoptic situations is considered to be representative of
mid-latitude weather. Figure 2b shows the distribution of the water vapour
observations covering 4 orders of magnitude, ranging from 10
ERA5 is the latest-generation reanalysis of the ECMWF based on the IFS Cycle
41r2 that was used for operational weather prediction in 2016. Atmospheric
quantities are provided on a global grid with a horizontal resolution
(TL639) of about 31 km and on 137 hybrid sigma-pressure model levels,
ranging from the surface up to 0.01 hPa (
Some example values of specific humidity and the according computed humidity bias.
Due to the variable altitude of the tTP, the distribution of water vapour in
the UTLS at individual altitudes is also highly variable. Hence, averaging
of the humidity profiles in geometrical coordinates strongly blurs the
vertical gradients across the tropopause. Therefore, bias statistics are
often performed in tropopause-relative coordinates (e.g. Kunz et al., 2014;
Bland et al., 2021). Different tropopause definitions have been established,
taking the thermal, dynamical, and chemical properties of the UTLS as a
reference. By definition, the tTP marks the reversal of the vertical
temperature gradient and thus the abrupt increase in static stability, which
is reflected in the sharp distribution of trace species across the
tropopause (Gettelman et al., 2011). We use the tTP as it best reflects the
strongest vertical gradients of water vapour (Birner et al., 2002; Pan et
al., 2004). From each ERA5 temperature profile interpolated to the 15 m
vertical grid of the lidar, we calculate the tTP altitude using the World
Meteorological Organization's (WMO) lapse rate-based definition (WMO, 1957).
A tTP is detected as the lowest level at which the vertical temperature
gradient
Histogram of the number of observations per thermal tropopause altitude bin (1000 m) and per campaign (coloured bars).
Vertical cross sections of
The selection of a suitable difference metric is crucial for a robust
quantification of model humidity errors, and different statistical approaches
can be found in the literature (Kunz et al., 2014; Bland et al., 2021). As
specific humidity rapidly decreases across the tropopause, absolute humidity
differences are not appropriate, and most studies rely on a relative
formulation of the error. However, since the simple ratio of model and observation as well as the absolute bias divided by the observed value are
statistical asymmetric quantities, we apply a logarithmic formulation with
base 2 (see Eq. 3), introduced by Kunz et al. (2014):
First, an example cross section of water vapour measurements of the research
flight on 1 October 2017 during the WISE campaign is presented in Fig. 4.
The case is selected as it possesses a good data coverage across the UTLS
and as it additionally provides ozone observations (see Sect. 3.3). HALO
flew meridional transects over the North Atlantic (50–60
Tropopause-relative
Binned distribution of DIAL specific humidity
observations relative to the thermal tropopause coloured by
Distributions of
For all 32 905 profiles from the 41 flights, the average profiles of specific
humidity and the humidity bias are presented in Fig. 5. The moisture
profiles of WALES and ERA5 show an exponential decline of specific humidity
in the UT, ranging from about
The higher moisture values in the ERA5 data become apparent in the vertical
profile of the humidity bias (Fig. 5b) that is weakly positive (0.2; 15 %) in the UT and associated with a high standard deviation. This is a
result of strong positive and negative bias values, as seen for example in
the case study (Fig. 4c). The weakest bias of 0.2 (
To better illustrate the variability of the water vapour observations in the
vertical, Fig. 6 shows the number of data and the mean bias in bins of
tropopause-relative altitude and specific humidity. Figure 6a indicates a
broader distribution of water vapour observations in the UT compared to the LS.
A small number of unusually low humidity values (
Tropopause-relative vertical profiles of
Tropopause-relative profiles of the
Vertical cross sections as in Fig. 4 but for
In this section, the variability of the LS bias between flights, campaigns,
and tropopause altitudes is investigated. Figure 7 shows the observed
humidity distribution within a 3 km layer above the tTP, i.e. the area of
the strongest LS moist bias. The observed humidity values of all flights
range from
The average profile of the bias and the number of observations for campaigns
with an increased data coverage is shown in Fig. 8. The data availability is
very different across the campaigns (Fig. 8b). During NAWDEX and WISE, a
large number of observations is present between
In addition, we explore whether the observed vertical structure of the moist
bias is sensitive to different synoptic situations. For this investigation,
the DIAL profiles are classified by their corresponding tTP altitude. Lower
tropopauses are typically associated with trough situations and high
tropopauses occur above ridges. For each category the corresponding average
bias profile and the number of observations is given in Fig. 9. The vertical
structure of the bias (Fig. 5b) is reproduced for each tropopause altitude
interval. No systematic differences between the bias profiles can be
revealed in the UT. Interestingly, each category shows an increased moist
bias of comparable magnitude as well as a decrease above, although its
vertical position relative to the tTP is different. The maximum bias is
located higher for low tropopause altitudes, while profiles with high tTP
altitude show a maximum closer to the tTP. For instance, the maximum bias
for low tropopauses (
In the following it is examined to what extent the observed air masses have
experienced mixing in their history and whether this is related to the
vertical structure of the moist bias. For this purpose, we examine
collocated ozone and water vapour observations that were collected during
four WISE research flights and that provide a suitable data coverage. First,
the observed ozone distribution for the same case study as introduced in
Sect. 3.1 is shown in Fig. 10a. Note that the ozone and water vapour
observations are given as volume mixing ratios (VMR) in the following. The
distribution of ozone is opposite to that of water vapour, with the lowest
concentrations (VMR
Binned distribution of water vapour and ozone
observations in T–T space for four WISE flights coloured by bin-average
Binned distribution of water vapour and ozone
observations in T–T space as in Fig. 11 but coloured by bin-average
Following the approach by Schäfler et al. (2021), the collocated water
vapour and ozone observations for four WISE flights are illustrated in
tracer–tracer (T–T) phase space in Fig. 11, and three classes of
observations are identified based on the characteristic distributions (e.g. Pan et al., 2004). First, tropospheric observations are characterized by low
VMR
For each bin in T–T space, the average humidity bias and the mean
tropopause-relative altitude are displayed in Fig. 12. The humidity bias is
weak for both tropospheric and stratospheric air (Figs. 12a and 11b),
ranging mostly between
The average vertical profile of the moist bias for the WISE flights (Fig. 13a) is similar to the full data set (Fig. 5b) at the tTP and in the LS, i.e. a local minimum is found at the tTP (0.1; 7 %) and a pronounced
maximum of 0.62 (54 %) peaking at about 1 km above the tTP. The
tropospheric part of the profile, however, is almost constant in the full
data set (0.2–0.25) but decreases with increasing altitude in the WISE data
(0.4–0.1). Figure 13b shows the relative proportion of the individual air
masses at a given tropopause-relative altitude and thus provides information
about the connection between the vertical structure of the moist bias and
the air mass classes. In the entire UT, the tropospheric air provides the
largest contribution of more than 80 % up to 500 m below the tTP. Across
the tTP, the proportion of tropospheric air rapidly decreases with altitude
in accordance with a rapid growth of the fraction of mixed air. This is
accompanied by an increase of the moist bias, and the altitude of the largest
bias (1–2 km above the tTP) coincides with the maximum relative
contribution of the mixed air class (
Recent studies document a lower-stratospheric moist bias in different NWP models (e.g. Kunz et al., 2014; Dyroff et al., 2015; Kaufmann et al., 2018; Woiwode et al., 2020; Bland et al., 2021). We find a comparable moist bias in ERA5 reanalyses based on a comprehensive multi-campaign water vapour lidar data set comprising 41 research flights (six campaigns) and roughly 33 000 vertical profiles obtained in the northern hemispheric mid-latitudes during different seasons. The observations from the surface up to the LS cover 4 orders of magnitude and represent typical mid-latitude data for the individual seasons (e.g. Pan et al., 2000; Randel and Wu, 2010; Zahn et al., 2014; Kunz et al., 2014; Dyroff et al., 2015; Bland et al., 2021). The high data availability around the tropopause makes the data set suitable for an evaluation of NWP fields in the UTLS. Although the number of observations reduces considerably towards the highest altitudes (up to 5 km above the tTP), the data set provides a valuable extension to previous humidity data sets which exhibit increased measurement uncertainties at altitudes larger than 2 km above the tTP (e.g. Bland et al., 2021).
In the troposphere we find strong positive and negative biases of small
spatial extent, which are likely related to insufficiently represented
tropospheric transport processes, to model errors of tropospheric processes
(e.g. clouds), or to the linear interpolation scheme that may have caused
increased differences especially in situations of strong horizontal or
vertical moisture gradients. The small positive and vertically almost
constant mean bias in the UT, which ranges between 0.2 (15 %) and 0.26
(20 %), confirms earlier findings (Dyroff et al., 2015; Bland et al.,
2021). It has to be noted that the UT bias is limited to cloud-free scenes,
as DIAL humidity profile observations cannot be retrieved inside or below
optical thick clouds. In agreement with Bland et al. (2021), a local minimum
of the bias (
In line with findings of Bland et al. (2021), who indicated little
sensitivity of the moist bias to various atmospheric conditions but revealed
a different depth of the moist bias for trough and ridge situations, low tTP
situations (which are typically associated with troughs) exhibit a maximum
bias at higher altitudes and a deeper layer of the increased bias compared
to high tTP situations. The magnitude of the moist bias is found to be
independent of the tropopause altitude. In addition, we detect a pronounced
LS moist bias in the summer (
For four flights during the WISE campaign, an air mass classification using collocated water vapour and ozone profile data (Schäfler et al., 2021) was applied to separate tropospheric (low ozone and large water vapour mixing ratio), stratospheric (large ozone and low water vapour), and mixed air (intermediate ozone and water vapour). In tropopause-relative coordinates, the vertical structure of the moist bias for the selected cases turned out to be comparable to the multi-campaign LS moist bias, so that these flights are considered to be representative of autumn. We find that the moist bias is increased in the mixed air class representing the ExTL and that the maximum is reached at the altitude where the proportion of mixed air is highest (near 100 %). The decrease of the moist bias above/below is accompanied by a growth of the proportion of stratospheric/tropospheric air. The high correlation in the distribution of the moist bias and the ExTL gives a strong hint at the importance of moisture injection into the LS, either due to numerical diffusion across the tropopause or due to insufficiently modelled transport and mixing processes. As the bias in the ExTL is increased in each of the evaluated WISE flights, we consider systematic uncertainties in the representation of mixing processes to play a key role for the LS moist bias. This is supported by the finding of a deeper bias layer above troughs which are characterized by a thicker ExTL above (e.g. Hoor et al., 2002; Pan et al., 2007). In addition, the maximum bias occurs in summer when cross-tropopause mixing is strongest (Hoor et al., 2002), and, finally, the bias is reduced in stratospheric background humidity at highest altitudes, which are not influenced by mixing processes at the extratropical tropopause. Schäfler et al. (2022) investigate the Lagrangian history of the observed air for the presented WISE case study on 1 October 2017 and find that the ExTL air experienced strong turbulent mixing in the jet stream during 48 h before the observation. They also find that the mixed air (in which we identified the increased bias) shows highly variable origins and transport pathways related to tropospheric weather systems which may be indicative of the relevance of different mixing processes. Additional collocated observations of ozone and water vapour in different seasons, near active mixing process (e.g. convection), or in the Southern Hemisphere where exchange at the polar jet stream is reduced (e.g. Bowman, 1995) could provide valuable information about the relevance of individual mixing processes and their role in forming the moist bias. The presented results suggest that improving the representation of mixing at the tropopause may reduce the humidity bias and be beneficial to improve the modelling of climate and weather. Davis et al. (2017) demonstrate that various reanalyses significantly overestimate LS humidity in the extratropics. The systematic moist bias in ERA5 reanalyses has to be kept in mind for climatological studies using ERA5 humidity fields in the LS.
In this study we applied a comprehensive data set of airborne water vapour
lidar profiles to investigate the representation of specific humidity in the
ERA5 reanalysis across the extratropical UTLS. The main conclusions of this
work are summarized below following the three research questions that were
raised in the Introduction:
The presented DIAL data set with its large number of high-accuracy and
high-resolution humidity profiles measured over the North Atlantic and
Europe during six research campaigns between 2013–2021 provides a valuable
extension to the available observational data sets that were used to
determine the lower-stratospheric moist bias. Beside the broad range of
observed humidity values (10 Our analysis demonstrates that a systematic lower-stratospheric moist bias
is also present in ECMWF's most recent global reanalysis ERA5. We find that
the vertical structure of the bias, which is analysed in tropopause-relative
coordinates, is characterized by a weak positive bias in the upper
troposphere (15 %–20 %) and a strong overestimation of humidity that reaches
a maximum (55 %) at 1.3 km above the thermal tropopause. Above this
maximum, we detect a steady vertical decrease of the moist bias towards a
constant small value (15 %) beyond 4 km above the tropopause. The moist
bias occurs in coherent and extended regions along the individual lidar
cross-sections. The above described unique measurement characteristics of
the DIAL data set together with the persistence of the bias structure in
different flights and campaigns allow the vertical decline at the highest
altitudes to be robustly confirmed. A high similarity for two campaigns
conducted in the same region over the North Atlantic in successive years
illustrates the persistence of the vertical structure. We find a seasonality
of the moist bias with a maximum in summer and a minimum in winter. Lower
tropopause altitudes, which are typically related to troughs, exhibit a
deeper layer of increased moist bias, while the moist bias over ridges is
confined to a shallow layer. For four flights of the DIAL data set, collocated water vapour and
ozone profiles are available and used to classify UTLS air masses according
to their chemical characteristics into tropospheric, stratospheric, and mixed
air. We find the strongest bias at altitudes dominated by the mixed air
class, representing the ExTL, while tropospheric or stratospheric air exhibits
a smaller bias. From this correlation, we deduce that insufficiently
represented mixing processes or numerical diffusion in ERA5
shapes the vertical structure of the lower-stratospheric bias, with the
maximum occurring at altitudes that are most frequently affected by exchange
processes between the troposphere and the stratosphere. The vertical
structure of the moist bias of the entire data set is comparable to the four
flights with collocated ozone and water vapour observations. In addition,
the deeper bias over troughs which typically feature a deeper ExTL, the
maximum moist bias in summer when cross-tropopause mixing is strongest, and
the reduced bias at altitudes of constant stratospheric background humidity
lead to the conclusion that the findings are applicable to the mid-latitudes
in general. In the future, it would be interesting to identify the
individual mixing processes that affect the moist bias most and the timescales on which it is formed.
The data used in this study are available (upon request from the mission PI) from the HALO database:
KK performed the data analysis, produced the figures, and wrote the manuscript. AS, MWi, MWe, and GCC supported the interpretation of the data, contributed with ideas, and commented on the paper. MWi performed the DIAL data processing.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the special issue “WISE: Wave-driven isentropic exchange in the extratropical upper troposphere and lower stratosphere (ACP/AMT/WCD inter-journal SI)”. It is not associated with a conference.
The authors thank the individual research teams that successfully conducted
the field campaigns NARVAL, NARVAL2, NAWDEX, WISE, EUREC
This research has been supported by the Deutsche Forschungsgemeinschaft (project A3 of the Transregional Collaborative Research Center SFB/TRR 165, “Waves to Weather” TRR 165).The article processing charges for this open-access publication were covered by the German Aerospace Center (DLR).
This paper was edited by Farahnaz Khosrawi and reviewed by two anonymous referees.