JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D20114, doi:10.1029/2009JD013587, 2010
A drop in upper tropospheric humidity in autumn 2001, as derived
from radiosonde measurements at Uccle, Belgium
R. Van Malderen1 and H. De Backer1
Received 23 November 2009; revised 9 June 2010; accepted 21 June 2010; published 26 October 2010.
[1] Simulations of climate models predict a doubling of the amount of upper tropospheric
water vapor by the end of this century, caused by the increasing concentrations of
greenhouse gases. Observations indicate that the tropopause height has increased by
several hundred meters since 1979. In this paper, we verify and link these two results by
carrying out a time series analysis on a uniform database of corrected radiosonde vertical
profiles gathered at Uccle, Belgium, and covering the 1990–2007 time period. The
most remarkable finding of this trend analysis is a significant drop in upper tropospheric
humidity (UTH) around autumn 2001, which marks an end to the upper tropospheric
moistening of the precedent decade. This UTH drop in autumn 2001 coexists with a
sudden lifting and cooling of the tropopause and with a significant stretch‐out of the free
troposphere. Therefore, we conclude that these autumn 2001 trends are certainly associated
with the dynamical behavior of the troposphere, triggered by the surface warming.
Links with the solar variability and the lower stratosphere were investigated but could not
be established definitely.
Citation: Van Malderen, R., and H. De Backer (2010), A drop in upper tropospheric humidity in autumn 2001, as derived from
radiosonde measurements at Uccle, Belgium, J. Geophys. Res., 115, D20114, doi:10.1029/2009JD013587.
1. Introduction
[2] Water vapor is a key variable for climate research. It is
the dominant greenhouse gas in the atmosphere and provides the largest known feedback mechanism for amplifying
climate change [Bony et al., 2006; Soden and Held, 2006].
While the total water vapor amount is the first‐order quantity
determining the water vapor greenhouse effect, the upper
tropospheric water vapor content has an especially strong
influence on the amount of outgoing long‐wave radiation
[see, e.g., Solomon et al., 2010]. The total amount of water
vapor is expected to increase due to global warming (and is
confirmed from an observational point of view) [see, e.g.,
Durre et al., 2009; McCarthy et al., 2009], and simulations
with coupled ocean‐atmosphere models and satellite measurements both point to an increase in upper tropospheric
specific humidity q with a near‐constant upper tropospheric
relative humidity (RH) [Minschwaner and Dessler, 2004;
Minschwaner et al., 2006; Soden et al., 2005; Gettelman and
Fu, 2008], despite the facts that (1) the different models
have very different humidity mean states and (2) systematic
specific humidity biases arise between the global climate
model (GCM) results and satellite measurements [Soden
and Held, 2006; John and Soden, 2007].
[3] Owing to their global coverage, the already mentioned
satellite measurements for upper tropospheric humidity have
1
Royal Meteorological Institute of Belgium, Brussels, Belgium.
Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2009JD013587
a great potential for climate studies [Buehler et al., 2008].
Their major drawback is the very coarse vertical resolution
they provide (measurements over layers that are typically
1–3 km thick). For instance, Gettelman et al. [2006] used the
data from the Atmospheric Infrared Sounder on the NASA
Aqua satellite to develop a climatology of upper tropospheric
relative humidity. They found that the highest variances in
humidity are seen around the midlatitude tropopause. Other
research satellite instruments measure upper tropospheric
water vapor (e.g., the Microwave Limb Sounder), but the data
from these instruments are only available for relatively short
time periods (see, e.g., Fetzer et al. [2008] for a comparison
of the upper tropospheric humidity (UTH) observations from
those two instruments). On the other hand, there exist some
instruments measuring water vapor on operational meteorological satellites which span longer time periods. The longest
available UTH record from satellite measurements dates
back to 1979 and comes from the High‐Resolution Infrared
Sounder instrument. On the basis of these observations, Bates
and Jackson [2001] described decadal trends in upper tropospheric (relative) humidity which are strongly positive in
the deep tropics, negative in the Southern Hemisphere subtropics and midlatitudes, and of mixed sign in the Northern
Hemisphere subtropics and midlatitudes. Since 1993, continuous humidity measurements have also been available
from instruments on operational satellites using the microwave range, especially a prominent water vapor line at
183.31 GHz [Milz et al., 2009]. The advantage of using the
microwave range instead of the IR (and most notably the
spectral region near 6.7 mm) is that the data are much less
affected by clouds.
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[4] In addition to satellite measurements, other data
sources for UTH are available. These are generally characterized by a higher vertical resolution. In situ aircraft
observations are of high quality but of limited extent [see,
e.g., Gettelman et al., 2004], as is the case for the sensitive
balloon‐borne instruments described by Vömel et al.
[2007]. The longest available data record is from operational balloon radiosondes. Unfortunately, the spatial coverage of radiosonde measurements is poor and radiosonde
UTH measurements can suffer from significant dry biases
[Soden et al., 2004; Ferrare et al., 2004; Vaughan et al.,
2005; Miloshevich et al., 2006]. Moreover, previous studies
have documented substantial spatial [Soden and Lanzante,
1996] and temporal [Elliott and Gaffen, 1991] discontinuities
in their historical records that are frequently related to differences in radiosonde instrumentation. In this paper, we focus
on the time series analysis of radiosonde UTH observations
of a single station, Uccle (Belgium, 50°48′N, 4°21′E, 100 m
above sea level). The major advantage of this approach is
that we know the exact metadata of these measurements, so
we can avoid inhomogeneities due to instrumental changes.
The use of these high‐resolution profiles also enables us to
link the UTH time variability to tropopause properties, tropospheric variability, surface warming, and lower stratospheric
temperature changes at the same site. We also take advantage
of this fact by defining the UTH relative to the tropopause,
and not only in absolute terms (as was done, e.g., by Milz et al.
[2009]). Of course, with this case study, we do not aim at
finding and explaining trends or changes which are representative for the whole globe, not even for the northern
midlatitudes or Europe, although we extend our analysis to
some radiosonde stations in the surroundings.
[5] The paper is organized as follows. In section 2, we
describe in detail the radiosonde observations on which the
present study is based. The methodology of the time series
analysis is covered in section 3. In section 4, the results of a
uniform, but short‐term (17‐year), time series analysis are
presented. The findings of this analysis are then put into a
larger perspective, both in space and time (section 5).
Section 6 is reserved for a discussion of the results and
drawing conclusions.
2. Data
[6] The observations which form the basis of the research
described in this paper are radiosonde vertical profiles of
temperature, pressure, altitude, and relative humidity, gathered
at Uccle. Uccle is located in the residential part of Brussels,
6 km south of the city center. For a detailed analysis of the
intensifying urban heat island effects on the surface air
temperature time series, we refer to the work of Hamdi et al.
[2009]. At Uccle, the radiosonde data are available in digital
form since September 1963 for the standard levels and since
January 1968 for the tropopause identification. However,
this data set, covering more than 40 years, is gathered by
different types of radiosondes and, hence, different types of
pressure and humidity sensors, different thermometers, etc.
This lack of uniformity of instruments most seriously
affects the humidity measurements, as large differences exist
between the response of different humidity sensors. Moreover, the humidity sensors of the early years were simply
not sensitive enough at lower temperatures, so the resulting
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measurements cannot be trusted. Another source of inhomogeneity is the difference in launch times throughout the
40 years of observations, which of course also has an effect
on the temperature measurements, next to the humidity
measurements. Therefore, to build up a homogeneous time
series of temperature profiles, we restrict ourselves to radiosondes launched at a fixed time; for a homogeneous time
series of humidity profiles, the radiosonde type is additionally imposed.
[7] As a consequence, the longest possible homogeneous
time series of radiosonde humidity measurements available
at Uccle spans a period of more than 17 years, from January
1990 until August 2007. During this period, the type of
radiosonde used was Vaisala’s RS80‐A. Until November
2001, the RS80 sondes were launched twice a day, at 0000
and 1200 UTC; after November 2001, this type of sonde
was only launched in the context of ozone soundings, with
a reduced frequency of three times a week (Monday,
Wednesday, and Friday) at 1200 UTC. For this 17 year
time period, we dispose of data points every 10 s, so that the
theoretical vertical resolution is about 100 m on average,
neglecting at the moment the time lag in the humidity sensor
response. Throughout this paper, the 1990–2007 Uccle time
series of RS80‐A radiosonde data, launched at 1200 UTC,
with data points every 10 s, is designated as the uniform
time series. The total Uccle time series is defined as all
available radiosonde observations at 1200 UTC, starting in
1963.
[8] The used Vaisala RS80‐A sondes contain the A‐
Humicap sensor, a planar thin‐film capacitive sensor using
a highly porous polymer electrode, whose capacity depends
on the amount of water vapor and the air temperature
[Verver et al., 2006]. The RS80‐A sonde was introduced in
the early 1980s and has been the most frequently used
radiosonde in the world for more than a decade, despite its
reported dry bias, especially at low temperatures [e.g.,
Miloshevich et al., 2001; Wang et al., 2002; John and
Buehler, 2005; Leiterer et al., 2005; Nash et al., 2005;
Sapucci et al., 2005; Vaughan et al., 2005; Häberli, 2006;
Suortti et al., 2008; and references therein]. A number of
error sources have been identified:
[9] 1. Temperature‐dependence error occurs when an
inaccurate calibration model is used for the temperature
dependence of the sensor response at low temperatures
(dominant error source for T −20°C [Suortti et al., 2008]).
[10] 2. Chemical contamination error occurs when nonwater molecules (e.g., from packaging material) occupy
binding sites in the sensor polymer. This contamination dry
bias was corrected by Vaisala by a change in the packaging
(absorption material, from September 1998, and a removable boom cover, from June 2000 onward). This adaptation
clearly reduces the RS80‐A dry bias; however, a dry bias
still remains in the data [Wang et al., 2002; Wang and Zhang,
2008].
[11] 3. Sensor aging, or long‐term instability of the sensor
material, can cause a dry bias. The sensor’s drift is mainly
caused by reduced polymer sensitivity to water vapor and
is therefore seen more clearly at high humidities. The A‐
Humicap drift at saturation is approximately −5% RH after
a 2 year storage time, and less than −0.5% RH per year
thereafter [Wang et al., 2002].
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[12] 4. Despite its aluminized protective cap, the humidity
sensor remains susceptible to solar heating.
[13] 5. A thin ice layer can form around the humidity
sensor, so it behaves more like a (bad) thermometer.
[14] 6. A time‐lag error can cause the humidity sensor to
have a too‐slow response at low temperatures.
[15] To retrieve unbiased and improved relative humidity
data, correction algorithms have independently been developed by Miloshevich et al. [2004] and Leiterer et al. [2005]
for the RS80‐A sondes. Both methods include an additional
improved temperature‐dependence correction and time lag
corrections. In addition to the temperature‐dependence error,
the method described by Leiterer et al. [2005] also addresses
the RH‐dependent part of the RS80‐A error, which is a factor
in temperatures above −40°C. This is provided by a modeled
ground check correction at 100% RH [Suortti et al., 2008].
The correction method of Miloshevich et al. [2004], in collaboration with Vaisala, is based on laboratory tests and
comparisons with fast‐response hygrometer measurements.
Leiterer et al. [2005] developed their own research sondes
(“FN sondes,” modified Vaisala RS90‐H sondes) and calibration method, and the RS80‐A correction scheme is based
on the comparison with those reference sondes.
[16] Both time lag correction schemes produce very
similar results and are able to recover more vertical structure
in the upper tropospheric humidity field. The differences
between these methods arise mainly from the temperature‐
dependence correction. On the basis of tropospheric comparisons of Vaisala radiosondes with balloon‐borne frost‐point
and Lyman‐a hygrometers during a dedicated experiment,
Suortti et al. [2008] classified the correction algorithm by
Leiterer et al. as the best available because the RS80‐A dry
bias can be almost totally removed. The case study comparing
radiosonde humidity data to advanced microwave sounding
unit (AMSU) satellite humidity observations described by
Buehler et al. [2004] demonstrated that the radiosonde correction performed at Lindenberg by Leiterer et al. significantly reduces the bias between simulated and measured
AMSU radiances, particularly in the upper troposphere. As a
result, the overall agreement is very good, with radiance
biases below 1.5 K (which translates to about 15% relative
error in relative humidity), but the corrected radiosondes still
underestimate the relative humidity under extremely dry
conditions, showing 0% RH when the true value is 2%–4%
RH.
[17] The other approach [Miloshevich et al., 2004] tends
to overcorrect in high RH conditions when T < −50°C. For
T > −30°C, it is ineffective and does not correct the RS80‐A
dry bias in high ambient RH [Suortti et al., 2008]. During a
small intercomparison campaign in Uccle with three types of
Vaisala radiosondes (RS80‐A, RS90‐H, and RS92‐H), the
overcorrection of the method of Miloshevich et al. [2004] also
came out. Another result from our intercomparison campaign
is that, even after correction with either method, the RS80‐A
humidity profiles show, on average, an absolute dry bias of
more than 10% at the surface, which decreases with increasing
height, with regard to simultaneous RS9x‐H profiles (with
x = 0 or 2). This discrepancy is probably caused by the
deterioration of the RS80‐A humidity sensors (sensor aging
plus chemical contamination) owing to their long storage
time (about 2 years in the case of the RS80‐A sondes used in
the intercomparison campaign).
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[18] Our uniform time series of RS80‐A radiosonde
measurements at Uccle is corrected by the schemes proposed by Leiterer et al. [2005]. Additionally, we also used
their icing recognition algorithm as a guidance, next to the
scrutinous visual inspection of the humidity profile for sensor
icing. It turns out that about one fifth of the observations are
retrieved by an ice‐contaminated sensor. About the same
amounts are found in the Finnish RS80‐A radiosonde records
[Suortti et al., 2008] and in the Lindenberg soundings [Leiterer
et al., 2005]. Humidity profiles resulting from an iced sensor
are discarded from our data set of radiosonde relative
humidity observations. There exist some interannual variations
in the relative frequency of measurements retrieved by iced
humidity sensors, but without a distinct relation with the
variability discussed in this paper.
3. Methodology
3.1. Statistics
[19] As stated in section 2, homogeneity of climate data is
indispensable for many aspects of climate research, especially for a realistic and reliable assessment of historical
climate trends and variability, and also for the calculation of
related statistics that are needed and used to define the state
of climate and climate changes. On the other hand, autocorrelation and periodicity are inherent in most climate time
series; for example, successive observations are unlikely to
be independent of one another and there is a clear seasonal
cycle in climate time series. Unfortunately, most statistical
analysis tests (linear regression, change‐point tests, and
correlation) rely on independent and identically distributed
time series. The rationale and theoretical basis, examples,
and technical details of such statistical tests can be found in
the work of Lanzante [1996]. Therefore, all data used in this
study are in anomaly form: the seasonal cycle has been
removed by subtracting from all monthly means the long‐
term monthly means.
[20] Despite our precautions to construct a uniform time
series (see section 2), variations between different RS80‐A
production batches, the introduction of new packaging
material and a new sensor boom cover, and changes in
measurement techniques can lead to artificial (nonclimatic)
discontinuities in our data set. On the other hand, natural
variability can cause a change (discontinuity) in the level
(mean) of the time series. Hence, as a first step we apply a
change‐point test to identify a possible stepwise shift in the
mean of the time series. We chose to use the Pettitt‐Mann‐
Whitney (PMW) test, which is a nonparametric test based on
the ranks of the values of the sequence. It seeks to find a single
change point; to find more change points, the sequence
should be cut into parts. Because it is based on ranks, the test
is not adversely affected by outliers and can be used when the
time series has gaps [Lanzante, 1996]. A drawback of the test
is the sensitivity to breaks in the middle of the time series.
Change points that exceeded the 90% confidence level were
retained. For comparison, we also applied the more commonly used Wilcoxon rank‐sum test and the cumulative
sum test to detect change points in the mean in a time series.
If not otherwise stated, the mentioned change points are
always detected by all three change point tests.
[21] Next to a stepwise shift in the mean of a time series, a
change point might also be responsible for a change in a
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Figure 1. (top) Uccle time series of monthly anomalies of the integrated specific humidity for (left) the
upper tropospheric layers between 500 and 200 hPa and (right) a layer that extends from the tropopause to
3 km below the tropopause. The mean before and after the detected change point are shown in grey. The
linear regression lines for (left) the entire time period and (right) the time periods before and after the
detected change point are also drawn. Red lines are used for positive trends, blue lines for negative trends.
A solid line denotes a statistically significant trend, and a dashed line denotes a statistically insignificant
trend. The statistical significance of the trends is investigated by Spearman’s test. Green lines denote the
zero anomaly lines. (bottom) Respective monthly cumulative deviations of the anomalies shown (Figure 1,
top) from the mean of the anomalies. Green lines denote the zero lines.
trend slope. Or, in case of a slowing of the rate of increase,
the change point might represent only a change in the trend
slope. Therefore, we also applied in some cases the change‐
point detection method of Lund and Reeves [2002] which is
designed to detect both step‐ and trend‐type change points.
This test is based on a classic simple linear regression model
that allows for two phases, one before and one after the
change point.
[22] A last tool or technique to evaluate the time behavior of
a data series is to visualize the (monthly) cumulative deviations: these are acquired by calculating for each monthly
value (mean, anomaly) the sum of the deviations of the preceding monthly values with the global mean. By definition,
the monthly cumulative deviations of a time series start and
end at zero. Examples are given later in this paper (see, e.g.,
Figure 1).
[23] Single linear trends are calculated by minimizing the
least squares. The slope of the linear regression line was
used as a quantitative indication of the rate of change over
the data period. The standard error of the linear regression
slope was also computed as an estimate of the uncertainty in
the slopes [Ross and Elliott, 1996]. Additionally, to test the
statistical significance of this trend, we applied Spearman’s
test of trend. This is a nonparametric measure of linear
association based on correlation of ranks. In this study, values
that rejected the null hypothesis of randomness at the 95%
confidence level were considered statistically significant
[see also Ross and Elliott, 2001]. If a real climatological
change point was detected by the PMW test, we calculated
trends for the two parts of the time series (before and after
the change point) to check if the change point is also a trend
turning point. These last two steps are immediately combined in the two‐phase linear regression scheme developed
by Lund and Reeves [2002].
[24] If a change point is present in a time series of climate
variables, the long‐term behavior might better not be quan-
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tified using the linear slope of a single straight line. In such a
case, an increments study should give some added value.
Seidel and Lanzante [2004], for instance, explored this idea
and assessed three alternatives to linear trends for characterizing global atmospheric temperature changes. Here, as in the
work of Añel et al. [2006], in the case of a statistically significant change point, we split the initial series at the change
point and we compute for each segment its increment by
multiplying the linear slope with the period covered by the
segment. The “increment” of the entire time series is then
defined as the value obtained by adding the increments of
the two segments, divided by the total length of the initial
time series.
[25] Finally, to determine the correspondence between
(1) different atmospheric variables measured at Uccle or
(2) different stations for a given atmospheric variable, linear
Pearson correlation coefficients were calculated and scatterplots were constructed. Correlations were taken for monthly
anomaly time series that are not detrended. However, the correlation coefficients calculated for time series first detrended
with single linear regression lines.
3.2. Tropopause Identification
[26] In this paper, we introduce an upper tropospheric
humidity relative to the tropopause. Therefore, the identification of the tropopause is of major importance. The tropopause used here is the standard (first) thermal tropopause,
defined as “the lowest level at which the lapse rate decreases
to 2 K km−1 or less, provided also the average lapse rate
between this level and all higher levels within 2 km does
not exceed 2 K km−1” [WMO, 1957]. In case of the uniform
1990–2007 time series of radiosonde data, we dispose of
profiles with a vertical resolution of about 100 m (data
points every 10 s), so that the tropopause is calculated, after
the sounding, using all these levels. Therefore, we used the
algorithm described in the appendix of Zängl and Hoinka
[2001] but also carefully checked the vertical (T, RH, and
ozone) profiles to avoid erroneous identifications of the thermal tropopause. The sounding profiles of the uniform 1990–
2007 time series were also used to determine the location of
a second tropopause, if present. Only soundings that reached
an altitude of 20 km were considered and the definition of
the World Meteorological Organization’s (WMO’s) Commission for Aerology is applied [WMO, 1957]: “if above the
first tropopause the average lapse rate between any level and
all higher levels within 1 km exceeds 3 K km−1, then a
second tropopause is defined by the same criterion as for the
first tropopause. This tropopause may be either within or
above the 1 km layer.”
[27] For the total Uccle time series of radiosonde measurements, starting in 1968, the first tropopause is identified
in a similar way. However, in the early years, only the
mandatory levels of the sounding are available. The tropopause determination of the other European radiosonde stations
considered further in this paper is based on the identification
(code 22) in the Integrated Global Radiosonde Archive
(IGRA). Antuña et al. [2006] showed in a case study that,
although there is a high amount of missing data in the IGRA
data set for a given station, the existing data set is statistically
representative of the complete data set for the tropopause
features.
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[28] Finally, for the uniform Uccle time series, we also
calculated the ozone tropopause, as defined by Bethan et al.
[1996] using the following three criteria:
[29] 1. The vertical gradient (evaluated over a depth of
≈200 m) in the ozone mixing ratio exceeds 60 ppbv km−1
(values of this gradient are generally in the range 50–
70 ppbv km−1 near the tropopause).
[30] 2. The ozone mixing ratio is larger than 80 ppbv.
[31] 3. The mixing ratio immediately above the tropopause
exceeds 110 ppbv. This criterion rejects layers of stratospheric air in the troposphere where the maximum mixing
ratio is less than 110 ppbv.
[32] Clearly, this definition of the border between the
troposphere and the stratosphere takes advantage of the fact
that ozone has very different tropospheric and stratospheric
concentrations, with a very sharp gradient at the tropopause.
Although there are clear differences between the thermal
tropopause and the ozone tropopause in individual soundings,
we want to stress that the time behavior of the properties
(height, pressure, and temperature) of both tropopause definitions is identical. This concerns both the trends as the
change points in the time series. Therefore, for the remainder
of the paper, we stick to the thermal tropopause.
3.3. UTH Definition
[33] Contrary to the thermal tropopause, there is no precise,
well‐established definition for the upper troposphere. And
there are also numerous parameters describing “humidity.”
Consequently, there is also a variety of definitions for the
upper tropospheric humidity. First, we prefer to work with the
(integrated) specific humidity as it represents the actual
amount of water vapor in the atmosphere. The relative
humidity, on the other hand, also depends on the temperature
of the atmosphere through the saturation vapor pressure (see,
e.g., Peixoto and Oort [1996] for more theoretical considerations on the relations between relative humidity, other
moisture parameters, and temperature). It is important to
note that the same trends occur when the UTH is defined in
terms of relative humidity instead of specific humidity.
[34] Second, when using satellite humidity observations,
the UTH is defined in absolute terms, that is, as the integrated specific humidity between fixed, absolute levels: for
example, between 500 and 300 hPa in the work of Soden
et al. [2002], and between 500 and 200 hPa in the work of
Milz et al. [2009]. Of course, satellite observations usually
do not provide direct or precise information about the tropopause location, so there is no other option for defining the
upper troposphere. However, if information about the tropopause location is present, as is the case for radiosonde
observations, the upper troposphere can be defined with the
tropopause as an upper limit and a height, temperature, and
pressure relative to the tropopause as a lower limit [e.g.,
tropopause height minus 3 km in Figure 1 (right)]. Defining
the UTH relative to the tropopause has many advantages with
respect to the UTH defined in absolute terms:
[35] 1. We prevent the potential mixing of moist upper
tropospheric air with dry lower stratospheric air (beyond the
detection limit of the radiosonde humidity sensor) in our
analysis. The mean tropopause pressure at Uccle, calculated
for the uniform time series, is 229.34 ± 45.18 hPa (1s), so
we would include for some soundings lower stratospheric
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air in the UTH value, if the upper troposphere were to be
defined between 500 and 200 hPa.
[36] 2. The upper troposphere always has the same thickness (in km, °C, or hPa, with only the last related to the mass),
so we are sure that the UTH is not affected by the stretching
or shrinking of the upper tropospheric layers.
[37] 3. Related to the previous point, we are independent
of the dynamics of the troposphere (and stratosphere) in
general and, for example, the lifting or descending of the
tropopause.
[38] In Figure 1, the UTH time series, in monthly anomaly
form, calculated from two different upper troposphere definitions, one in absolute terms and one relative to the tropopause,
are presented. It should be obvious that the delimitation of the
upper troposphere plays an important role in assessing the
time behavior of the UTH, as large differences exist between
both UTH time series. The time variation before 2001 is very
similar [and nicely demonstrated by the monthly cumulative
deviations in Figure 1 (bottom)]: the UTH decreased until
around the year 1995 and then recovered until the years 2000–
2001. On the other hand, the behavior after 2000 is very
distinct: the “absolute” UTH more or less remains constant,
whereas the “relative” UTH drops down after autumn 2001.
In this paper, we first focus on this large discontinuity around
autumn 2001 in the relative UTH time series. Exploring the
nature of this apparent and rather sudden drying out of the
upper troposphere, defined relative to the tropopause, might
also illustrate why the same phenomenon does not arise in
the upper troposphere, defined in absolute terms. We nevertheless stress again that, in our opinion, the UTH should be
defined relative to the tropopause and, consequently, it is
especially this time series of UTH that is studied in great
detail.
3.4. Homogeneity Checks
[39] In this section, we check if the discontinuity in the
UTH time series around autumn 2001 can be ascribed to an
inhomogeneity in the uniform time series of radiosonde
observations. In any case, we underline that the occurrence
of this change point is independent of (1) the definition and
the extension of the upper troposphere (ranging from 1 to
4 km below the tropopause, or from 100 to 300 hPa below
the tropopause), (2) the used change‐point test (all mentioned change‐point tests found a statistically significant
change point in autumn 2001), (3) the correction method
used (the Miloshevich correction also resulted in an autumn
2001 change point), and (4) the used subset of the uniform
database (allowing all launch times or considering only
ozone soundings, three times a week, does not affect the
presence of the autumn 2001 change point).
[40] We first investigate a nonphysical (e.g., changes in
equipment, measurement technique) origin of this change
point. As mentioned in section 2, Vaisala made changes in
the sonde packaging in September 1998 and June 2000.
Because we made an inventory of all metadata of the radiosondes launched since 1990, we dispose of the production
dates of the individual sondes and we can calculate their
ages at launch. The mentioned packaging changes might
have led to a discontinuity (moist bias) in our time series in
October 1999 and in November 2000, respectively. Even if
we take the uncertainties of the change‐point detection into
account, these packaging changes cannot account for the
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autumn 2001 change point in the UTH time series. Moreover, after autumn 2001, a “dry bias” instead of a moist bias
is observed. On the other hand, our PWM test statistic does
not reveal any traces of change points around October 1999
and November 2000. Furthermore, there is no metadata event
around autumn 2001 for the uniform radiosonde database;
the storage conditions of the sondes or the technicians
performing the radiosoundings did not change during the
1990–2007 time period.
[41] However, when we apply the PMW test to the time
series of the sonde ages at launch, a change point in autumn
2001 is detected. On average, the sondes are older at launch
after autumn 2001 than before. This can be explained by the
fact that the operational synoptic radiosoundings (performed
daily by our weather office, twice a day, except when an
ozone sounding took place) switched from Vaisala’s RS80‐A
to RS90‐H in autumn 2001. As we only consider RS80‐A
radiosondes launched at 1200 UTC in this study, this radiosonde type change gives rise to a reduced frequency of
radiosonde launches (three times a week, at 1200 UTC; see
also section 2), next to the even more important increase of
sonde ages after autumn 2001. Indeed, our radiosonde launch
practice was such that radiosondes used for ozone soundings
were on average older than the synoptic radiosondes. As
noted in section 2, the dry bias in the UTH after autumn
2001 might be caused by the older radiosondes used due to
the sensor aging and the chemical contamination errors. This
would also mean that the proposed correction schemes for
these errors by Wang et al. [2002], which we included in the
so‐called Miloshevich correction, are insufficient, because,
also in the Miloshevich‐corrected UTH time series, a dry bias
is noted after autumn 2001. But we believe that there exist
even stronger arguments which rule out the autumn 2001
change point in the sonde’s ages as the major contributor to
the UTH change point. First of all, the autumn 2001 change
point is also present in the UTH database formed by only
considering ozone radiosoundings. This subset of the uniform radiosonde database has no change point in the sonde
ages time series, so also not around autumn 2001. Second,
there is no autumn 2001 change point in the precipitable
water time series. Nevertheless, this variable is most susceptible to the aging and the chemical contamination of the
humidity sensor. So the UTH time series should also not be
so strongly affected by the radiosonde age time series. Third,
the autumn 2001 change point also arises in the time series of
other atmospheric variables, like the tropopause temperature,
pressure and height, and tropospheric temperatures, although
it is not statistically significant in all of these cases. As far as
we know, radiosonde temperature and pressure sensor aging
or contamination are not as much of a major issue as they are
for the humidity sensor, so the sonde age change point is
likely not responsible for the temperature and pressure
change points around autumn 2001. Moreover, to our
knowledge, Vaisala has never made modifications in their
radiosonde temperature, pressure, and humidity sensors all at
once within a given radiosonde type.
[42] It is important to make another consideration. As we
mentioned in section 2, serious doubts exist about the quality
of RS80‐A humidity measurements at low temperatures.
Therefore, we removed humidity profiles resulting from an
iced humidity sensor and we applied the best available
correction method, the one developed by Leiterer et al.
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Figure 2. Uccle time series of monthly anomalies of the integrated specific humidity for upper troposphere
layers of 10°C thickness and with temperatures in the intervals (a) tropopause temperature (Ttropo) < T <
Ttropo + 10°C, (b) Ttropo + 10°C < T < Ttropo + 20°C, (c) Ttropo + 20°C < T < Ttropo + 30°C, and (d) Ttropo +
30° C < T < Ttropo + 40°C for the different subplots. Each time series has a statistically significant change
point in autumn 2001, and the linear regression lines before and after these change points are shown. Red
lines are used for positive trends, and blue lines for negative trends. Solid lines denote statistically significant
trends, and dashed lines are statistically insignificant trends. The different layers, from top to bottom, have
the following mean pressures and thickness of geopotential height (where minima and maxima are denoted
in parentheses): 262 hPa (204–319), 328 hPa (263–395), 397 hPa (319–478), 482 hPa (384–587), and
1.522 km (1.224–1.884), 1.205 km (1.088–1.420), 1.218 km (1.115–1.447), 1.330 km (1.139–1.538).
[2005]. Suortti et al. [2008] pointed out that after this correction, for T < −50°C and at high RH, the dry bias did
diminish considerably, but on average there still remained an
≈5% RH dry bias in the upper troposphere. However, we are
convinced that the UTH trend captured by the Uccle RS80‐A
radiosondes (see Figure 1) reflects a real UTH climatology.
Indeed, when descending through the upper troposphere, and
hence enhancing the reliability and data quality of the radiosonde humidity measurements, the described humidity trend
persists. This is obvious from Figure 2, in which the (specific)
humidity trends are shown for layers of 10°C thickness and
with top temperature equal to the tropopause temperature
+n*10° C (n = 0,1,2,3).
[43] To summarize, we rule out any instrumental cause for
the drop in the UTH around autumn 2001, and we ascribe it
to natural variability. The main issue in the remainder of the
paper is then to find the origin of this UTH decrease, given
the information available from the radiosonde data.
4. Analysis of the Uniform Uccle Time Series
[44] In this section, we describe the time variations (trends
and change points) present in the uniform Uccle 1990–2007
database of the UTH (section 4.1) and related properties
such as the tropopause (section 4.2), double tropopause
occurrences (section 4.3), and tropospheric (section 4.4) and
lower stratospheric (section 4.5) variables.
4.1. UTH
[45] The similar time behavior given by both UTH definitions before 2001 (see Figure 1) is also in agreement with the
observations of the upper tropospheric water vapor between
300 and 500 hPa from the NASA Water Vapor Project and
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from TIROS Operational Vertical Sounder products [see
Soden et al., 2002, Figure 2]. In their paper, they explain the
upper tropospheric drying in the early 1990s as a response to
the Mount Pinatubo eruption in June 1991. Owing to the
volcanic aerosols, the reduced solar heating led to a global
cooling of the lower troposphere. Associated with this cooling was a reduction in the global water vapor concentrations,
which closely tracked the decrease in temperature [see Soden
et al., 2002, and references therein]. The authors demonstrated that their GCM reproduces the observed temperature
profile changes only if the water vapor feedback (through the
radiative calculations scheme) is turned on. The time variability of the UTH in the second half of the 1990s can then be
interpreted as the return to normal values before the eruption.
[46] In contrast to the UTH time variability before ∼2000,
the behavior after 2000 is not understood. However, not
only in the upper troposphere but also in the lower stratosphere a substantial, persistent drop in humidity since 2001
is found in both global (60°N–60°S) satellite observations
from the Halogen Occultation Experiment (HALOE, at
82 hPa) and balloon observations at Boulder, Colorado
(40°N) [Randel et al., 2006]. This feature is complemented
by an anomalously cold tropical tropopause and a decrease of
ozone near the tropical tropopause during this period and is
also believed to be associated with enhanced deep convection between 20°N and 20°S [Rosenlof and Reid, 2008] and
with an increase of total stratospheric NO2 in the tropics after
2001 [Pastel et al., 2009]. These phenomena are ascribed to
an enhanced tropical upwelling (Brewer‐Dobson) circulation
after 2001 [Randel et al., 2006; Garcia and Randel, 2008],
caused by an enhanced planetary wave driving [Dhomse
et al., 2008]. This is suggested to be a result of enhanced
mixing in the extratropics, leading to additional air being
drawn from the lower stratospheric tropics and causing
cooling in the tropical tropopause region due to adiabatic
expansion and thus reducing water vapor values [Jones et al.,
2009].
[47] Of course, if there is a link between the UTH drop at
Uccle in autumn 2001 and the lower stratospheric humidity
decrease after 2000 and, consequently, this event is a larger‐
scale climate event, it would be very hard to infer any causes
or mechanisms from just one (or even a few) station’s data.
Therefore, first we focus on the trend analysis of some local,
related variables and try to find a mechanism for the UTH
drop at Uccle on a local (or regional) scale. As the humidity
drop in autumn 2001 is only present when the upper troposphere is defined relative to the tropopause, we first concentrate on the time behavior of the tropopause itself to find
the physical phenomenon that drives the UTH discontinuity.
4.2. Tropopause Properties
[48] First, we want to mention that the tropopause at Uccle
has a mean height of 11.02 ± 1.36 km and a mean temperature of −59.00 ± 6.34°C for the uniform time series.
The close connection between the 1990–2007 time series of
the tropopause and the relative UTH is nicely demonstrated
by the correlation analysis. The UTH, defined in a layer
extending from the tropopause to 3 km below it, for instance,
is positively correlated with the tropopause temperature (the
linear Pearson correlation coefficient, R2, equals +0.68) and
negatively correlated with the tropopause height (R2 = −0.72).
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As already marginally mentioned, the autumn 2001 change
point is also present in the time series of the tropopause
temperature, pressure, and height, though not statistically
significant at the 90% confidence level in the case of the
temperature and height. Before autumn 2001, the tropopause
height was descending (especially due to a dip in the year
2001; see Figure 3) and the pressure increasing, both significantly, and heating up (not significantly). Around November
2001, the tropopause started to lift up (see again Figure 3)
and cooled down. Hence, from then on, the opposite trends
(not significant) or no trends are observed. Clearly, the
tropopause properties (height and pressure, temperature) are
highly intercorrelated.
[49] In general, the long‐term variability of the tropopause
can be of stratospheric and/or tropospheric origin [e.g., Santer
et al., 2003; Seidel and Randel, 2006]. Indeed, the tropopause
height is negatively correlated with lower stratospheric temperatures and positively correlated with tropospheric temperatures (see Figure 4, which is a remake of Figures 6 and
7 from the work of Seidel and Randel [2006] for the Uccle
station and only for the 1990–2007 time period): the tropopause rises (descends) with a warming (cooling) troposphere
and cooling (warming) stratosphere, which is in midlatitudes
due to balanced dynamical structure in cyclones and anticyclones [Seidel and Randel, 2006, and references therein].
Additionally, there are other possible causes of the tropopause long‐term variability (rising) closely related to the
tropospheric and stratospheric variability but nevertheless
with their own featuring, such as the broadening of the
tropical belt and the poleward movement of tropospheric jet
streams [Seidel et al., 2008], the acceleration of the Brewer‐
Dobson circulation under rising concentrations of greenhouse
gases [Garcia and Randel, 2008], and the strengthening of
the meridional thermal gradients in the upper troposphere/
lower stratosphere (UTLS) at the subtropics and midlatitudes
and consequently an increase of the UTLS wave baroclinicity in these regions [Castanheira et al., 2009]. This
latter phenomenon is also associated with an increase in the
frequency of double tropopause events.
4.3. Double Tropopauses
[50] Before going into more detail about the possible
causes for the time behavior of the tropopause, we take
some time to describe the occurrence of double tropopauses
above Uccle. Double tropopauses are associated with a
characteristic break in the thermal tropopause near the subtropical jet, wherein the low‐latitude (tropical) tropopause
extends to higher latitudes, overlying the lower tropopause
[Randel et al., 2007]. At Uccle, for the uniform time series,
a double tropopause is located on average at about 17.05 ±
3.37 km high (which is derived through the atmospheric
hydrostatic equation which uses the temperature, pressure,
and humidity as data inputs, with a mean pressure of 97.21 ±
44.36 hPa) and has a mean temperature of about −60.34 ±
6.13°C. The frequency of the double tropopause events in
Uccle is about 57% in winter, 26% in spring, 18% in
summer, and 35% in autumn. Both these numbers and their
seasonality agree with the results mentioned in the studies
by Añel et al. [2007] and Randel et al. [2007]. In this paper,
we are mostly interested in the time variability of the frequency of double tropopause occurrences, shown in Figure 3
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Figure 3. (top) Uccle time series of monthly anomalies of (left) the tropopause height and (right) the
relative frequency of double tropopause events. The mean before and after the detected change point
are shown in gray. The linear regression lines for (left) the time periods before and after the detected
change point and (right) the entire time period are also shown. Red lines are used for positive trends,
and blue lines for negative trends. A solid line denotes a statistically significant trend, and a dashed line
a statistically insignificant trend. The statistical significance of the trends is investigated by Spearman’s
test. Green lines denote the zero anomaly lines. Over the entire time period, the tropopause height is
increasing at a rate of 30 m/decade (single linear regression slope) or decreasing at a rate of 177 m/decade
(incremental). (bottom) Respective monthly cumulative deviations of the anomalies shown (Figure 3, top)
from the mean of the anomalies. Green lines denote the zero lines.
in terms of their monthly anomalies and cumulative deviations. It is striking that the monthly cumulative deviations
reveal a rather similar time behavior as the UTHs before the
year 2000 (see Figure 1) but do not show a drop around
autumn 2001. Indeed, the frequency of double tropopause
events decreased in the early 1990s, started to increase in the
second half of the 1990s, and was followed by a leveling off
after the year 2002. The net result over the entire 1990–2007
time period is no significant trend in the double tropopause
occurrences.
[51] The analysis of the variation in time of the height,
pressure, and temperature of the second tropopause is
hampered by the reduced frequency of radiosonde launches,
and hence an increased scatter, after November 2001.
However, we are still able to detect an increase in the second
tropopause height at the end of 2001, in agreement with the
lifting of the first tropopause. On the other hand, we can
study the effect of the presence of a second tropopause on
the properties of the first tropopause. Therefore, we split our
uniform database into (1) a subset containing all measurements in which a single tropopause is detected and (2) a
subset of measurements characterized by the occurrence of
at least a double tropopause. Although there is an increased
scatter in the monthly anomaly time series of (first) tropopause properties of these two subsets with regard to the entire
database of measurements, the general tropopause time variability of both subsets is very similar and is also nearly
identical to the tropopause behavior described so far.
4.4. Tropospheric Variables
[52] We now come back to Figure 4. Compared to Figures 6
and 7 in the work of Seidel and Randel [2006], we add an
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Figure 4. Vertical profile of the correlation between temperature anomalies at a given level and tropopause height anomalies, calculated for the uniform database at Uccle. The different symbols indicate the
presence of a (significant) change point in the temperature anomaly time series around autumn 2001.
extra feature by marking if the temperature time series exhibit
a (statistically significant) change point around autumn 2001.
It turns out that this change is present in the time series of all
tropospheric temperatures from 850 to 350 hPa. In these
cases, the change point is also a trend change point (see
Figure 5 for the temperature at 500 hPa): before autumn
2001, these tropospheric temperatures have a tendency to
decrease; around autumn 2001, there is an increase in the
tropospheric temperatures and, afterward, a tendency to
increase or to remain constant. These trends are opposite to
the tropopause temperature trends. Overall, the troposphere
warms by, for example, 0.28°C/decade at 500 hPa if quantified by the single linear regression slope or cools at −0.19°C/
decade if expressed in terms of the total incremental change
(see section 3.1).
[53] Another interesting tropospheric parameter for which
we analyzed the time behavior is the thickness of the free
troposphere. This variable is defined here as the difference
between the geopotential heights at 300 and 700 hPa. As
could be expected from Figure 4, a (significant) change
point in the time series occurs in autumn 2001. This is also a
trend change point: prior to this date, the free troposphere
shrinks (not significantly), followed by a (nonsignificant)
stretching afterward (see Figure 5). Around autumn 2001,
the free troposphere is stretched out rather suddenly. If the
thickness of the geopotential height is computed for the
1000–400 hPa pressure interval, as in the work of Añel et al.
[2006], a similar time variability is found, with a (trend)
change point around autumn 2001. A last point to consider
is the strong positive correlation between the thickness of
the free troposphere and the surface temperature (R2 = 0.68),
although the surface temperature does not exhibit a similar
trend (see again Figure 5).
4.5. Lower Stratospheric Variables
[54] We now study the time behavior of some lower
stratospheric parameters. As pointed out again recently by
Son et al. [2009], the cooling of the lower stratosphere
associated with ozone depletion has a large impact on the
tropopause height (e.g., next to the tropospheric warming due
to greenhouse gas increases [see, e.g., Santer et al., 2003]).
The temperatures observed in the lower stratosphere with the
uniform Uccle radiosonde data set decrease in time in the
period 1990–2007: the higher up in the stratosphere, the more
significant the cooling becomes (see Figure 5 for the temperature time series at 70 hPa). The different change‐point
tests do not agree about one significant change point, but
from our analysis it stands out that the early years of the
time period (before the year 1998) contribute most to the
overall cooling. This is also the period in which the stratospheric ozone above Uccle has not started its recovery yet.
Additionally, the thickness of the geopotential height for
the lower stratospheric 100–50 hPa layer (introduced by Añel
et al. [2006]) also remains more or less constant during the
last two decades. So, the lower stratospheric temperatures and
thickness do not reveal any event or change at the end of
2001. Although the study of the stratospheric ozone time
series of Uccle is far beyond the scope of this paper (it is the
subject of a forthcoming paper), we want to mention here
that we also do not find any discontinuity or trend change
around autumn 2001 in the (lower) stratospheric ozone
content, even after detrending the data with the dominant
component of the ozone decadal variability. Apparently, at
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Figure 5. Uccle time series of monthly anomalies of (top left) the surface temperature, (top right) the
temperature at 500 hPa, (bottom left) the thickness of the free troposphere, and (bottom right) the temperature at 70 hPa. In case of a relevant and statistically significant change point, the means and the linear
regression lines for the time periods before and after this change point are shown in gray (means) and in
color (linear regression). When there is no change point, the linear regression lines for the entire time
period are shown. Red lines are used for positive trends, and blue lines are used for negative trends. A
solid line denotes a statistically significant trend, and a dashed line denotes a statistically insignificant
trend. The statistical significance of the trends is investigated by Spearman’s test. Green lines denote
the zero anomaly lines. For the entire time period, the temperature at 500 hPa and the thickness of the
free troposphere increase at a rate of 0.28°C/decade and 4.84 m/decade, respectively, or decrease at incremental rates of −0.19°C/decade and −6.41 m/decade, respectively.
Uccle, this is the equivalent effective stratospheric chlorine
(EESC) content.
5. Relevance of the Autumn 2001 Change
[55] The existence of a change point in autumn 2001 in
the Uccle tropospheric temperatures, tropopause properties,
UTH, and (free) tropospheric thickness is a very interesting
feature on its own, but is it also present in the time series of
observations at other midlatitudes? And how exceptional is
it in a larger time perspective? We try to answer these two
questions in sections 5.1 and 5.2.
5.1. Spatial Uniformity
[56] In this section, we examine to which extent the studied
Uccle time series trends are representative for the European
midlatitudes. We especially focus on the presence of a
change point around autumn 2001 in the time series of
other European radiosonde stations. Therefore, we selected
radiosonde stations within 10° latitude from Uccle and with
a longitude range of −10° to +30° and downloaded their
data provided in the Integrated Global Radiosonde Archive
(IGRA) described by Durre et al. [2006]. In this section, we
deal with the tropopause temperature time series of these
stations, rather than with (upper tropospheric) humidity data
records, as the data homogeneity is not guaranteed for an
individual IGRA station. However, at least for the Uccle data,
there was a clear correlation between the (relative) UTH and
the tropopause temperature trends.
[57] Certainly, there is extensive evidence that the long‐
term variation in radiosonde temperatures as well is affected
critically by inhomogeneities introduced through changes in
instruments and measurements practices [Lanzante, 2009,
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Figure 6. Time series of moving averages of the monthly anomalies of tropopause temperatures for
Uccle, De Bilt (WMO code 06260, NL, 52°6′N, 5°11′E), Larkhill (WMO wode 03743, UK, 51°12′N,
1°48′W), and Meppen (WMO code 10304, D, 52°44′N, 7°20′E). The data of the last three stations are taken
from the IGRA database. Autumn 2001 is marked with a vertical dashed line.
and references therein). But, as the existing “homogenized”
data sets [e.g., Haimberger et al., 2008; McCarthy et al.,
2008; Sherwood et al., 2008] apply only to the mandatory
pressure levels of radiosonde analysis, these data sets do not
provide enough vertical resolution for a trend analysis of
tropopause temperatures [Rosenlof and Reid, 2009]. Recently,
McCarthy et al. [2009] extended their homogenization
procedure to tropospheric humidities (standard levels up to
300 hPa) and introduced as such a greater consistency
between temperature and specific humidity trends from day
and night observations. However, as this homogenization
system is neighbor‐based, we chose not to use such a database to detect a single change point in different neighboring
stations.
[58] The autumn 2001 change does occurs not only at
Uccle, but also at other European stations. Examples for the
tropopause temperature are given in Figure 6. The other
three European radiosonde stations shown in Figure 6 lie in
the vicinity of Uccle, and especially the overall resemblance
between Uccle and De Bilt is very striking. As the autumn
2001 change is also a distinct feature in other European
radiosonde stations (with different equipment, and instrumental changes occurring at other periods), the physical
nature of the tropopause temperature drop at the end of the
year 2001 stands out without any doubt. Furthermore, we
also calculated the correlation coefficients between the
monthly anomalies (“correlation between months”) of the
entire time series of tropopause temperatures of the selected
IGRA stations with the corresponding Uccle data. A contour
plot in the latitude‐longitude field of these correlation
coefficients is shown in Figure 7. This figure shows a rather
concentric contour pattern around Uccle, meaning that, also
for the entire time series of tropopause temperatures (typically starting in the late 1960s), (1) the Uccle data series
(trends) are typical for Western Europe and (2) deviations
from the Uccle data series (trends) occur rather smoothly,
both in latitude and longitude.
[59] However, the contours in Figure 7 have a more longitudinal structure which could resemble the fact that the
features of the global tropopause are more homogeneous in
longitude than in latitude. Moreover, the contour plot might
also suggest a discrimination of trends between the western,
more maritime, and eastern (continental) part of Europe.
This might point to the fact that the autumn 2001 event is
not a large‐scale natural variability event.
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Figure 7. Latitude‐longitude contour plot of the correlation coefficients between the tropopause temperature monthly anomalies of selected IGRA radiosonde stations (red crosses) and the Uccle station (red
asterisk) for the longest time period possible.
5.2. Extended Study
[60] To put the autumn 2001 change in a larger time perspective (“how exceptional is this change in tropospheric
and tropopause properties”), we now consider the entire
database of radiosonde measurements at Uccle (at 1200 UTC),
starting in 1963 (1968 for the tropopause identification). But
we have another reason to deal with a longer time series. The
autumn 2001 change point is, for a number of tropospheric
and tropopause variables, a trend turning point, or at least
it breaks the preceding trend. Therefore, the extension of
our Uccle data set helps to study the relevance of either time
period. This entire database of vertical profiles is gathered
by different radiosonde types, so we do not investigate the
humidity field trends but limit ourselves to the temperature,
height, and pressure measurements. These are estimated to
suffer less from sensor changes than the humidity measurements, although their long‐term variation is also affected by
inhomogeneities [Lanzante, 2009, and references therein],
as already discussed at the beginning of section 5.1.
[61] Generally, it comes out that the surface temperature
at Uccle increases at a rate of 0.50 ± 0.06°C/decade, the
lower tropospheric temperature (at 500 hPa) increases 0.18 ±
0.06°C/decade, the tropopause cools by −0.33 ± 0.08°C/
decade (see Figure 8, for the period 1968–2009), and the
lower stratospheric temperature (at 100 hPa) decreases by
−0.66 ± 0.06°C/decade. The tropopause height increased
36 ± 19 m/decade. All these trends are statistically significant.
The numbers are also in line with the literature reports for
Northern Hemisphere midlatitudes [see, e.g., Seidel and
Randel, 2006; Sherwood et al., 2008; Schmidt et al., 2008].
So, the aforementioned opposite (but not always statistically significant) trends in the 1990–2001 decade for the
restricted RS80‐A database represent a temporary disruption
of the general trends.
[62] In the entire radiosonde database, the autumn 2001
change point is less pronounced: the major features of the
entire time series are the strong overall decreasing or
increasing trends (leading to an artificial change‐point
detection in the middle of the time series). After detrending
the time series, no significant change points are identified in
the tropospheric temperatures or the tropopause features.
For the surface and the lower stratospheric temperatures as
well as the thickness of the free troposphere, a change
point at the end of the 1980s is present. Only in the case of
the surface temperature a trend reversal does occur in this
period (i.e., the surface temperature decreases significantly
before November 1988 and increases significantly thereafter,
but this increase is mostly due to the high values during the
last decade). The same change point is present in the time
series of surface temperatures retrieved by the weather station
at Uccle. For the lower stratospheric temperatures, the end
of the 1980s (and beginning of the 1990s) marks the end of
the strong cooling, followed by a slight increase of temperatures. Here, the lower stratospheric ozone recovery since
the mid 1990s certainly plays a role, but the change from VIZ
to RS80‐A radiosondes in January 1990 has without doubt a
strong effect, too.
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Figure 8. Uccle time series of the tropopause temperature monthly anomalies for the 1968–2009 period.
A change point is found around October 1988; the autumn 2001 change point is indicated by the vertical
dashed line.
[63] So, to conclude this section, we mention that the
autumn 2001 change point in temperatures and tropopause
properties is less pronounced when a longer time series of
radiosonde measurements at Uccle is considered. This is an
important consideration, as the HALOE stratospheric water
vapor database, in which a prominent drop was observed in
2001, only spans from 1992 onward.
6. Discussion and Conclusions
[64] On the basis of a uniform (one radiosonde type,
identical vertical resolution) data set of corrected radiosonde
vertical profiles, we investigated the properties of the upper
troposphere above Uccle, Belgium, in the 1990–2007 time
period. Over the entire period, no significant trends in upper
tropospheric humidity are detected, which is in line with
climate model predictions of a constant relative humidity
but opposes the predicted specific humidity increase in the
upper tropospheric levels (see section 1). As a matter of fact,
up to the year 2000, the most prominent feature is a period
of negative UTH anomalies in the early 1990s, which is a
response to the Pinatubo eruption: volcanic aerosols led to a
global cooling of the lower troposphere and reduced the
global water vapor concentrations. In the late 1990s, the
UTH then slowly recovered from the Pinatubo response.
However, the most curious finding of this study is the drop
in upper tropospheric humidity in autumn 2001, which
marks an end to this significant moistening of the upper
troposphere. We argued exhaustively that we do not find
any instrumental or environmental cause in our data set and
we ascribe it to natural variability.
[65] As this drop is prominently present in the time series
of UTH defined relative to the tropopause (with the tropopause as the upper limit and height, temperature, and pressure relative to the tropopause) and absent in the absolute
UTH time series (e.g., between 500 and 200 hPa), the cause
of the UTH decrease is without doubt associated with the
variability of the tropopause itself. As a matter of fact, the
tropopause underwent a lifting and cooling around autumn
2001, with the opposite behavior before autumn 2001. A
similar change in the tropopause properties around autumn
2001 is detected in the time series of other European radiosonde stations. This variability of the tropopause can be of
tropospheric and/or stratospheric origin.
[66] In the time series of the Uccle tropospheric temperatures from 850 to 350 hPa, a change point around autumn
2001 also exists. Before this change point, the troposphere
has a tendency to cool down; afterward, it has a tendency
to warm up. Hence, only for the short time period of almost
two decades, the association between a cooling (warming)
troposphere and descending (ascending) tropopause is well
established. In this context, we also mention the change in
the thickness of tropospheric geopotential heights around
autumn 2001: the tropospheric shrinking trend of the 1990s
is interrupted by a stretching of the troposphere in 2001,
which can be expected from the temperature records. As
such, the tropospheric vertical movement (stretching and
shrinking) might provide the link between the tropospheric
temperature changes and the tropopause properties. The tropospheric dynamics (circulations) contributing to the change
around autumn 2001 can then be summarized as follows: in
autumn 2001, the tropospheric temperature rose significantly,
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VAN MALDEREN AND DE BACKER: DROP IN UTH IN 2001 ABOVE UCCLE
so vertical turbulent motions or convection led to a considerable stretching out of the free troposphere. As a consequence, the tropopause is also lifted up and cooled down,
and the upper tropospheric layers started to freeze‐dry in the
fall of 2001.
[67] This hypothesis puts all the pieces of the tropospheric
puzzle together. However, the tropopause behavior is also
known to be affected by stratospheric processes such as
ozone depletion, which cools the lower stratosphere. In any
case, in the time series of lower stratospheric temperatures,
thickness of geopotential height, and (EESC‐detrended)
ozone amounts, we do not find any change around autumn
2001. The lower stratospheric temperatures hardly decrease
during the studied time period of 17 years, possibly because
of the onset of the ozone recovery in the second half of the
1990s. The time variability of height and pressure of the
second tropopause also exhibits a steplike change in autumn
2001 and hence seems to follow the vertical motion of the
first tropopause. The frequency of double tropopause occurrences varies in time with the UTH humidity, with the
exception that the relative frequency of double tropopauses
only started to level off after 2002. Pan et al. [2009] showed
that the occurrence of double tropopauses is, at least frequently, associated with tropospheric intrusions of subtropical air into the extratropical lower stratosphere. As the
frequency of double tropopause events can be interpreted as
an indication for the strength of the UTLS wave baroclinicity
[Castanheira et al., 2009], there is also a leveling of the
increase in UTLS wave baroclinicity at Uccle after 2002.
[68] We elaborate more on the (meridional) cross‐
tropopause transport of air and we want to discuss the
possible link between the autumn 2001 drop in UTH and
tropopause temperature and the enhanced tropical upwelling
after 2001, leading to a substantial, persistent, global
decrease in stratospheric water vapor since the end of 2000
[Randel et al., 2006]. Climate model simulations indicate that
a strengthening of the tropical upwelling, and hence an
intensification of the Brewer‐Dobson circulation, in response
to climate change will lead to an enhanced downwelling at the
(northern) midlatitudes [Li et al., 2008; McLandress and
Shepherd, 2009]. This increased downwelling could inject
dry stratospheric air, which enters the upper troposphere
across the tropopause. This might be a possible contributing
cause to the observed drop in UTH in autumn 2001 above
Uccle, taking some time lag with the increased tropical
upwelling into account. Jones et al. [2009] noted a time lag
of about 6 months between the drop in the stratospheric
water vapor in the tropics and in the 20–25 km altitude
midlatitude bins. On the other hand, increased downwelling
would also lead to a downward shift of the tropopause,
which is likely to warm up then. Exactly the opposite tropopause behavior is observed around autumn 2001 in the
Uccle data set. The consequences of the strengthening tropical upwelling on the midlatitude troposphere and stratosphere certainly deserve some additional research, from both
modeling and observational points of view.
[69] However, in a more indirect way, the lower stratospheric water vapor might trigger the UTH variability.
Increases in stratospheric water vapor act to cool the stratosphere but to warm the troposphere, whereas the reverse is
true for stratospheric water vapor decreases [Solomon et al.,
2010]. In our uniform time series, after 2001, we find high-
D20114
er mean tropospheric temperatures and similar mean stratospheric temperatures compared to the decade before, so the
association of these temperatures with a possible drop in
stratospheric water vapor above Uccle is not straightforward.
But Solomon et al. [2010] showed also that the decrease in
stratospheric water vapor concentrations after 2000 acted to
slow the rate of increase in global surface temperature over
2000–2009 by about 25% compared to that which would
have occurred due only to carbon dioxide and other greenhouse gases. In this sense, the stratospheric variability might
also act to slow down (or spin up) the tropospheric circulation
discussed earlier in this section. To conclude, we do not find
any observational evidence of a direct link of the UTH drop
around autumn 2001 with stratospheric processes. This is
not very surprising, however, because if the autumn 2001
decrease in UTH is a large‐scale climate event, a more global
observational record at midlatitudes (and also of photolytic
tracers), next to climate model simulation studies, are needed
to establish and test mechanisms that give rise to abrupt and
unexpected trend changes.
[70] For the last consideration we want to make, we return
to Figure 8. The tropopause temperature time series seems to
have some periodic behavior, under the form of a decadal
oscillation. An obvious candidate to account for this cyclic
variability in the tropopause and tropospheric properties is
the 11 year solar cycle. Indeed, a number of independent
analyzes reported on a relatively robust but modest influence of solar cycle forcing on the stratosphere‐troposphere
system. The lower stratosphere warms at solar maximum
due to the solar UV effect on increased stratospheric ozone;
the troposphere generally appears to warm and moisten
during solar maximum conditions (see Gleisner et al.
[2005], Salby and Callaghan [2006], van Loon et al.
[2007], and many more references therein), but there are
disagreements as to whether the tropical region warms, or
primarily the subtropics through midlatitudes [Rind et al.,
2008]. Two mechanisms have been suggested by modeling studies to explain the solar influence on the troposphere:
the top‐down dynamical response to the stratospheric variations and the bottom‐up coupled ocean‐atmosphere surface response [Rind et al., 2008; Meehl et al., 2009].
[71] However, when looking at Figure 9, in which the
time series of the 10.7 cm solar flux, the Uccle tropopause
temperature (1968–2008), and UTH are shown, only during
the last considered solar cycle (roughly from about 1995
on), both the tropopause temperature and the UTH variability are in phase with the solar cycle. The variability of
the entire tropopause temperature time series (starting in
1968) seems to be less coupled with the solar cycle. So, the
solar cycle maximum in 2001 could be partly responsible for
the autumn 2001 change in tropopause temperature and
UTH, but the solar cycle variability alone cannot account for
the observed variations, on more or less a decadal timescale,
in the tropopause temperature. This correspondence in variability between the solar cycle and the tropopause temperature in the 1990–2007 time period is not restricted to the
Uccle radiosonde database alone but also applies to other
IGRA radiosonde stations (see section 5.1) located in the
western part of Europe. As a matter of fact, there is a clear
discrimination between the west (strong) and the east (weak)
of Europe with regard to the correlation of the tropopause
temperature (and temperature at 100 hPa) with the solar
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VAN MALDEREN AND DE BACKER: DROP IN UTH IN 2001 ABOVE UCCLE
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Figure 9. Time series of normalized, 2 year running means of the monthly anomalies of the 10.7 cm
solar flux, the Uccle tropopause temperatures, and UTH. The autumn 2001 change is marked by a vertical
dashed line. We consider 2 year running means in order to compare the variability at timescales larger
than 2 years.
cycle during 1990–2007. This again underlines the representativeness of Uccle for only the western, maritime, part
of Europe. However, for all chosen European IGRA stations,
the entire time series of tropopause and lower stratospheric
temperatures shows decadal variations which are not directly
linked to the solar cycle.
[72] The discussion about the possible influence of the
solar cycle on the tropopause and UTH variability once
more underlines the need for a long‐term time series. Indeed,
when considering the entire, although inhomogeneous, time
series of radiosonde measurements, the change in tropopause
properties around autumn 2001 becomes less pronounced
and is not unique in time at all. These examples underscore
the bottom line for any climate change research: there is a
strong need for long‐term, homogeneous time series of
observations.
[73] Acknowledgments. This research was supported by the AGACC
project (contract SD/AT/01A) funded by the Belgian Federal Science
Policy Office. R. Van Malderen is now a fellow of the Solar‐Terrestrial
Center of Excellence (STCE), also funded by the Belgian Federal Science Policy Office. We thank U. Leiterer, H. Dier, and L. Miloshevich
for providing their code to correct the humidity profiles and for their
help with implementing it. This research was not possible without
the commitment of the technical staff that performed the radiosoundings at Uccle throughout the years. We also want to thank the three
anonymous reviewers for their in‐depth comments which substantially
improved the manuscript.
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