Naturwissenschaften (2006) 93:603–609
DOI 10.1007/s00114-006-0147-5
SHORT COMMUNICATION
Changes in tropopause height for the Eurasian region
determined from CARDS radiosonde data
Juan A. Añel & Luis Gimeno & Laura de la Torre &
Raquel Nieto
Received: 8 June 2005 / Revised: 22 June 2006 / Accepted: 25 June 2006 / Published online: 19 September 2006
# Springer-Verlag 2006
Abstract Previous studies have identified the tropopause
height (TH) as a promising fingerprint of climatic change.
In the present paper, we report variations in TH for the
Eurasian region over the period 1973–1998 and analyse the
influence of the Northern Annular Mode (NAM) on these
variations. As previous studies indicate that the greatest
increases in TH occur in the extratropics, we focused our
attention on this area. We applied a set of homogenization
procedures to radiosonde data and considered three different scenarios that take into account change points and the
main volcanic eruptions over the study period. Our results
demonstrate that the number of stations with positive TH
trends is very sensitive to the quality of data and the
methods used to remove inhomogeneities. Consequently,
when change points were included in the analysis, the
number of stations with positive trends decreased markedly.
Furthermore, stratospheric NAM appears to control TH in
stations located at latitudes higher than 55°N.
Keywords Tropopause . Radiosonde data .
Parallel climate model
Introduction
The height of the tropopause (TH)—the boundary between
the troposphere and stratosphere—has recently been pro-
Electronic supplementary materials Supplementary material
is available for this article at http://dx.doi.org/10.1007/s00114-0060147-5 and is accessible to authorized users.
J. A. Añel (*) : L. Gimeno : L. de la Torre : R. Nieto
Facultad de Ciencias de Ourense, Universidad de Vigo,
Ourense, Spain
e-mail: [email protected]
posed as a promising fingerprint of human effects on
climate (Santer et al. 2003a,b; Sausen and Santer 2003).
Recent increases in TH have been identified from radiosonde data (Highwood et al. 2000; Seidel et al. 2001), from
optimal combinations of observations and numerical
weather forecast reanalyses (Randel et al. 2000), and from
climate models forced by combined anthropogenic and
natural effects (Santer et al. 2003a,b).
Santer et al. (2003b) claimed positive identification of
a parallel climate model (PCM) pattern of TH change (in
response to combined anthropogenic and natural effects)
from TH data inferred from two different reanalyses. They
noted inhomogeneities in these early reanalysis products
(e.g. Kalnay et al. 1996; Gibson et al. 1997). Analysis of
improved second-generation reanalysis products (Santer et
al. 2004) confirms the earlier TH detection findings.
Previous analyses of reanalysis data indicate that the
greatest increases in TH have occurred in the extratropics in
both hemispheres. Accordingly, in the present study, we
focus our research on the extratropical region of the North
Hemisphere. Rather than employing reanalysis methods, we
made direct use of radiosonde data to estimate changes in
TH. We note that radiosonde-derived temperatures are one
of the multiple observational data streams assimilated by
reanalysis. Previous studies of TH changes inferred from
radiosonde data have focused on tropical sites (Seidel et al.
2001) and on the Arctic (Highwood et al. 2000), with no
studies to date in extratropical latitudes.
In this study, we estimate changes in TH from
25 years of radiosonde data for the Eurasian region
(20°N–80°N, 46°E–90°W), which represents roughly
13% of the globe. This is an area of strong interannual
variability in TH, where the main mode of extratropical
climate variability in both the troposphere and stratosphere—the Northern Annular Mode (NAM; Baldwin and
604
Fig. 1 Stations from the CARDS subset of Wallis (1998) for the
studied area (20°N–80°N and 46°E–90°W) for each of the three
studied scenarios. Upward triangles indicate a positive increment in
tropopause pressure, while downward triangles indicate a negative
increment (hectopascals per decade); if the increment is lower than 1.0
in absolute values, it is indicated with a lozenge (⋄). Colours indicate
the range of the increment in absolute values (green, 1.0–5.0; yellow,
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5.0–10.0; red, >10.0). Square symbols indicate those stations for
which we were unable to calculate an increment. The symbols under
the triangles indicate the correlation of tropopause pressure with NAM
at the 700-hPa level, while symbols over the triangles indicate
correlation with NAM at the 50-hPa level: x indicates a significant
correlation at the 5% level, while * indicates a significant correlation at
the 1% level (blue, negative correlation; red, positive correlation)
Naturwissenschaften (2006) 93:603–609
605
Fig. 1 (continued)
Dunkerton 2001)—can strongly influence TH (Ambaum and
Hoskins 2002).
Materials and methods
Original radiosonde data were provided by the Comprehensive Aerological Reference Data Set (CARDS; Eskridge
et al. 1995). We selected an initial subset of 36 stations
(Fig. 1) in the Eurasian area. According to Wallis (1998),
this subset of CARDS provides optimal temporal and
spatial coverage. The standard synoptic observation times
are 0000 and 1200 UTC. Although we dealt with both of
these observation times, only the results for 0000 UTC are
displayed in this paper because they are comparable to
those for 1200 UTC. The standard period of study was
1973–1998, although for several stations, we used the
longest period available.
For each station, we first computed daily TH at 1200 and
0000 UTC using the standard World Meteorological
Organization (WMO) thermal definition: the lowest level
at which the lapse rate decreases to 2°C/km, provided that
the average lapse rate between this level and all higher
levels within 2 km does not exceed 2°C/km (WMO 1957).
Occasionally, soundings were available at either 2300 or
0100 UTC rather than the standard 0000 UTC. In these
cases, we used either of the later or earlier soundings. When
both the 2300 and 0100 UTC soundings were available (but
not 0000 UTC), we used the 0100 UTC result. The TH was
set to “missing” on days with insufficient sounding levels
or erroneous or missing data.
Several homogenization studies and procedures for
adjusting radiosonde data have been proposed and conducted in the past (Lanzante 1996, 1998; Lanzante et al.
2003a,b; Parker and Cox 1995; Seidel et al. 2001). The
homogenization procedure used in the present study was
developed from the method used by Lanzante et al. (2003a)
and is described in Fig. 2. First, we estimated change points
from four information sources: (a) computational method—
abrupt changes in the variance of monthly series of
tropopause pressure (TP) anomalies using an iterative
method based on non-parametric tests, as explained in
Lanzante (1996); (b) strong differences between 1200 and
0000 UTC series of monthly mean TP; (c) CARDS project
metadata; and (d) metadata and considerations of Lanzante
et al. (2003a,b).
On this basis, we classified the detected change points as
one of four types:
1. Type 1: detected using the computational method
(CM) + confirmation from 0000 and 1200 difference
series (CDS) + confirmation from metadata (CFM);
2. Type 2: CM + (CDS or CFM);
3. Type 3: CM without CDS or CFM but plus detected
change point for the nearest station;
4. Type 4: only CM (supposed as natural variability).
606
Fig. 2 Methodology applied to
the original daily radiosonde
data
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After detecting the change points, three different
scenarios were studied. Scenario 1 (S1) uses the raw data
series of TP annual mean, computed without considering
the detected change points. The slope of the linear
regression function was used to estimate temporal variations. Scenario 2 (S2) is similar to S1, except that we
deleted those years that include any of the Type 1, 2, or 3
change points.
The increment for each segment (derived by splitting the
initial series at change points) was computed as the
difference between the value of TH for the last point and
the first point of the segment (using the values obtained
from the linear regression function for the segment). We
then define the “increment” as the obtained value after
adding the results of increments of each segment and
dividing by the period covered by the segments, as
suggested by Seidel and Lanzante (2004).
Scenario 3 (S3) is the same as S2 except that we
removed the 2 years following the main volcanic eruptions
of the study period: El Chichón in 1982 and Pinatubo in
1991. For S2 and S3, those segments with less than five
usable annual means were not considered.
To evaluate the role that the NAM plays in moderating changes in TP, we calculated Pearson correlations of
TP with the NAM index for the 700- and 50-hPa levels
using the NAM index as computed by Baldwin and
Dunkerton (2001) (from http://www.nwra.com/resumes/
baldwin/nam.php).
607
continent and a negative increment over the Atlantic
sectors.
Increments range from −16.27 hPa/decade for Barencburg
in S2 to 16.60 hPa/decade for Pechora in S3. The number of
stations with an increment (positive or negative) in TP in
excess of 10 hPa/decade (red triangles in Fig. 1) is three for
S2 and two for S3; the obtained results for 1200 UTC are
similar to those for 0000 UTC.
The NAM is the main mode of extratropical natural
climate variability in the troposphere and the stratosphere,
controlling the structure of the temperature field at an
interannual scale in both layers. Because it is possible that
the NAM has a strong influence on the interannual
variability of TP, we correlated our series with the NAM
at 700 hPa (representative of the mode in the troposphere)
and NAM at 50 hPa (representative of the mode in the
stratosphere). The use of NAM indices at both troposphere
and stratosphere levels is necessary because there are long
periods over which both modes are uncoupled (de la Torre
et al. 2006).
Figure 1 shows that the influence of the stratospheric
NAM is higher than that of the tropospheric NAM. In
general terms, these correlations have a sound physical
basis. An increment in stratospheric NAM is associated
with intensification of the polar vortex and a cooling of the
polar stratosphere, which in turn leads to an increment in
TH. These significant correlations with tropospheric NAM
are also supported for the tropospheric spatial pattern of
temperature anomalies associated with extreme phases of
NAM (Hurrell 1995).
Results
Table 1 summarizes most of the results of our analysis,
including station positions, the initial analysed period,
temporal variation in TP and correlation of TP with the
NAM at 700 and 50 hPa. This information is also shown in
Fig. 1, where increments and correlations with the NAM
are displayed via symbols overlaid upon a map.
A brief examination of Fig. 1 reveals the first interesting
result of the analysis: a significant number of stations
record positive increments in the TP. Four S1 stations
record TP increases, and this number increases when more
rigorous analyses are conducted (10 for S2, 11 for S3). The
obtained results for 1200 UTC are similar to those for S3
and only slightly different to those for S1 and S2. This
result diverges slightly from previous results derived from
general trends in TH, which describe decreases in TP. If we
only consider those stations where the sign of the increment
is constant for the three scenarios (the safest results), we
find a clear temporal decrease in TP over the North Atlantic
at latitudes higher than 60°N. Other less consistent results
(only S2 and S3) for both times (0000 and 1200 UTC)
include a positive increment in TP over the northern Asian
Discussion
Comprehensive Aerological Reference Data Set radiosonde
data were used to estimate trends in TH for the Eurasian
region, an area with strong inter-annual variability in TH
and where a mode of natural climate variability, NAM,
potentially controls part of this variability.
In general terms, results are very similar in sign,
although not in magnitude, to those achieved by Santer et
al. (2003a,b, 2004) using reanalysis and numerical model
experiments. The positive TH trends recorded for a
significant number of stations disappear when restrictive
conditions related to change points in the radiosonde data
are applied. This shows the sensitivity of the detected
increments to the method used to remove inhomogeneities.
Furthermore, one of the most reliable results, the increment
in TH over the Atlantic at latitudes higher than 60°N,
appears to be modulated by the positive trend of the
stratospheric NAM and its effect on cooling of stratospheric
polar temperatures. Ambaum and Hoskins (2002) showed
that an increment in the NAM index leads to stronger
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Table 1 WMO station codes for all the stations used in our analysis and study periods for each station
The increments (I) (hectopascals per decade) and r700 and r50 levels are shown for each of the studied scenarios. When it was not possible
to compute the increment, the data were omitted from the table. Significant correlations at the 5 and 1% levels (underlined) are presented
in blue for negative correlations and in red for positive correlations.
N Number of years of available data for the station; r700 correlation with NAM at the 700-hPa level; r50 correlation with NAM at the 50-hPa level.
stratospheric vortex, which is associated with a higher
tropopause over the Arctic. Therefore, the negative trend in
TP recorded over the extratropical northern hemisphere can
be associated with the positive trend in the NAM since
1970 (Hurrell 1995); this does not exclude the possible
influence of greenhouse gases on the recorded increment in
TH. Many studies have identified the effects of humaninduced increases in well-mixed greenhouse gases and the
depletion of stratospheric ozone on atmospheric temperatures (e.g. Ramaswamy et al. 1996, 2001; Tett et al. 1996).
These radiative perturbations may well modulate the
behaviour of natural modes of variability such as the NAM.
Given the nature of the present study, it is not possible to
evaluate the statistical significance of recorded increments,
but we have determined physical signals that support the
obtained results. The annual mean thickness of geopotential
height was computed for 400–1,000 and 50–100 hPa to
obtain a representation of the variation in mean temperature
of the stratosphere and troposphere (electronic material S1
and S2). The results are largely concordant with the
increments determined in our analysis.
Acknowledgements We would like to thank John R. Lanzante for his
collaboration in performing the homogenization procedures. We also
thank David Parker, Dian J. Seidel, Imke Durre and Benjamin D. Santer
for their helpful comments. Finally, we would like to acknowledge the
assistance of José M. Castanheira. This work has been founded by the
Spanish Science Ministry through TROJET project.
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