Introduction
Model simulations, reanalyses data sets, and satellite observations show
large discrepancies of the global humidity climatology. Tian et al. (2013) showed that
the tropical boundary layer in the Modern-Era Retrospective Analysis for
Research and Applications (MERRA) is 10 % drier than the Atmospheric
Infrared Sounder (AIRS) observations. Yet, above 700 hPa MERRA shows a
wetter environment than AIRS by more than 20 %. These values are recorded
over the Intertropical Convergence Zone (ITCZ) – a region characterized by
deep convection and persistent cloud coverage. They also reported that a
composite of 16 climate models from the Coupled Model Intercomparison
Project Phase 5 (CMIP5) archive is 15 % drier than the AIRS observations
below 600 hPa but 30 % wetter in the middle and upper troposphere.
Jiang et al. (2012) showed that CMIP5 models are twice as moist as the AIRS and the
Microwave Limb Sounder (MLS) observations in the upper troposphere, but in
the middle troposphere CMIP5 models are moister than AIRS and MLS by 10 %.
Chuang et al. (2010) reported large differences in the interannual anomaly of the upper
troposphere humidity among CMIP5 models, European Center for Medium-range Weather Forecast (ECMWF) data sets, and AIRS
observations over deep convective regions. Chen et al. (2008) showed disparities in the
humidity field in ERA-40 and National Centers for Environmental Prediction (NCEP) reanalyses – also documented by Huang et al. (2005),
who had found inconsistent interannual variabilities of the tropical
humidity among the ERA-40 and the NCEP reanalyses with respect to the
Geophysical Fluid Dynamics Laboratory AM2 model and the High-resolution Infrared Radiation Sounder (HIRS) observations. John and Soden (2007) documented
that CMIP3 models show a PBL that is 25 % drier than AIRS and ECMWF data
sets, while they reported a significant moist bias in the free troposphere
of up to 100 %. Such discrepancies lead to undesirable inconsistencies
among models, reanalyses, and remote sensing platforms that have greater
repercussions in weather forecasting and climate research and their future
projections.
A viable path towards improving the current models, reanalyses, and
satellite observational skills in capturing the water vapor's dynamics is to
have observations that are as independent from weather and climate models
and reanalyses as possible. Despite the advancements in space-based remote
sensing, caveats still exist even in the satellite records. In particular,
clouds may contaminate infrared (IR)-based observing platforms (e.g., AIRS; Fetzer et al., 2006), while modeling errors of the Earth's limb radiances can impact
microwave (MW) sounder retrievals (e.g., MLS; Read et al., 2007), introducing biases in
the derived humidity climatologies. Both IR and MW sounders have a coarse
vertical resolution (e.g., 2–3 km) that is inadequate to resolve the
detailed vertical structure of water vapor. Lin et al. (2012) and Boyle and Klein (2010) emphasized
that having vertically resolved high spatial resolution atmospheric data
makes model convection parameterization more responsive to environmental
conditions, while Tompkins and Emanuel (2000) quantified the required vertical resolution to
properly characterize the humidity climatology to be 25 hPa (or
∼ 100 m). Ground-based in situ measurements (e.g., radiosondes,
lidars, and radars) are limited over land and lacking information over oceanic
regions, while different reanalyses exhibit considerable differences (even
after the assimilation of satellite observations).
There is an increased need for an improved definition of the Earth's global
humidity climatology that could help discern current discrepancies in
models, reanalyses, and observations. Carlowicz (1996) emphasized that better tools
are needed to measure water vapor, suggesting the Global Positioning System
Radio Occultation (GPSRO) technique as a strong candidate due to its unique
characteristics that are valuable to atmospheric monitoring: all-weather
sensing, high vertical resolution (100–200 m; Kursinski et al., 2000; Schmidt et al., 2005), high
specific humidity accuracy (< 1.0 g kg-1), high temperature accuracy
(< 0.5 K), and sampling of the full diurnal cycle. For these reasons,
we propose constraining past and present-day humidity climatologies by using
GPSRO observations. Together with state-of-the-art reanalyses, GPSRO data
sets have the potential to greatly improve the current global humidity
climatology and its related feedbacks.
In 1995, the GPS/METeorology (GPS/MET) radio occultation (RO) experiment
demonstrated how atmospheric refractivity, temperature, and water vapor
profiles are obtained (Rocken et al., 1997). Since then, numerous RO missions have flown, and currently fly, exploring the
capabilities of the RO technique as a complementary data set to the existing
data records. The National Research Council Decadal Survey for Earth
Science (NRC, 2007) identified radio occultations as a critical measurement
for weather and climate observations, highlighting the fact that all of the appropriate low Earth orbit missions should include a GPS
receiver to augment operational measurements of temperature and water vapor.
Kursinski et al. (1997), Rocken et al. (1997), Kursinski and Hajj (2001), and Colard and Healey (2003) described the retrieval process of
humidity profiles from GPSRO observations. Steiner et al. (1999), Gorbunov and Kornblueh (2001), Divakarla et al. (2006), Ho et al. (2007),
Chou et al. (2009), Ho et al. (2010), Sun et al. (2010), Gorbunov et al. (2011), Kishore et al. (2011), Wang et al. (2013), and Vergados et al. (2014) validated the
GPSRO-based humidity retrievals against reanalyses, radiosondes, and
satellite observations, while recently Kursinski and Gebhardt (2014) reported an innovative
technique to further reduce and eliminate retrieval biases in the middle-troposphere humidity products.
The overarching objective of this study is to use the GPSRO data sets to
characterize the tropical humidity climatology. We will conduct our analysis
over a seasonal timescale. This is because the spatial patterns and the
seasonal cycle of relative humidity (RH) are fundamental energy balance quantities and play a
critical role in climate research. We will compare the GPSRO observations
against ECMWF and MERRA data sets to observationally constrain the strength
of seasonal variability in the reanalyses. Our effort on constraining
humidity exemplifies an end-to-end application of evaluating and validating
the complementarity of GPSRO observations, while gaining new insights about
the representation of moist convection that is not properly captured by the
reanalyses (e.g., Dai, 2006; Holloway and Neelin, 2009; Hannay et al., 2009; Frenkel et al., 2012) and helping to provide guidelines for
future model improvements.
The novelty of our study lies in the fact that we are the first to compare
GPSRO observations with MERRA data sets. The motivation for this study
comes from the fact that MERRA does not assimilate GPSRO products (unlike
ECMWF), providing an additional step towards assessing the GPSRO humidity
profiles. Such a study will also provide further insight about the water
vapor dynamics as well as help us constrain current model physics. We
attempt to properly characterize the GPSRO-based humidity climatology and
place it into perspective with current reanalyses in order to explore its
potential for advancing our knowledge on tropical weather and climate
research. This paper is organized as follows. Section 2 describes the
data sets, while Sect. 3 presents and discusses our results. Section 4
provides a summary of our current research and our concluding remarks,
followed by recommendations on future directions.
Data sets
We analyze RH climatologies from GPSRO observations and ECMWF and MERRA
data sets during winter 2007–2009 (December–January–February, DJF) and
summer 2007–2009 (June–July–August, JJA). We focus on the tropics and
subtropics (40∘ S–40∘ N) around the globe (180∘ W–180∘ E),
because this latitudinal belt contains the majority of water vapor and has
been identified to be the most sensitive to climate change.
Constellation observing system for meteorology, ionosphere, and
climate
COSMIC is a constellation of six microsatellites placed in near-circular low
Earth orbit at ∼ 800 km altitude (Schreiner et al., 2007). They record the
phase and amplitude of dual frequency L-band GPS signals (f1=1.57542 GHz; f2=1.22760 GHz) as a function of time. The time derivative of
these phase measurements provides an estimate of the Doppler shift of the
GPS signals due to the presence of the Earth's atmosphere (provided
ionospheric contributions have been removed from the observations). Together
with COSMIC and GPS orbital information (position and velocity vectors), the
Doppler is used to estimate the bending of the GPS signals from which the
refractivity is extracted (Ho et al., 2009). The relative motion of the COSMIC and GPS
satellite pair allows for the vertical scanning of the atmosphere and the
retrieval of vertical profiles of atmospheric refractivity, which in turn
contains temperature and humidity information. The GPS L-band frequencies
have low sensitivity to clouds and precipitation, making them especially
useful over cloudy regions.
Here, we use the forward refractivity operator (e.g., Smith and Weintraub, 1953; Kursinski et al., 1997; Hajj et al., 2002;
Heise et al., 2006) to compute the water vapor pressure:
N=77.6PT+3.73⋅105eT2⟺e=13.73⋅105(NT2-77.6PT),
where N (unitless) is the COSMIC refractivity, P (mbar) is the pressure, T (K) is
the ECMWF temperature, and e (mbar) is the GPSRO-derived water vapor pressure.
The refractivity data are obtained from the “wetPrf” COSMIC data files
with a vertical resolution of 100 m in the troposphere, while the
temperature profiles are provided by ECMWF analysis. We decide to use this
method, instead of the “wetPrf” profile humidity that is the product of
a variational assimilation using a priori atmospheric state, because we would like
to be as independent of a priori humidity information and its associated errors as
possible. Given the COSMIC refractivity accuracy of ∼ 1 % at
2 km and ∼ 0.2 % at 6–8 km (Schreiner et al., 2007), the major error in the
humidity retrieval is the a priori temperature information and its error
characteristics. Thus, given robust temperature retrievals from independent
data sets, we can solve for the humidity while meticulously quantifying the
uncertainties arising from the temperature profiles (cf. Sect. 3.3).
Because Eq. (1) requires that both the GPSRO and the ECMWF data sets be
reported at the same pressure levels, we interpolate the ECMWF temperature
profiles into the vertical grid of the GPSRO profiles using linear
interpolation.
Rienecker et al. (2011) report that MERRA follows closely the ECMWF temperature variability
at monthly and seasonal timescales, especially in the lower and middle
troposphere that is well constrained by radiosonde observations. In
particular, at 500 hPa both analyses show indistinguishable interannual
variability; MERRA exhibits only at 200 hPa a bias of the order of 0.5 K,
while ECMWF shows half of that. Therefore, there is no advantage to
selecting one analysis over another given that our own analysis treats
multi-year climatology data sets. Hence, in Sect. 3.3 we performed a
sensitivity analysis of the retrieved GPSRO relative humidity products on
temperature uncertainty by introducing a ±1.0 K temperature error
throughout the vertical extent of the troposphere. These results serve to qualitatively and quantitatively guide the reader through the structural
differences of the GPSRO relative humidity products. Additionally, ECMWF is
the analysis routinely used by numerous researchers and by the COSMIC Data
Analysis and Archive Center (CDAAC) for the retrieval of the GPSRO water
vapor pressure profiles.
The CDAAC provides both the COSMIC
and the ERA-Interim profiles (cf. cdaac-www.cosmic.ucar.edu/cdaac/). We use
the water vapor pressure derived from Eq. (1) to estimate RH with respect to
liquid water, which is the World Meteorological Organization (WMO) standard
measurement, using
RH=ees×100%,es=6.112⋅exp17.62⋅TT+243.12,
where es (hPa) is the saturation water vapor pressure and T (∘C) is
the temperature. This formula is from the WMO Guide to Meteorological
Instruments and Methods of Observation (CIMO Guide, WMO No. 8) formulation
(WMO, 2008).
MERRA v5.2.0
From MERRA (v5.2.0) (Rienecker et al., 2011), we use relative humidity estimations
with respect to liquid water available at the GES DISC Giovanni Interactive
Visualization and Analysis. The data can be downloaded from
http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=MERRA_MONTH_3-D and are given in a
1.25∘ × 1.25∘ latitude–longitude grid and 25 vertical pressure
levels in the troposphere. The vertical resolution between the surface and
up to 700 hPa is 25 hPa, while between 700 hPa and 300 hPa the vertical
resolution becomes coarser, decreasing to 50 hPa.
MERRA is a NASA analysis based solely on assimilation of satellite
observations using Goddard's Earth Observing System (GOES) version 5.2.0
Data Assimilation System (Rienecker et al., 2008). It primarily assimilates radiances
from the AIRS instrument, the Advanced Television and Infrared Observatory
Spacecraft Operational Vertical Sounder (ATOVS), and the Special Sensor
Microwave Imager (SSM/I). We refer the reader to Fig. 4 in Rienecker et al. (2011) for a
detailed description of the rest of the data sets currently being
assimilated. The major advantage of using MERRA data sets in this study is
that MERRA does not assimilate GPSRO products.
European Center for Medium-range Weather Forecast Reanalysis Interim
ERA-Interim is one of the most advanced global atmospheric models simulating
the state of the atmosphere with accuracy similar to what is theoretically
possible (Simmons and Hollingsworth, 2002) using a 4D-Var method (Simmons et al., 2005). Primarily, it assimilates
radiosonde humidities and AIRS radiances and, as of 1 November 2006, GPSRO
bending angle profiles (Dee et al., 2011). As a global analysis grid, it can be
interpolated to a desired location and its accuracy is based on the error
characteristics of the assimilated data. Currently, ERA-Interim uses the
T255 grid scheme that translates to approximately 80 km horizontal
resolution and uses 37 vertical pressure levels between 1000 hPa and 1 hPa,
with 11 pressure levels available in the troposphere. The ERA-Interim
profiles are obtained by the CDAAC database.
Pressure–latitude cross sections of relative humidity
during winter (DJF; left column) and summer (JJA; right column) seasons
averaged over the 2007–2009 period using GPSRO (a and
b) observations and MERRA (c and d) and ECMWF (e and f) reanalyses.
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Results
Diagnosing the spatial distribution of relative humidity using
GPSRO observations
Figure 1 presents the 3-year zonal-mean RH climatology over the tropics and
subtropics (±40∘) during summer and winter as a function of
pressure level and latitude. A direct comparison among all data sets
indicates that the spatial distribution patterns of the RH fields match up
remarkably well. All data sets display an upward current of moist air from
the lower to the upper troposphere around the equatorial latitudes, which
coincides with the ITCZ location. In the middle troposphere, we identify
regions of low RH fields centered at ±20–25∘ between 600 and 500 hPa in both hemispheres, representing areas of dry air subsidence.
All these are well-documented features of the Hadley Cell circulation, which
are also captured by GPSRO data.
Despite the qualitative agreement among the data sets, we are interested in the magnitude
of the RH differences with respect to one another, as we
want to (a) investigate the GPSRO products and (b) examine the
reanalyses' representativeness of tropical moist convection. To the best of
our knowledge, this is the first time that GPSRO observations are used to
study the 3-D spatial patterns of the moist thermodynamic budget of the
Hadley Cell circulation (that encompasses the ITCZ) and place an
observational constraint on the reanalyses data.
Boundary-layer zonal-mean moisture climatology during winter
(DJF; left column) and summer (JJA; right column), averaged over the
2007–2009 period from GPSRO (solid blue) observations and MERRA (dashed
dot green) and ECMWF (dashed orange) reanalyses.
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Comparing GPSRO observations with ECMWF reanalysis
GPSRO observations indicate that the boundary layer (900–700 hPa) over the
ITCZ (and in all other latitudes) is systematically moister than ECMWF (cf.
Figs. 1, 2). The RH differences are the largest around the equatorial belt,
and their magnitude varies with pressure level and geographic location.
During winter, we report a maximum absolute difference of ∼ 10 % at 900 hPa that grows to ∼ 20 % at 700 hPa, while
during summer these differences are smaller. In the winter middle
troposphere (700–500 hPa), GPSRO shows again a wetter ITCZ than ECMWF by
5–15 %, but at higher latitudes both GPSRO and ECMWF agree remarkably
well because the computed RH differences fall within the GPSRO RH retrieval
errors. During summer we notice the same behavior, although the RH
differences are smaller than the winter season.
Moving higher into the troposphere (< 500 hPa), the GPSRO
observations and the ECMWF data set capture well the moisture budget of the
ITCZ; however, moving northward the GPSRO observations indicate a moister
environment than ECMWF. This behavior is again the same during both seasons.
Quantitatively, the GPSRO results are in very good agreement with Kursinski and Hajj (2001),
who also reported that the NCEP reanalysis captures a wetter ITCZ than the
GPS/MET observations by more than 10 % in the summer of 1995. Also,
Kishore et al. (2011) showed that the COSMIC observations are moister than those of both the ECMWF
(by 3–8 %) and the Japanese 25-Year Reanalysis project (by 2–20 %)
in
tropical regions (±20∘) during the 2006–2009 period. Chou et al. (2009),
although conducting their analysis over a small region off the coast of
Taiwan, also reported that the NCEP/NCAR reanalysis is more than 30 %
moister than the COSMIC observations at the 400–300 hPa pressure layer.
Same as Fig. 2 but for the middle-to-upper troposphere.
![]()
Comparing GPSRO observations with MERRA reanalysis
Relative to MERRA data sets, both during the summer and winter seasons,
GPSRO observations show a slightly drier boundary layer at 900 hPa, but this
dryness quickly disappears at higher altitudes, demonstrating an excellent
agreement between the two data sets (cf. Figs. 1–3). Quantitatively, the
maximum absolute RH difference is found over the ITCZ at 900 hPa with a
value of ∼ 15 % but decreases significantly down to less
than 3 % aloft. The magnitude of the reported differences is smaller than
the GPSRO RH retrieval errors, marking an excellent agreement between MERRA
and GPSRO across the entire tropical region, which is statistically
significant to the 95 % confidence level. In the middle troposphere,
between 700 hPa and 400 hPa, GPSRO and MERRA data sets show again an
excellent agreement: the magnitude of the RH differences has a value
of less than 3 % at all latitudes.
At 400 hPa we start noticing that GPSRO observations are drier
than the MERRA data sets by 5 %. This dryness increases to 15 % at
300 hPa over the ITCZ and the rest of the tropical region. Such discrepancies
are shown in both seasons. Despite the quantitative differences of the RH in
the upper troposphere, GPSRO and MERRA data sets are qualitatively in
excellent agreement as they both capture the spatial variability of the RH
in both hemispheres.
Pressure–latitude cross sections of seasonal variability
(summer versus winter) of the relative humidity climatology averaged over
the 2007–2009 period using (a) GPSRO observations and (b)
MERRA and (c) ECMWF reanalyses.
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Diagnosing the seasonal variability of relative humidity from
GPSRO observations
Previous studies by Su et al. (2014), Fasullo and Trenberth (2012), and Hall and Qu (2006) highlighted the fact that
seasonal variations of RH are representative of their relationship under
global warming. Hence, it is of first-order importance to cross compare and
constrain the present-day seasonal cycle of RH among different data sets
in order to advance our knowledge of the behavior of the Earth's energy and
humidity climatology in future climate projections. Figure 4 shows the
seasonal RH variability as the difference between the summer and winter
climatologies derived in Sect. 3.1, separately for each data set.
Qualitatively, all data sets match up remarkably well, capturing the same
spatial patterns.
Current analysis indicates that the middle troposphere (700–500 hPa)
centered at ±5–25∘ in both hemispheres shows the maximum RH
seasonal differences, indicating that it is the most sensitive region to
seasonal variations. Quantitatively, both GPSRO observations and MERRA data
sets show RH differences of -30 (Southern Hemisphere) and +36 %
(Northern Hemisphere), whereas the ECMWF reanalysis differences range
between -22 (Southern Hemisphere) and +28 % (Northern Hemisphere).
Quantitatively, our estimated differences from GPSRO, MERRA, and ECMWF are
in very close agreement with recently published research using the latest
AIRS (v. 6) observations (Ruzmaikin et al., 2014), which reported equatorial RH fluctuations of
∼ 30 %. Although GPSRO observations and MERRA reanalysis
show the same range of RH seasonal variations, the ECMWF reanalysis presents
a weaker seasonal variability by about 10 %.
Over the ITCZ, around the equatorial belt, all data sets indicate that RH
varies the least between winter and summer throughout the vertical extent of
the troposphere. We report RH differences from GPSRO observations and ECMWF
and MERRA reanalyses of the order of ∼ 3–5 %,
∼ 3–7 %, and ∼ 2–9 %, respectively. All
data sets agree on the magnitude of the seasonal variations of RH, whereas
their small range implies that ITCZ climatology is not as sensitive to
seasonal cycles, unlike the middle troposphere inside the dry subsidence
regions of the Hadley Cell circulation.
GPSRO RH sensitivity error analysis on ±1.0 K
temperature uncertainty for summer (left) and winter (right), using 1 year
of data from 2007, as a function of pressure level. The orange shaded
region shows the boundaries of the errors.
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Error characterization of the GPSRO humidity on temperature
uncertainty
The percentage error of the GPSRO-derived RH profiles, due to temperature
errors, at a certain pressure level is mathematically expressed as (after
accounting Eqs. (2, 3)), and is shown in Fig. 5 as a function of pressure
level:
δRHRH=∂RH∂TRH⋅δT⇔RH=ees2NTb⋅es-aPb⋅es-4.284×103⋅T2⋅N-aPTb⋅es(T-30.14)2⋅δTRH.
In Fig. 5, we have used 1 year of data (summer and winter 2007) and
have assumed a temperature error of ±1.0 K at all pressure levels and
latitudes. The results indicate that the RH error increases with increasing
altitude due to the decreasing water vapor concentration (and consequently
its contribution to the atmospheric refractivity). Quantitatively, the RH
error obtains a value smaller than 5 % in the lower troposphere and
smaller than 9 % in the middle troposphere. These results are also in a
very good agreement with Vergados et al. (2014), who estimated a < 3 and
< 8 % GPSRO RH retrieval error in the lower and middle troposphere
with respect to collocated radiosondes at ±30∘, respectively,
for a temperature error of ±1.0 K. Above 400 hPa, Fig. 5 shows an
increase of the RH error up to 30 % at 300 hPa.
The magnitude of the retrieval error in the lower and middle troposphere is
smaller than the reported differences between the GPSRO and ECMWF reanalyses
in Sect. 3, marking the statistical significance of the observed
discrepancies within the boundary layer and above. However, in the upper
troposphere the retrieval error grows larger than the documented GPSRO and
ECMWF differences, and consequently we can not derive a statistically
significant conclusion about the observed discrepancies.
Discussion and conclusions
Figures 1–3 show that MERRA reanalysis and GPSRO observations are in
excellent agreement when capturing the tropical humidity climatology, both
qualitatively and quantitatively, in the lower and middle troposphere.
Excluding pressure layers below 900 hPa and above 400 hPa (where the
atmospheric conditions render the GPSRO-derived RH fields less accurate),
the Pearson correlation coefficient between the two data sets for both
seasons is greater than 0.80 at the 95 % confidence level based on the
Student t test statistics. In the upper troposphere, the observations suggest
a drier environment than MERRA by ∼ 15 %. Most importantly,
these two data sets are independent, as MERRA does not assimilate any GPSRO
product; hence, their degree of correlation and statistical differences is a
strong indicator of the quality of the GPSRO-derived RH climatology.
Figures 1–3 show that the ECMWF reanalysis is systematically drier than the
GPSRO observations throughout the vertical extent of the troposphere,
although this disagreement becomes smaller closer to the upper troposphere.
The maximum differences are found over the ITCZ location and can reach up to
30 %, suggesting that ECMWF underestimates the moisture budget of the
ascending branch of the Hadley Cell circulation. Northward from the ITCZ and
at higher altitudes, the disagreement between the two data sets diminishes
and falls within the estimated GPSRO RH uncertainty errors (e.g., Vergados et al., 2014;
Kursinski and Gebhardt, 2014), thus becoming statistically insignificant. In the upper troposphere,
both ECMWF and GPSRO data sets capture properly the moisture budget of the
ITCZ, although we start noticing small RH differences within the dry subsiding
regions northward from the ITCZ.
Figure 1 demonstrates that both MERRA and GPSRO data sets capture the same
strength of the winter and summer large-scale atmospheric ascent, which
hydrates the middle and the upper troposphere, markedly noticeable over the
ITCZ. During summer, we observe a sharper and more organized convection than
during winter. Although ECMWF is qualitatively similar to MERRA and GPSRO
data sets during summer, it underestimates the strength of hydration during
winter. Based on the theory of Huang et al. (2006) and John and Soden (2007) that the vertical
transport of moisture
from the lower to the upper troposphere (mainly due to deep convection)
should be responsible for the documented model discrepancies, we conclude
that GPSRO captures stronger convection than ECMWF.
Figure 4 shows that at seasonal timescales GPSRO observations and MERRA
and ECMWF reanalyses capture the same RH patterns, and the middle
troposphere over the regions of dry air subsidence (cf. Figs. 1 and 4) is
most sensitive to seasonal oscillations. The GPSRO and MERRA data sets show
an excellent agreement in capturing the magnitude of the seasonal
variability of RH; however, ECMWF shows a weaker seasonal oscillation by
∼ 10 %.
Finally, we must clarify that during summer (JJA) in 2007, 2008, and 2009,
the El Niño–Southern Oscillation (ENSO) index was < 0.4 (in
absolute value). We had a weak El Niño event in the winter (DJF) of
2006–2007 (+0.7), a moderate La Niña in the winter of
2007–2008 (-1.5), and a weak La Niña during the winter of
2008–2009 (-0.8). For reference, the ENSO index time series from
1950 to present fluctuates within the [-3, 3] range
(http://www.esrl.noaa.gov/psd/enso/mei/). Hence, although ENSO is contained
in all data sets, there is no strong forcing present. Such a natural
variability affects the Earth's temperature field throughout the vertical
extent of the troposphere and stratosphere (Randel et al., 2009), when not higher up, at all
latitudinal belts. Additionally, GPSRO observations' unprecedented vertical
resolution and global coverage provides a more detailed picture of the
tropical 3-D thermal structure than MERRA and ECMWF reanalyses. Consequently,
one could argue that the GPSRO observations might better capture the ENSO
signal than the ECMWF and MERRA reanalyses. To date, numerous studies have
demonstrated GPSRO observations' potential of capturing such a natural
variability (Lackner et al., 2011; Steiner et al., 2011; Scherllin-Pirscher et al., 2012).