A merged time series of stratospheric water vapour built from the Halogen Occultation
Instrument (HALOE) and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)
data between 60

Water vapour is one of the Earth's most important greenhouse gases, having
the strongest long-wave radiative forcing effect on the atmosphere

In this work, stratospheric H

While a large number of altitude-resolved H

The Halogen Occultation Instrument (HALOE)

The Michelson Interferometer for Passive Atmospheric Sounding

The MIPAS H

The MIPAS instrument stability has been assessed (Michael Kiefer, personal
communication, 2015). A possible drift due to detector-aging and resulting
changes of its non-linear response was estimated at approximately

The combined HALOE–MIPAS H

H

The eruption of Mount Pinatubo on 15 June 1991 brought enormous amounts of
aerosol into the stratosphere. This aerosol layer affected the radiative
transfer of solar radiation through the atmosphere and led to artefacts in
the HALOE analysis

For harmonization with respect to altitude resolution we use the method
suggested by

H

Figure

The MIPAS–HALOE overlap period from July 2002 to August 2005 allows for de-biasing of MIPAS with respect to HALOE. This de-biasing was performed independently for the MIPAS HR and RR data, because these two data sets rely on different processing schemes and thus could theoretically have different characteristics. By the independent de-biasing of each of the two MIPAS data sets with respect to HALOE, biases between both the MIPAS data sets are also removed implicitly. These, however, were found to be small anyway.

Three different approaches to determine the bias were tested, one relying on
coincident measurements, the other relying on latitudinal mean values, and
the third minimizing the root mean square difference of the MIPAS and HALOE
time series during the overlap period. The third method proved to be most
robust and was finally selected. The other two candidate approaches suffered
from sparse statistics or sampling artefacts, respectively. De-biasing was
performed separately for each 10

In order to better understand the temporal variation of H

The merged time series (top panel, black curve) with the standard
errors of the data (black) and the best fitting standard regression model
(top panel, red curve) and the linear term of the regression (green line). In
the lower panel the residual time series between the measured data and the
fitted regression model is shown. The latitude bin of 0–10

Besides the constant and linear term, the annual cycle and its first three overtones (wavenumbers two, three, and four waves per year) were considered. Wavenumber two represents the semi-annual oscillations, and wavenumbers two to four help to better model the annual cycle when it is not perfectly harmonic. The following proxies were considered:

The quasi-biennial oscillation (QBO) was parametrized using Singapore winds
at 30 and 50 hPa, as obtained from the Institut für Meteorologie of the
Freie Universität Berlin,
(

For the El Niño–Southern Oscillation (ENSO) signal, the Multivariate ENSO
Index (MEI) (

In the fitted time series there are pronounced systematic residuals. Some of
them are related to an apparent discontinuity in the water vapour abundance
in 2001, the well-known millennium water vapour drop

The fit residuals obtained by the regression analysis described in the
previous section resemble a harmonic with a period of about 11 years.
Besides, strong H

Approach 1: the solar cycle was modelled by a harmonic of 127 months with an
overtone of 63 months

The root mean square improvement of the fit residual with respect to the standard approach, gained by the inclusion of the solar cycle approximated by harmonic parametrization as described under Approach 1 in Sect. 4.2. White bins are positive values, i.e. deterioration of the fit.

Top panel: Fitted regression model with solar cycle approximated by
harmonic parametrization as described under Approach 1 in Sect. 4.2. The blue
curve is the fitted contribution of these harmonics and the green line is the
linear component. The middle panel (blue curve) shows the original solar
cycle F10.7 parametrization in arbitrary units. In the lower panel the
residual time series between the measured data and the fitted regression
model is shown. The rms for this fit is 0.30 ppmv. For further details, see
Fig.

Approach 2: alternatively to the treatment with harmonics, the solar cycle
has been fitted using the radio flux index at a wavelength of 10.7 cm
(F10.7) as a proxy. This index, which is available via the Solar and
Heliospheric Observatory (SOHO,

The root mean square improvement of the fit residual with respect to the standard approach gained by the inclusion of the solar cycle approximated by the F10.7 proxy as described under Approach 2 in Sect. 4.2. White bins are positive values, i.e. deterioration of the fit.

Both approaches reveal a strong relation between the water vapour abundances and
the solar cycle. The correlation is phase-shifted in a sense that lowest water
vapour abundances are seen a couple of years after the solar maximum
(see Fig.

The amplitudes of the solar component in the regression model are shown in
Fig.

The propagation of the data errors through the regression model leads to
uncertainties of these amplitudes of generally less than 2 % within the
tropical pipe and less than 5 % outside. Fit residuals, however, are not
compliant with

Top panel: fitted regression model with solar cycle approximated by
the F10.7 proxy as described under Approach 2 in Sect. 4.2. The blue curve is
the fitted solar signal contribution with the F10.7 proxy. The middle panel
(blue curve) shows the original solar cycle F10.7 parametrization in
arbitrary units. In the lower panel the residual time series between the
measured data and the fitted regression model is shown. The rms for this fit
is 0.31 ppmv. For further details, see
Fig.

“Quasi-amplitudes” of fitted terms representing the solar cycle in the regression, i.e. the halved differences between the maxima and minima along the time series of these contributions. Top panel: harmonic parametrization; lower panel: F10.7 parametrization.

The phase shift of the solar signal (Fig.

The distribution of the phase shift between the solar maximum and negative water vapour response over latitude and altitude. Positive phase shifts represent a delay of the response of water vapour to the solar cycle.

For higher altitudes and latitudes, the phase shift shows a different
behaviour. After having reached a maximum in the lower stratosphere
(green/yellow belt in Fig.

Inherent time lag of the solar signal in water vapour, i.e.
difference of the phase shift of the solar signal in water vapour and the age
of stratospheric air as derived in

Inclusion of a solar cycle by either approach discussed in Sect. 4.2 has
improved the fit of the regression model to the measured H

Linear terms of the multivariate regression of water vapour time series with and without the inclusion of a solar term in the regression model. Top panel: standard approach without solar term; lower panel: including F10.7 parametrization.

The analysis of the merged MIPAS–HALOE time series by multivariate linear regression, including a solar cycle proxy as described above, suggests that a solar signal is imprinted on the water vapour abundance entering the stratosphere at the tropical tropopause, and this signal is then transported to the middle stratosphere via the Brewer–Dobson circulation. The signal vanishes in the middle stratosphere. The solar signal in the water vapour time series is phase-shifted anti-correlated to the solar cycle, i.e. lowest water vapour after solar maximum is found. The phase shift consists of two components: the first component is an inherent time lag of about 25 months; the second component results from transport times in the stratosphere by the Brewer–Dobson circulation as approximated by the mean age of air.

Two obvious candidates to explain a solar signal in lower stratospheric water vapour are methane oxidation and the import of water vapour through the tropical tropopause into the stratosphere.

The photochemical oxidation of methane is an important contribution to the
stratospheric water vapour budget

The import of water vapour from the troposphere into the stratosphere is to the first order controlled by the tropical cold-point temperature which implies that any mechanism leading to solar cycle influence on the tropical tropopause temperatures could explain the solar cycle signal in water vapour.

Different studies exist that analyse the influence of the solar cycle
onto the tropical tropopause temperature with different results:

To put these results into context of our observations, we have estimated the
temperature variation necessary to produce our observed solar-cycle-driven
water vapour variations. Using the relation between temperature and
saturation vapour pressure, such 2 K variation corresponds to a variation in
water vapour of about 1 to 1.5 ppmv, assuming long-term average temperature
conditions for the tropical cold-point tropopause (

As a second approach to estimate the temperature variations needed to explain
our observed water vapour variations, a regression of observed water vapour
variations at the tropical tropopause (

In contrast to

Both the “top-down” solar influence based on solar heating of the
stratosphere and the “bottom-up” mechanism (based on solar heating of the
sea surface and dynamically coupled air–sea interaction) strengthen the
tropical convection and produce an amplified sea surface temperature (SST),
precipitation, and cloud response in the tropical Pacific to a relatively
small solar forcing (see

According to

Assuming that the cause of the solar signal seen in water vapour comes from
the ocean,

There is, however, some evidence that weakens the hypothesis of solar-cycle-driven
tropopause temperatures, causing the solar signal in lower
stratospheric water vapour:

Regarding the water vapour trends, there was agreement until recently that
water vapour in the lower stratosphere has increased over the previous
decades

Only recently,

The analysis performed by

The findings by

A parametric fit of a 20-year time series of lower stratospheric water vapour
based on a merged MIPAS–HALOE data set is improved by inclusion of a solar
cycle term. The water vapour data records within
60

Inclusion of the solar cycle term in the multivariate linear regression of
the water vapour time series has another important consequence: the linear
term, interpretable as a trend over the 2 decades of observations, becomes
considerably more negative after inclusion of the solar cycle proxy and in
the lower stratosphere the “trend” even changes sign from slightly positive
without the solar proxy term to significantly negative. Thus, including the
solar cycle term as an additional proxy of a driver that rules stratospheric
water vapour has the potential to help to resolve the water vapour conundrum:
increasing water vapour abundances in the tropical and extra-tropical lowest
stratosphere

A robust causal

It is a general truism that statistical co-occurrence never assures a causal relation, but we can neither imagine that Earth's atmosphere affects solar activity or that both lower stratospheric water vapour and solar activity are controlled by a third driver. Thus we consider pure coincidence, caused by other processes of a similar timescale, e.g. variability in the ocean / atmosphere system with coincidently similar periods and time phases, as the only serious alternative hypothesis.

attribution of the lower stratospheric water vapour fluctuations to solar effects is admittedly a challenge because of the small temporal coverage of the time series, which includes less than two solar cycles. But at least it can be said that in descriptive terms the lower stratospheric water vapour time series shows a signal which can be well modelled by a solar cycle signal and whose disregard can affect water vapour trend estimation. Consideration of other HThe provision of MIPAS level-1b data by ESA is gratefully acknowledged. HALOE
data were downloaded from