Measurements of the atmospheric HDO

The hydrological cycle plays a key role in the uncertainty of climate change.
More specifically, water vapour plays an important role, since it is the
strongest natural greenhouse gas. Water vapour is involved in positive feedback
mechanisms

Global satellite measurements, as well as ground-based Fourier transform
spectrometer (FTS) measurements of HDO have become available in recent years.
The measurements of HDO are normally expressed as a ratio of the HDO
abundance to the abundance of the main isotopologue

Certain measurements directly retrieve

With new satellite HDO data sets available, such as from the
Greenhouse gases Observing Satellite

Here, we present an updated version of the SCIAMACHY

The updated SCIAMACHY

The SCIAMACHY HDO

in the lower
troposphere where the concentration is highest. The spatial footprint of a
single measurement is

As mentioned earlier, the values for

The new HDO

First: map of the new IMAP v2.0 2003–2007

The output format of the HDO

no. of iterations

residual RMS of the spectral fit

The extension of the data product beyond the original 2003–2005
time period is complicated due to various instrument issues with the channel 8
detector

Nevertheless, we have extended the

In Fig.

In the bottom panel of Fig.

Top: density distribution of the a priori column-averaged

The retrieval algorithm uses variable a priori information from ECMWF for the
profiles of H

Figure

Top: typical column averaging kernels of a bottom layer scaling
retrieval (blue) and a profile scaling retrieval (magenta). Bottom: impact of
the profile scaling approach with respect to a bottom layer scaling approach
for a month of measurements above the Sahara. Both approaches assumed no prior
depletion profile for

The bottom panel of Fig.

An offset in the humidity total column, i.e. the total column that
would be measured under perfectly dry conditions, will lead to a
humidity-dependent shift in

Cumulative density distributions (showing the 25, 50 and 75
percentiles) of H

Since the existence of humidity-dependent biases can potentially hamper the
correct use of the

It is difficult to validate the impact of the offset correction for the driest
areas, as either no FTS stations exist in these regions or the stations have
a very sparse sampling throughout the year and limited SCIAMACHY coverage due
to their high latitudes and resulting high SZAs (such as for the dry high
(ant-)arctic stations at Eureka, Ny-Ålesund and Arrival Heights).
The corrected values of

Our validation data set is based on ground-based FTS stations from two different networks with different retrieval approaches: TCCON and NDACC. The NDACC data are produced within the framework of the MUSICA project. Six stations from both networks were selected with data available between 2003 and 2007. Two stations, Bremen and Lauder, are part of both networks albeit with different temporal coverage. Below we describe the TCCON and MUSICA data sets separately.

The TCCON network consists of about 20 operational ground-based FTS stations
that use direct solar spectra in the near-infrared to measure the
column-averaged abundances of various atmospheric constituents, including
H

Fifteen spectral windows are used for H

The average measurement precision of the a
posteriori calculated

Overview of the FTS stations used in the validation study for
the period 2003–2007.

The ground-based component of the project MUSICA

We have used the 2012 version of the MUSICA data (column-averaged values for
H

For co-locating the SCIAMACHY data with the ground-based FTS data, we
first selected all SCIAMACHY measurements within a 500 km radius of
the FTS stations. The measurements were filtered according to the

Time series of

Same as Fig.

Figures

For every station we have determined various statistics, which are printed at
the top and bottom of the figures and will be explained below. These statistics
are based on the spatially and temporally co-located data points from
the algorithm as described in Sect.

The bias is defined as the mean of the 2003–2007 SCIAMACHY–FTS

Results of the SCIAMACHY-FTS comparisons before and after the SCIAMACHY offset correction. The last two rows show the weighted mean of the bias (including error in the mean) for all six TCCON stations and the four low-altitude MUSICA stations. The values within brackets are the station-to-station standard deviation. The other values in the last two rows are unweighted averages for all six TCCON and MUSICA stations.

The standard deviation of the SCIAMACHY–FTS differences is defined as

The standard deviation is larger than the mean measurement noise error in
SCIAMACHY

The reduced chi-square

The Izaña station shows the highest

Finally, the values of

The last statistical parameter we show in
Figs.

Overall, considering all statistical parameters, we can conclude that the
uncorrected SCIAMACHY

As mentioned above, the averaged bias with respect to MUSICA is larger than with respect to TCCON, while for the two individual stations that participate in both networks the bias with respect to MUSICA is either consistent with TCCON, or even smaller. Since the MUSICA and TCCON stations are spread across different geographical locations, this could be the result of a selection effect in combination with a latitudinal bias.

In Fig.

Bias as a function of latitude for all low-altitude TCCON and MUSICA
stations. The letters “T” and “M” indicate a TCCON and MUSICA station
respectively. For Lauder (

Three of the four stations at low to moderate latitudes
(

As a result of Rayleigh distillation (i.e. the preferential condensation of the
heavier isotopologues due to their lower vapour pressure), seasonal variations in

We show monthly means for all stations in
Figs.

Monthly means for the TCCON stations. FTS data within

Same as Fig.

Figures

As a reference, we have also plotted the seasonalities of the a priori

For Ny-Ålesund and Lauder (and to a lesser extent Kiruna) the similarity in

Monthly means of

Due to the averaging, the correlation coefficients for the monthly means are
much higher than the correlation coefficients for the single co-located
observations (as were shown in Figs.

Interestingly, the shape of the seasonality for the high-altitude Izaña station is reproduced quite well (except for the negative shift due to the altitude effect), even though many SCIAMACHY observations for that station are measured above the Sahara. In contrast, the seasonality measured from the top of the Jungfraujoch mountain is much steeper than observed by SCIAMACHY, possibly related to the higher altitude of Jungfraujoch (compared to Izaña) or differences between Central Europe and the subtropics in the dominating circulation patterns that control moisture transport at high altitudes.

Finally, it has been suggested that SCIAMACHY might overestimate seasonalities
in

To study if

Same as Fig.

Monthly means of

Same as Fig.

Following

Correlation diagram of the H

The FTS stations of Jungfraujoch and Izaña show much more depleted

It is important to note that the seasonal asymmetry is not present yet in
the a priori information. Figures

A simultaneous validation in two dimensions (

We have presented a validation study of the SCIAMACHY HDO

Throughout this work we have also studied the impact of an offset correction on
the retrieved total columns HDO and H

The latitudinal dependency of the bias also explains why the average bias
determined using MUSICA (with more stations at higher latitudes compared to
TCCON) is larger than the average bias determined using TCCON, even though we
find smaller biases with MUSICA at the combined stations of Bremen and Lauder.
The retrieval setups of MUSICA and TCCON are considerably different (including
different algorithms, wavelength regions, spectroscopy, a priori inputs and
calibrations), which seems to result in lower

Regardless of any bias, the SCIAMACHY HDO

There have been a few other studies that compared SCIAMACHY or other satellite
retrievals of the ratio HDO

Finally,

This researched was funded by the Netherlands Space Office as part of
the User Support Programme Space Research under project GO-AO/16. US
funding for TCCON comes from NASA's Terrestrial Ecology Program, grant
number NNX11AG01G, the Orbiting Carbon Observatory Program, the
Atmospheric CO