Observation of tropospheric δ D by IASI over the Western Siberia : comparison with a GCM

Introduction Conclusions References


Introduction
Siberia covers 77 % of the Russian territory but it is almost unpopulated (3 inhab km −2 ).
Siberia is important in the context of anthropogenic climate change which largely impacts this region (IPCC, 2007) mainly covered by tundra, forests, and lakes.The area has undergone a sharp increase of temperatures for the last decades, which in turn has induced changes the biogeochemical cycles and the hydrological cycle (e.g.Kane, 1997;Lapshina et al., 2001).
Water vapor is a key atmospheric compound and the dominant greenhouse gas in the Earth's atmosphere (Kiehl and Trenberth, 1997;Schmidt et al., 2010).It plays an important role in the radiative transfer (Hartmann, 2002;Soden and Held, 2006;Bony et al., 2006), the large-scale circulation (Sherwood et al., 2010), cloud formation (Luo and Rossow, 2004), precipitation (Bretherton et al., 2004), and stratospheric chemistry (Fueglistaler, 2009).The water cycle strongly controls various feedback mechanisms of the Earth system in response to perturbations of the energy balance of the troposphere, such as cloud and ice/snow-albedo feedbacks.The water cycle is also coupled to the surface energy budget by latent heat fluxes associated with evaporation and condensation.However, many aspects of the water cycle are still uncertain.The measurement of the isotopic ratio of water, comparing the abundance of the heavier isotopologues (principally H 2 18 O, H 2 17 O and HDO) to the main isotopologue H 2 16 O, can provide an additional information about these processes and about the sources of water vapor.Indeed, the chemical properties of all isotopologues are similar but their masses are different.There is a fractionation of the different isotopologues during evaporation/condensation processes due to the differences in vapour pressures and diffusivities in air.
The isotopic composition is given in per mil (‰) units using the conventional δ notation relative to the V-SMOW standard (Vienna-Standard Mean Ocean Water).This is the reference standard for water isotope ratios (Craig, 1961).Our work focuses on δD as δ 18 O and δ 17 O are still challenging to retrieve from space.δD is expressed as below: where R VSMOW = 0.31152 × 10 −3 .Hence δD gives an indication on the abundance of HDO relative to that of H 2 O.A δD of 0 ‰ indicates that the ratio is equal to that of sea water.A value of −500 ‰ means that the ratio in the sample is only half that of the ratio in sea water, and a value of −1000 ‰ means that the sample contains no HDO.
The study of the water isotopes has been carried out for many years in various scientific fields.The analysis in ice core improved our understanding of past climate (e.g.Jouzel et al., 2007).The spatial and temporal analysis of stable water isotopologues distribution in precipitation across the globe has a potential to improve our understanding of the hydrological cycle (e.g.Gat, 2000).In ecology, stable water isotopologues are used to evaluate soil evaporation, plant transpiration, drought effects on vegetation and 11057 water sources for different plants (e.g.Farquhar et al., 2007).These studies highlight the importance of a detailed understanding for the mechanism of the isotopic signal in atmospheric water vapor and thus on the improvement of its measurements.

Discussion
In recent years the improvement in instrumental performances allowed to measure the water vapor isotopic signal from the measurements acquired by spaceborne instruments with the Infrared Atmospheric Sounding Interferometer (IASI) among them.Herbin et al. (2009) showed first the possibility to detect δD with IASI and they analyzed the distribution during a typhoon event over South-East Asia, while Schneider and Hase (2011) performed a validation work over Tenerife and Lacour et al. (2012) presented results for the 3-6 km distribution.Prior to IASI, the Interferometric Monitor for Greenhouse gases (IMG) (Zakharov et al., 2004;Herbin et al., 2007) and the Tropospheric Emission Spectrometer (TES) provided quasi-global distributions of δD representative of the mid-troposphere (e.g.Worden et al., 2006Worden et al., , 2007) ) whereas the SCanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) instrument provided total column distributions, with sensitivity down to the boundary layer (Frankenberg et al., 2009).Both TES and SCIAMACHY distributions required significant time averaging to reach the global scale, which puts a limit on what can be expected for monitoring short-term variability of water.The Thermal And Near infrared Sensor for carbon Observation -Fourier Transform Spectrometer (TANSO-FTS) (e.g.Boesch et al., 2013;Frankenberg et al., 2013) can also be used to derive the δD total column distribution, and the Atmospheric Chemistry Experiment -Fourier Transform Spectrometer (ACE-FTS) gave information on the δD variability in the Tropical Tropopause Layer (Nassar et al., 2007).
This work documents for the first time δD retrievals from IASI radiances over land and at high latitudes.While the methodology to retrieve spectra measured above land was already developed in Lacour et al. (2012), the present dataset is interesting in that it includes cases with high thermal contrasts (∆T ) which greatly impact the sensitivity of the retrieval.Generally high latitudes retrievals are more challenging than low and mid-latitudes retrievals, mainly due to the cold temperature profiles of the atmosphere and the lower concentration of water vapor that affect the information content present in the measurement.To cope with that difficulty, a specific a priori covariance matrix for high latitudes retrievals was built in order to better constrain the solutions of the inverse problem.
One of the rationales for the present study is to acquire a first IASI δD dataset for the high-latitude regions for comparison with model simulations, which have been suggested to have deficiencies at these high-latitudes (Risi et al., 2012a).Furthermore, models also underestimate the role of the continental recycling at mid-latitudes as over Siberia (e.g.Risi et al., 2013).Thanks to the joint effort from different communities within the Russian-French WSIBISO project (http://wsibiso.ru), the new dataset from IASI retrievals allows us to evaluate the performance of isotopic General Circulation Models (GCMs) over Siberia.Here we use the isotopic version of the LMDZ model, LMDZ-iso (Risi et al., 2010).
A global description of the IASI mission is given in Sect. 2. Section 3 presents the methodology used and provides a detailed characterization of the retrieved δD, under different thermal contrast conditions.The results are presented, compared with LMDZiso in Sect. 4. The conclusions are presented in Sect. 5.

Overview of IASI observations
IASI is a nadir-viewing Fourier Transform Spectrometer instrument.The first model (IASI-A), was launched in October 2006.The second instrument was launched in September 2012.The wide swath of the IASI instrument provides near global coverage twice daily with a local time of observation close to 9:30 a.m. and PM .IASI measures in the thermal infrared part of the spectrum, between 645 and 2760 cm −1 .It records radiance from the Earth's surface and the atmosphere with an apodized spectral resolution of 0.5 cm −1 , spectrally sampled at 0.25 cm −1 .It has a low noise of 0.1-0.4K for a reference blackbody at 280 K (Hilton et al., 2012), with the lower noise values in the range used for δD retrievals (e.g.Lacour et al., 2012).The IASI atmospheric view is composed of 2 × 2 circular pixels each with a ground footprint of 12 km diameter at nadir and it has an across-track scan with a swath width of 2200 km.Owing to its large swath IASI provides a large number of observations per day even over small areas; this is illustrated for instance Fig. 1, which shows a sampling example around the city of Yekaterinburg for one day.This area is referred hereafter as YEK.O is very challenging as its variation in the atmosphere is small.This is not the case for the HDO/H 2 16 O ratio for which IASI was shown earlier to provide results of sufficient accuracy.In the following, we therefore focus on the retrievals of HDO and H As there are numerous IASI data (more than one million observations per day) and as the retrieval procedure for δD currently relies on a line-by-line approach that is computationally expensive, we have chosen to process only 1 pixel out of 4. Furthermore, only spectra with a cloud fraction (see IASI Level 2 Product Guide, 2008) below or equal to 10 % are considered and the few unrealistic positive retrieved δD values are filtered out.Overall this restricts the dataset to 9 % of the observations (15 234 out of 175 528) over the Siberia area for the year 2011.Despite these strict criterions and as shown in Fig. 1 for one day, the YEK area is still well covered by IASI.

Retrieval methodology summary
The methodology used to retrieve HDO/H 2 O ratios from IASI radiances is overall similar to that presented in Lacour et al. (2012), which itself was stimulated by the earlier developments on the retrieval of δD presented by Schneider et al. (2006) and Worden  (2006).As compared to Lacour et al. (2012), the retrieval settings have been optimized for the specificity of the high-latitude region analyzed here.We also apply the methodology developed by Schneider et al. (2012) to ensure that we analyze H 2 O and δD products representative of the same air mass and that the cross-dependence between them is minimized.Hereafter we briefly introduce the general concepts of the optimal estimation method (OEM), we describe the new information a priori and the new retrieval characterization.

General aspects of the retrieval methodology
In this study we use for the retrieval the line-by-line radiative transfer code "Atmosphit" developed at Université Libre de Bruxelles to retrieve HDO/H 2 O ratios from IASI spectra, using the HITRAN database for spectroscopic parameters (Rothman et al., 2003).
It relies on the OEM described by Rodgers (2000) and has been adapted to perform the retrieval on a natural logarithmic scale and to optimally retrieve HDO/H 2 O ratios (Lacour et al., 2012).
A forward model F needs to describe the physics of the measurement.An atmospheric state vector can be related analytically to F by: where y corresponds to the measurement vector (IASI radiances), ε is the instrumental noise, x the state vector that contains the parameters to be retrieved, and b the vector containing all other model parameters impacting the measurement (e.g.emissivity, temperature profiles, etc.).Equation ( 2) highlights the linear approximation of relationship between x, the a priori vector x a , weighted by the weighting function matrix K (also named Jacobians matrix) and ε.The Jacobians matrix K is expressed as: As explained in Rodgers (2000), the solution is found by: S ε is the measurement error covariance, and S a is the a priori covariance matrix.Equation (4) shows that the retrieved profile x combines the information from the measurement and the a priori.
The OEM solution can be found by iteratively applying: The iteration starts with the initialization of the a priori conditions (x a and S a ) and terminates when convergence is achieved.

Particularity of the δD retrieval: the HDO/H 2 O correlated approach
The retrieval of water isotopologues requires applicability over a wide dynamical range (because of the high variability of water) and at the same time high precision to capture the small isotopic variations.The HDO/H 2 O correlated approach allows to constrain the retrieval taking into account the probability density function of the ln(HDO)−ln(H 2 O) distribution which is more stable than H 2 O variability (Schneider et al., 2006).
Performing the retrieval on a natural logarithm scale and including the correlation between ln(HDO) and ln(H 2 O) helps to constrain the joint HDO/H 2 O retrieval to a physically meaningful solution.This method presents also the large advantage to minimize the error on the δD profile (e.g.Schneider et al., 2006) as demonstrated for example by Worden et al. (2006Worden et al. ( , 2007Worden et al. ( , 2012) ) on TES measurements, on IASI measurements by Schneider and Hase (2011) and Lacour et al. (2012), or for ground-based measurements by Schneider et al. (2006Schneider et al. ( , 2010Schneider et al. ( , 2012)).

A priori information
We use a single a priori profile developed specifically for Western Siberia (Fig. 3) for all conditions (season).The use of a single a priori allows for a simpler analysis of the 11062 latitudinal, longitudinal and seasonal distribution of HDO, H 2 O and thus of δD, as there is no change in the a priori.The a priori matrix, which corresponds to a measure of the knowledge of the state vector prior to the measurement, is provided by the covariance matrix S a .The choice of the a priori is a tricky task in order to pose correctly the problem.The covariance matrix describes to which extent parameters co-vary, for an ensemble of vectors.The diagonal elements of these matrices correspond to the variance of each parameter.Our state vector is based on correlations between ln(H 2 O) and ln(HDO) profiles.Within one profile, different altitude levels are correlated and there are also strong correlations between ln(H 2 O) and ln(HDO).These correlations can be captured in a total covariance matrix S a , which can naturally be grouped into four sub-blocks as: where the two sub-blocks S a hh and S a dd are the covariance matrices of the ln(H 2 O) and ln(HDO), respectively.The matrices S a hd and S a dh contain the correlations between ln(H 2 O) and ln(HDO).The a priori informations were built from outputs from the LMDZ-iso model (Risi et al., 2010).
As can be seen in Fig. 3 (right panel), the δD a priori variability reaches up to 18 % and varies from 8 % to 14 % from the surface to 5 km.The H 2 O variability introduced in our retrieval is about 90 % from the surface to the free troposphere.In comparison with IASI retrievals used in Lacour et al. (2012), our variability is more constraint.

The measurement noise
The measurement variance-covariance matrix should include the instrumental noise and the impact of other error sources such as the uncertainties on the temperature profiles.Hereafter, it is assumed to be diagonal, at 5 × 10 −9 W (cm 2 sr cm −1 ) −1 .This value is slightly higher than the IASI spectral noise estimated in the selected spectral range, for a temperature at 280 K (∼ 3.5 × 10 ) (Clerbaux et al., 2009).This choice should reflect the impact of the other sources of errors in our retrievals.Note also interestingly that the value of σ ε used here is smaller than the one used in Lacour et al. (2012) (8 × 10 ).This reflects the better spectral fits obtained with IASI in regions with lower humidity, such as those analyzed here.For the sake of illustration, Fig. 2 gives an example of a spectral fit in the selected windows.More generally, the fits are characterized by a yearly root-mean-square on (RMS) average of the residuals of 5.5 × 10 −9 W (cm 2 sr cm −1 ) −1 , with better RMS obtained in winter and in fall than in the summer since humid scenes give saturated spectral features that are more difficult to adjust.

Sensitivity of the measurement to the different isotopologues
The Jacobians are the partial derivative of the radiance with respect to the concentration for each isotopologue and integrated over each retrieval layer (1 km).The Jacobians show how the radiance would change to a fractional change in the concentration as illustrated in Fig. 4.
In Fig. 4, the Jacobians of H 2 O and HDO are plotted as a function of the altitude in the selected spectral window.It is worth noting that the sensitivity of the measurement varies as a function of thermal contrast, which is the difference between the surface temperature and the temperature of the first layer of the atmosphere.In Siberia, the yearly absolute mean thermal contrast |∆T | is 5 K, depending on the time of the year.Note for example that the mean ∆T in January is −8 K and in July +4 K.
This plot illustrates the impact of the thermal contrast with two cases, one with a low (Fig. 4a) and one with a high (Fig. 4b With a large thermal contrast, the isotopologues are detectable closer to the surface.With a low thermal contrast, the maximum sensitivity is achieved between 1 and 6 km for H 2 O, while it ranges from 1 to 5 km for HDO.

Vertical sensitivity of the retrieval
The averaging kernel (AK) matrix is composed of elements that are the derivatives of the estimated state x with respect to the state vector x: G is the gain matrix whose rows are the derivatives of the retrieved state with respect to the spectral points and it is defined by: x and x are expressed in logarithmic space and thus the AK noted A is unitless.This AK can be written as: Thus Eq. ( 5) becomes: AKs are commonly used to evaluate the vertical sensitivity of the retrieval with the peak indicating the altitude of maximum sensitivity and the full width at half maximum the vertical resolution.The degree of freedom of the signal (DFS), corresponding to the trace of the AK, expresses the number of independent vertical information in the retrieval.In the case of the HDO/H 2 O correlated retrieval, the averaging kernels corresponding to the state vector x are defined as follows: With |∆T | < 4 K, the vertical sensitivity ranges between 1 and 5 km for HDO (Fig. 5a, bottom right) while for H 2 O, it is more homogeneous on the vertical (Fig. 5a, top left) with clear surface sensitivity.HDO is generally the limiting component (with respect to the sensitivity) to the HDO/H 2 O retrieval.HDO AKs can usually be been interpreting as a proxy of δD AK.As described before, the retrieval presents a larger sensitivity close to the surface with a higher thermal contrast (Fig. 5b).For the year 2011, over YEK, the averaged AKs present a higher sensitivity in the mid-troposphere even if we have to be aware that the thermal contrast highly impacts the vertical representativity of δD.Schneider et al. (2012) suggested an optimal use of humidity and δD retrievals by a posteriori correction.Their method allows to transform the {ln(H 2 O), ln(HDO)}state onto a {humidity, δD}-proxy state, useful for a common study of both parameters in a same air mass.The advantage of their posteriori processing, it is to determine a proxy-δD AK reducing the cross dependence of both isotopologues.The final result of this procedure named A (Schneider et al., 2012) is shown on Fig. 6 for each compound (A hh , A dd , A hd , A dh ) and for both thermal conditions, low (Fig. 6a) and high With their technique, the humidity and δD present the same vertical resolution and cross dependence between δD and H 2 O is minimized.Even if the DFS is limited, this shows that IASI resolves the same air mass for both isotopologues.This transformation of the AKs sheds further light on the dependency of the retrieval to the local thermal contrast, with the A characterized by very different shapes.For low thermal contrast (Fig. 6a), the surface sensitivity for δD is vanishing and the information reduces to the integrated [1-5 km] column; the DFS is typically 0.7.When the thermal contrast is larger (Fig. 6b), the AK peaks at the surface and decrease rapidly, reaching very small values already at 4 km; the DFS is then slightly larger, around 0.8.Note; however, that the number of observations with large values of |∆T | is not so frequent so that on the yearly average, the most representative sensitivity is that depicted in Fig. 6a.

Error characterization
The error on the retrieval can be calculated as the difference between the retrieved state and the true state.Thus from the Eqs.( 8) and ( 10) it is expressed as: x The total error is so composed by three terms.(A − I) (x−x a ) represents the smoothing error, which accounts for the vertical sensitivity of the measurements to the retrieved profile; the second term Gε is the measurement error, associated to the spectral noise; and GK (x − x a ) is the model parameters error representing the imperfect knowledge of the model parameters as temperature, water line-list of spectroscopic parameters or surface emissivity for example.Their covariance matrices are respectively given by: S smoothing = (A − I)S a (A − I) T  (13) with S b representing uncertainty on the forward model parameters and K b is the sensitivity of the forward model to the forward parameters (= ∂F ∂b ).In Eq. ( 13), the covariance matrix of a real ensemble of states generally approximated by the a priori covariance matrix S a .Equation ( 13) explicitly includes a vertical sensitivity through the A matrix, and it is obvious that the smoothing error decreases when AKs are close to one and/or if the variability in S a is small.
Following the technique described by Schneider et al. (2012), we can determine directly the δD errors.The error budget for H 2 O and δD is presented for both, a low (Fig. 7a) and a high (Fig. 7b) thermal contrast.
Reducing 4 Observations of the seasonal variability: comparison with a GCM

The isopotic General Circulation Model: LMDZ-iso
The resolution of the GCM LMDZ4 (Hourdin et al., 2006)  physical package includes the Emanuel convective scheme (Emanuel, 1991;Emanuel and Zivkovic-Rothman, 1999) and a statistical cloud scheme (Bony and Emanuel, 2001).Water vapor and condensate are advected using a second order monotonic finite volume advection scheme (Van Leer, 1977;Hourdin and Armengaud, 1999).Water isotopic species (H 2 16 O, H 2 18 O and HDO) are transported and mixed passively by the large-scale advection and various air mass fluxes.There is no fractionation during the evapotranspiration over land, as done in most other GCMs due to the simplicity of the land surface parameterization.The representation of the reevaporation and diffusive exchanges as the rain falls is significantly different compared to other GCMs.The relative proportion of evaporative enrichment and diffusive equilibration is calculated depending on relative humidity.LMDZ-iso also takes into account the evolution of the compositions of both the rain and the surrounding vapor as the rain drops reevaporate.The isotopic version of the LMDZ GCM is described in more details by Risi et al. (2010).

Seasonal variability of the tropospheric water
In order to compare the satellite retrievals to the simulation values with a consistent vertical sensitivity, it is necessary to convolve the model profiles with the IASI AKs.
Based on study by Schneider et al. (2012), the smoothed profiles of water mixing ratios (q smoothed ) from the model are obtained as: Where q model is the water mixing ratios from LMDZ-iso, I the identity matrix and q apriori the IASI a priori water mixing ratios.The real ratio R = HDO/H 2 O as seen with the sensitivity of our retrieval is: Figure 8 shows the IASI and LMDZ-iso daily means over YEK area presented in Fig. 1 the specific humidity and for the δD, in 2011, between 0 and 3 km altitude for a high thermal contrast.Figure 9 shows the same time series in the free troposphere, with a low thermal contrast.A relative difference (RD) is calculated for both quantities allowing to get a common parameter to compare simultaneously their variation.For the RD δD calculation, each δD is normalized by δD + 1000.A good agreement is found for both, H 2 O and δD even if higher differences are observed in winter for both altitude ranges.

4.2.1
Variability in the lower troposphere (0-3 km layer, Fig. 8) In spite of these biases, reaching a larger RD of −65 % for H 2 O in the lower troposphere on 7 December (Fig. 8a), the model agrees well with the retrieval of δD from IASI (RD δD = −0.6 % on this date) (Fig. 8b).A systematic δD underestimation by IASI is however observed (mean annual RD δD = −2.9%, corresponding to a mean difference of −22.8 ‰).The higher RD for δD (−17.2 %) is reached on 5 January for 0-3 km.This corresponds to a RD for H 2 O of −64.3 %.

Variability in the free troposphere (1-5 km layer, Fig. 9)
The same trend is observed as at lower altitudes.For the humidity, a RD of −84.4 % in the [1-5 km] range is reached on 27 January (Fig. 9a) but the model still agrees well with the retrieval of δD from IASI (RD δD = −7.9% on this day) (Fig. 9b).The δD underestimation by IASI is slightly higher than in the lower troposphere, with an annual mean of −4.3 % for [1-5 km], corresponding to a mean difference of −32.6 ‰.The higher RD for δD is reached on 4 October with a value of 19.7 % (difference of 147.9 ‰), corresponding to a RD for H 2 O of −56.2 %.

Comparison with other measurements
The seasonal variation is relatively well captured by IASI in comparison with the LMDZiso modeled values for both, H 2 O and δD.This is a different finding compared to conclusions from Risi et al. (2012b) and references therein.They highlighted the underestimation of the δD seasonal cycles by GCMs in mid and high latitudes.
LMDZ-iso also presents a bias on δD at mid and high latitudes with other atmospheric sounders as TES or TANSO-FTS (Risi et al., 2012a(Risi et al., , 2013)).The model underestimates the latitudinal gradient compared to these sounders (Risi et al., 2012a).
There is an overestimation of δD by TANSO-FTS in summer, while this bias was less pronounced with TES.Nevertheless, the absolute δD values between both instruments are not directly comparable due to the lack of absolute validation.As suggested by Risi et al. (2013), the difference of sensitivity between all these instruments calls to perform more cross-validation exercises, not only on the mean δD but also on his variation.

Day-to-day variability
Section 4.2 shows the IASI performance to detect the seasonal cycle, both for δD and the specific humidity.On the time series shown in Figs. 8 and 9, a large fraction of the variability results from the seasonal cycle.To analyze whether the satellite data and the model agree on the synoptic variability, it is necessary to remove this cycle.We model the cycle as a cosine function that is fitted against the data.The synoptic variations are then derived from the original estimates by substracting the seasonal cycle.The process and results are illustrated in Fig. 10 in lower altitudes and in Fig. 11 in the free troposphere.Figures 10a and 11a show the seasonal fit.Figures 10b and 11b show the δD deseasonalized cycle obtained for IASI, smoothed LMDZ-iso (convolved with the IASI AKs) and LMDZ-iso.
In the [0-3 km] altitude range, the variability obtained by the smoothed LMDZ values is underestimated by 2 compared to IASI one (Fig. 10b) but higher correlation are observed (Fig. 10c).This correlation is higher with the smoothed LMDZ distribution (r ∼ 0.7).The daily variability simulated by LMDZ in [1-5 km] is similar to that observed by IASI (Fig. 11b), in terms of both standard deviation and range.However, convolving with the AKs reduces the variability, so the smoothed LMDZ actually underestimates the daily variability compared to IASI one.Figure 11c  these daily averages values, with a good agreement (r ∼ 0.6 in both cases).For both altitudes range, the correlation is higher in summer (JJA) (r ∼ 0.8) (Table 1).This shows that LMDZ-iso and IASI are also able to capture well the δD day-to-day variability and mostly in lower altitudes, confirming for the higher latitude what was shown earlier in tropical and sub-tropical regions (Lacour et al., 2012).The fact that two time series (LMDZ GCM and remotely sensed data) show a significant correlation provides some support to both of them even if the variability is underestimated with the smoothed values from the model.It does demonstrate that the short term variability does not result from random noise but is linked to true atmospheric processes.

Effect of thermal contrast on retrieved seasonal variations of the isotopic composition
As the IASI retrievals provide a good representation of the day-to-day variability, in agreement with the simulation from LMDZ-iso, they can be used to describe the isotopic composition in the air mass over Siberia.For this, we plot the δD values as a function of the specific humidity.
As described in the Sect.3.6, the thermal contrast impacts on the vertical information provided by IASI.In our case, IASI is more sensitive close to the surface with a high thermal contrast, which occurs mainly in winter, and in the free troposphere with a low thermal contrast.Figure 12 shows the integrated δD and H 2 O between 0 and 3 km, both for a |∆T | > 8 K, and between 1 and 5 km for a |∆T | < 4 K.In both cases, IASI detects well the seasonal change in the water isotopic composition with for example the depletion on δD in December-January-February between the surface and 3 km altitude.Tech., 5, 397-411, doi:10.5194/amt-5-397-2012, 2012. Zakharov, V. I., Imasu, R., Gribanov, K. G., Hoffmann, G., andJouzel, J.: Latitudinal distribution of deuterium ratio in the atmospheric water vapor retrieved from IMG/ADEOS data, Geophys.Res.Lett., 31, L12104, doi:10.1029/2004GL019433, 2004.
Paper | Discussion Paper | Discussion Paper | Discussion Paper |

11059
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | are the three main isotopologues of water having large absorption bands in the thermal infrared region.Spectral signatures of these species are well detected by IASI.However, as explained byHerbin et al. (2009), a meaningful retrieval of

Discussion
Paper | Discussion Paper | Discussion Paper | Discussion Paper | et al.

) 11061 Discussion
Paper | Discussion Paper | Discussion Paper | Discussion Paper | where σ ε is a constraint representing the noise equivalent spectral radiance and estimated 11063 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) absolute value of the thermal contrast (for Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | maximum sensitivity are different for each isotopologue.This maximum is highlighted by the dark blue color showing the most negative derivatives corresponding to the largest derivatives.

) 11065 Discussion
Paper | Discussion Paper | Discussion Paper | Discussion Paper | expressing the sensitivity of the estimate state to the true state for ln(H 2 O), ln(HDO) respectively; and A hd and A dh , the real ln(HDO) affecting the retrieved ln(H 2 O) and real ln(H 2 O) affecting the retrieved ln(HDO).An illustration of the AKs is given in Fig. 5a for a |∆T | < 4 K and in Fig. 5b for |∆T | > 8 K.

11067
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | used here is 2.5 • in latitude, 3.75 • in longitude and 19 vertical levels from the surface up to 3 hPa (∼ 35 km).The 11068 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Figure8shows the IASI and LMDZ-iso daily means over YEK area presented in Fig.1, using theSchneider et al. (2012) method.The panels show the seasonal variation for 11069 shows the correlations between 11071 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | pospheric vertical resolution from Aura TES radiances, Atmos.Meas.
Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .Fig. 2
Fig. 1.IASI cloud-free observation of 1-5 km δD (in ‰) over the Yekaterinburg area (YEK) on 1 July 2011; only 1 pixel out of four is processed and shown.Each circle represents the observation at the location of the center of each pixel.The city is marked with the square.

Fig. 2 .
Fig. 2.Upper panel: IASI observed spectra (blue curve), the calculated spectra (red curve) in radiance units for August 2011.Bottom panel: The corresponding residual is plotted in black, with a RMS of 1.05 × 10 −6 W cm −2 sr −1 /cm −1 .Note the factor 100 between the residual and the radiance.The gap on the residual highlights the part where the retrieval is not performed, avoiding major CH 4 and N 2 O interferences.

Fig. 3
Fig. 3 A priori profiles used in the retrieval for δD in ‰ (a), for H 2 O and HDO (in g/kg), plotted in blue and red curves, respectively (b).The a priori variability 1σ for H 2 O and δD are expressed in % (c).

Fig. 3 .Fig. 4
Fig. 3.A priori profiles used in the retrieval for δD in ‰ (a), for H 2 O and HDO (in g kg −1 ), plotted in blue and red curves, respectively (b).The a priori variability 1σ for H 2 O and δD are expressed in % (c).

Fig. 5 Fig. 5 .Fig. 6 Fig. 6 .
Fig. 5 Mean AKs (A) with a |∆T| < 4 K (a) and with a |∆T| > 8 K (b) on 2011.See text for the 743 definition of Ahh, Add, Ahd and Adh and for the explanation of both panels.The colored curves 744 and dots aim to help the reader to follow the variability of each AK with altitude.745 746 747 Fig. 5. Mean AKs (A) with a |∆T | < 4 K (a) and with a |∆T | > 8 K (b) on 2011.See text for the definition of A hh , A dd , A hd and A dh and for the explanation of both panels.The colored curves and dots aim to help the reader to follow the variability of each AK with altitude.11085
the vertical sensitivity of H 2 O to the δD one gives a higher smoothing