Importance of the Saharan Heat Low on the control of the North Atlantic free tropospheric humidity deduced from IASI δ D observations

The isotopic composition of water vapour in the North Atlantic free troposphere is investigated with IASI measurements of the D/H ratio (δD) above the ocean. We show that in the vicinity of West Africa, the seasonality of δD is particularly strong (160‰), which is related with the installation of the Saharan Heat Low (SHL) during summertime. The SHL indeed largely influences the dynamic in that region by producing deep turbulent mixing layers, yielding a specific water vapor isotopic footprint. The influence of the SHL on the isotopic budget is analysed at various time and space scales and is shown to 5 be large, highlighting the importance of the SHL dynamics on the moistening and the HDO-enrichment of the free troposphere over the North Atlantic. We also report important inter-annual variations of δD above Izana (Canary Islands) that we interpret, using backward trajectory analyses, in terms of the ratio of air-masses coming from the North Atlantic and air-masses coming from the African continent. Finally, we present spatial distributions of δD and humidity above the North Atlantic and we show that the different sources and dehydration pathways controlling the humidity can be disentangled thanks to the added value of 10 δD observations.


Introduction
In the North Atlantic, the free tropospheric humidity is the result of a complex interplay between moistening and dehydrating processes of air parcels originating from different sources.While the large scale subsidence largely controls the dryness of the subtropics, numerous other processes have been shown to moisten the subsiding air, such as large scale transport from the tropics (Sherwood, 1996) or vertical mixing associated with convection, or evaporation of condensate in the convective towers (Sun and Lindzen, 1993).Due to the difficulty to disentangle the relative contributions of these processes and of the different sources, controls on the tropospheric humidity remain imprecise.Additionally, these different sources and processes may be affected by the modulation of regional environmental influences such as the migration of the Inter Tropical Convergence Zone (ITCZ) (Wilcox et al., 2010), the activity of the Saharan Heat Low (SHL) (Lavaysse et al., 2010) or the West African monsoon and associated mesoscale convective systems.Furthermore, the subtropical North Atlantic is a particularly climate sensitive area (Spencer and Braswell, 1997) as the radiative forcing associated with changes in water vapour is the strongest in the free troposphere (Held and Soden, 2000) and as the dryness of the subtropical atmosphere allows for important variations of the humidity.Efforts in understanding the controls on the North Atlantic humidity are thus crucial.
The measurement of water vapour isotopologues has proved to be a helpful observational diagnostic to study the atmospheric moistening/dehydrating processes in a novel way (e.g.Worden et al. 2007;Frankenberg et al. 2009;Risi et al. 2012;Yoshimura et al. 2014;Tuinenburg et al. 2015).This is because the water isotopologues (HDO, H 2 16 O, H 2 18 O) preferentially condense/evaporate during the phase changes of water, and therefore their isotopic ratio is sensitive to key processes of the hydrological cycle such as airmass mixing (Galewsky et al., 2007), convection (Risi et al., 2008), transport (Galewsky and Samuels-Crow, 2015).The observation of water isotopologues in the vapour provide thereby useful information on the processes that affected the air-masses downwind.While the number of δD (δD= 1000 * [(HDO/H 2 O)/Rsmow−1], Rsmow being the HDO/H 2 O ratio in the standard mean ocean waters) measurements has increased this last decade (e.g.Lacour et al. 2012;Worden et al. 2012;Schneider et al. 2016), it is necessary to understand the factors controlling their variations in order to apprehend their utility for studying hydrological processes.With its demonstrated capabilities to provide δD measurements in the free troposphere (Schneider and Hase, 2011;Lacour et al., 2012Lacour et al., , 2015)), the Infrared Atmospheric Sounding Interferometer (IASI) flying onboard MetOp has since a decade a key role in supplying δD observations.IASI has the advantage to make about 1.3 millions measurements a day, which almost ensures one measurement everywhere twice a day.Up to now, IASI δD retrievals have been sparsely used for geophysical analyses (Bonne et al., 2015;Tuinenburg et al., 2015).
In this study, we explore δD distributions derived from IASI at various time and space scales above the North Atlantic near the West African coast and we interpret their seasonal to inter-annual variability as well as their spatial variations.This enables us to investigate the potential of such observations to improve our representation of the moistening processes in this region.
Because the retrieval of δD above deserts is difficult due to uncertainties in the surface emissivity and the presence of dust, we have chosen to not analyse the measurements above the Sahara.Nevertheless, because of the integrated nature of δD, we show that some information can be derived from δD signature of air parcels coming from the desert.Former studies have already been dedicated to the interpretation of isotopic variations observed in precipitation and in water vapour in West Africa (Frankenberg et al., 2009;Risi et al., 2010;Okazaki et al., 2015) by combining models with observations, but solely focusing on the role of convection.From in situ measurements at Izana, González et al. (2016) have also shown that different airmass pathways could be detected in H 2 O-δD pairs distribution.Here, we use IASI measurements of H 2 O and δD to first show that the Saharan Heat Low (SHL) -which is a key component of the West African Monsoon system -has a large influence on the seasonal cycle of the budget of water isotopologues above the North Atlantic in summertime, when the SHL is most active.Then, we present inter-annual variability of the isotopic composition at Izana (Canary Islands, 100 km off the coast of Africa) and analyse the causes of the variability.Finally, we detail the spatial variations of water vapour and its isotopic composition above the North Atlantic for July 2012 and discuss the different sources and dehydration pathways controlling the free tropospheric humidity.
In section 2 we present the IASI datasets and the different numerical weather prediction model re-analysis that we have used and we provide some guidance on the interpretation of δD-humidity variations.We analyse the seasonal and inter-annual δD variations observed at Izana in sections 3 and 4, respectively.Then in section 5, we describe the spatial distribution of δD observed in July 2012.Finally, the results are discussed in the conclusion section.
2 Data and methods

IASI δD retrievals
This study is mainly based on H 2 O and δD profiles derived from IASI radiances measurements (Lacour et al., 2012).IASI is a Fourier Transform spectrometer flying onboard the MetOp platform measuring the thermal infrared emission of the Earth and the atmosphere (Clerbaux et al., 2009).The high quality spectra (good spectral resolution -0.5cm −1 -and low radiometric noise) allow to retrieve information on H 2 O and δD in the troposphere after an inversion procedure following the optimal estimation method (Rodgers, 2000) adapted for the requirements of δD retrieval (Worden et al., 2006;Schneider et al., 2006).
With respect to supplying δD observations in the free troposphere, IASI is the unique successor of the Tropospheric Emission Spectrometer (TES) instrument which has been used in many isotopic applications studies (i.e.Worden et al. 2007;Risi et al. 2010Risi et al. , 2013)).IASI with its high spatio temporal sampling (a measurement almost everywhere on the globe, twice a day) is of great interest for studies on short terms variations of δD (Bonne et al., 2015;Tuinenburg et al., 2015) and for an optimal sampling of δD natural variability (Lacour et al., 2015).
The δD profiles retrieved from IASI have limited information on the vertical, with degrees of freedom (dofs) varying between 1 and 2 depending on the local conditions (thermal contrast, temperature and humidity profiles, e.g.Pommier et al. 2014).In general, the maximum of sensitivity of the retrieval lies in the free troposphere between 3 and 6 km.For our analysis we use only the retrieved δD profiles that have more than 1.5 degrees of freedom and that yield a maximum of sensitivity between 4 and 6 km.The retrievals are also filtered based on the residual of the fit.On an individual basis, the observational error on δD between 4 and 6 km has been estimated and cross-validated to 38‰ (Lacour et al., 2015).When several retrieved values are averaged (from N measurements), this error is reduced by a factor √ N .Because of the large number of IASI measurements, there is presently no near-real-time processing of IASI radiances for δD.The availability of this quantity is thus limited.In this study we use three different datasets to investigate the isotopic characteristics of water vapour in the North Atlantic: Figure 1.Simple models describing the domain of existence of δD-q pairs.The two purple curves describe the progressive depletion of a tropical boundary layer source and a drier one according to a Rayleigh distillation.The green curve describes the mixing between humidity from the tropical boundary layer source with humidity of the upper troposphere.

TES δD retrieval
In order to derive a climatology of δD seasonality at a global scale, we also used δD profiles retrieved from the Tropospheric Emission Spectrometer (TES) measurements (Worden et al., 2012).The TES instrument is, like IASI, a thermal infrared sounder but with a better spectral resolution which makes it more sensitive to the lower troposphere.The spatiotemporal sampling is however lower than IASI.Nevertheless, δD retrievals from TES are available since September 2009 and allow for global analysis of δD.

Backward trajectories analyses and reanalysis data
To help in the interpretation of δD data we use backward trajectory calculations from the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) (Stein et al., 2015) where GDAS (Global Data Assimilation System) re-analyses have been used as the meteorological fields.Backward trajectories have been mainly used in the analysis of the dataset above Izana for which we computed a trajectory reaching the centre of the 4 • by 4 • box for every day.We also used ECMWF re-analysis data to characterize atmospheric dynamics.

Background on δD analysis
Variations in δD are to a first order tied to those in specific humidity (noted q).For this reason the interpretation of the information contained in δD is generally done in the δD-q space, which allows for a joint analysis of their variations.While the interpretation of δD-q pairs can be complicated as numerous processes can produce a same δD-q combination, simple models are helpful to understand their position in the δD-q space (Noone, 2012).The isotopic depletion of water vapour that undergoes condensation at equilibrium can be described by a Rayleigh distillation model as: δD = (α − 1) ln q q 0 + δD 0 ; (1) with q 0 and δD 0 are the specific humidity and the isotopic composition of the water vapour source, and α is the coefficient of fractionation.This model is shown in Figure 1 for two different sources of water vapour (purple lines).A Rayleigh model with a tropical water vapour source can generally be used to describe the lower limit of the domain of existence of δD-q pairs.The superior limit of this domain can be described by a mixing model between depleted and dry air from the upper troposphere that mixes with enriched and humid air from the tropical boundary layer (green line in Figure 1).The mixing between a source A and B produces a resulting air parcel of mixing ratio q which is the weighted average of the mixing ratio of the two air parcels: with f , the mixing fraction.The resulting ratio of isotopologues is given as (Galewsky and Hurley, 2010): The mixing model is shown in green in Figure 1.Mixing and distillation of water vapour from different sources can occur over a wide range of combinations and produce δD-q pairs in between these two boundary models.Noteworthy, intense convective activity act to over deplete water vapour and δD-q pairs can be found below the Rayleigh distillation model (Worden et al., 2007).
3 Seasonal variations: Influence of the SHL on δD in the subtropical North Atlantic

Seasonal cycle of water vapour and its isotopic composition over Izana
Figure 2-a shows the composite (2009-2013) seasonal cycles of specific humidity and its isotopic composition at 5.5 km above Izana.The troposphere is moistened from April to August as the large scale subsidence weakens due to the northward migration of the ITCZ.Then, it progressively dries as the ITCZ retreats south.Interestingly, the δD composition of water vapour does not follow the exact same cycle as humidity: the air masses are indeed progressively enriched (8‰ per month) from January to June, before exhibiting an abrupt enrichment from June to July (140‰ in one month).The enrichment persists throughout in August with values nearly as important as in July.Afterwards, the content in HDO rapidly decreases from August to November, by ∼150‰.This strong seasonality in δD values is particularly striking and exceeds the seasonality generally found elsewhere (see section 3.4).The period of enrichment in HDO over Izana appears to coincide with the period when the SHL is present over the Western Sahara as the climatological onset of the SHL occurs at the end of June as the SHL retreats toward the South at the end of September (Lavaysse et al., 2009).In the following, we conduct our analyses with the aim at understanding the

The Saharan Heat Low and the associated atmospheric dynamics
In summer, strong heating of the Saharan surface creates a low pressure system (SHL) which enhances convergence in the low levels (see left panel of Figure 3 illustrating the low level circulation for July 2012).In the lower troposphere, the cyclonic circulation around the SHL strengthens simultaneously the south westerly monsoon flow east of its center and the northeasterly Harmattan flow to the west.The near-surface convergence generates enhanced rising motion in an environment prone to dry convection, leading to the formation of deep well mixed boundary layers, whose top often reach 600 hPa or higher.The divergent circulation at the top of the SHL generates an anticyclonic circulation in the middle troposphere, which contributes to the intensification of the African easterly jet (AEJ) further south (Thorncroft and Blackburn, 1999) and which is responsible for the horizontal transport of continental air masses above the Atlantic.The middle tropospheric circulation for July 2012 represented in Figure 3 shows how the anti-cyclonic circulation contributes to strengthening the AEJ and to bringing mid-level air masses from Northwest Africa over the Northeastern Atlantic.
The development of a heat low due to the surface heating has several consequences on the atmospheric dynamics.In the summer, before the displacement of the West African Heat Low (WAHL) over the Western Sahara, the Saharan atmospheric boundary layer (SABL) depth is on the order of 5 km, which it one of the deepest ABL over the globe in summer.Once the SHL settles in, the top of the SABL can reach an even higher altitude of 6 to 7 km (∼550 hPa) during the months of July and August due to the enhanced near-surface convergence in the SHL region.The SABL is fully developed in the late afternoon (Chaboureau et al., 2016).During most of the day, while the mixed layer is developing, the characteristics of air masses in the upper part of the SABL (the residual layer) are controlled by advection from the East (Flamant et al., 2007(Flamant et al., , 2009;;Chaboureau et al., 2016).As the warm and dry air moves off the African coast, the SABL rises and becomes the Saharan Air Layer (SAL) undercut by the cool and moist marine boundary layer.
The intensity of the SHL also has an influence on deep convection over the Sahel (Lavaysse et al., 2010)  show the monthly temperature at 850 hPa over the domain, this variable being a proxy of the SHL location over the Sahara.
The δD-q diagrams in the top panel of Figure 4 show a clear change in the repartition of the δD-q pairs from May to June.
In May, air masses are characterized by low δD values and a wide range of specific humidity.These observations are localized close to or below the Rayleigh model or close to the dry end of the mixing model.The measurements below the Rayleigh model are associated with air masses coming from the tropics and from lower altitude and can thus be explained by convective processes (e.g.Worden et al. 2007).In June there is a clear separation of the measurements in two clusters, with the cluster of enriched air masses close to the mixing model and the cluster of depleted air masses located between the Rayleigh model and the mixing model.In July, only the enriched cluster is seen; it is associated with larger specific humidity values.The same applies in August with slightly more depleted values.In September and October, the observations are located at the left corner of the diagram (more depleted and less humid).The distribution of the δD-q observations in October 2012 is similar to that of May 2012.This situation then persists until the end of the year (not shown here).In summer, the position of the δD-q pairs along the mixing model suggest important mixing between dry and depleted air parcels with moist and enriched ones.Additionally, the fact that they are localized on the moist branch of the mixing model indicate a mixing for which the proportion of the moist term is quite important; this is surprising at an altitude of 5.5 km.
The backward trajectories shown in the middle panel of Figure 4 highlight that the shift in the cluster of δD-q pairs towards higher δD values (from -300 to -100 ‰) corresponds to a change in the origin of the airmass.During most of the year, the main origin of the air masses arriving at Izana is the upper troposphere above the Atlantic Ocean.Conversely, from June to August, the air masses come from lower altitudes and from a more localized area in the western Sahara.The situation in June, which is characterized by a HDO-enriched δD-q cluster and an HDO-depleted one, is particularly noteworthy and can be explained with the trajectories analysis: the depleted cluster would there be associated with air masses coming from the Atlantic and from higher altitudes while the enriched cluster would correspond to the air masses coming from the African continent and from lower altitudes.Such differences in the δD-q distributions due to different origins of air masses have already been reported from in situ measurements in González et al. (2016).
The bottom panels of Figure 4 indicate that the change in airmass trajectories is concomitant with the onset of the SHL, the installation of the SHL over the Western and central Sahara (Lavaysse et al., 2009).The concomitant shift of δD-q pairs clusters from a δD value of ∼-300 ‰ to a value of ∼-100 ‰, with the change of airmass trajectories and the installation of the SHL above the Western Africa shown here for the year 2012 is observed for the entire 2009-2013 period with some differences For all variables, the ones corresponding to the green and purple boxes strongly deviate from the majority of the observations (characterized by the blue and orange boxes where most of the δD-q pair lie).The humidity profiles corresponding to the purple box show high humidity (relative and specific) values, which strongly suggest convective processes at play, furthermore confirmed by the precipitations found along the backward trajectory (Figure 5-c).In the case of the enriched and humid values, corresponding to the summer enrichment (green box), the different profiles characterize a very particular atmosphere.
The specific humidity profile has very high values close to the surface, which rapidly decrease with altitude; the relative humidity profile shows also very high values in the first layer of the atmosphere than very dry values up to around 550 hPa;

Spatial extent of the SHL influence
As the high seasonality in the water isotopic composition observed at Izana is closely associated with the activity of the SHL, it can serve as a diagnostic to evaluate the spatial influence of the SHL.In Figure 7-a we first present the seasonality in δD signal (defined as δD July−August -δD January−February ) observed from the TES instrument.We use the TES δD data here as they are available over a longer time period than the IASI dataset considered here and at a global scale.We plot the seasonality of δD as a zonal mean with its associated standard deviation, calculated for the 2005-2010 period.The seasonality observed off the Western African coast, on an entire latitudinal band of narrow longitudinal extent (30 • W-25 • W), is also drawn in orange.
The latter exhibits a sharp maximum around 22 • N, which exceeds values found globally.We attribute this high seasonality as the result of the SHL activity and therefore suggest its influence on the isotopic budget of water vapor extends over a large part of the North Atlantic.Figure 7-b shows the same as Figure 7-a but for IASI data in 2010.These also show the enrichment in July August from 15 • to 30 • .The bottom panel of Figure 7, which shows the seasonality for the specific humidity (in percent), reveals a different behaviour and this can also be interpreted as the signature of the SHL, as mixing processes produce a stronger isotopic signal for a given specific humidity than any other hydrological processes (Galewsky and Hurley, 2010;Noone, 2012).
The differences between humidity and δD are also clearly visible in the spatial distributions of δD and q for July 2012 shown in Figure 8.The water vapour distribution strongly differs from its isotopic composition as the maximum in Deuterium enrichment does not appear along the ITCZ, where high δD values are generally associated with high humidity in convective 5 areas (Risi et al., 2012), where convection act to bring enriched air masses at higher altitudes.Instead, we find the maximum of enrichment further North, around 20 • N at the northern edge of the AEJ, for a wide range of specific humidity values.

Inter-annual variations above Izana
Figure 2 shows that there is significant inter-annual variations observed in δD signal at Izana.In this section, we investigate the reasons that could explain this variability.

Control of the zonal transport
As explained previously, the δD variations are sensitive to the source of water vapour.During summertime, the air masses reaching Izana have contrasting isotopic signatures and water vapour content: the air masses from the Atlantic are dry and depleted, while conversely, the air masses from the African continent are wet and enriched.Thus, the origin of the air masses must control δD.In Figure 9-a, we show the correlation between summer (June to August) δD (and q) daily variations and their longitudinal origin for each time step of the backward trajectory analyse.The correlation plot of the δD (and q) daily variations and the longitudinal origin shows that there is a maximum of correlation (r=0.68 with p<0.001), between δD and the longitudinal origin of the airmass back to 140 hours (6 days) prior to the arrival of the airmass at Izana.Interestingly, this does not correspond to the same maximum for specific humidity variations, which is 60 hours prior to the arrival.The daily variations of δD are thus largely controlled by the longitudinal origin of the airmass.The fact that the correlation between δD and the longitudinal origin is the highest back to 6 days before their arrival compared to 3 days for specific humidity, probably translates the memory effect of δD.Indeed, while the specific humidity is reset at its saturation value when there is saturation, δD keeps memory of its history before the saturation (Risi et al., 2010).
In Figure 9 the air masses are exclusively coming from the Atlantic while a ratio <<1 indicates that the air-masses mainly come from the continent.We found a linear relation (r=-0.91,p<0.001) between δD and the ratio indicating that the summer monthly averages of δD observed at Izana reflect the balance between the two main origins of the air masses.Logically, this translates into the inter-annual seasonal variations of δD shown in Figure 9-c.The inter-annual variability of summer δD values can thus be explained by the relative contributions of air-masses coming from the Atlantic and the ones coming from the SHL.

Control of the mixing fraction
In this section we translate the control of western to eastern in terms of mixing fraction of the mixing.Indeed, assuming that the monthly average isotopic composition at Izana in summer is the result of mixing between upper tropospheric air and boundary layer air, the control of the ratio of the number of the western air-masses to the eastern airmasses can be understood in terms of the mixing fraction of the humid source (f in equation 2 and 3) in a mixing model.This is shown in Figure 10 where daily δD-q pairs from June to August are smoothed on 30 days moving average filter and placed in δD-q diagrams for each year from term of the mixing model (in orange in Figure11) could be associated with tropical boundary layer water vapor or boundary layer water vapor from the Mediterranean region (drier) both being potential sources of water vapor fed into the SHL (so called SHL ventilation from south or north, respectively, Lavaysse et al. 2009).This means that moisture transported at low-level from the oceans and the seas surrounding the continent towards the SHL contribute to the moistening of the free troposphere over the Northeast Atlantic.The SHL is a key player in this process as the relatively moist and enriched air masses are mixed vertically over the depth of the Saharan ABL before being transported over the Ocean due to the divergent, anticyclonic circulation at the top of the SHL.In Figure 10, the colours indicating the ratio of the number of western air-masses to the total number of airmasses show that from June to August, as δD increases along the mixing model, the ratio progressively decreases.Assuming constant dry and humid terms, this displacement along the mixing model can be explained by an increase of the mixing fraction (f) in equations 2 and 3.The ratio of the number of western air-masses to the total number of air-masses acts thus like the mixing fraction in controlling the δD composition of water vapour.The magnitude of the enrichment is important because in the dry member of the mixing model, a small increase of the fraction of boundary layer air acts to significantly enrich the resulting mixed air-mass (mixing fractions are indicated by the red crosses in Figure 10), conversely, the specific humidity increase is small.The summer enrichment observed at Izana can therefore be interpreted as the progressive increase of the boundary layer air fraction in the mixing as the SHL acts to efficiently blend boundary layer air over increasing depths from June to August, bringing moisture to altitudes where only upper tropospheric air is observed during the rest of the year.Figure 10  observations could be explained by the mixing of a constant source such as the MBL with air becoming more enriched and more humid.This could be explained by a stronger influence of the African easterly jet and a weaker influence of the subsidence as we get closer to S3.The Figure 11 shows how this mechanism could explain the δD-q pairs observed.
-P3: This pathway corresponds to the western part of the Northern Atlantic where S3 is probably mixed with air from the large scale subsidence as the end member of the mixing model corresponds to the location of the Azores high.
-P4: This pathway describes the depletion of S2 northward and southward of the ITCZ and can be described by a simple Rayleigh distillation model which has the characteristics of the tropical boundary layer moisture.
-P5: This pathway is found between S3 and S2 or corresponds to distilled air parcels from S2.The position of the observations in the δ-q space could be explained again by mixing.However, in that case, the enriched source is drier than the humid source and the corresponding mixing model presents an inverse curvature.The blue curves showing the mixing models computed from S3 and S2 or more distilled terms allows to explain the scatter observed.

Conclusions
In this study we have explored δD-q distributions derived from the IASI sounder above the North Atlantic for different time and space scales with the objective of providing an interpretation on the controls of δD in that region.We have shown that the seasonal enrichment of δD observed at Izana was closely linked to the installation of the SHL above the Sahara from June to August.By the end of June, the intense surface heating during the summertime period generates deep boundary layers, which can then be transported above the Atlantic within the so-called SAL.The SAL top reaching the altitude of IASI sensitivity, HDO-enrichment is observed over Izana.We have shown that the influence of the SHL expands far off the coast, suggesting a large influence of the SHL on the isotopic budget and thus on the humidity budget.The summertime δ-q distributions at Izana are mainly the result of mixing processes between dry and depleted upper tropospheric air with humid and enriched boundary layer air from the oceans and seas surrounding the West African continent.In the summer, the SHL acts to efficiently mix these contrasting sources and transport anomalously moist and enriched air masses (when compared to the rest of the year) over the Northeast Atlantic Ocean.Inter-annual variations of δD were also interpreted as the differences in the fraction of western to eastern air-masses arriving at Izana.The combination of δD and q observations from IASI in July 2012, together with the knowledge of the key components of the West African Monsoon system, allowed interpreting the variety of processes driving the water budget over the Northeast Atlantic.More generally this analysis demonstrates the usefulness of δD measurements from IASI as we show it is possible to disentangle the respective contribution of the different sources of water vapor together with their respective interactions.
The demonstrated capabilities of IASI to provide unique observational constraints on the different sources and processes controlling the free tropospheric humidity in the North Atlantic would be useful to evaluate the representation of these sources and processes in isotopes-enabled climate models.In particular, the strong isotopic signature associated with the SHL and its interactions with the monsoon and the AEJ could be used to assess the correctness of its representation in climate models.

-a 5
year (2009-2013) dataset above Izana (26 • N-30 • N, 18 • W-14 • W) with an average of 65 measurements available per day; a 1 year dataset (2011) along a latitudinal band of narrow longitudinal extent (0 • N-60 • N, 30 • W-25 • W), which is used in section 3.4 to evaluate the variations of δD seasonality along the Western African coast; a 1 month dataset (July 2012) above the North Atlantic (0 • N-40 • N, 40 • W-5 • W) which is also used to illustrate the spatial extent of the influence of the SHL (subsection 3.4) and to analyse the different sources and processes controlling the humidity above the North Atlantic in section 5.
Figure 2. a) Composite seasonal cycles of δD (red) and specific humidity (green) above Izana for the 2009-2013 time period.b) Monthly variations of δD (red) and specific humidity (green) above Izana for the 2009-2013 time period

Figure 3 .
Figure 3. Low level (left panel) and mid-level (right panel) circulation associated with the SHL.The location of the SHL is indicated by the temperature field at 850 hPa above 300 K on both panels.The arrows represent wind fields at 850hPa (left panel) and 600hPa (right panel).On the left panel, the red arrows indicate the different sources converging in the depression and the blue arrow represents the anticyclonic circulation associated with the SHL.On the right panel, the red arrows shows how the anti-cyclonic circulation is divided into a part strengthening the AEJ.
Figure 2, we analyse the monthly δD-q diagrams from March to October 2012 in Figure 4-a using Rayleigh and mixing models.In parallel, we provide monthly analyses of airmass trajectories reaching Izana from HYSPLITT backward trajectory analyses (Figure 4-b).Air masses arriving at Izana from lower altitudes are identified by dark to light orange lines while air masses coming from higher altitudes are identified by dark to light purple lines.The bottom panels of Figure 4 (Figure 4-c)

Figure 4 .Figure 5 .
Figure4.Top panels: δD and q daily variations from May to October 2012.The mixing (orange line) and Rayleigh models (blue) are also shown.The temperature lapse rate is given by the colour scale.Middle panels: backward trajectory analyses of the air parcels arriving at Izana from May to October 2012.The altitude of the air parcels are indicated relatively to the observation altitude (5.5 km).Bottom panels: monthly temperatures at 850 hPa from May to October 2012.

Figure 6 .
Figure 6.Development of deep boundary layers during summer above the Sahara (7 • W-5 • E,20 • N-30 • N).Boundary layer heights are extracted from ECMWF ERA re-analysis and are averaged from 2009 to 2013.

Figure 8 .
Figure 8. δD (left panel) and q (right panel) distributions from IASI at 4.5 km for July 2012 together with the averaged wind fields at 600 hPa.

Figure 9
Figure9.a) Correlation between δD (red curve) and q (purple curve) daily variations and the longitudinal origin of the airmass at various time steps (number of hours prior the arrival of the airmass at Izana).b) δD monthly averages (June, July and August) as a function of the ratio of the number of air masses arriving from the Atlantic (Longitude<-20 • W) and the number of air masses arriving from the African continent.c) Same as b) but for summer averages (July, August).

2009 to 2013 .Figure 10 .
Figure 10.Summer enrichments (from June to August) observed at Izana from 2009 to 2013 along a mixing model defined by the mixing between upper tropospheric water vapor (UT) and boundary layer vapour above the Mediterranean (MBL).IASI daily observations at 5.5 km are smoothed on a 30 days moving average filter and the corresponding ratios of western air-masses to total number of air-masses is shown in colour.The 10, 50, 75 and 90% fractions (f) of MBL of equations 2 and 3 are indicated with the red crosses.

Figure 12
Figure 12. a) δD-q composition of the three main sources identified in the δD-q diagram with a mixing model (orange curve) between upper tropospheric air (black circle) and Meditteranean boundary layer vapor (orange circle); a Rayleigh model from a tropical boundary layer vapor (purple circle); and a mixing model between MBL and water vapor distilled according to the Rayleigh model defined.b) Geographical location of the differents sources identified in a).c) All δD-q pairs for July 2012 together with different mixing models that could explain the variations observed (see text for details), the colours indicate the different pathways identified.d) Geographical location of the different pathways identified in c).e) Illustration of the mechanism explaining the variations observed in yellow (P2): mixing models between a constant humid term (MBL) and a dry term more and more enriched.
It is flown on-board the Metop satellites as part of the EUMETSAT Polar System.The IASI L1 data are received through the EUMETCast near real-time data distribution service.Jean-Lionel Lacour is grateful to the CNES for post-doctoral grant.The research in Belgium was funded by the F.R.S.-FNRS, the Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex arrangement 4000111403 IASI.FLOW).C. Clerbaux is grateful to CNES for scientific collaboration and financial support.20 Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2017-152,2017 Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 21 March 2017 c Author(s) 2017.CC-BY 3.0 License.