ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-4251-2016Detecting moisture transport pathways to the subtropical North Atlantic
free troposphere using paired H2O-δD in situ measurementsGonzálezYennyyglezram@mit.eduSchneiderMatthiashttps://orcid.org/0000-0001-8452-0035DyroffChristophRodríguezSergiohttps://orcid.org/0000-0002-1727-3107ChristnerEmanuelGarcíaOmaira ElenaCuevasEmiliohttps://orcid.org/0000-0003-1843-8302BustosJuan JoseRamosRamonGuirado-FuentesCarmenhttps://orcid.org/0000-0002-4578-8204BarthlottSabinehttps://orcid.org/0000-0003-0258-9421WiegeleAndreasSepúlvedaEliezerSieltec Canarias, S. L., Hábitat 2, 38204, San Cristóbal de La Laguna, Santa Cruz de Tenerife, Canary Islands, SpainIzaña Atmospheric Research Centre (IARC), Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Canary Islands, SpainInstitute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germanynow at: Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology,
77 Massachusetts Avenue, Cambridge, MA 02139-4307, USAnow at: Aerodyne Research Inc., 45 Manning Road, Billerica MA 01821, USAYenny González (yglezram@mit.edu)5April20161674251426913August20158October201515March201617March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/4251/2016/acp-16-4251-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/4251/2016/acp-16-4251-2016.pdf
We present two years of in situ measurements of water vapour (H2O) and
its isotopologue ratio (δD, the standardized ratio between
H216O and HD16O), made at two remote mountain
sites on Tenerife in the subtropical North Atlantic. We show
that the data – if measured during night-time – are well
representative for the lower/middle free troposphere. We use the
measured H2O-δD pairs, together with dust measurements
and back trajectory modelling for analysing the moisture pathways to
this region. We can identify four principally different transport
pathways. The air mass transport from high
altitudes and high latitudes shows two different scenarios. The first scenario brings dry air masses to the stations,
as the result of condensation events occurring at low temperatures. The second scenario brings humid
air masses to the stations,
due to cross-isentropic mixing with lower-level and more humid
air during transport since last condensation (LC). The third pathway is
transportation from lower latitudes and lower altitudes, whereby we can
identify rain re-evaporation as an occasional source of moisture. The
fourth pathway is linked to the African continent, where during summer,
dry convection processes over the Sahara very effectively inject
humidity from the boundary layer to higher altitudes. This so-called
Saharan Air Layer (SAL) is then advected westward over the Atlantic
and contributes to moisten the free troposphere. We demonstrate that
the different pathways leave distinct fingerprints on the measured
H2O-δD pairs.
Schematic depiction of the processes influencing the water vapour
balance over Tenerife in the subtropical North Atlantic. A temperature
inversion layer separates the marine boundary layer (MBL) and the free troposphere (FT). In the MBL, NE trade winds blow, while in the FT, the
regular NW subsidence regime is alternated in summer with Saharan dust
outbreaks. On the island, the upslope winds prompt MBL air to reach the
low FT during the daytime. IZO and TDE stations are represented as red dots.
Introduction
In the subtropical free troposphere, in the region of the descending
branch of the Hadley cell, the humidity is not conserved along the
mean subsidence . Instead, this dry air is
often moistened . The few studies
based on atmospheric modelling points to the turbulent transport of water
vapour from the surface upwards as the dominant moistening process,
balancing the drying in the subtropical free troposphere
. Additional processes, such as evaporation of
condensate and isentropic eddy transport of moist air from the
tropics, are suggested to also contribute to moisture in this region
and references therein, but the sources and
dynamics involved in this moistening are still unclear.
Several studies indicate that water vapour isotopologue observations
are very promising for investigating the different moisture pathways
in this region. Such observations can be used to distinguish between
the different mechanisms associated with moistening
e.g. .
Stations located in the subtropical free troposphere are key emplacements for studying the synoptical scale
humid transport in the subtropics. Studies based on water vapour isotope observations in the
subtropical Pacific free troposphere highlight the relevance of moisture exchange between the boundary
layer and the free troposphere due to shallow convection at local scale .
In addition, subtropical moisture is strongly affected by the mixing of air masses, with humidity properties that are defined
by their condensation and evaporation history on a regional scale . In the North Atlantic, studies based on in situ
water vapour isotopologue measurements have been focused on the moisture sources influencing the marine
boundary layer . However, studies in relation to
transport pathways of moisture in the subtropical North Atlantic free troposphere including long-term
measurements of water vapour isotopologues have not been reported yet. In contrast to the Pacific,
the atmosphere over the subtropical North Atlantic is regularly affected by
dust outbreaks (from the Sahara desert) and consequently, the scheme of
moisture exchange in the subtropical Pacific region can not fully describe
those occurring in the subtropical North Atlantic. In order to fill this gap, we present and discuss the
first multi-year observational in situ data set of free-tropospheric water vapour isotopologues in the
subtropical North Atlantic region.
In the following we express the isotopologues H216O and HD16O as H2O
and HDO. The δ-notation expresses the per mil difference of the
stable isotope ratio of a water sample (R=HDO/H2O)
from that of the isotope ratio of Vienna Standard Mean Ocean Water,
i.e. δD=1000×(R/RVSMOW-1)RVSMOW=3.1152×10-4,.
In Sect. 2 of this paper we present the measurement sites and discuss
the methodology of our study. In Sect. 3 we document that our
night-time measurements are representative for the subtropical North
Atlantic lower free troposphere. We use backward trajectories and dust
measurements as tracers for detecting the main meteorological
processes. Then, we document that these processes leave unique
fingerprints in the H2O-δD distribution. Section 4
summarizes the work.
MethodologyMeasurement site
As a study area we used Tenerife (Canary Islands, Spain;
28.3∘ N, 16.5∘ W). On this island, two high mountain
sites are run by the Izaña Atmospheric Research Centre. IZO (Izana Observatory) is
a mountain top station located at 2367 m a.s.l., whereas TDE
(Teide Observatory)
is a small measurement site located at the volcanic cone at
3550 m a.s.l. In the context of the project MUSICA
(MUlti-platform remote Sensing of Isotopologues for investigating the
Cycle of Atmospheric water), we installed two Picarro instruments at
IZO and TDE stations. These instruments provide continuous
isotopologues data for altitudes above 2000 m, which are unique
for the subtropical North Atlantic.
A schematic depiction of the locations of IZO and TDE and the
meteorological processes affecting the measurement sites are shown in
Fig. . In the subtropical North Atlantic region, the
atmospheric stability is determined by the combination of two synoptic
processes that well define the marine boundary layer (MBL) and the
free troposphere (FT). In the MBL, a quasi-permanent north-north-east
(NNE) trade wind blows <1000m a.s.l.,. In the FT, the descendent branch
of the Hadley cell around 30∘ N results in a north-western (NW)
subsidence regime . This subsidence is
frequently alternated in summer with south-eastern (SE) Saharan dust
outbreaks . Episodes of air mass transport from
a south-westerly (SW) direction are also occasionally observed.
Time periods covered by water isotopologue in situ observations at
IZO (upper panel) and TDE (lower panel). All 10 min
averages of δD measured at any time of the day are shown .
The local moisture exchange between the MBL and the FT is limited by
a temperature inversion layer. In the surroundings of the
island, the top of the MBL, which is frequently located just below the
temperature inversion layer, is characterized by a stratocumulus layer formed by the condensation
of water vapour onto the pre-existing particles . This
layer creates a quasi-continuous foggy and rainy regime between 800
and 2000 m a.s.l., which is more pronounced on the northern
part of the island. The effectiveness of this layer in separating the MBL
from the FT is reflected in the relative humidity (RH) profile. In the
FT, the RH is typically around 20 %, whereas it is normally above
60 % in the MBL . Vertical mixing between MBL
and FT air is observed during the daytime due to the upslope flow
regime (taking place on a very local scale).
Measurements of water vapour isotopologues
Two commercial cavity ring-down spectrometers (Picarro model L2120-I)
have been used for water vapour isotopologues, monitoring at the two
mountain sites on Tenerife. At IZO, measurements started in March
2012 and at TDE in July 2013. The data sets consist of about 75 000
(at IZO) and 40 000 (at TDE) 10 min averages. Time series of
δD measurements at IZO and TDE stations are shown in
Fig. . The measurement gaps are due to instrument failure, maintenance, and shut downs due to extreme weather conditions. Although during summer
data have been registered almost continuously, the measurements have often
been interrupted during winter. There are only a few measurement days in
December and January (at IZO) and in January (at TDE).
At IZO, H2O data between 200 and 16 000 ppmv and δD between -500
and
-90 ‰
were recorded. At
TDE, H2O ranged between 250 and 12000 ppmv and δD ranged between
-430 and -100 ‰. At Mauna Loa free troposphere station, reported δD values between
-100 and -400 ‰ and humidities between 500 and 15 000 ppmv. On the Chilean
hyperarid Chajnantor plateau, recorded H2O-δD pairs ranging from
95 ppmv and -465 ‰ to 12 500 ppmv and -45 ‰. Thus, the in situ H2O-δD measurements made at the different subtropical free-tropospheric stations registered data that are within the same order of magnitude.
At IZO, the sampling inlet is installed 4 m above the roof of a 6-floor building at a height of
30 m above ground (2397 m a.s.l.). It consists of a stainless steel tube of 18 m that goes
from the terrace of the tower, through the service channel to the Picarro laboratory.
A vacuum pump generates an inflow of 2810 L min-1
(standard conditions) throughout the sampling line,
which has an inner diameter of 80 mm. The manifold has an inner diameter of 80 mm and a length
of 250 mm. The instrument takes the air sample using a stainless steel tube with an inner diameter of
4 mm, which goes from the manifold to the inlet of the analyzer (1.6 m). An additional pump with
an inflow of 5 L min-1 is connected in series at the inlet of the instrument. The residence time of the air inside the tubing is approximately
8 s.
At TDE, the inlet is also 4 m above the roof at a height of 6 m above the ground
(3556 m a.s.l.). The sampling line is connected to a manifold with an inner diameter of 60 mm and
a total length of 5 m where the air is pumped at 20 L min-1. The air then flows to the instrument
through stainless steel tubes with an inner diameter of 4 mm (3.3 m). At TDE, an additional
pump with an inflow of 5 L min-1 is also connected in series at the inlet of the instrument. The residence time
of the air is approximately 44 s.
No heated tubing was implemented at any of the stations. The relative humidity values were above
90 % for only 7.6 % and 4.6 % of
the data at IZO and TDE stations. In these conditions, the outside temperature at IZO ranges
between -6
and 15∘ C, whereas at TDE it varies between 6 and 11∘ C. Since the tubing in the
buildings is kept around 21∘ C, condensation events in the inlet lines can be excluded.
For calibration purposes we used two liquid working standards of composition
δDS1=-142.2±0.7 ‰
and δDS2=-245.3±0.7 ‰. The working standards were prepared from a mixture
of ground water from Karlsruhe, Germany, melted Antarctic snow (S1), and the pure melted Antarctic
snow (S2). The isotopologue composition of these standards was measured by Le Laboratoire des Sciences
du Climat et de l'Environnement (LSCE-CEA, France) and referenced to the VSMOW2/SLAP2
scale . For routine measurements we need a significant amount of liquid
standards (1 L year-1
and instrument). Large amounts of our two standards are available and we can
perform continuous calibrations during long measurement periods.
The instruments are calibrated every 8–12 h using liquid standards injected with the
Picarro Standards Delivery Module (SDM). The instrument precision for 10 min averages and at 15 700 ppmv is better
than 0.2 ‰ (see , Fig. 7a), and for very dry conditions within a few permil. The error estimation accounts for the described instrument precision as well as errors
due to the following corrections:
uncertainty of the standards (0.7 ‰ for both),
humidity dependence (from 0.3 ‰ for
10 000 ppmv, up to 8.0 ‰ at 200 ppmv),
extrapolation of VSMOW2-SLAP2 scale outside the range of calibration (for humid air: <2.0 ‰;
up to 5 ‰ for strong depleted, i.e. generally dry air),
calibration (1 ‰ for the whole humidity range).
The absolute uncertainties in δD are then <15 ‰ at 500 ppmv, <4 ‰ at
4500 ppmv and even smaller for higher humidity. More detailed information about the calibration procedure, stability, and uncertainty estimations of the two instruments
is given in Appendix .
The mean diurnal cycle and annual cycles of H2O-δD
(left panels for IZO and right panels for TDE). Grey dots show the mean
diurnal cycles for each month (mean value for each hour of the day) and the
blue squares the annual cycle (mean and standard deviation of all
observations that fall within an individual month).
The leading errors are due to the calibration corrections, meaning that they
are systematic errors for the period between two calibrations. Thus,
calculating 1 or 3 h averages instead of 10 min averages will
not significantly reduce these errors. Actually, the variations we see within
1 h are mostly real atmospheric variations (recall the high precision
of the 10 min averages). Calculating averages of these real
atmospheric small-scale variations is not trivial and might affect the
H2O-δD distribution: firstly, H2O is often log-normally
distributed and it will make a difference whether we use the mean or the
median H2O concentrations. Secondly, H2O-δD pairs do not
vary along a single straight line. By averaging the δD and the
H2O values, the averaged H2O-δD data point will be
below the mixing line or the Rayleigh curve described by the individual data
points. If we average the δD weighted by H2O, the pairs will
lie close to the mixing line, but we will not capture the situation of a
Rayleigh process well. In order to avoid artefacts caused by data averaging, all
the H2O-δD distribution plots of our study are made with high
resolution data (10 min averages).
Measurements of dust
Long-term measurements of aerosol at Izaña include chemical
composition, dust concentrations, and size distribution. The
methodology and quality control of aerosol in situ techniques are
described in . In Izaña, bulk mass concentrations
of aerosols are clearly dominated by Saharan dust
. We used records of dust at Izaña to
detect the arrival of north African air .
Unfortunately, in situ dust measurements are not available at TDE. In
order to distinguish between clean and dust-laden conditions at this
station we use the AERONET columnar-integrated aerosol optical depth
(AOD) level 2.0 obtained at a wavelength of 500 nm at IZO
(http://aeronet.gsfc.nasa.gov). See more details of calibration
procedures, data acquisition, and processing in .
Back trajectories
Transport pathways of moisture are analysed by integrating the Global
Data Assimilation System archive information (GDAS1, NCEP) in the
Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT
4.0). The GDAS1 is available every 6 h, and the post-processing
converts the data to 1∘× 1∘ latitude–longitude grids
and from sigma levels to the 23 pressure levels between 1000 and
20 hPa. HYSPLIT performs a linear
interpolation between the times of the available input data
(6 h) for calculating 5- day back trajectories of 1 h resolution
. The trajectories are released at the height above sea level of the stations. The vertical component
of the back trajectories was computed using the vertical model velocity. The end points of the
trajectories were set at Tenerife (28.3∘ N, 16.5∘ W)
at the elevations of the IZO and TDE stations.
ResultsThe effect of the local diurnal upslope flow
The airflow regime at IZO is driven by the occurrence of upward
transport of humid air during the daytime and downward transport of dry
FT air at night. The upward transport is caused by the combination of
the thermally driven growth of the MBL volume and the buoyant airflows
caused by the heating of the air located just above the terrain
. This upslope flow transports the gases emitted
on lower parts of the island, which is captured in the marked daily
cycle of gases and particles measured at IZO
(e.g. ). The highest concentrations of pollutants are
observed in the early afternoon.
Figure shows the monthly mean daily cycle of
H2O and δD at IZO and TDE stations respectively (grey dots).
The annual mean and standard deviation of H2O and δD at both stations is also shown in blue dots.
There is a marked daily cycle in the H2O and δD data of the
IZO station (left column in Fig. ) and a weaker one at the
TDE station (right column in Fig. ). At IZO, the diurnal
cycle of H2O and δD is more pronounced than the annual
cycle. At TDE, the H2O annual cycle is stronger than the
diurnal cycle. However at TDE, δD data also show a diurnal
cycle with an amplitude similar to the amplitude of the annual cycle.
Distribution of the H2O-δD pairs (10 min
averages) at IZO (left) and TDE (right) stations. Grey dots show all data for
the individual stations. Black dots represent the data collected during
night-time (from midnight to 1 h after sunrise). Orange crosses
represent the data collected during the daytime (from 3 to 10 h after
the sunrise). The whole data set is presented in grey dots.
The distribution of the 10 minH2O-δD pairs collected
at IZO and TDE stations are shown in Fig. . The whole
data set (10 min average) is presented in grey dots; black dots
represent the data collected during night-time (from midnight to 1 h
after sunrise). Orange crosses represent the data collected during the daytime
(from 3 to 10 h after sunrise, when the upslope breeze is
active).
At IZO, the lowest δD values are measured at night-time (Fig. ). During the daytime, we observe less
depletion. The H2O-δD pairs measured at IZO during the daytime are assembled in the upper side
of the H2O-δD distribution (δD=-146±39 ‰, orange crosses, left
column in Fig. ). At midday, the increase of humidity is associated with a mean
isotopic composition of δD=-131±35 ‰. This value is similar to the one
measured close to the ocean by . The δD mean value of these in-flight measurements
measured around the top of the MBL, 600–900 ma.s.l. in front of the Tenerife coast was -124±43 ‰.
Thus, the shifting of the H2O-δD pairs to the upper side of the distribution is
the result of the mixing of dry FT air with water evaporated from the
surrounding ocean and exported by turbulent mixing to the top of the
MBL. This result is in agreement with the one found by
in Mauna Loa (Hawaii).
At TDE, the difference between the daytime and night-time
H2O-δD distribution is not as clear as at IZO (right
column in Fig. ). The weaker diurnal effect at
TDE station is due to its location. TDE is located at a higher
altitude on a rather sharp peak (Pico del Teide). There it is less
exposed to the slope breezes and consequently, the influence of the
MBL-FT air mixing is weaker.
At IZO, gases and particles measured at night-time well represent FT
conditions . To examine the
variability of the H2O-δD pairs in the subtropical
North Atlantic FT, and in order to avoid the possible local MBL-FT
mixing, only the H2O-δD pairs measured at night-time at
IZO and TDE will be considered in the following study.
Appendix complements this study of the diurnal signals in the
H2O-δD distribution. Figure shows a
two-dimensional plot of the density of the H2O-δD pairs for
night-time and daytime observations.
The Saharan Air Layer and the moisture in the subtropical North Atlantic FT
The meteorological pattern of the subtropical North Atlantic region is
characterized by a constant transport of Atlantic air masses that
alternate with Saharan dust events, especially in summer. Previous
work carried out in this region show that these two regimes can
easily be distinguished by their dust content. At IZO, dust
concentrations under background clean conditions are lower than
2 µgm-3, whereas during Saharan
events, dust concentrations above 25 µgm-3 are
usually observed . The Izaña AOD levels for
background conditions are usually lower than 0.05
and frequently above 0.10 during dust events .
The left column in Figs. and
show the distribution of 5 day
back trajectories for non-dust and dust-laden conditions at IZO and
TDE stations respectively. We considered that an air mass experiences
condensation when RH along the trajectory to the station exceeds the
limit of 80 % during a 3 h time interval
. If this occurs, we set this point as the
initial point of the back trajectory. If no condensation was observed
along the 5- day path, the back trajectory was fully
drawn. Clean dust-free air masses originate from the FT over the
Atlantic (first row in Figs. and
). Dust-laden air masses originate in
north Africa close to the surface (dust > 25 µg m-3,
AOD > 0.1, second row in Figs. and
).
Air masses reaching IZO station as a function of the dust load. First
row shows non-dust conditions, second row, dust-laden conditions. The
colour-bar indicates the altitude (km) of these air masses in each grid. The
left column shows the trajectories. The right column shows the
H2O-δD distribution (pairs are presented on 10 min
average): all data (grey dots), data measured under non-dust conditions
(green dots) and data for dust-laden conditions (red dots). Orange lines
represent the evolution of an air mass that has experienced Rayleigh
distillation for RH = 80 % and SST = 18 ∘C
(thin line) and SST = 25 ∘C (thick line) respectively.
These temperatures cover the annual mean sea surface temperatures around the
Canaries. The thin black line represents the mixing between a moist air mass
(initial conditions: H2O= 18 000 ppmv, δD=-84 ‰) and a dry air mass (initial conditions:
H2O=400ppmv, δD=-600‰). The
thick black line represents another mixing process (initial conditions for
moist end member: H2O= 16 000 ppmv, δD=-93 ‰; initial conditions for dry end member:
H2O=400ppmv, δD=-600 ‰).
The right column in Figs. and
show the 10 min average
H2O-δD pairs measured under non-dust (green dots) and
dust-laden conditions (red dots) at IZO and TDE stations
respectively. The whole H2O-δD distribution (grey dots)
is quite well confined within two theoretical curves. The theoretical
line on the low δD value side of the distribution represents
the evolution of an air mass that has experienced Rayleigh
distillation, a gradual dehydration in which all
condensate is immediately removed from the vapour phase (initial conditions:
RH = 80 % and SST = 25 ∘C, orange line). The Rayleigh process starts
when saturation is reached (dew point). The equations and coefficients
for saturation over water conditions were taken from . For
the theoretical mixing we assume moistening by evaporation from the ocean
surface and drying by middle/upper tropospheric air (initial
conditions of air mass 1:
H2O= 18 000 ppmv,
δD=-84 ‰; air mass 2:
H2O= 400 ppmv,
δD=-600 ‰, solid black line). The calculation of this mixing line
is based on the simplified solution of the diffusion mixing model shown by Eq. 6.
These two theoretical lines (Rayleigh and mixing) mostly cover the set of H2O-δD measured during
this study.
Same as Fig. for the data collected at
TDE station.
The H2O-δD pairs associated with clearly non-dust
conditions represent 55 % of the data collected at IZO and
51 % of the data collected at the TDE. The origin of the
North Atlantic air masses reaching IZO and TDE stations covers a wide
area (0–60∘ N, 45∘ W–20∘ E), and also
a wide range of altitudes (0–10 km; left column in
Figs. and
). A wide H2O-δD
distribution is measured under the arrival of dust-free air masses
(green dots in right column in Figs. and
). This distribution illustrates the
effect of non-Rayleigh processes affecting H2O and δD,
such as air mass mixing and different H2O-δD-relations
(Rayleigh-curves) of air masses from different source regions
. At both stations, night-time H2O-δD
pairs below a minimum Rayleigh curve for air masses with subtropical
origin (initial H2O=27 000 ppmv,
δD=-71 ‰) cannot be explained by condensation or
mixing. Therefore, these occasional super-Rayleigh observations
indicate additional fractionation related to either intracloud or
subcloud processes or post-condensational exchange and
references therein. These observations will be discussed later.
Saharan dust conditions (dust > 25 µgm-3) were
observed in 20 % of IZO and 19 % of TDE of the whole
recorded data. At IZO summer data represent 74 % of the
Saharan dust measurements, and 70 % of TDE measurements. The
H2O-δD pairs collected under dust-laden conditions were
confined to the upper part of the distribution (red dots in
Figs. and
). During these events, relatively
enriched and moist H2O-δD pairs were measured.
Mean values for IZO were -133±35 ‰ for IZO and
7000±3000ppmv; and for TDE -152±44 ‰ and 5000±2000 ppmv
respectively. The H2O-δD distribution under dust-laden
events is limited by theoretical lines that simulate the mixing
between BL and FT air. The dashed black line drawn in
Figs. and
represents the mixing between the following air masses: air mass (1) H2O= 16 000 ppmv,
δD=-93 ‰ and air mass (2) H2O= 400 ppmv,
δD=-600 ‰. This mixing line was determined as
best fit of the border of the data of dust-laden air.
Episodes of moderate dust content (2 < dust < 25 µg m-3, 0.02 < AOD < 0.1) are related to air masses that have
travelled westward from the African continent towards the Atlantic
Ocean and then return eastward laden with the remaining dust, where they
are measured at IZO. These events are the mixing product of clean
North Atlantic and Saharan dust-laden air masses. As a consequence, they
do not make a unique fingerprint on the H2O-δD
distribution and are not shown here.
Dust-laden Saharan air masses contribute to moisten the dry subtropical
North Atlantic FT. The information of the H2O-δD pairs
measured under these conditions indicate typical dehydration/mixing
process also observed over the ocean. The transport of dust starts
over the Sahara desert, where dust storms are driven by local thermal
low pressure systems at the surface. In summer, the northward shift of
the north-eastern trade winds and the intertropical convergence zone
(ITCZ) in combination with a convective boundary layer prompts the
strong injection of dust at high altitudes . The dust is
then exported westwards at subtropical latitudes (20–30∘ N)
within the Saharan Air Layer (SAL), a stratum of warm dust-laden air,
normally located between an altitude of 1 to 5 km with
a maximum dust load at an altitude between 2 and 3 kme.g. . Previous work
indicates that the moisture content of the SAL during summer has its
origin in evaporation of the warmer Mediterranean Sea, which is
then driven by the trade winds over north Africa, mixed with dust in the Sahara desert and
then transported into the SAL to the subtropical North Atlantic FT
. showed column vapour δD summer mean from satellite
observations and atmospheric models varying between -100 and -160 ‰ for the
region covering the Mediterranean Sea and the Sahara desert (period 2003–2005). These mean values are
in agreement with our data set.
Trajectory-based analysis of the last condensation (LC) point for
non-SAL conditions. Upper panels: location (pressure-latitude) of LC point.
Bottom panels: histograms for
ΔH2O=log[H2Ot=0]-log[H2OLC].
The different colours correspond to different TLC groups:
TLC<250K (blue line),
250 K<TLC< 270 K (grey line) and
TLC> 270 K (red line).
Classification of North Atlantic air masses as a function of the last condensation temperature
In this section, we focus on the moisture transport pathways over the
North Atlantic. For this purpose, we only work with air masses
corresponding to clean conditions (not linked to SAL) and we use the
theory of the last condensation (LC) point. According to this theory,
the mixing ratio is determined by the specific humidity at the point
of LC, in which case it is not affected by subsequent mixing and
references therein.
We use GDAS1 data and HYSPLIT back trajectories to derive information
about the LC point and mixing after LC and then relate this
information to our H2O-δD observations. The LC point is
identified at the area where RH exceeds 80 % during
a 3 h time interval . We use the
corresponding 3 h averages of temperature and specific
humidity at this LC point (in the following referred to as
TLC and H2OLC) for classifying the
air masses.
We create three main data groups: TLC< 250 K, 250 K<TLC< 270 K, and
TLC> 270 K. The temperatures TLC=250K and
TLC=270K correspond to humidities of approximately 1700 and 8000 ppmv
respectively. The exact values of the temperatures that define the three temperature groups are more or
less arbitrary.
The absolute value is not important here. What is important is that we distinguish air masses according to their temperatures at the
last condensation point. We group all air masses for low TLC (last condensation at low temperatures,
i.e. dry at the last condensation point) and for high TLC (i.e. last condensation at high temperatures
and accordingly humid air masses). Furthermore, we create a group that lies in between.
The top panels of Fig. (left for IZO and right for TDE) show that TLC is a good proxy
for the locations of the LC point, since the data groups automatically with respect to the location and humidity
of the last condensation point. The coldest saturation temperatures (220–250 K, blue)
typically correspond to air transported from the upper extra-tropical troposphere
(> 40∘ N, < 450 hPa). These air masses represent 32
and 43 % of the air masses reaching IZO and TDE station
respectively. Air masses experiencing LC at warmer temperatures
(250–270 K) usually originate between 20 and 50∘ N
at 400–600 hPa (grey, Fig. ). They represent 48 and 42 % of
the air masses reaching IZO and TDE respectively. The warmest LC temperatures (270–290 K) usually
originate between 0 and 40∘ N and pressure levels above 600 hPa
(red,
Fig. ). They represent 20 and 14 % of the air masses reaching IZO and
TDE respectively.
H2O-δD distribution (10 min data within ±3 h of the arrival time of the air mass) measured at IZO station and
analysed with regard to the LC point. The data set has been grouped in three
groups corresponding to different condensation temperatures:
TLC< 250 K,
250 K<TLC< 270 K and TLC>270K. The H2O-δD pairs measured for reduced mixing
since LC (ΔH2O± 0.1) are presented in green. Pairs
measured for increased mixing since LC (ΔH2O outside ±0.1)
are presented in dark grey. Rayleigh and mixing curves are plotted as in
Figs. and .
We use the logarithmic difference between the humidity given by
GDAS1/HYSPLIT at the station (H2Ot=0) and the humidity at
the LC point (H2OLC), in order to analyse if the
air mass experienced variations on the moist content during the
transport from the LC point to the stations
(log[H2Ot=0]-log[H2OLC], hereafter
ΔH2O). We postulate that the air masses with
ΔH2O within the ±0.1 bin do conserve the
properties of the LC point during their transport to IZO and
TDE. Negative ΔH2O indicates that the air masses mix
with drier air masses during transport to the stations (we exclude
situations of rainout by requiring that RH never reaches 80 %
before the LC point). Positive ΔH2O indicates that the
air masses get moister. The bottom panels of Fig.
show the normalized distribution of ΔH2O for the three
TLC groups (blue, black, and red coloured lines
respectively). Left panel for IZO and right panel for TDE.
At IZO the humidity concentrations since the LC point are best
conserved for TLC> 250 K (red and grey lines). At
TDE we observe a relatively clear moistening for
TLC< 270 K (grey and blue lines) and in general a drying for air masses
with TLC> 270 K (red line). The total
contribution of air masses with ΔH2O<-0.1 (i.e. drying
since LC) is 19 % at IZO and 43 % at TDE, revealing
that drying by mixing with dry air is more frequently observed at IZO. For TLC> 270 K, i.e. for
air that has been rather humid at the LC point, the drying by mixing
with subsiding dry air is very clearly observed at TDE (red line,
bottom right panel of Fig. ). The total
contribution of air masses with ΔH2O>+0.1
(i.e. moistening since LC) is 46 % at IZO and 29 %
at TDE. This moistening is most pronounced for air with the LC point
in the upper troposphere of the extra-tropics (TLC< 250 K) and it becomes particularly evident at IZO (blue
line, bottom left panel of Fig. ). This moistening
process is more important at IZO than at TDE, due to IZO's location at
a lower altitude, which is directly affected by turbulent mixing from
the marine boundary layer.
Same as Fig. for the data collected at TDE
station.
Figure shows the H2O-δD distribution
as measured at IZO for the different TLC groups. The
H2O-δD data pairs correspond to 10 min averages
measured within ±3 h of the air mass arrival time of the
trajectory. For each TLC group, the
H2O-δD data points have been colour-coded as a function
of ΔH2O. Green highlights those data
associated with ΔH2O within ±0.1, thus conserving
water vapour concentrations since LC. Dark grey marks data
with ΔH2O out of the ±0.1
range. Figure shows the same for the station TDE.
For the cold LC temperatures (TLC< 250 K)
and at TDE we typically observe dry air (H2O< 3000 ppmv,
see left panels in Fig. ). For IZO we also observe dry air if
there has been
no mixing since LC (ΔH2O±0.1, green dots,
upper left panel in Fig. ). However, this is only the
case for about 14 % of all the air masses that have their LC
at these low temperatures. This extratropical subsiding dry air has already been reported in other subtropical
free troposphere stations in the Pacific such as Chajnantor plateau in Chile, and
Mauna Loa station in Hawaii, e.g. . Generally at IZO humidity is not conserved
since LC if TLC is low. In most cases these
air masses are mixed with humid air during their transport. This mixing
can then be observed in the measured H2O-δD pairs. If
there is a lot of mixing (H2O> 8000 ppmv) the vapour is clearly enriched in HDO,
which is consistent with moistening by mixing with humid air (dark grey
dots in the bottom left panel of Fig. ). At the subtropical Pacific FT Mauna Loa
station, also showed that higher-latitude air masses are more humid than expected and attributed this to local mixing processes.
For the warmest LC temperature group (TLC> 270 K, right panels in Fig. ) we occasionally observe
H2O-δD pairs below the exemplary Rayleigh
distribution, which can be explained by evaporation from a rather warm
ocean or by re-evaporation of falling rain droplets. Under these
conditions the subtropical North Atlantic free troposphere is
particularly humid. These tropical air masses moving north-east towards the Canaries, moistening the
atmosphere, are also observed in the subtropical Pacific in Mauna Loa station,. In
some occasions, mixing with air from higher atmospheric levels can also dry these air masses. The distribution
of H2O-δD pairs describing this mixing lie above the exemplary Rayleigh line (dark
grey dots in bottom right panels of Figs. and ).
For air masses linked to intermediate TLC (above 250
and below 270 K; central column in Figs. and
) we observe a mix of the different processes
discussed above.
It is important to keep in mind that the meteorological parameters upwind of the stations as deduced
by HYSPLIT/GDAS1 are the result of a dispersion model and analysis fields, each with an uncertainty. It is
likely that our analyses are affected by these uncertainties. See
more details in Appendix .
Nevertheless, we are able to demonstrate a clear relation between the HYSPLIT/GDAS1 data and the
measured H2O-δD pairs which a posteriori justifies our approach. Our simplified analysis
suggests that the distribution of the moisture in the subtropical North Atlantic FT is controlled by the
temperature at last condensation and subsequent mixing of air masses. Thus, these results are in agreement
with the idealized advection-condensation model proposed by .
Summary and conclusions
We report continuous measurements of water vapour isotopologues made
at two mountain observatories (IZO at 2370 m and TDE at
3550 ma.s.l.) on the island of Tenerife. We assume that the
measurements made in the second half of the night are only very weakly
affected by the local circulation on the island and represent the FT well. This assumption is consistent with previous
studies of trace gases made on the island's mountain and it is also
consistent with the wide distribution of the H2O-δD pairs
corresponding to the night-time observations. Thus, our measurements
generate a unique continuous water vapour isotopologue data record for
the lower/middle FT and can be used for studying free-tropospheric
water pathways. In this paper we perform such a study and therefore combine the isotopologue data with
back trajectory calculations and dust measurements.
The distribution of water vapour isotopologues collected during
dust-free North Atlantic conditions show wide variability. This
variability has been analysed in the context of the last condensation
point. The results show that the lowest δD values
registered at IZO are found in relation to air masses that have
experienced condensation at lower temperatures, and at higher latitudes and
altitudes over the North Atlantic Ocean (TLC< 250 K, > 40∘ N, < 400 hPa). The
condensation at low temperatures is responsible for the dryness of the
subtropical North Atlantic FT. These air masses seem to frequently
experience mixing with more humid air masses during transport to
the subtropical region. Moistening during subsidence was detected by
analysing GDAS1/HYSPLIT data and identified in the measured
H2O-δD distribution. Humid air masses are also detected when
the last condensation takes place close to the surface at lower latitudes not
far from the Canaries (> 270 K, > 600 hPa),
whereby the observed H2O-δD distribution indicates rain
re-evaporation or evaporation over a warm ocean as moisture source (increased
depletion with respect
to Rayleigh).
H2O-δD distributions as obtained for the four
different moisture pathways that determine the free troposphere moisture
budget in the surroundings of Tenerife (Left panel for IZO and right panel
for TDE). Contour lines indicate the areas of highest data
point density. The thin dashed and thick solid lines mark the areas that
include 95 and 66 % of all data respectively. The different colour
of the contour lines mark the different pathways (1–4) as given in the
legend. Rayleigh and mixing curves are plotted as in
Figs. , ,
, and .
For dust episodes, rather humid and enriched vapour is detected at the
stations, indicating a strong injection of boundary layer air
into the FT. These dust-laden air masses, which are the product of a strong
convection over the Sahara desert, reach the Canaries without having
experienced significant condensation and are found as an influential
contribution for moisture in this region. We show that the
measurements of water vapour isotopologues at regions located to the
west of the African continent provide new insights into the influence
of the African continent on the moisture budget of the
subtropical Northern Atlantic FT.
In summary, our results indicate that four different moisture
transport pathways have to be considered in order to understand the
subtropical North Atlantic moisture budget. While the dominant dryness
of the region is determined by the mean subsidence of dry air from
high altitudes of the extra-tropics (pathway 1), there are three main
processes that moisten the FT over the Atlantic: horizontal mixing
over the Atlantic with air from the Saharan Air Layer (pathway 2),
transport of air from low altitudes (P>600hPa) and
latitudes (whereby humidity occasionally originates from
rain-evaporation, pathway 3), and vertical mixing close to the
Canaries (pathway 4).
Figure gives an overview on the
H2O-δD distributions as observed when one of the four
different pathways is clearly prevailing. It depicts the areas with
the highest density of the observed H2O-δD pairs. Pathway 2
dominates when there is high aerosol load
(dust > 25 µg m-3 or AOD > 0.1, red contour lines). The other pathways
dominate for low aerosol load (dust <2µg m-3
or AOD < 0.02) and when TLC and ΔH2O are
situated within specific ranges. Pathway 1 prevails for
TLC< 250 K and ΔH2O within ±0.1
(grey contour lines). Pathway 3 prevails for
TLC> 270 K and ΔH2O within ±0.1 (green contour
lines). Finally pathway 4 dominates for TLC< 250 K and ΔH2O>1.0 (blue contour
lines). This pathway is mainly limited to altitudes below
3000 ma.s.l., and it is more frequently observed at IZO than
at TDE. In the range of high humidity, pathways 2 and 4 are difficult to distinguish, because both pathways show the mixing product between dry and humid air masses.
The use of aerosol measurements allows us to distinguish these two situations and also gives new insights into the properties of the
air masses over the Sahara desert and their subsequent advection over the Atlantic Ocean.
The summary as shown in Fig. reveals that the
H2O-δD pairs measured in the subtropical North Atlantic
FT well reflect the dominating moisture transport pathways to this
atmospheric region. A continued long-term monitoring of water vapour
isotopologue ratios would offer a unique possibility for investigating
the importance of the different mechanisms responsible for the
expected moistening of the subtropical North Atlantic FT in response
to climate change.
Calibration procedure of Picarro instrumentsMixing ratio calibration
Measurements of absolute humidity obtained from the meteorological
sensors have been used for correcting Picarro humidity
measurements. The meteorological stations are located close to
respective Picarro inlets. The temperature and relative humidity of
these stations have been used for calculating the water vapour
pressure (e=esat⋅RH/100). The saturation
vapour pressure is calculated from the Magnus–Tetens formula
esat=6.1094⋅exp(17.625⋅T)/(243.04+T),.
Figure shows 1 h resolution of
simultaneous measurements of humidity from the meteorological station
and the Picarro at IZO and TDE stations respectively. The slope (S),
intercept (i), and coefficient of determination (r2) of the
linear fit at each station are shown. The linear fit obtained at each
station is applied for correcting the Picarro humidity
measurements.
Isotopic calibrations with the SDM system
Calibration measurements are carried out every 8–12 h by sequentially measuring the two working
standards at three different water vapour mixing ratios of around 6000, 12 000, and 18 000 ppmv using
a Picarro SDM. Therein, a micro-litre volume of liquid working standard is injected
into a heated oven where it evaporates completely without fractionation. It is subsequently diluted into a flow of
dry synthetic air. The amount of liquid working standard can be adjusted in a certain range
and,
together with the flow rate of synthetic air, determine the humidity of the calibration gas mixture produced.
This calibration gas mixture is then measured by the Picarro spectrometer for 20 min at each humidity
level. A 6 min time interval is discarded between ambient and calibration measurements, as well as
between different humidity levels and working standards. This allows for appropriate isotope
and humidity exchange in the measurement cell of the Picarro spectrometer. The calibration procedure thus
takes slightly over 2 h.
The calibration frequency, as well as the calibration humidity levels, have been modified slightly during the course
of our measurements depending on demands during certain campaign periods. At TDE, the airflow is controlled
by an electronic mass flow controller, while at IZO the flow is controlled by a rotameter. The reduced precision
of this second device is translated in a larger variability of the calibration humidity at IZO in comparison to
TDE (Fig. ).
Grey dots show the 1 h humidity data pairs of the Picarro
and the meteorological at IZO and TDE. Red lines indicate the linear fit of
the distribution. S= slope, i= intercept,
r2= coefficient of determination.
Difference between the measured δD and that from the two
liquid standards used for calibrating the instruments at IZO (first row) and
TDE stations (second row). The left column depicts the dependence of this
difference to the humidity during calibration. The right column shows the
evolution of the difference with the time for all humidity levels.
Measured humidity dependence of δD determined by measuring
water vapour produced in a bubbler dilution system at IZO (left) and TDE
(right) stations respectively. Data shown are 1 min averages. The
error bars indicate the 1σ standard deviation of the δD
measurements, within which the average δD of each humidity bin (black
symbols) are identical to the average of all data.
In order to apply the calibration gas measurements to our ambient measurements, we first filter out
calibration measurements where any of the syringes of the SDM was clogged. We then analyse the
humidity dependence of the measured raw isotope ratios during calibration, using
the three recorded humidity levels. If a humidity dependence significantly larger than the precision of the
calibration measurements was found, we would have to apply a correction function. This was not the case for
our calibration measurements. Figure shows an example measurement of humidity
dependence of δD at IZO and TDE stations respectively. An uncalibrated working standard was used in
a bubbler to saturate synthetic air with water vapour. This air was then diluted into a variable flow of synthetic air
to produce a gas mixture of variable humidity. The data are averaged for 1 min, and the error bars denote
the 1σ-standard deviation of the δD measurements. For low humidity measurements, where
the instrument is
most susceptible to a humidity dependence of δD, the dependence remains within the scatter of the
data (1σ standard deviation). Please also be aware that we use a Picarro L-2120i model. The Picarro
models L-21xxi have significantly less dependency on humidity concentrations than the Picarro models L-11xxi
e.g. .
The stability of the humidity dependence for SDM calibrations along the whole study period is shown in the
left column of Fig. . We analyse the differences obtained from the
regular calibrations made below and above 15 000 ppmv. No significant differences are observed along the
humidity range covered with the SDM, being the 3σ below 0.8 ‰ at IZO (1.8 ‰ at
TDE) (instrumental precision < 0.5 ‰).
Subsequently we determine a linear regression function of the two working standards S1 and S2 measured
during calibration (Fig. ). This function is of the form
δDVSMOW=a+b×δDmeasured, and
it takes the uncertainty of the isotopic composition of the working standards as well as the precision of
the calibration measurements into account. We determine a calibration function for every calibration
measurement described above (1 to 2 per day). We apply this function to the ambient measurements in order to
transfer these measurements onto the VSMOW2/SLAP2 scale. The uncertainty in this linearity is
about ±2 ‰ (determined with standards between +15 and -428 ‰,
see Fig. 2 of. Since our working standards cover 100 ‰, the uncertainty in
slope (a) of the aforementioned calibration function is 2 ‰/100 ‰.
Each day, the data are calibrated with the resulting
combination of the calibration at three different humidity points (no humidity dependence was found) and a linear
fit between the responses of the two standards. The right column of this figure shows the time series of the
SDM calibrations carried out at IZO and TDE stations (gaps are due to instrumentation repairs). No temporal
drifts are observed in the different time series, indicating that both Picarro instruments are very stable and
consistent over time.
For the total uncertainty estimation we consider the instrumental precision as well as uncertainty components:
uncertainty of the standards (0.7 ‰ for both dry and humid air),
humidity dependence (from
0.3 ‰ for 10 000 ppmv, up to 8.0 ‰ at 200 ppmv),
extrapolation of
VSMOW2-SLAP2 scale outside the range of calibration (for humid air: < 2.0 ‰; up to
5 ‰ for strong depleted air),
calibration (1 ‰ for the whole humidity range).
The
absolute uncertainties in δD are then < 14.7 ‰ for strong depleted air at 500 ppmv
and < 4 ‰ at 4500 ppmv.
Schematic graph showing the use of two isotope working standards S1
and S2 with known δDVSMOW. When the two standards are
measured by an instrument one obtains δDmeasured. A linear
regression of the form y=a+bx can be used to transfer
δDmeasured onto the VSMOW scale. Note that uncertainties in
x and y are considered.
Diurnal signals as seen in H2O-δD distribution density plots
Figure shows all the observed 10 min averaged
H2O-δD data pairs, which is a large amount of data (about
75 000 and 40 000 data points at IZO and TDE respectively). From
Fig. it becomes clear that the IZO daytime
observations cover only a limited area in the H2O-δD
distribution space. There is no single IZO daytime observation with δD
below -345 ‰ (and almost no daytime observations with δD
below -220 ‰ for H2O above 10 000 ppmv). For TDE the
situation is not clear from Fig. .
In order to give a statistical insight into the differences between the
H2O-δD distributions during day and night, we additionally
calculate the density of the H2O-δD distributions for the
daytime and night-time data pairs. The respective distribution plots are shown
in Fig. and clearly confirm that for IZO the
daytime and night-time H2O-δD distributions are significantly
different. For TDE the day-night differences are less pronounced, but they
become visible in the density plot (right panel of
Fig. ). For instance, at TDE the probability for
δD being below -300 ‰ is significantly higher during night
than during day (compare the areas marked by the thick black and orange
lines).
H2O-δD distributions as obtained for the “night-time
observation” (10 min averages made between midnight and 1 h
after sunrise, black contour lines) and the “daytime observations”
(10 min averages made between 3 and 10 h after sunrise,
orange contour lines). The thin and thick lines mark areas that include 95
and 66 % of all the data. The plot shows the density distributions of the
data points from Fig. 4.
Discussion of the representativeness of the used TLC and ΔH2O parameters
There are two different reasons for a not
perfect representativeness: First, there is an uncertainty in the trajectories, which is the larger the longer
the trajectory. Second, the model does not well resolve the fine structured topography of Tenerife, which
might affect the flow of air masses and already the height attribution of the trajectories might be
incorrect.
In our case, the trajectories are released at the height above sea level of the stations. We tested the uncertainty
we could have in the representativeness of our backward trajectories by looking also in the backward
trajectories released 500 m above and 500 m below. The data set describes those days in
which last condensation has been observed along the pathways at the three altitudes (only during the
night-time period, 00:00 and 06:00 UTC), and we calculate for all trajectories the parameters TLC
and ΔH2O=log(H2Ot=0)-log(H2OLC).
A 3-year (2012–2014) analysis of the uncertainty in the vertical
resolution of the model using TDE as reference station. The correlation
between TLC at 3500 m with 3000 and 4000 m
respectively, is shown.
A 3-year (2012–2014) analysis of the uncertainty in the vertical
resolution of the model using TDE as reference station. The correlation
between ΔH2O=log[H2Ot=0′] -log[H2OLC] at
3500 m with 3000 and 4000 m respectively, is shown.
The test is made for the TDE station and we correlate the parameters as obtained at 3500 m (altitude of the site), with
the parameters as obtained at 4000 m (blue stars in Figs. and )
and 3000 m (red stars). The green line represents the diagonal (3500 m). The uncertainty test
was carried out with 3 years of back trajectories (2012–2014). The Pearson's coefficients obtained for
TLC and ΔH2O (for each of the three TLC categories) are shown in the figures.
The scatter in the plots documents the uncertainty in the representativeness of the TLC
and ΔH2O as used in our study. However, we think that this scatter is a
conservative uncertainty estimation. The reason is that we work with night-time data (midnight – one hour
after sunrise). During that time the atmosphere above the island is rather stable and local effects (not resolved
by the model) should by far be less important than during the daytime. That is we think that the air mass
recorded during night-time the stations IZO and TDE corresponds to air travelling over the ocean around the island at very
similar altitudes and the scatter for altitude differences of as large
as ±500 m likely overestimates the actual uncertainty.
Acknowledgements
This study has been conducted in the framework of the project MUSICA
(MUlti-platform remote Sensing of Isotopologues for investigating the
Cycle of Atmospheric water), funded by the European Research Council
under the European Community's Seventh Framework Programme
(FP7/2007-2013/ ERC Grant agreement number 256961). We thank Dan Smale
(NIWA, New Zealand) and the staff of Arrival Heights/Scott Base for
providing the Antarctic ice water probes we used for preparing our
isotopologue standards. The isotopologue composition of the probes was
kindly determined by LSCE-CEA, France. Aerosol measurements are part
of the project POLLINDUST (CGL2011-26259), funded by the Minister of
Economy and Competitiveness of Spain. The AERONET sun photometer at
Izana (PI: Emilio Cuevas) has been calibrated within AERONET
EUROPE TNA supported by the European Community Research Infrastructure
Action under the FP7 Capacities programme for Integrating Activities,
ACTRIS grant agreement number 262254. Eliezer Sepúlveda is
supported by the NOVIA project (Ministerio de Economía y
Competitividad of Spain, CGL2012-37505). We thank the editor and the
three anonymous referees for their very constructive input and their help in
improving the presentation of our results.
Edited by: H. Wernli
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