TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-13-413-2019Moisture transport in observations and reanalyses as a proxy for snow accumulation in East AntarcticaMoisture transport in observations and reanalyses as a proxy for snow accumulationDufourAmbroiseambroise.dufour@univ-grenoble-alpes.frCharrondièreClaudinehttps://orcid.org/0000-0002-1150-9764ZolinaOlgaIGE, Institut des Géosciences de l'Environnement, CNRS/UGA, Grenoble, FranceIORAS, P. P. Shirshov Institute of Oceanology, Russian Academy of Science, Moscow, RussiaAmbroise Dufour (ambroise.dufour@univ-grenoble-alpes.fr)4February201913241342530July201827August201818December201819December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://tc.copernicus.org/articles/13/413/2019/tc-13-413-2019.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/13/413/2019/tc-13-413-2019.pdf
Atmospheric
moisture convergence on ice sheets provides an estimate of snow accumulation,
which is critical to quantifying sea-level changes. In the case of East
Antarctica, we computed moisture transport from 1980 to 2016 in five
reanalyses and in radiosonde observations. Moisture convergence in reanalyses
is more consistent than net precipitation but still ranges from 72 to
96 mm yr-1 in the four most recent reanalyses, ERA-Interim, NCEP CFSR,
JRA 55 and MERRA 2. The representation of long-term variability in reanalyses
is also inconsistent, which justified resorting to observations.
Moisture fluxes are measured on a daily basis via radiosondes launched from a
network of stations surrounding East Antarctica. Observations agree with
reanalyses on the major role of extreme advection events and transient eddy
fluxes. Although assimilated, the observations reveal processes that reanalyses
cannot model, some due to a lack of horizontal and vertical resolution,
especially the oldest, NCEP DOE R2. Additionally, the observational time
series are not affected by new satellite data unlike the reanalyses. We formed
pan-continental estimates of convergence by aggregating anomalies from all
available stations. We found statistically significant trends neither in
moisture convergence nor in precipitable water.
Introduction
East Antarctica stores the equivalent of 53.3 m out of
58.3 m of sea-level rise for the whole continent . The mass of the ice sheet
decreases when icebergs are calved off the coast and increases when snow
accumulates inland. The surface mass balance is expected to become more
positive as precipitation increases due to higher humidity
, though not as fast as the loss of ice
increases, thus leading to net sea-level rise . However, as
we shall see, the additional accumulation has yet to be observed.
The in situ methods that are used to determine snow accumulation are reviewed
in . The most reliable consist of measuring the emergence of
stakes on a yearly basis or annual snow layers in firn cores
. To make up for their limited spatial coverage, the
ground-based observations can be combined with remote-sensing data as in
or with regional climate models . Lately, both satellites and models have been used without the
need for surface data: the snowfall measurements from CloudSat
and the high-resolution calibration-free model of
.
Not all the aforementioned methods yield accumulation time series. Among
those that do, none present statistically significant trends in accumulation
on a continental scale, e.g. or for
East Antarctica, in particular. The situation is more contrasted on a local
scale with recent increases in Dronning Maud Land
and Law Dome , mixed trends
on the traverse to Dome A and none in Adélie Land
. The 1000-year ice core synthesis of
confirms the high spatial variability, both regional and small-scale due to
post-deposition processes. In East Antarctica and for the 20th century, the
authors report a decrease in accumulation in Victoria Land and contradictory
trends in Dronning Maud Land and over the plateau.
The accumulation can also be inferred from the convergence of moisture blown
from the oceans onto the continent. To compute Antarctica's atmospheric
moisture budget, one needs vertical wind and moisture fields along the coast,
including locations with few research stations, like West Antarctica.
Reanalyses synthesize historical observations and short-term weather
forecasts and provide gridded atmospheric fields constrained by observations
even in such remote regions .
The analyses will shift to accommodate new observations sources making the
interpretation of time series equivocal. This issue was raised in the case of
Antarctica by under a telling title: “Precipitation
changes in high southern latitudes from global reanalyses: A cautionary
tale.”
Major characteristics of reanalyses compared during the study.
ReanalysisReferenceModelModelAvailableDatavintageresolutionresolutionassimilationNCEP DOE R22001T62 L282.5∘ L103D-VarERA-Interim2006T255 L600.75∘ L234D-VarNCEP CSFR2009T382 L640.5∘ L233D-Var + FOTOJRA 55,2009T319 L600.5∘ L234D-VarMERRA 22012∼50×50 km L720.5∘×0.625∘ L233D-Var + IAU
To reduce the ambiguity, we cross-examined the reanalysis moisture transport
with the only in situ measurements of humidity and wind – radiosondes – in
the footsteps of and . Rather than
compute the climatological moisture budget, our contribution will be to
investigate the temporal variability of the transport. Radiosoundings cannot
provide an independent validation of reanalyses, but they will yield more
homogeneous time series. Besides, in Antarctica, humidity soundings are
sometimes rejected during the assimilation process, even by state-of-the-art
analyses . Like our predecessors, we restrict ourselves to East
Antarctica because of the lack of long-term upper-air measurements in West
Antarctica and in the peninsula.
Data and methods
We build upon a similar study in the Arctic and the
reanalyses presented herewith. The first NCEP NCAR reanalysis was not
included due to a well-known moisture diffusion problem over Antarctica
as well as unrealistic evaporation .
Its successor, NCEP DOE R2, solved the main errors from the first attempt
. At 2.5∘, it has the lowest horizontal resolution of our
ensemble (Table ) as well as the fewest vertical
levels. We left out JRA 25 and MERRA 1 because they were not extended to
2016. CFSR has the highest resolution (T382 or approximately 35 km) and
includes ocean and sea-ice physics in its forecast model. Our period of study
starts in 1980 to adjust to the new reanalysis from NASA and MERRA 2. Among
other things, the data assimilation system from MERRA 1 was improved to take
into account satellites launched after NOAA-18 (2005), for instance the
hyperspectral infrared radiances of EOS Aqua . ERA-Interim and
JRA 55 implement 4D-Var assimilation, whereas CFSR and MERRA 2 run improved
versions of 3D-Var. ERA-Interim has been commended as especially reliable
over Antarctica .
The domain of the study is constrained by the location of stations running
long-term radiosonde programmes, which restricts us to East Antarctica
(Fig. ). SANAE (east of Neumayer) and Molodeznaja (east of
Syowa) had practically no humidity soundings available inside our time
window. Inland, the radiosonde programme at Vostok Station stopped in 1992; it
began at Dome C in 2006 only. The boundary between East and West Antarctica
is the transect between the Ross and Filchner-Ronne ice shelves. We excluded
ice shelves because they do not contribute to sea-level rise. We smoothed the
shape of the domain (red line in Fig. ) to avoid making
the direction normal to the boundary too dependant on the local shape of the
coastline (black line). The largest gaps between the stations are at the
boundaries between the Ross and Filchner-Ronne ice shelves and around Dumont
d'Urville.
Radiosondes can measure moisture advection directly as it is the product of
humidity with the horizontal wind vector. The Integrated Global Radiosonde
Archive (IGRA) version 2 provided the sounding data . IGRA stores
quality-controlled soundings from more than 2700 stations worldwide dating
as far back as 1905. We applied additional climatological checks to the wind
and specific humidity variables, similarly to . Based on
monthly distributions, they exclude values further than 3 times the
interquartile range from the median. Due to the persistence of unrealistic
values, we removed the data from Syowa above 400 hPa.
Location of the radiosonde launch sites and boundary of the East
Antarctic ice sheet. The diameter of the dots is proportional to the number
of archived soundings (maximum is Casey with 39 000 soundings between 1957
and 2016). Only the sites selected for the study are labelled.
Monthly density of valid humidity and wind soundings per station and
per synoptic time (midnight and noon GMT).
The hygrometers on board radiosondes suffer from known biases some of which
can be compensated retrospectively . For instance, at
low temperatures, the hygrometer can no longer adjust to sharp variations in
humidity, leading to the so-called time-lag bias. Moreover, the default
calibration model of the sensor is inaccurate below -20∘C
(temperature-dependence error). Unfortunately, the suggested corrections
apply only to specific radiosonde models and require metadata and high-resolution vertical data absent from IGRA. However, as we shall see, in
Antarctica most of the transport of humidity occurs near the surface, often
in warm storm conditions. Finally, solar heating of the sensor also can
generate errors which were documented in the case of Antarctica in
with the help of an Atmospheric Radiance Emitted
Interferometer.
The temporal availability of the soundings after filtering is shown in
Fig. . The temporal sampling is biased against the early
1980s and winters. To build time series and compute trends, we only kept
the monthly data of a station if it involved at least 10 valid soundings per
month for at least 10 years. The same criterion was used to exclude
reanalysis data at given locations and pressure levels that are too often
masked due to the topography and variations in surface pressure. When we
needed to compare the data sets at specific locations, we co-located the
gridded reanalysis data with the stations using bilinear interpolation. In
such cases, the year-round 6-hourly reanalysis values were masked to match
the irregular availability of observations.
Mean vertically integrated moisture flux convergence over Antarctica
in ERA-Interim (a) and the standard deviation of that variable for
all the studied reanalyses (b).
The study of snow accumulation via upstream atmospheric processes relies on
the conservation of water vapour. Over long timescales, the rate of change
of precipitable water can be ignored so that the moisture
budget equation is reduced to
∮∂EAa∫0psqvndldpg=∬EAa∫0ps(c-e)dsdpg,
where E Aa refers to the East Antarctic ice sheet and ∂ to its
boundary. ps is the surface pressure, q is the specific humidity and
vn is the wind component normal to the boundary. c and e are the
condensation and evaporation rates per unit mass. One must then assume that
the vertical integral of condensation and evaporation is equal to net
precipitation, i.e. that the transport of water only occurs in the gas phase.
As it happens, the convergence of cloud frozen and liquid water is in the
order of 10 % of the vapour convergence in Antarctica according to the few
reanalyses that provide these variables . The final step is
to equate net precipitation with snow accumulation. Liquid run-off is indeed
negligible given the low temperatures . Sublimation and
hoarfrost will appear under evaporation. However, our method cannot account
for snow blown out of the domain by the wind.
We use the trapezoidal rule to approximate the path integrals along the
domain boundary, e.g. to compute the left-hand side of Eq. (). In the case of a variable f available at M stations
surrounding the domain this translates to
∮fdl≈∑m=0M12fm+1+fmlm+1-lm.
The lm are the location of the stations after the domain boundary is
parameterized by the arc length l. Likewise, the vertical integral of a
function f defined on N pressure levels pn is given by
∫0psf(p)dp≈∑n=n0N-112fpn+1+fpnpn-pn+1+fpn0ps-pn0.n0 refers to the last pressure level above the surface.
Our aim was to investigate trends rather than absolute amounts so we worked
with anomalies with respect to each station's monthly climatology as in
. We could thus consider the whole 1980–2016 period and not
be as affected by the biased temporal and spatial sampling. Trends and
correlations were considered statistically significant for p values higher
than 95 % on a two-tail Student t test.
Assessment of reanalyses
The map of mean moisture convergence in ERA-Interim (Fig. a)
is broadly similar to the accumulation map in . The
convergence is highest over the peninsula where the westerlies intersect the
orography. The lower altitude of West Antarctica allows moisture advection
further inland than in the eastern half. The area stretching from Dome Fuji
to Dome C receives less than 20 mm of moisture a year.
Magnitude of the long-term-averaged (1979–2016) atmospheric
moisture budget terms of the East Antarctic ice sheet (a). Linear
trends of the moisture budget terms (b): colour indicates rate of
change and hatches statistical significance.
Along the coast of East Antarctica and in the Transantarctic Mountains, there
are zones of net moisture divergence neighbouring local maxima in excess of
1000 mm yr-1. In comparison, the accumulation from
is always positive and peaks at 500 mm equivalent water per year. The
important wind contrasts in mountain ranges and outlet glaciers are a likely
source of numerical artefacts. Theoretically, at least, strong winds erode and
sublimate snow, which is a genuine cause of moisture divergence, but these
processes are not represented in most climate models .
Generally speaking, the ensemble standard deviation is proportional to the
mean convergence, being highest in the peninsula and along the coast (Fig. b). The inter-data-set spread is disproportionately high in
the Transantarctic Mountains and the East Antarctic Plateau. The 100 km
scale inhomogeneities on the plateau are due to JRA 55. Neither the
precipitation nor the evaporation exhibit these features (not shown). Unlike
the other data sets, in JRA 55, evaporation is on average negative (net
deposition) above 2000 m.
Magnitude of the long-term-averaged (1979–2016) atmospheric
moisture budget terms in mm yr-1 for the East Antarctic ice sheet and
the whole Antarctic continent (ice-shelves excluded). The columns stand for
precipitation (P), evaporation (E), convergence (C), net precipitation
(P-E) and residual (P-E-C). Strictly speaking, the values of
represent accumulation rather than net precipitation. The
figure for East Antarctica is a weighted average of the accumulation in the
sectors from 45∘ W to 180∘ E.
East Antarctica Antarctica Data setPEP-ECP-E-CPEP-ECP-E-CNCEP DOE R2126666065-517264108115-7ERA-Interim110199193-2159191401383NCEP CFSR1373410272311963815812039JRA 5511921998991752115414015MERRA 21421912396272042018414342112143
Given the dispersion between data sets on a local scale, we cannot expect them
to converge to a common estimate of convergence on the scale of East
Antarctica (Fig. a, magenta bars). This is all the more
true for evaporation and precipitation, which are forecasted variables and
thus even less constrained by observations (light- and dark-blue bars,
respectively). One issue with reanalyses is the non-closure of their moisture
budget, witnessed by the gap between upper and lower bars in Fig. a. The small mismatch in NCEP DOE R2 is illusory, a
consequence of its excessively high sublimation . The
4D-Var reanalyses exhibit the smallest residual. The cloud water fluxes were
not included: they could improve the balance for CFSR, JRA 55 and MERRA 2
but will worsen it for NCEP DOE R2 and ERA-Interim. For comparison, we also
give the break-down of the moisture budget in the case of the whole continent
(still excluding ice shelves) in Table , along with the
figures of . West Antarctica and the peninsula are more
exposed to moisture fluxes, and indeed the convergence is higher for the whole
continent. The ranking between reanalyses remains the same.
Distribution of the mean incoming vertically integrated moisture
flux on the boundary of the Antarctic ice sheet in different reanalyses. The
grey-shaded bands indicate the ranges corresponding to plus or minus 1
interannual standard deviation from the mean. Stars represent the radiosonde
and dots the reanalyses interpolated over the launch sites, with the same
temporal sampling as the observations. The red dots in the lower panel
indicate the location of the sites and the arrows the direction normal to the
boundary.
The interannual variability in reanalyses is doubtful due to the
discontinuities introduced by changes in the observation system
. This is especially the case in Antarctica, where
analyses rely heavily on satellite data as opposed to conventional
observations . The changes in the components of the
moisture budget over time are summarized as linear trends in Fig. b. Generally speaking, the trends are weak and
inconsistent. Precipitable water has not changed significantly in the modern
reanalyses, yet it is the purported mechanism for the future increase in
moisture fluxes.
Comparison with observations
The coastal profile in the upper panel of Fig. was built
by interpolating the mean moisture flux field on the smoothed boundary of the
East Antarctic ice sheet (lower panel). Overall, the moisture transports are
positive over East Antarctica with occasional negative values in valleys
where the katabatic winds are channelled, e.g. on the Amery Ice Shelf
(70∘ E). Because of its low resolution, NCEP DOE R2 is oblivious to
these local effects and is frequently outside of the ensemble envelope. On
the other hand, NCEP CFSR, which has the highest spectral resolution (T382),
shows stronger offshore fluxes at these locations than ERA-Interim, MERRA 2
and JRA 55.
Above the launch sites, the reanalysis profiles can be compared with the
radiosoundings (black stars in Fig. ). The observations
are within the plus or minus 1 standard deviation envelope. In several
cases (Syowa, Mawson, Casey), the presence of the radiosounding coincides
with an extremum in the reanalysis profile, perhaps a deviation to accommodate
the observation.
Annual cycle of moisture transport into East Antarctica
(a) computed from reanalyses averaged over East Antarctica,
(b) observations and reanalyses (with the same spatial and temporal
sampling) averaged over all stations.
The reanalysis climatology is built on 6-hourly data, whereas there are two
radiosoundings a day at the very best with extensive gaps in the data. To
correct the sampling bias, we now only consider the reanalysis time steps that
coincide with a radiosounding. The resulting time averages are shown as dots
in Fig. . As expected, there is a gap between profiles and
the dots, a measure of the sampling bias. This is particulary true for
Mirny as well as for Novolazarevskaya in the case of NCEP CFSR. The values
for South Pole Station are shown at the abscissa of the closest point on the
domain boundary; the direction of fluxes normal to the boundary is also
defined at this point. At the South Pole, the difference between profiles and
dots is also a consequence of the distance to the boundary. Otherwise, the
subsampled reanalyses are quite representative of the year-round values,
including the summer-only data at Mario Zucchelli Station, surprisingly.
Mean vertical profiles over radiosonde sites, simultaneously with
observations (solid lines); along the coast of the East Antarctic ice sheet
for the whole 1980–2016 period (dashed lines). The variables displayed in
the panels are moisture flux (a), specific humidity (b),
mean wind (c) and transient eddy fluxes (d).
This is likely a coincidence: the annual cycle of moisture convergence
experiences a two-fold increase during the austral winter (Fig. a). In the Southern Hemisphere, the lower winter humidity is
more than compensated by the stronger storm activity . NCEP
DOE R2 has a much weaker convergence cycle perhaps due to the unrealistic
evaporation in summer. When the reanalyses are interpolated on the stations
and masked in time to match the radiosonde launches, the annual cycle is
quite different (Fig. b). The transition from summer to
winter is no longer as sharp in most data sets and it is altogether absent
from NCEP DOE R2 and IGRA.
In the vertical, the moisture flux profiles demonstrate two different regimes
(Fig. a). In most data sets, including observations, the
transport changes polarity around 900 hPa: into the continent above and away
from it below. The level of the transition is higher in ERA-Interim (850 hPa)
and higher still in NCEP DOE R2 (750–700 hPa). The 1000 hPa level in NCEP DOE R2 was too often below the ground to be shown. The surface fluxes are weaker
above stations (solid lines) than in the continental average (dashed lines).
The inter-data-set spread also increases near the surface in the case of
humidity (Fig. b). The IGRA observations exhibit a shallow
inversion that is also present in the modern reanalyses but deeper and stronger.
There is practically no mean wind above 800 hPa, but below the
prevailing winds blow out from the ice sheet. This is the signature of
katabatic flows. There is some difference between the original reanalyses and
when they are both sampled in time and interpolated in space. We suggest that
the stations are not representative of their surroundings because they were
generally built in places sheltered from the winds. Only the high-resolution
data sets can represent both the katabatic outflow and the relative haven of
the station. What is more, the radiosonde launch is more likely to be
cancelled or the balloon may be lost during a strong katabatic event, which biases
the observations against these conditions.
Due to the correlation between wind and humidity, the mean moisture flux
(qv‾) is more
than the product of the mean wind (v‾) and the mean
humidity (q‾). The residual is the transient eddy term of the
Reynolds decomposition: q′v′‾=qv‾-qv‾. In the presence of an extratropical cyclone, the
warm sector advects exceptionally moist air inland and vice versa in its cold
sector. This corresponds to the inward fluxes above 900 hPa in
Fig. d. They peak at 750 hPa and then decrease with altitude.
Below 900 hPa, the fluxes averaged along the boundary remain positive, but
they change polarity at the sonde locations. Intermittent katabatic conditions
imply unusually strong downslope winds: they will lead to positive transient
eddy fluxes if they advect the unusually dry air from the plateau. However,
they will count as negative if that air is moistened by the sublimation of
blowing snow or precipitation . The
former process, at least, is not modelled in the reanalyses. Its effect on the
humidity profile will only be felt where radiosoundings are assimilated.
Returning to panel (a), we now interpret the outward low-level fluxes as the
effect of katabatic winds, but we suspect that this effect is underestimated
for different reasons depending on the location. At the stations, the
prevailing katabatic wind is weaker than average. Away from the observations,
the reanalyses are too dry during katabatic events. The inward high-level
fluxes are a consequence of transient eddies, presumably extratropical
cyclones, with good agreement between sites and data sets.
We finish the intercomparison with the statistical distributions of
vertically integrated moisture transport, both in the non-sampled and
subsampled cases (Fig. a and b). The positive (inward)
transport dominates, particularly the last decile, but the negative (outgoing)
fluxes are nonetheless important: they comprise more than a third of the absolute
transport. The inter-data-set variability is as strong for the positive and
the negative transport, highlighting once more the role of katabatic winds.
In the subsampled case, the observations fall within the reanalysis envelope,
but the inter-data-set variability is also greater.
To sum up, radiosondes and reanalyses diverge in narrow valleys and near the
ground, especially for low-resolution models. Otherwise, since radiosondes
are usually assimilated into reanalyses, the difference between the two is
predictably small. The added value of radiosondes is the study of time
series.
Interannual variability
Time series from reanalyses reflect modifications to the observation system
in addition to changes in the climate. The radiosonde record also has its
homogeneity issues, e.g. when instruments are upgraded. These events are
recorded in the IGRA metadata and we found no association with
discontinuities in the times series. We will therefore interpret sudden
divergences between observations and reanalyses as artefacts of the data
assimilation. From Fig. , the most conspicuous ones are in
NCEP DOE R2, followed by JRA 55, but they do not take the form of the shifts we
would expect with, say, the introduction of a new satellite. Rather,
anomalies are amplified intermittently compared to observations or one
reanalysis departs for several years from the ensemble. At Amundsen Scott
Station (South Pole), observations exhibit variations in higher amplitude
than the reanalyses. The extreme climate in our only inland station must have
been a test to both measurements and reanalyses.
Breakdown of the climatological vertically integrated moisture flux
into the contribution of its different deciles. When a decile is represented
below the origin, its contribution is negative. The top of the bar indicates
the mean transport when negative values are set to zero. The difference
between the top and bottom is the mean transport, symbolized by the bold
black line. In panel (a), the flux deciles are averaged over the
East Antarctic boundary, whereas in panel (b) the deciles are
computed by aggregating the data over stations only with the same temporal
sampling as IGRA.
Time series of vertically integrated moisture transport anomalies
into East Antarctica (in kg m-1 s-1) at the location of the
selected stations in observations and reanalyses.
Linear trends of vertically integrated moisture transport into East
Antarctica. The profiles are built from reanalyses over the 1980–2016 period
interpolated along the domain boundary, whereas the dots are computed from
reanalyses with the same temporal sampling as the observations. The stars
correspond to trends computed from observations. The bold sections of the
profiles and the solid dots and stars indicate statistically significant
changes.
We now examine the long-term evolution of moisture transport in terms of
linear trends, this time analysed along the horizontal dimension (Fig. ). Most longitudes show no statistically significant change
in the advection. Among those that do, even fewer display an agreement
between data sets. Mirny Station and its surroundings to the east have seen
less moisture travelling inland according to the observations, NCEP CFSR and
MERRA 2. The other reanalyses agree in polarity but not in significance. There
has been a similar decrease over the Amery Ice Shelf between Davis and
Mawson stations. The radiosondes and the interpolated reanalyses at Mawson
Station present an opposite trend, which is greater in magnitude than the
uninterpolated reanalyses, indicating a sampling bias. This increase evokes
the accumulation measurements of on the eastern side of the
nearby Lambert Glacier, but it is unrelated: the timing of the rise is off by
a decade. Radiosondes at Syowa Station report an increase in the onshore
moisture flux that is stronger than the reanalyses. Syowa Station is located at the
edge of the domain surrounding Kohnen Station, identified in
and where ice cores have revealed a similar increase in
accumulation. In Adélie Land, trends are incoherent in polarity. The positive
trend in radiosondes is not significantly different from zero, in line with
the findings of . recorded anomalous
precipitation at Law Dome that peaked in the mid-1970s before returning to
normal by the turn of the century, but there is no trace of this trend in the
observed transport at the nearby station of Casey. The decreasing
accumulation in Victoria Land from is not apparent in the time
series of McMurdo and Mario Zucchelli stations, presumably because the ice
core sites were located further north.
Time series of transport into East Antarctica: (a) absolute
amounts computed from reanalyses averaged over the domain boundary,
(b) anomalies of observations and reanalyses (with the same spatial
and temporal sampling) averaged over all stations.
On the scale of the ice sheet, we know from Fig. that
the transport series from reanalyses will be offset by up to 30 mm yr-1 and that they will
present contradictory trends. In Fig. a, ERA-Interim, JRA 55 and MERRA 2 are grouped
together, especially after 2000. NCEP DOE R2 is once again the outlier. It is the only
data set to not peak in 1980. Surprisingly, NCEP CFSR diverges from the other
modern reanalyses in the 1980s.
When we restrict the reanalyses to the locations and times of radiosonde
launches, they display a more consistent behaviour (Fig. b): a downward trend in the 1980s and an upward trend in the two following
decades. Observations follow the same pattern with comparatively higher
fluxes in the 1980s and 2000s. During the same period, precipitable water
above the launch sites has remained constant, both in observations and
analyses (not shown). The interdecadal pattern in transport is therefore a
consequence of variable winds rather than thermodynamics.
From Fig. b, we deduce a straightforward assimilation
of radiosoundings by reanalyses when available. The reanalysis time series at
the stations would be less consistent if the assimilation relied more on the
model first guess or on remote-sensing data. While the time series at the
stations may be accurate, they would be irrelevant if they were not
representative of East Antarctica as a whole. If that were the case, the two
panels of Fig. would have little in common. As we saw,
this true of is decadal variability. However once trends are removed, the
continental and the station detrended time series are significantly
correlated on a year-to-year basis for all reanalyses except NCEP DOE R2. The
significant correlation coefficients lie between 0.48 (JRA 55) and 0.61
(MERRA 2). This gives credibility to the claim that the network of stations
is representative of the entire boundary of the ice sheet.
Conclusions
We have compared moisture transport to Antarctica in five reanalyses and over
the 1980–2016 period. For East Antarctica, estimates of moisture convergence
range from 65 to 96 mm yr-1 and net precipitation from 60 to 123 mm yr-1 (respectively 72–96 and 91–123 mm yr-1 when excluding NCEP
DOE R2). The polarity of the linear trend for moisture convergence is not
consistent. In any case, changes are not driven by precipitable water
increases.
The scarce situ observations and the unreliable satellite observations
make Antarctica a fitting object of study for
reanalyses. The differences we found between the data sets remind us of their
familiar limitations: model dependence in the absence of observations and
unreliable time series . Additionally, the
non-closure of their atmospheric hydrological budget is an issue for
estimating accumulation over the ice sheet. Fortunately, the gap between
moisture convergence and net precipitation is reduced by 4D-Var assimilation.
Although radiosondes are assimilated by reanalyses, lower-resolution models
have difficulties in taking into account observations near the surface and on
irregular terrain. This is relevant to representing the humidity inversion and
how valleys funnel moisture offshore during katabatic events. Most of the
onshore moisture transport occurs above 950 hPa as transient eddy fluxes and
in winter. This consistently represented in all but the oldest reanalysis.
Changes in the observing system confounds the interpretation of temporal
variability in reanalyses. At the cost of spatial coverage, the original
observations provide a homogeneous time series that can be used as a proxy for
accumulation. After a sharp decline in the 1980s, the moisture transport
recovered gradually in the 1990s and early 2000s and has since slightly
decreased. This time series can now serve as a reference for identifying spurious
trends in the reanalyses.
The seasonal resolution of stake farms and ice cores is not enough to study
short and intense accumulation events such as those described in , but
radiosondes are launched on a daily basis. The distribution of moisture
transport highlights both positive and negative extreme events. Regarding
exports, the conflation of net precipitation with accumulation ignores wind
erosion in particular. The reanalyses only represent its signature on the
humidity profile near radiosonde sites. Conversely, the observations are made
in sheltered locations that experience a weaker transport by the mean wind.
In the long term and averaged over all the selected East Antarctic stations,
there has been no statistically significant increase, either in incoming
moisture or in precipitable water. In contrast, surface temperature trends
are much stronger in West Antarctica and the peninsula , with
probable consequences for the moisture budget. It remains to be seen whether
the limited radiosonde data in these regions can provide the corresponding
evidence.
The reanalysis data sets were available by courtesy of
NCEP, JMA, ECMWF and NASA. In practice, the NCEP reanalyses and JRA 55 were
found at https://rda.ucar.edu/, ERA-Interim at
https://www.ecmwf.int/en/forecasts and MERRA 2
at https://disc.gsfc.nasa.gov/. The IGRA sounding
data were made available by the NOAA National Centers for Environmental
Information at ftp://ftp.ncdc.noaa.gov/pub/data/igra.
The bulk of the article was completed as part of AD's PhD
thesis under the supervision of OZ. CC contributed specifically to the
question of biases in humidity soundings.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the project
“Mechanisms of moisture advection in high latitudes in the present and
future climate” funded by CNRS, IGE and UGA and partly through the BELMONT
Forum Fund ARCTIC-ERA project funded by ANR. Olga Zolina also benefited from the
project 14.613.21.0083 (ID RFMEFI61317X0083) funded by the Ministry of
Education and Science of Russia. We are grateful to the logistics crew at Cap
Prudhomme, Vincent Favier and the TA64 Météo France team, particularly
Didier Lacoste for investigating the station's present weather archives on
our behalf. Finally, we thank the editor and the two anonymous reviewers for their consideration and constructive comments.
Edited by: Christian Beer
Reviewed by: two anonymous referees
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