Hydrological Sciences-Journal-des Sciences Hydrologiques, 42(5) October 1997
747
Deuterium and oxygen-18 in present-day
precipitation: data and modelling*
J. JOUZEL
Laboratoire de Modélisation du Climat et de l'Environnement, CEA/DSM, CE Saclay,
F-91191 Gif/Yvette, France
K. FROEHLICH
Isotope Hydrology Section, International Atomic Energy Agency, PO Box 100, A-1400 Vienna,
Austria
U. SCHOTTERER
Department of Climate and Environmental Physics, University of Bern, Sidlerstrasse 5,
CÙ-3012 Bern, Switzerland
Abstract The cycles of the water isotopic species (HDO and H2I80) have been
incorporated in three atmospheric General Circulation Models (GCMs) with the
main objective of predicting the distribution of water stable isotopes in precipitation,
The performances of those models are examined for present-day climate through a
comparison with existing data available from the IAEA/WMO network established in
1961 and from other sources.
Deuterium et oxygène-18 dans les précipitations contemporaines:
données et modélisation
Résumé Les cycles des formes isotopiques de l'eau (HDO et H2180) ont été
désormais incorporés dans trois modèles de circulation générale de l'atmosphère
(MCG) avec pour objectif principal de prédire la répartition de ces isotopes stables
dans les précipitations. Dans cet article de synthèse, nous examinons les
performances de ces modèles pour le climat actuel à travers une comparaison avec
les données disponibles grâce au réseau AIEA/OMM établi en 1961 et à d'autres
sources.
INTRODUCTION
HDO and H2180 are two stable isotopic forms of water. Their concentrations are
expressed, as 5D and ô180, in permil (%o) units with respect to the Vienna Standard
Mean Ocean Water (V-SMOW, with D/H and 180/160 ratios equal to 155.76 x 10"6
and 2005.2 x 10~6). Variations are observed in the 6D and ô180 of precipitation both
on a spatial and on a temporal basis. These variations result from the successive
isotopic fractionation processes occurring at each phase change of water during its
atmospheric cycle because of differences in physical properties (saturation vapour
pressure and molecular diffusivity in air) of the isotopic species, HDO and H2180,
and of H2160 the main component of water. The global distributions of 8D and 8180
in modern precipitation are well documented largely through the IAEA/WMO
* Paper presented at the International Workshop on Tracing Isotopic Composition of Past and Present
Precipitation—Opportunities for Climate and Water Studies (Berne, Switzerland, 23-25 January, 1995).
Open for discussion until 1 April 1998
748
J. Jouzel et al.
network (IAEA, 1992) and from other sources such as rivers, groundwaters and
surface samples of the Greenland and Antarctic ice caps. Moreover, many
monitoring programmes have been developed to study, in particular areas and often
in individual events, the 8D and 5 ,8 0 of the precipitation and, in a few cases, of the
atmospheric water vapour. The objectives of these studies was to develop the use of
stable isotopes in such fields as cloud physics, climatology, hydrology and palaeoclimatology.
The focus of this review article is on present-day precipitation with an emphasis
on the various modelling approaches aiming to explain observed characteristics. First
a summary is given of how isotopic contents vary with time and space and how they
relate to meteorological parameters. Isotopic models are then examined ranging from
simple Rayleigh type models to complex isotopic General Circulation Models in
order to be able to explain the main characteristics of the distribution of water
isotopes in precipitation.
PRESENT-DAY DISTRIBUTION IN PRECIPITATION
Precise measurements of the natural abundance of deuterium and 180 in meteoric
waters started in the late 1940s and the interest of such measurements for studying
various aspects of the hydrological cycle was soon realized (Dansgaard, 1953;
Friedman, 1953; Epstein & Mayeda, 1953). In 1961, a first attempt to summarize
data was published by Craig (1961) who defined the Meteoric Water Line
Fig. 1 Geographical distribution of the IAEA/WMO network stations for which a
minimum of one complete year of stable isotope record is available (adapted from
RozanskieraZ., 1993).
Deuterium and oxygen-18 in present day precipitation: data and modelling
749
(MWL: 5D = 8 x ô180 + 10) on which fall non-evaporated continental
precipitations. The same year IAEA and WMO initiated the world-wide survey of the
isotope composition of monthly precipitation that has been in operation since then
(Rozanski et al., 1993). Figure 1 shows the geographical distribution of stations from
the IAEA/WMO Global Network for Isotopes in Precipitation (GNIP) for which a
minimum of one complete year of stable isotope record is available. This network
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Fig. 2 Seasonal cycles of 8lsO in precipitation and surface temperature for different
locations. Solid line is for the 3-year GISS model results and the dashed line is for
observations (adapted from Jouzel et al., 1987).
J. Jouzel et al.
750
has been the basis of several comprehensive studies in which the isotopic
composition of global precipitation has been discussed (Dansgaard, 1964, Friedman
et al, 1964, Craig & Gordon, 1965, Merlivat & Jouzel, 1979, Gat, 1980,
Yurtserver & Gat, 1981, Rozanski et al, 1992, 1993). A detailed description of the
available data is given in the recent review by Rozanski et al. (1993). For the present
purpose, oriented towards a model/data comparison, the description following is
limited to the main characteristics of observed distributions. With this in mind, the
temporal and spatial patterns are examined along with the relationship with
meteorological parameters.
This presentation of data based on 8 ,8 0 contents would also apply for 8D as these
two parameters are very strongly correlated. Analysis of the long-term annual mean
8D and 8180 values of all GNIP stations confirms that the MWL line defined by
Craig (1961) is a good approximation of the locus of points representing the average
isotopic composition of freshwaters worldwide (Rozanski et al., 1993). Beyond the
general relationship, an analysis of both 8D and 8180 in a given precipitation,
however, brings additional information about the process leading to its formation.
The deuterium excess defined by Dansgaard (1964) as d = 8D - 8 x 8180 is a very
useful parameter with which to examine this information (Jouzel et al., 1986).
Studies of individual precipitation events have revealed that the stable isotope
~T^~i
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T =10°C
Vs Condensation
Temperature Tc
T =30->C
Vs Inversion
Temperature T(
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20
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Fig. 3 S O in precipitation as calculated with a Rayleigh model. The three sets of
curves are for different initial sea surface temperatures (Tw). The approach of
Merlivat & Jouzel (1979) is used for the liquid phase, and that of Jouzel & Merlivat
(1984) is used for snow formation. The solid lines correspond to East Antarctic data
plotted with respect to either Ts (surface temperature) or Tt (inversion temperature).
-A0
ls
-30
-20
-10
0
Deuterium and oxygen-18 in present day precipitation: data and modelling
751
composition may vary dramatically during single events (Rozanski et al., 1993).
However, an examination of GNIP data shows that a clear temporal pattern (also
seen in Greenland and Antarctic snow) emerges after averaging on a monthly basis.
As illustrated in Fig. 2 for a few selected sites and large regions, precipitation at
continental mid- and high latitude stations exhibits a seasonal cycle with precipitation
isotopically depleted in winter and enriched in summer whereas there is a general
absence of a defined seasonal cycle for island stations. These seasonal differences are
due to several factors (Rozanski et al., 1993): (a) seasonally changing temperature at
mid- and high latitudes, with only minor fluctuations in the tropics. (Figure 3 shows
that a simple Rayleigh model predicts a decrease of the isotopic content of a
precipitation when its temperature of formation decreases); (b) seasonally modulated
évapotranspiration flux over the continents inducing seasonal differences in the
atmospheric water balance; and (c) seasonally changing source areas of the vapour
and/or different storm trajectories. Over continental areas, there is a gradual
enhancement of the seasonal variations with increasing distance from the coast with,
for example (Rozanski et al., 1993), an amplitude of the 8180 signal of 2.5%> at
Valentia station in Ireland and of ~10%o at Moscow, 3200 km inland, this being
largely due to an increase of the seasonal temperature range when going inland.
Note also that the deuterium excess may vary seasonally. In Vienna for example,
d is higher in winter than in summer which results in a slope lower than 8 (Rozanski
et al., 1993). This seasonal variation is also seen in the long-term monthly averages
of d derived from the GNIP data base (Fig.4(a)). The lower d value of the northern
hemisphere during the summer months corresponds with the observed higher relative
air humidity related to the SST in the oceanic source regions of the air masses
concerned. On the contrary, during the winter months, the relative humidity at the
oceanic source regions is lower giving rise to higher d values.
It is interesting to note that a hysteresis effect can also be observed for the
deuterium excess, both for marine and continental stations. The examples in Fig.4(b)
from the northern hemisphere (GNIP stations Ponta Delgada, Azores and Vienna)
show that at a given surface temperature (or vapour pressure), the deuterium excess
is lower in the period April-July than in the period between September and
November. This effect will be the subject of further consideration taking into account
the phase shift between the seasonal variation in the SST and the air temperature (or
vapour pressure). A seasonally inverse trend can be observed in regions where the
atmospheric vapour forming the summer precipitation is dominated by air moisture
evaporated from continental basins (Schotterer et al., 1993; Schotterer et al., 1997).
Figure 5 displays the annual average S180 of precipitation obtained from GNIP
observations and complementary data (Jouzel et al., 1987). There is a clear latitudinal pattern, with 8lsO decreasing as one approaches the poles. This latitudinal
pattern is modulated by a continental effect, i.e. at a given latitude, 8180 decreases
when moving inland. Another aspect of these spatial variations, which is of practical
significance for hydrological applications, is the altitude effect (in a given region
8!80 at higher altitudes generally will be more negative); the magnitude of the effect
depends on local climate and topography, with gradients in 8180 of between 0.15 and
J. Jouzel et al.
752
Southern Hemisphere
Northern Hemisphere
1
Jan
1
Feb
1
Mar
1
Apr
1
May Jun
1
1
Jul
1
Aug
1
Sep
1
Oct
r
Nov
Dec
12
16
Mean monthly vapour pressure (mb)
Fig. 4 Hemispherically-averaged seasonal distribution (a) of the deuterium excess;
(b) and its seasonal relationship to vapour pressure. The hysteresis effect in Spring
and Autumn may be explained by leads and lags of SST with respect to air
temperature.
0.50%0 per 100 m (Yurtsever & Gat, 1981). For several GNIP stations, in Portugal
and Turkey for instance, an increase of the deuterium excess with altitude has also
been observed. So far, no explanation has been found for this apparent altitude effect
of the deuterium excess.
At mid- and high latitudes there is a strong correlation between ô180 and surface
temperature fields which does not hold true for tropical inland equatorial regions.
The 8180 surface temperature plot of Fig. 6 illustrates the strength of this correlation
for Greenland and Antarctic sites whereas Fig. 7 was established using available
IAEA/WMO stations and those complementary polar sites (Jouzel et al., 1987). For
temperatures below 15°C, the 5nO/Ts gradient is 0.64%o per°C and the correlation
coefficient is 0.96. Another way to relate ô ,8 0 to temperature is to obtain the 8nO/Ts
slope from monthly values at a single site. This slope is well defined over continental
areas, but its value is generally lower than that calculated from a spatial 8nO/Ts
gradient (Rozanski etal., 1992).
The relationship between mean annual values of temperature and precipitation
,8
0 essentially vanishes above approximately 15°C. In contrast, for tropical and
Deuterium and oxygen-18 in present day precipitation: data and modelling
753
S180 in PRECIPITATIOH (per dill)
3 year average
Annual
S180. in PRECIPITATIOH (per . i l l )
Observations
Annual
Fig. 5 Global distribution of 8lsO in precipitation for the present-day climate. The
lower map is derived from observations, while the upper map is produced from a
three-year simulation with the NASA/GISS model.
equatorial regions, there is, at least for oceanic areas, some relationship between the
annual amount of precipitation and its 8180, the so-called "amount effect"
(Dansgaard, 1964), with rainy regions having low 8180 and dry regions having high
§180. Figure 8 shows the relationship between precipitation 8180 and precipitation
amount for sites at tropical islands and moonsoon related stations (Hoffmann &
Hermann, 1995).
In an attempt to derive a relationship between the mean 5180 of precipitation and
basic geographical and climatological parameters multiple regression have been
performed by Yurtserver & Gat (1981). These authors concluded that the mean 5180
J. Jouzel et al.
754
8 D (%o)
8180
(%o)
-,.20
-150
-200
-250
Greenland
-300
-350
-20
-10
Temperature (°C)
Fig. 6 Isotope content of snow versus local temperature (annual average). Antarctic
data (5£>, left scale) are from Lorius & Merlivat (1977), and Greenland data (SlsO,
right scale) are from Johnsen et al. (1989).
Observations
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Fig. 7 Annual mean SlsO in precipitation as a function of local temperature (annual
average) from (a) observations, and from present-day simulations performed with the
(b) ECHAM, (c) LMD, and (d) GISS isotope GCMs.
Deuterium and oxygen-18 in present day precipitation: data and modelling
755
ECHAM3: Control - Amount effect
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Fig. 8 Annual means (filled dots) and monthly means (light sqares) of 8 I8 0 vs
precipitation amount (mm per month) for tropical islands ((b) observations for
selected sites and (a) corresponding results simulated by the ECHAM 3 model) and
for moonsoon related stations ((d) observations and (c) model results). The linear
regressions are calculated for the monthly means (Figure adapted from Hoffmann &
Hermann, 1995).
was, for the available network stations essentially correlated with the temperature
variations. The correlation with other parameters is not significant at least at a global
scale. However, as illustrated above for the amount effect, this may not hold true on
a regional scale.
THE MODELLING APPROACH
Water isotopes have been incorporated into a hierarchy of models, including
dynamically simple Rayleigh-type distillation models, two-dimensional models and
atmospheric general circulation models (GCMs). Here, through a short description of
simple distillation models, the main climate parameters or processes that influence
the global distribution of isotopes in precipitation are examined. The current state of
development of isotope GCMs and their performance in simulations of present-day
climate are than discussed.
Fractionation processes and simple Rayleigh-type models
Phase changes of water generally lead to isotopic fractionation for two reasons. First,
because the saturation vapour pressures of HDO and H2180 are slightly lower than
756
J. Jouzel et al.
that of H2160, the condensed phase (either liquid or solid) is, at equilibrium,
isotopically enriched with respect to the vapour phase. The equilibrium fractionation
coefficient, which corresponds to the ratio of D/H (or I80/160) in the condensed
phase to that in the vapour phase, is essentially equal to the ratio of the saturation
vapour pressures of the corresponding molecules, and thus it varies only with
temperature and the phase change considered. Second, a "kinetic effect" results from
the fact that the molecular diffusivity in air of HDO (and H2lsO) is lower than that
for H2160. This effect, which is independent of temperature, applies to nonequilibrium processes of evaporation and condensation. The equilibrium isotopic
effect is 8-10 times higher for HDO than for H2180, whereas their kinetic effects are
of the same order. This difference in relative importance is one reason why a joint
analysis of both isotope species is of interest (Jouzel, 1986).
A Rayleigh model (Dansgaard, 1964) considers the isotopic fractionation
occurring in an isolated air parcel travelling from an oceanic source towards a polar
region. The condensed phase is assumed to form in isotopic equilibrium with the
surrounding vapour and to be removed immediately from the parcel. Under these
assumptions, the isotope content of this precipitation is a unique function of the
initial isotope mass and water vapour mass within the air parcel and of the water
vapour mass remaining when the precipitation forms. The parcel's water vapour
content is proportional to the saturation vapour pressure, a function of temperature
and phase change, and is inversely proportional to the air pressure. Thus, in this
simple model, the isotope content of precipitation depends only on initial isotope
mass and on the initial and final condensation temperatures and air pressures.
Merlivat & Jouzel (1979) showed that the initial isotope concentration in an air
parcel may, under certain simplifying assumptions, be expressed as a function of sea
surface temperature (Tw), relative humidity (h), and wind speed. They extended the
Rayleigh model by allowing some of the liquid condensate to remain in the parcel
(Craig & Gordon, 1965) and by accounting for the kinetic fractionation associated
with snow formation by inverse sublimation in a supersaturated environment (Jouzel
& Merlivat, 1984). In the extended model, then, the isotope content of precipitation,
ÔP, essentially depends on the evaporative source conditions, h and Tw, on the
proportion of liquid kept in the cloud, and on the condensation temperature, Tc.
Figure 3 illustrates the observed global scale decrease of 5P with Tc and thus with the
surface temperature at the precipitation site, Ts, which varies with Tc over most of the
globe (Polar regions, particularly Antarctica, feature a strong temperature inversion
and thus are an exception). The straight line in Fig.6 representing Antarctic data
(Dumont d'Urville—Dôme C axis) is reported with respect to both Ts and the
temperature at the inversion level, Th which is very close to Tc (Robin, 1977). The
observed slope obtained in the latter case (1.12%o per °C) is very close to that
predicted by the Rayleigh-type model assuming a source temperature of 20°C (1.11.2%o per °C). Thus, given adequate values for its parameters, the Rayleigh-type
model can reproduce the data observed over East Antarctica and, more generally,
can reproduce the relationship between ÔP and local temperature for mid- and high
latitudes.
Figure 3 also shows the influence of sea surface temperature, Tw, on SP.
Deuterium and oxygen-18 in present day precipitation: data and modelling
757
Merlivat & Jouzel (1979), Johnsen et al. (1989) and Petit et al. (1991) show that
source conditions (temperature and humidity) also influence the relative amounts of
HDO and H2180 in the parcel and thus the deuterium excess in precipitation. For
Tc> -20°C, the isotope concentrations derived from a Rayleigh model fall on the
straight line S£>= s x 8180 + d, with s and d depending essentially on h and Tw and
not on atmospheric processes (Merlivat & Jouzel, 1979). An excellent fit with the
Meteoric Water Line, 8£> = 8 x 8 1 8 O + 10 (Craig, 1961) is obtained when the
global-mean estimates of h = 81% and of Tw = 26°C are used in the model. Using
the global-mean values is reasonable because the main water vapour source at the
global scale is in the subtropics. Extended to the solid phase (Jouzel & Merlivat,
1984), the model also reproduces deuterium excess values observed in Greenland
(Johnsen et al, 1989) and in Antarctica, where d becomes higher than 15 %> in
central regions (Petit et al., 1991; Qin Dahe et al, 1994).
These results, as well as those concerning the isotope / temperature relationship
(Fig.3), are further confirmed by Ciais & Jouzel (1994), who introduced mixed
clouds into the Rayleigh-type model, thereby allowing supercooled liquid droplets
and ice crystals to coexist between ^15°C and -40°C. Such coexistence could be
important because the differing saturation conditions over water and ice allows liquid
droplets to evaporate while water vapour condenses on ice crystals. Accounting for
the associated isotopic fractionation processes in the mixed clouds, however, did not
significantly modify the simulated S£> and ô ,8 0 of the condensed phase.
Simple Rayleigh-type models are inadequate for examining isotope behaviour in
convective storms, which can feature large drops that are out of isotopic equilibrium.
Although some isotope cloud models can account for large drops (Jouzel et al.,
1980; Fédérer et al., 1982; Gedzelman & Arnold, 1994), they are limited to the
study of idealized clouds. They cannot account for the complexity of large convective
systems, such as those occurring in the tropics, for which 8P depends on
precipitation amount rather than on temperature.
Despite such limitations, simple isotope models are able to reproduce the basic
behaviour of SD and 8180 in precipitation, at least in mid- and high latitudes, where
large convective systems do not dominate precipitation production. Indeed, their
ability to simulate correctly the observed present-day temperature/isotope
relationships in those latitudes has been, up until now, the main justification for the
standard practice of using these spatial relationships to estimate palaeotemperatures
from the isotope content of ancient precipitation (Rozanski et al, 1997). However,
recent empirical estimates of temporal slopes appear consistently lower than presentday spatial slopes, in particular in central Greenland for glacial/interglacial changes
(see Jouzel et al., in press).
Isotope modelling with GCMs
Atmospheric general circulation models (GCMs) simulate the time evolution of
various atmospheric fields (wind speed, temperature, surface pressure, specific
humidity), discretized over the globe, through the integration of the basic physical
758
J. Jouzel et al.
equations: viz. the hydrostatic equation of motion; the thermodynamic equation of
state; the mass continuity equation; and the water vapour transport equation. To
reproduce the observed regime of atmospheric circulation, these equations are
supplemented with parameterizations for radiative transfer, surface fluxes of
momentum, latent heat, and sensible heat, latent heat release through condensation,
and various internal processes that operate at scales not resolved by the relatively
coarse mesh size of the model. These latter processes include turbulence in the
boundary layer and cumulus convection, which drives convective precipitation and
which redistributes momentum, heat and water vapour over an atmospheric column.
Parameterizations for non-convective precipitation are also included, as are
treatments of heat and water storage in land and ice reservoirs. A full discussion of
general circulation modelling is beyond the scope of this paper.
The incorporation of the HDO and H2I80 cycles into a GCM involves following
the two isotopes through every stage of the GCMs water cycle. Simply put, the
model transports the water isotopes between the atmospheric grid boxes and among
the surface reservoirs with the same processes as used to transport regular water.
Isotopic fractionation, including both equilibrium and kinetic effects, is accounted for
at every change of phase, i.e. during surface evaporation, atmospheric condensation,
and re-evaporation of falling precipitation. The formulations implemented for
isotopic fractionation distinguish between convective and nonconvective systems and
are largely based on what is used in, or has been learned from, the simple Rayleightype models described above. Although other parts of the hydrological cycle, such as
surface hydrological processes and water vapour transport, do not involve fractionation, they still must be extended to include water isotopes. Indeed, a realistic
transport scheme for advecting water vapour and isotopes between grid boxes is
absolutely critical to the reproduction of observed isotope fields (Joussaume et al.,
1984; Jouzel et al., 1991). In particular, the occurrence of negative water mass,
which is not a serious problem for some GCMs, is catastrophic for isotope
modelling.
Joussaume et al. (1984) pioneered GCM isotope modelling, producing global
fields of 5Z) and ô180 for present-day January climate using a low resolution version
(32 points in longitude, 24 points in latitude) of the LMD GCM (Laboratoire de
Météorologie Dynamique, Paris). Jouzel et al. (1987) generated a full annual cycle
of isotope fields with the 8° x 10° (36 points in longitude, 24 points in latitude)
GISS GCM (NASA Goddard Institute for Space Studies, New York) and examined
the robustness of the approach through an extensive sensitivity study (Jouzel et al.,
1991). Simulations using finer spatial resolutions have now been performed for
February and August with the LMD model (Joussaume & Jouzel, 1993) and for the
full annual cycle with the GISS model (Charles et al., 1995). Water isotopes have
now been incorporated into a third model, the ECHAM GCM (Hoffmann &
Heimann, 1993), which is the Hamburg version of the European Centre for MediumRange Weather Forecast GCM. A five-year simulation for present-day climate has
now been produced using the ECHAM 3 version (Hoffmann & Heimann, 1995); the
resolution of this spectral model (T42 resolution) corresponds on a physical grid to a
2.8° x 2.8° resolution.
Deuterium and oxygen-18 in present day precipitation: data and modelling
759
These simulations of modern climate aim to determine how well isotope GCMs
can reproduce observed present-day isotope distributions. The modelling efforts,
however, have a further common objective, viz. the reconstruction of palaeoclimatic
isotope fields to help in the interpretation of palaeodata. Isotope behaviour during the
Last Glacial Maximum (LGM) which has been examined with the three isotopic
models (Joussaume & Jouzel, 1993; Jouzel et al, 1994, Hoffmann & Heimann,
1995). The main results obtained for the present day, are now summarized focusing
on how simulated isotope fields compare with available data.
Figure 5 compares the annual mean present-day 8180 in precipitation simulated
by the GISS model to that obtained from the GNIP database and complementary
data (Jouzel et al, 1987). The GISS model (and, in fact, the other two isotope
GCMs) reproduces the clear decrease of 8180 with increasing latitude. The model's
6180 field compares well with observations, though simulated values in middle and
high latitudes are slightly too low and polar values are slightly overestimated, at
least over Greenland. Overestimation of S180 at the poles is even more pronounced
in the LMD and in the first version of the ECHAM models. The GISS model
predicts values of -55%o in central East Antarctica, only 2%o higher than that
recorded at Vostok (Lorius et al., 1985), while the corresponding ECHAM and
LMD values are too high by 6-8 %o and by up to 15%o, respectively (Hoffmann &
Heimann, 1993; Joussaume & Jouzel, 1993). For the GISS model, the small
difference is easily explained by the slight overestimation of the predicted
temperature in this region. This might also explain the large difference observed
with the LMD model, for which temperatures there are overestimated by 15-20°C
(Joussaume & Jouzel, 1993) but not the ECHAM model results (Hoffmann &
Heimann, 1993), since this model's computed temperature in the region is lower
than the observed value (up to 6°C). However, the simulation recently obtained
with the ECHAM 3 version (Hoffmann & Heimann, 1995) agrees quite well with
data in those polar regions: in Central Greenland the model result of -33.2%o is in
fairly good agreement with the observed -34.8%o (Johnsen et al., 1992) whereas
the difference between predicted and observed 8180 values is reduced to only 4%o
over East Antarctica.
As discussed in Hoffmann & Heimann (1995), isotopic GCMs simulate quite
satisfactorily the continental effect. With the ECHAM 3 model, the simulated
gradient over Europe and the Amazon Basin, where the GNIP data allow a
comparison with observations, falls within 10% of the observed gradient.
Figure 7 summarizes the simulated relationships between annual mean isotope
concentration and annual mean temperature for the three models. The linear
regressions are performed over two temperature ranges, viz. temperatures above and
temperatures below 15°C (0°C for ECHAM). The upper range encompasses tropical
sites for which the isotope content of precipitation is controlled mostly by
precipitation amount and not by temperature; the three models reproduce this
observed behaviour well. In the lower range, the models correctly simulate the
observed linear relationship between ô180 and temperature. The observed and
predicted gradients are within -10%, except for the ECHAM model, which predicts
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J. Jouzel et al.
a slightly lower value.
Isotopic GCMs are successful in simulating the amount effect that characterizes
tropical and equatorial precipitations. However, predicted S,80/P slopes may be
lower than those observed as seen for tropical islands with the ECHAM 3 model
(Fig. 8) and for tropical and equatorial regions taken as a whole with the GISS model
(Jouzel et ah, 1987).
A successful isotope GCM must also reproduce the observed seasonal cycles of
8180. Seasonal cycles generated with the GISS 8 x 10 model, the first to simulate a
full seasonal cycle (Jouzel et al., 1987), are generally realistic, as shown in Fig. 3.
The Figure compares the observed seasonal cycle of 8180 in precipitation for some
selected GNIP stations with the simulated cycles in the corresponding model grid
boxes. (Observed and simulated temperature cycles are also compared). The seasonal
amplitude of 8180 in precipitation is generally larger at continental stations than at
island stations (IAEA, 1981), and these larger amplitudes, though slightly
underestimated in Canada and Siberia, are well simulated in central North America.
Simulated amplitudes for western Europe, Northeast America and Southeast Asia are
also quite reasonable. The GCM reproduces the general absence of a defined
seasonal cycle over the island stations, the northern hemisphere character of the
cycles in South America and Southern Africa, and the early spring maximum in
Australia. The simulated seasonal cycles over Antarctica are realistic, but that over
Greenland is not. This defect over Greenland disappears in the simulation with the
higher resolution (4 x 5) GISS model; apparently, with the coarser resolution, air
masses sent over Greenland have an excessive maritime character (Jouzel et ah,
1987). The higher resolution LMD simulation also produces a realistic seasonal
contrast (Joussaume & Jouzel, 1993). The ECHAM model (Hoffmann & Hermann,
1993) produces mixed results, with realistic cycles simulated over central North
America and poorer cycles simulated over the western Pacific and central Europe.
Some of the coarse grid ECHAM model's deficiencies in this regard (e.g. over the
western Pacific) can easily be explained by deficiencies in the simulated climate
itself, whereas others require a different explanation, such as low model resolution.
The observed linear relationship between 8D and 8180 is also well reproduced,
both the SZ)/8180 slope and the intercept being predicted correctly; for example the
relationship obtained in the GISS model 5D = 8.06 x 8180 + 10.4 is very close to
the meteoric water line. This model also captures some of the regional characteristics
of the 8D/8lsO relationship such as the lower slope observed for tropical islands
(Jouzel et al., 1987). The agreement between observed and predicted deuterium
excess values is also relatively good (the difference does not exceed a few per mil)
when one considers that d is a second order parameter and that the observations are
less complete than those of 8 I8 0 alone.
Finally, the GCM approach is particularly well suited to examining the link
between the evaporative origin of a precipitation mass and its isotope content. Water
evaporating from a well-defined source region on the Earth's surface can be
"tagged" in the GCM and followed through the atmosphere until it precipitates.
Through this approach, the relative contributions of many different evaporative
Deuterium and oxygen-18 in present day precipitation: data and modelling
q(,\
regions to a given region's precipitation can be quantified exactly. Joussaume et al.
(1986) determined the evaporative contribution of ten global divisions to local
continental precipitation in the LMD model. The GISS model was used to determine
the sources of local precipitation in the Northern Hemisphere (Koster et al., 1986)
and the differences in the sources of Sahelian precipitation during wet and dry years
(Druyan & Koster, 1989). Using the GISS model, Koster et al. (1993) found that a
region's 8180 in precipitation is significantly related to the extent of continental water
recycling in the region. Koster et al. (1992) followed both the H 2 0 and the HDO
coming from a given source (defined by sea surface temperature) during simulations
of July climate with the GISS model. Their results show that the deuterium content
of Antarctic precipitation decreases as the temperature, Te, of the evaporative source
for the water increases by about the amount predicted by simple Rayleigh type
models (Fig. 3); the SD/Ts slope for the GCM is -4.8%o per °C, while that for the
simpler model is -4.2%o per °C. The situation appears to be more complex over
Greenland. Charles et al. (1994) performed a similar experiment with the 4 x 5
version of the GISS model, focusing on Greenland precipitation and defining the
evaporative source regions geographically rather than according to sea surface
temperature. As was found for Antarctica, several evaporative source regions contribute to the precipitation at a given Greenland site, and the isotope contents of the
different contributions vary significantly. For example, moisture from the North
Pacific source arrives at the Greenland coast with a 8180 value roughly 15 %c lower
than its North Atlantic counterpart, a difference comparable to that predicted for
Antarctica when comparing the contributions from the warm and intermediate
sources (8D differences larger than 100%o in coastal Antarctica). Charles et al.
(1994) attributed the lower ô180 to the fact that North Pacific moisture is advected
along a much colder path before reaching Greenland, though the tagging of evaporated moisture by large regions rather than by temperature makes a comparison
with simple models less straightforward than in the Antarctica study.
CONCLUSION
The overview of stable water isotope modelling presented above is not comprehensive in that it treats neither the modelling of isotope behaviour within individual
storms (Jouzel et al., 1987; Fédérer et al., 1982; Smith, 1992; Gedzelman &
Arnold, 1994) nor the global approach using two dimensional atmospheric models
(Fisher & Alt, 1985). The focus here has been on what one can learn from the
combined use of simple Rayleigh-type models and isotope GCMs. Simple Rayleightype models are useful because they help understand the main features of the 5D and
5 ,8 0 distributions in precipitation and how they are influenced by the precipitation
site and evaporative source temperatures. Isotope GCMs are indispensable tools
because they account for the complexity of atmospheric processes and circulation.
Existing isotope GCMs reproduce well the main characteristics of worldwide
precipitation as observed mainly from the IAEA/WMO network (GNIP) but also
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from complementary sources. Geographical distribution, continental effect, seasonal
features and the close relationship between 5D and ô l8 0 are all correctly simulated.
The strong present-day 8nO/Ts relationship documented from observations in middle
and high latitude precipitation is well captured as well as the lack of such a
relationship in tropical and equatorial regions where the influence of the precipitation
amount is generally well predicted by the models.
Beyond these generally satisfying results, it must be admitted that isotopic
models show noticeable differences when examined on a regional basis, this being
also true when model results are compared with data for a given grid point. Such
model-model and model-data differences have not been examined in sufficient detail
to attribute them to specific causes. One way to quantify model performances better
and hopefully to improve them is to intercompare model results for simulations
performed with the same boundary conditions (including for climates different from
the present-day one). In addition to the three models discussed above (LMD-Paris,
NASA/GISS-New York and ECHAM-Hamburg), isotopes are now being incorporated into the GENESIS/NCAR model. A programme aiming to intercompare
these models and, thanks to the invaluable data produced by the GNIP, to compare
closely model results and observations should be envisaged for the near future. To
ensure continuation of long-term observations and the further provision of high
quality isotopic data GNIP is being reorganized. Special attention should also be
given to the deuterium excess as an additional isotopic parameter relevant for
climatological investigations especially with respect to identification of air moisture
source regions and thus characterization of air mass circulation patterns.
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