Decadal variability in Caspian Sea thermohaline properties is
investigated using a high-resolution ocean general circulation model
including sea ice thermodynamics and air–sea interaction forced by prescribed
realistic atmospheric conditions and riverine runoff. The model describes
synoptic, seasonal and climatic variations of sea thermohaline structure,
water balance, and sea level. A reconstruction experiment was conducted for
the period of 1961–2001, covering a major regime shift in the global climate
during 1976–1978, which allowed for an investigation of the Caspian Sea response to
such significant episodes of climate variability. The model reproduced sea
level evolution reasonably well despite the fact that many factors (such as possible
seabed changes and insufficiently explored underground water
infiltration) were not taken into account in the numerical reconstruction.
This supports the hypothesis relating rapid Caspian Sea level rise in
1978–1995 with global climate change, which caused variation in local
atmospheric conditions and riverine discharge reflected in the external
forcing data used, as is shown in the paper. Other effects of the climatic shift
are investigated, including a decrease in salinity in the active layer,
strengthening of its stratification and corresponding diminishing of
convection. It is also demonstrated that water exchange between the three
Caspian basins (northern, middle and southern) plays a crucial role in the
formation of their thermohaline regime. The reconstructed long-term trends in
seawater salinity (general downtrend after 1978), temperature (overall
increase) and density (general downtrend) are studied, including an
assessment of the influence of main surface circulation patterns and model
error accumulation.
Introduction
The Caspian Sea is the largest enclosed water body on earth
with a surface area of more than 370 000 km2 and a catchment area
almost 10 times greater. Yet it is highly sensitive to variations in the
global and regional climate systems as well as economic activities that
include major schemes of river regulation. This is vividly reflected in the
evolution of the Caspian Sea level, which is subject to large fluctuations on
seasonal and decadal timescales. The water balance of the isolated sea varies
significantly due to the seasonal character of the riverine discharge,
accounting for sea level oscillations with an amplitude of 20–40 cm.
Long-term fluctuations of the level are even larger: in the second half of
the 20th century they amounted to 2.5 m.
Prediction of the long-term impacts of climate change and man-made activities
on the Caspian represents a great scientific challenge important for
fisheries, coastal development and other industries of the region. Ocean
general circulation models (OGCMs) have greatly advanced our understanding of
the Caspian Sea circulation patterns, particularly its seasonal variability
. The
increasing production of global atmospheric reanalysis datasets and their
availability over several decades have made possible retrospective studies of
the long-term evolution of the marine environment based on numerical
reconstruction of its response to external forcing, as will be done in the
present paper. This approach was applied in our previous work
with emphasis on the long-term variability of the
Caspian Sea water balance and its sensitivity to external factors. Now we use
the same model to study the evolution of thermohaline properties
(temperature, salinity and density) of the Caspian Sea in 1961–2001. The
period is particularly interesting, as it covers one of the most notable
events of global climate change – the climate shift of 1976–1978, also
referred to as the Great Pacific Climate Shift, widely discussed in the
literature . The shift was
associated with a change in major climatic indicators such as the North
Atlantic Oscillation, with significantly increased cyclonic activity and air
humidity in Europe consequently leading to a sharp rise in the Caspian Sea
level and changes in its stratification. The weakened ventilation of the deep
sea, in turn, has led to degradation of the ecological situation in the sea
.
In the present paper we analyze the long-term evolution of the Caspian Sea water
parameters obtained in a numerical reconstruction experiment, which is
described in Sect. 2. In order to better understand model results, the
evolution of the prescribed atmospheric and riverine forcing is briefly
considered in Sect. 3. In Sect. 4 we discuss main patterns of surface
circulation, which will help explain further results. Then we proceed with
model validation based on a comparison of the obtained evolution of several
in situ parameters with observations (Sect. 5). Finally, in Sect. 6 we
analyze the long-term variability of thermohaline properties of the sea and its
response to climatic variations. The Caspian Sea comprises three basins,
partly separated by peninsulas extending into the sea interior: northern,
middle and southern (respectively referred to as NorthCS, MidCS and SouthCS).
Due to great differences between the basins in terms of bottom relief,
nonuniform distribution of river runoff and large sea extent in the
latitude direction, the thermohaline circulation of each basin is distinctively
different from the others. Therefore, the analyzed properties of water masses
have been averaged over a certain horizon for every Caspian basin separately.
Averaging in the horizontal plane simplifies the analysis but conceals many
subbasin-scale features of the fields, which must be kept in mind. In the
following figures vertical dashed lines mark instances in which climatic shifts
occur.
Experiment setupModel description
In and a three-dimensional
primitive equation numerical model MESH (Model for Enclosed Sea
Hydrodynamics) was presented, which was developed to study Caspian Sea
seasonal variability. The model successfully coped with this task and was
used as the basis for the present research. However, an investigation of the
Caspian Sea circulation on a decadal timescale imposes additional requirements
on the model, and therefore it has been considerably redesigned. The geopotential
vertical coordinate (z coordinate), which was used in MESH, was
replaced by a hybrid system with a terrain-following sigma coordinate in the
upper 30 m of the sea covering shallow regions and a z coordinate below
30 m of depth. The long-term fluctuations of the Caspian Sea surface height
(CSSH) are greater than the seasonal by an order of magnitude and could cause
numerical instabilities and errors in a z coordinate model. The use of a
sigma coordinate ensures model stability during CSSH lows and allows for much better
resolution of the surface boundary layer structure and diurnal air–sea
interaction cycle during CSSH highs. A sigma coordinate grid also provides an
accurate representation of the northern Caspian shelf bathymetry with
increasingly flat slope (see Fig. 1). This is necessary to reconstruct the
evolution of the sea surface area, which is essential for the evolution of air–sea
fluxes in an environment of coastal flatlands subject to large CSSH
fluctuations. Additionally, the model has been equipped with a
flooding–drying algorithm, enabling it to describe shoreline variations
related to mean sea level change and wind surges. A more detailed description
of the model used in this work is presented in
and . Here we will note only the
main points.
Caspian Sea bathymetry used in the model (depths relative to mean sea level, in meters).
The average sea surface height and model vertical grid origin are 28 m.
Dashed lines indicate conventional separation of the sea into three basins:
northern (NorthCS), middle (MidCS) and southern Caspian (SouthCS). Arrows
designate water inflows due to rivers accounted for in the model and the seawater outflow into the Kara-Bogaz-Gol Bay. Numbers 1 and 2 indicate locations
of the deepwater stations from Tuzhilkin and Kosarev (2004), for which
reference observational T and S data will be given.
We use the Caspian Sea bathymetry based on the ETOPO1 dataset
, while particular attention has been paid to correctly
interpolating the data onto the model grid and to preserving fine details such as
islands and the shoreline. The Kara-Bogaz-Gol Bay was erased from the relief,
as its connection to the sea is unilateral, and the corresponding boundary
condition was set to account for the outflow of seawater into the bay. The
resulting bathymetry is presented in Fig. 1. The model has a resolution
of ∼4.3 km in the horizontal plane, which is relatively high, as the
Rossby baroclinic deformation radius is 17–22 km in deepwater areas of the
Caspian . The eddy-resolving ability of the model is
important to adequately simulate heat and salt transfer in the sea interior
and obtain a correct circulation pattern. To ensure model stability without
excessively damping the physical mode of its solution a parameterization of
lateral viscosity has been implemented based on a bi-harmonic operator with
Smagorinsky coefficient C=3 as discussed in . Lateral
diffusivity is parameterized with a simple Laplacian scheme with constant
coefficient Ah=1. Heat and salt advection is approximated using a
total variation diminishing scheme from , which acts as a
second-order scheme in most cases, except high-gradient frontal zones. In the
vertical a grid is set with rather fine resolution varying from 2 m in the
upper sea layer to 30 m in deep waters. This minimizes numerical errors in
advection terms and prevents excessive accumulation of the errors in the
long term. Vertical viscosity and diffusivity are parameterized via a scheme
based on the Richardson number with variable coefficients:
Km=(10-5–10-3) m2 s-1 for viscosity and Kh=(10-7–3×10-4) m2 s-1 for
diffusivity and thermal conductivity. The model time step is 5 min.
External forcing
Monthly mean river runoff data were used to prescribe the discharge of the
Volga, Ural, Kura, Terek and Sulak rivers. The outflow into the
Kara-Bogaz-Gol Bay was set using annual mean data. Atmospheric forcing was
prescribed using the ECMWF ERA-40 atmospheric reanalysis dataset
, chosen for several reasons. First, the data cover an
extended period (from 1957 to 2002) comprising one of the most vivid episodes
of global climate change – the climatic regime shift of 1978. This allows
for an investigation of the Caspian Sea response to such global events. The other
advantage of the ERA-40 reanalysis is its relatively high spatial resolution
(1.125∘), which is still rather coarse for the Caspian Sea with
dimensions 8∘×11∘, but it is sufficient to resolve the main
features of atmospheric circulation in the region, as has been shown by
. The ERA-40 temporal resolution of 6 h allows for the
simulation of
the diurnal air–sea interaction cycle and the synoptic variability mode. As
is the case for any global reanalysis product, ERA-40 has errors specific to a particular
region of the planet . Therefore, we have
partially corrected the ERA-40 wind and precipitation fields for better
consistency with the available climatology atlases of the Caspian region
: wind speed was increased by 15 %, and
precipitation was decreased by 30 %. The performed corrections as well as
the model sensitivity to them are considered in detail in
. The prescribed atmospheric parameters, together with the
parameters of the sea surface obtained in the model, are used to compute
air–sea fluxes based on the approach of : evaporation,
sensible and latent heat fluxes, and the momentum flux. Precipitation and
radiative heat fluxes are taken directly from ERA-40. The fluxes are
dynamically amended due to sea ice cover and simulated in the submodel of sea
ice thermodynamics, which is described in .
Initial conditions and model spin-up
The model was initialized with the climatic mean 3-D fields of temperature
and salinity for January . These fields have been
considerably smoothed and averaged over an extended period of time and
therefore lack realistic cross-shore gradients and many other details,
particularly in shallow regions. While the distribution of temperature in
such areas adjusts rather quickly due to atmospheric impact, the salinity
field is a lot more passive and requires an additional spin-up model run:
the model is run for 5 years with a relaxation of sea surface salinity (SSS)
in the southern Caspian basin. This is necessary to avoid excessive growth of
salinity in the upper layer of the basin until a freshwater anomaly,
associated mainly with the Volga River's runoff, appears along the western
coast of the middle Caspian. It is this anomaly that supplies relatively fresh
water to the south in an amount sufficient to compensate for intense evaporation.
After 5 years a realistic salinity distribution in the middle Caspian is
achieved, and the SSS relaxation in the southern Caspian is no longer
required to balance the salt budget of this basin. The resultant salinity
field is then used as the initial condition for the main model run, as discussed
in the following sections.
Long-term variability of the forcing components: (a) sum
of riverine water input and precipitation with the deduction of the outward
flux into the Kara-Bogaz-Gol Bay; (b) riverine water input alone;
(c) precipitation; (d) air temperature (∘C);
(e) wind speed module (m s-1); (f) relative
humidity (%); (g) surface solar radiation (W m-2);
(h) surface thermal radiation (W m-2). All atmospheric
parameters and fluxes were averaged over the sea area; water fluxes
(a, b, c) are given in terms of the corresponding sea level
increment.
External forcing variability
Figure 2 shows the evolution of external forcing components for the period
considered. The Caspian Sea water budget is a sum of river discharge (∼300 km3 yr-1) and precipitation (∼100 km3 yr-1)
approximately balanced by evaporation (∼400 km3 yr-1) and the
outflow into the Kara-Bogaz-Gol Bay (∼30 km3 yr-1)
. The underground water contribution is thought to be
insignificant (∼4 km3 yr-1) . Evaporation is
the only component that cannot be directly measured, and therefore it is
computed by the model based on air and sea surface parameters. The evolutions
of the net input of the other three water budget components, as well as river
discharge and precipitation, are separately presented in Fig. 2a, b and c,
respectively. In the late 1970s one can note a sharp rise (∼20 %) in
the net water input, which was a consequence of the climatic regime shift
mentioned earlier. The shift was also associated with an increase in air
humidity in the Caspian region, followed by a trend change in the evolution
of radiative fluxes: both solar and thermal radiation (absolute value)
intensities imply warming after 1980. We will refrain from discussing the
reasons and mechanisms of such abrupt variations and merely ascertain the
fact that the data used to prescribe the external forcing in the model
contain the signal associated with the climatic shift of 1978. Notably,
there is no significant long-term change in the average air temperature and
wind speed module present in the data (Fig. 2d and e).
Surface circulation
We start the discussion of the model results with a brief review of surface
circulation patterns. This will help to shed some light on further results,
as it is the circulation in the upper active layer that mostly determines
physical processes occurring in the entire water column. Figure 3 shows
monthly mean sea surface currents (SSCs) in January and July, averaged over
1961–1977 (before the regime shift of 1978) and 1978–2001 (after the
shift). This division allows for an assessment of how the climatic shift influenced the
SSC field. The July pattern was altered insignificantly, so only the plot for the
first period is presented. Winter circulation, in contrast, was altered
rather noticeably but only in the MidCS basin where the direction of the
open sea main flow changed by 45∘ counterclockwise. We provide these
fields only for reference and will not further investigate their variability,
as the impact of the climate shift on the SSC is beyond the scope of the
present study.
Model monthly mean surface currents in January (a, b) and
July (c) averaged over 1961–1977 (a, c) and 1978–2001
(b).
Typical distributions of sea surface salinity (SSS) and temperature (SST) are
shown in Fig. 4. Unlike the currents in Fig. 3, these are instantaneous fields,
though they clearly correlate with many SSC features. Figure 4a vividly
demonstrates the differences in the thermal regime of the three Caspian basins in
late winter: while SST in NorthCS is around zero (the basin is covered by an ice
sheet), SouthCS waters are much warmer (up to 11 ∘C). The MidCS basin is
subject to intrusions from both the north (cold elongated current propagating
along the western shore) and south (warm anomaly in the open sea). The most
distinctive feature in the SST field during summer is a cold anomaly in the
eastern part of MidCS (Fig. 4b) created by an upwelling, which occurs along
the eastern shore due to the northwest wind typical for this region in summer.
The same wind accounts for a large freshwater intrusion from NorthCS into
MidCS (Fig. 4c), which is formed by a relatively strong jet current near the
Mangyshlak Peninsula (see Fig. 3c). Although the existence of this jet is
consistent with satellite imagery , the intensity of the
corresponding freshwater transport is evidently overestimated by the model,
as these regularly occurring intrusions decrease average SSS in MidCS below
that observed by ∼0.5 psu. This is likely due to excessive numerical
viscosity of the tracer advection scheme implemented in the model. Figure 4d
shows a reverse situation characterized by an intrusion of relatively salty
MidCS waters entering NorthCS and an opposite process occurring along the
western MidCS shore. Similar intrusions are noted between the MidCS and SouthCS
basins (Fig. 4c, d). Thus, the exchange of water masses with contrasting salinity
between Caspian basins plays an important role in the formation of the
thermohaline regime of the sea.
Model instantaneous sea surface temperature (SST) and salinity
(SSS): (a) SST (∘C) on 1 March 1976; (b) SST
(∘C) on 1 July 1973; (c) SSS (psu) on 1 August 1974;
(d) SSS (psu) on 1 July 1975.
Model validation
To assess the magnitude of model errors we will compare the evolution of its
solution with in situ observations. First let us consider the reconstructed
sea surface height (CSSH), which is an integral indicator of the model
quality, as it depends on the sea surface temperatures that reflect
the thermohaline circulation of the entire sea. Figure 5 compares the observed
sea level in the vicinity of Baku (Abşeron Peninsula) with that obtained by
the model. Until the sharp decline in 1975 there is a good match of the two
curves, which indicates a correct description of seawater balance
components. Yet sharp changes in the sea level are not well reproduced in the
following period, possibly due to errors in the model and/or inaccuracies in
the external forcing data, which led to a considerable discrepancy (up to
35 cm). As a result, the sea surface area is overestimated by the model,
and, in turn, so is the net evaporation flux, which is why the model CSSH has
a slow downtrend relative to observations and matches them again in 1992. This
negative feedback between the Caspian Sea level and its surface area was
shown to be significant in earlier work by . In an
auxiliary experiment, accurate water balance was demonstrated when the model
was started from 1978 with the correct initial CSSH, which suggests that
errors in water budget components occur only in the mid-1970s, i.e., when
the first changes in the regime were detected. Overall, the evolution of the
Caspian Sea surface height is reconstructed reasonably well, and this fact
alone refutes all of the hypotheses relating its rapid rise in 1978–1995
to various earlier speculations in the literature such as changes in the
seabed, underground infiltration of the Aral Sea waters into the Caspian,
variations in underground riverine discharge and other factors that were not
taken into account in the model. Indeed, our results are consistent with the
theory of the dominant role of global climate fluctuations in the Caspian
Sea level variability on a decadal timescale .
Thus, the sharp growth was caused by the abovementioned climate regime
shift of 1978, and the corresponding signal is present in the forcing data we
use (Fig. 2b, f).
Caspian Sea surface height (CSSH) in the vicinity of Baku:
observations and model reconstruction (in meters relative to mean sea level; MSL).
To analyze deepwater properties we use the long-term measurements of T and
S at two particular points located in the central parts of the middle and
southern Caspian basins (locations 1 and 2 in Fig. 1) from
, who studied the evolution of temperature and
salinity in deepwater zones of the Caspian Sea in 1956–2000. This interval
was divided into four distinct periods: (1) quasi-stationary conditions in
1956–1967, (2) harsh winters and low river influx in 1968–1977,
(3) increased river influx in 1978–1995, and (4) regime saturation in
1995–2000. Unfortunately, we do not have the source data available, so only
the mean T and S values for these four periods will be used in each
location. Figures 6 and 7 compare these averaged values at 100 m of depth to
those obtained by the model. Overall, one can note a correlation of the
reconstructed evolution with observations, more so in SouthCS than in MidCS.
The significant discrepancy in salinity at location 1 is caused by the
aforementioned SSS error in MidCS. As a result, salinity stratification of
the upper active layer is overestimated in this basin, which obstructs
the intense deep convection responsible for the observed increase in salinity at
100 m in 1968–1977 (Fig. 6) caused by the harsh winter conditions of the
period. In SouthCS (location 2) model salinity is much closer to observations
with the exception of the first period, apparently due to inadequate initial
conditions corresponding to the climate mean rather than instantaneous
values of 1961. However, in 3 years model salinity reaches values close to
the mean observed ones. Systematic overestimation of temperature by
0.5–1.5 ∘C at both locations reflects errors in the description
of vertical mixing, including insufficient convection intensity, and can be
considered the model error.
Salinity (a) and temperature (b) at 100 m of depth
at location 1 (MidCS; see Fig. 1) obtained in the model and those observed.
The observational data are plotted as a mean value that is constant for four
different periods. Mean model values for the corresponding periods are shown
in thin black lines.
Same as Fig. 6 for location 2 (SouthCS; see Fig. 1).
Long-term trends of thermohaline propertiesNorthern Caspian
The northern Caspian is a very shallow estuary of the Volga and Ural rivers,
and the circulation of its waters is strongly influenced by their discharge and
wind. Due to the shallow depth (4–5 m in most of its area) the water column
is almost always well mixed throughout the year, allowing us to analyze
surface properties only. Figure 8 shows the evolution of sea surface salinity
(SSS) in all three basins. The amplitude of the SSS annual
oscillations increases northward, which is a direct consequence of the
riverine runoff distribution in space. As one can see from Fig. 8, SSS
in the northern basin fluctuates around 8 psu until the climate regime shift
of 1978 and then reaches a new quasi-equilibrium state with an annual mean value
slightly below 7 psu. The time required for this transition period is rather
small and amounts to 3–4 years. After 1980 the SSS trend stabilizes, but an
additional drop down to 6 psu occurs in the 1990s. In the other two Caspian
basins SSS trends are similar, but their rates are smaller by an order of
magnitude. Overall, SSS evolution in the entire sea correlates with river
discharge and air humidity, and the results presented here are consistent
with the observations .
Evolution of sea surface salinity (SSS) averaged over three Caspian
basins (psu).
Notably, the reconstructed evolution of the NorthCS salinity field is rather
sensitive to model design, particularly to the bottom drag parameterization. An
important feature of this basin is that it serves as a transit zone for fresh
riverine waters, moving into the MidCS and SouthCS basins to be evaporated
there. This leads to a continuous loss of the net mass of salt in the
northern basin, which can be compensated for only by recurrent intrusions of the
MidCS saline waters induced by wind, as shown in Fig. 4d. However, such
intrusions are usually brief, so the amount of salt that enters and remains
in the northern basin greatly depends on the bottom drag resistance to the
currents transporting it. Use of a bottom drag parameterization
that is too viscous in our prior experiments caused a gradual decline of mean NorthCS salinity
down to zero within a decade. Therefore, a new parameterization scheme that
is more adequate for such shallow regions has been devised, which allowed us to
stabilize the salinity evolution here at a level close to that observed.
Nonetheless, salinity distribution in NorthCS is still somewhat inaccurate
compared to observations: the freshwater tongue associated with the Volga
River extends southward too far, often shifting the salinity gradient maximum
close to MidCS waters (see Fig. 4c). In these conditions, wind drives
freshwater masses into the MidCS basin, decreasing its surface salinity down
to ∼12 psu on average (Fig. 8), which is about 0.5–0.7 psu
lower than observed.
Middle Caspian
The middle and southern Caspian basins have maximum depths of 800 and
1000 m,
respectively, so vertical mixing processes play a much greater role in
thermohaline circulation here than in the north. In MidCS autumn–winter
convection is thought to create a mixed layer of 200 m depth and to mix the
entire water column during the coldest winters, e.g., in the winter of 1969
. However, more recent papers
suggest that throughout the period
considered, convective mixing occurred only in the upper 100 m layer and did
not reach the Caspian abyssal waters, even in the most severe winters. Our
results support these conclusions: in the numerical reconstruction the average
depth of winter convection in the deep parts of the MidCS basin is about
80 m. The abovementioned 0.5–0.7 psu underestimation of MidCS surface
salinity significantly decreases convection intensity, but auxiliary
sensitivity experiments have shown that convection depth does not exceed
110–120 m, even when this error in the SSS field is artificially
compensated for.
Figures 9, 10 and 11 show the reconstructed evolution of salinity,
temperature and density at different depths in the MidCS basin. At a depth of
250 m the effects of convective mixing are noted only in the coldest winters
and are absent at 500 m and below (Fig. 10). In the active layer (upper
100–150 m) the thermohaline properties exhibit a clear seasonal cycle and
have no long-term trend until 1978, which indicates a quasi-stationary
circulation regime. After the climate shift of 1978 the upper-layer salinity
begins a gradual decline (Fig. 9) associated with the intensification of river
discharge and an increase in air humidity in the Caspian region. These
downtrends cease only after the next climatic shift in the mid-1990s, when a
new quasi-stationary sea circulation regime is achieved. Because the
freshening signal, associated with the first shift, originates at the
surface, the rate of the salinity downtrend decreases with depth, which
strengthens sea stratification and diminishes the convection-driven ventilation
of deep waters. These results are qualitatively consistent with observations
. The weakening of convection also
accounts for the reduction of winter SST, noted after the shift of 1978 in
MidCS, and for the upward trend in subsurface temperatures (Fig. 10), as was
suggested in .
Evolution of salinity (psu) at different depths averaged over the
MidCS basin.
Evolution of temperature at different depths (SST – sea surface
temperature) averaged over the MidCS basin (∘C).
Evolution of a density anomaly (kg m-3) at different depths (SSD
– sea surface density) averaged over the MidCS basin.
At greater depths (500 m and deeper) the influence of changes in external
conditions becomes almost indistinguishable from the accumulating model
errors that account for a slow downtrend (∼0.1 psu every 40 years) in the
average salinity and an uptrend (∼1∘C every 40 years) in the
average temperature. These trends are caused by advective and diffusive
mixing and are inevitable in the presence of small but nonzero T and S
vertical gradients. According to , the only process that
can counteract it is down-slope cascading – slow sinking of cold saline
waters along the slope of the northern and eastern continental shelves.
Despite its important role, this process is not fully taken into account by
the model, which is why it yields these erroneous slow trends. The reason is
that at depths greater than 30 m the model uses a z coordinate grid, and
bottom slope is represented as a set of horizontal stairs obstructing
cascading process. To overcome this z coordinate deficiency a
parameterization of cascading should be implemented in the model. In the
active layer, in contrast, the model errors do not conceal the actual
variability of water properties: the long-term trends alternate with
quasi-stationary circulation regimes in correlation with external forcing
variations.
Southern Caspian
The reconstructed salinity, temperature and density in the SouthCS basin are
presented in Figs. 12, 13 and 14. The southern Caspian basin is the most
distant one from the Volga River's mouth and has the strongest evaporation
throughout most of the year ; therefore, the salinity field in
its active layer is rather sensitive to water exchange with the relatively less
salty MidCS basin. In order to attain a circulation regime that would balance
the salt budget of SouthCS, a 5-year spin-up model run with SSS relaxation
was necessary, as we have described in Sect. 2.3. However, after the SSS
field had been released, it took three more model years to reach
quasi-equilibrium circulation in the upper 100 m sea layer (Figs. 12, 13).
During the first 2 years surface salinity grows rapidly, which leads to an
intensification of convection-driven mixing in the active layer during the
third year of the run with relatively sharp rises in temperature and salinity
at the depth of 100 m. By the fourth year of the run (in 1964) a vertically
quasi-homogeneous salinity distribution is achieved (Fig. 12), characterized
by a slight positive deviation (∼0.1 psu) in the active layer from the
mean climatic values from . As a result, the maximum
convection depth exceeds that observed by 10–15 m: according to
convective mixing processes in SouthCS span the upper
70–80 m layer and reach 100 m only in its northern part. In the model
reconstruction the lower boundary of the convective mixed layer is located at the
depth of 80–90 m in the central area of the basin. Thus, average
temperatures at 100–150 m are overestimated by 1–2 ∘C due to
overly intense mixing with warmer surface waters during winter.
Evolution of salinity (psu) at different depths averaged over the
SouthCS basin.
Evolution of temperature at different depths (SST – sea surface
temperature) averaged over the SouthCS basin (∘C).
Evolution of a density anomaly (kg m-3) at different depths (SSD
– sea surface density) averaged over the SouthCS basin.
After the first 4 years of the model run a steady circulation regime is
achieved in the upper 100 m layer, which persists until the 1980s. The
impact of the climatic shift of 1978 on thermohaline properties in SouthCS is
similar to that obtained in MidCS, but it has a 3-year time lag required to
adjust MidCS circulation to the forcing variation. In 1981 a transition
begins to a new circulation regime characterized by a restoration of stable
salinity stratification and an additional increase in temperatures in the
lower part of the active layer. Thereby, autumn–winter convection in SouthCS
weakens, as can be clearly seen in the evolution of density at 75 m in
Fig. 14. Like in the middle Caspian, slow trends in temperature and salinity
below 250–300 m in SouthCS are a result of vertical advective and diffusive
mixing in the absence of sufficient deepwater ventilation via down-slope
cascading from the eastern shelf. At the depth of 250 m the effects of the
second climatic shift are still observed, and this is the maximum depth to
which a signal of external forcing variability propagates in both the MidCS and
SouthCS basins.
Summary and conclusions
We have considered a long-term numerical reconstruction of the Caspian Sea
thermohaline circulation in 1961–2001. The model reproduced a
quasi-stationary regime that lasted until 1978 and, at least qualitatively,
the sea response to the global climate shift that occurred in 1976–1978.
The influence of surface circulation on the thermohaline regime of the sea
has been discussed, and the crucial role of the exchange of waters with
contrasting
parameters between the three Caspian basins has been demonstrated. A correct
reconstruction of the water balance in 1978–1995, i.e., during a period of
rapid sea level rise (∼2.5 m), confirms that the level rise was
associated with the variability of riverine and atmospheric forcing rather
than other factors that are not accounted for by the model. Thus, our results
are consistent with the commonly recognized theory relating the Caspian Sea
level fluctuations to global climate changes.
During the first 15–17 years of the experiment a quasi-stationary
circulation pattern was obtained with a clear seasonal cycle and almost no
long-term trends in the evolution of temperature and salinity in the active
layer. Due to model errors in reproducing the surface salinity field, the depth of
winter convection in the middle Caspian is about half of that estimated in
, although it is in better agreement with the results of more
recent studies . At greater depths, below
the active layer, slow trends in the evolution of thermohaline properties
were obtained as a result of the insufficient ventilation of these waters. The
reason is that the model does not fully take into account down-slope
cascading processes because of the z coordinate. The error accumulation
rate amounts to ∼1∘C every 40 years for temperature and ∼0.1 psu every 40 years for salinity. At intermediate depths (200–300 m)
both these trends and the effects of external forcing variability are noted,
while below 250–300 m the latter are absent.
After 1978 the non-trend circulation mode was replaced by a transition to a
new circulation regime due to a shift that occurred in the global
climate. This transition was associated with downtrends in the salinity field,
which led to the strengthening of density stratification in the upper sea layers
and weakening of autumn–winter convection. As a result of an increased
isolation from surface waters during winter, the temperature at 100–200 m
showed an uptrend. The surface salinity in the northern and middle Caspian
responded to the increased river discharge almost simultaneously, while the
corresponding trend in the southern Caspian SSS occurred with a 3-year time
lag, which indicates a much greater interdependence of the middle Caspian
with the northern basin rather than the southern one. Overall, the reproduced
sea response to the climatic shift of 1978 is discernible despite
considerable model errors, and it is consistent with the observational data
analysis presented in . The next climatic shift of 1995
stabilized the salinity trends, and a new circulation regime was achieved.
When modeling Caspian Sea circulation, the greatest challenge is to keep
salinity distribution in the active layer close to that observed. Even slight
errors in the salinity field significantly modulate the intensity and depth of
convective mixing and consequently alter the thermohaline circulation patterns
of the entire sea. Two major factors determine the deviations of salinity:
external forcing errors and model quality, particularly the description of
interbasin water mass exchange, as the three Caspian basins have different
salinity regimes. The correct simulation of deepwater properties requires taking
into account down-slope cascading, which is an important mechanism of
ventilation and renewal of the abyssal Caspian waters. However, despite all
of its errors and simplifications, the model qualitatively reproduced the
evolution of the Caspian Sea thermohaline circulation and its response to
external forcing variations.
Data availability
The air forcing was prescribed using data from the ECMWF
ERA-40 reanalysis available at
https://apps.ecmwf.int/datasets/data/era40-daily/levtype=sfc/. The gridded ETOPO1 bathymetry is provided by NOAA's
National Centers for Environmental Information available at
https://www.ngdc.noaa.gov/mgg/global/global.html. The
Caspian Sea model source code currently has no software license and therefore
cannot be made publicly available.
Author contributions
The research was carried out by GD under the supervision of RI.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The research was carried out using equipment from the shared research
facilities and HPC computing resources at Lomonosov Moscow State University
and Joint Supercomputer Center of the Russian Academy
of Sciences.
Financial support
The work was carried out at the Northern Water Problems Institute
of the Karelian Research Center
with the financial support of Russian Science Foundation grant no. 14-17-00740.
Review statement
This paper was edited by Markus Meier and reviewed by Emin
Özsoy and two anonymous referees.
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