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Marine Hydrophysical Institute, Sevastopol, the CRIMEA
Ocean variability is a crucial factor in regulating
the decadal-to-century changes of the coupled
ocean-atmosphere system and hence of the
climate (Bjerknes, 1964; Kushnir, 1994;
Polonsky et al., 1995). Certainly in principle,
atmosphere can vary in the broad range of
periods (including decadal-scale, see e.g., [Power
et al., 1995]) without active sea-air interaction. It
seems however, this statement concerns mostly
the planets without Oceans. The Earth on the
contrary is more close to the aquaplanet. Oceans
are more slow and inertial component of the
coupled system. Low-frequency changes of the
meridional ocean circulation and of the surface
heat fluxes due to the sea surface temperature
(SST) variability are the principal causes of the
long-term variability of the coupled system
resulting in the climate changes. So, the reliable
detection of the low-frequency ocean variability
is crucial factor to study the long-term climate
changes. Because of the limitations in the
quantity and quality of oceanographic
observations, the joint analysis of different
datasets and discussion of the problem of
climatic signal detection are necessary in order to
study the long-term variability. This is the aim of
the present paper with the strong focus to the
deep Atlantic Ocean. It continues the discussion
of this topic initiated at the 10th Conference on
Applied Climatology (Polonsky, 1997).
2.1 Deep-sea observations
Deep-sea observations have been performed for
more than a century. Before the 1970's, Nansen
profiles were the main source of deep-sea data.
Then the XBT and CTD soundings essentially
replaced the Nansen measurements. However,
the routine oceanographic observations are too
sparse and noisy for the reliable detection of lowfrequency (long-term) changes of the oceanic
fields in the deep ocean (below about upper 800
m layer), except perhaps near a few standard
oceanographic sections and some specific regions
with strong oceanographic activity (Dzhiganshin
and Polonsky, 1993; Parilla et al., 1994).
Moreover even in these regions, activity was
concentrated in recent decades. This means that
even there, only decade-to-decade (not longerterm) variations may be estimated with
reasonable accuracy. Regular XBT profiles have
been collected during the last 25 years and the
maximum depth of XBT sounding is usually 800
m. Therefore the XBT data cannot eliminate
the lack of long-term deep-sea observations. At
the same time, from the point of view of a
long-term variability study, the systematic
discrepancy between the Nansen, CTD and XBT
data sets may also be an important problem
(Gaysky et al., 1992).
While the systematic errors may be corrected in
some cases (as done e.g., by Wright (1986) for
SST), the random high-frequency noise cannot
be eliminated in principle. This may be only
reduced using optimal filtering or/and
interpolation, if one has at least a few
observations near a considered grid point.
However, high-amplitude noise and the small
amount of deep sea observations do not permit to
obtain an accurate depiction of "ocean climate"
even for the most recent decades. Moreover, the
unknown statistical structure of regional deep sea
oceanographic fields leads to errors of
interpolation which are of the same order as the
typical magnitude of low-frequency variability
(Gaysky et al.,1992). As a result of all kinds of
errors the joint combined climatic datasets
consisting of all types of observations, such as
Levitus' (1982) datasets, demonstrate strong
artificial vertical inversions due to different
space-time distribution of various measurements
on different standard levels (Bulgakov et al.,
Recently White (1995) gave the assessment of
statistical structure of the temperature fields in
the upper 400m layer in all Oceans to the North
of about 30˚S. For the tropical Atlantic his results
confirm the assessment of Gaysky et al. (1992)
He concluded that the sampling rate is adequate
for detecting the space-time evolution of biennial
signal over the interior global ocean, but it is
inadequate for detecting year-to-year changes in
upper ocean heat storage averaged over large
portions of the global ocean. It is clear the
situation with the decade-to-decade changes is
much worse. In fact, rather optimistic White’s
result is due to quite numerous XBT observations
during 80’s. During most decades of XX century
the amount of observations is poor and even the
biennial fluctuations in the upper 400 to500m
layer cannot be detected reliably. Below 500600m, all the time of oceanographic observations
is not suitable to detect the long-term ocean
variability over the globe. The precise long-term
observations are necessary to improve such case.
The regular observations at the Ocean Weather
Station (OWS) and the World Ocean Circulation
Experiment (WOCE) field program are good
examples of such strategy.It is unacceptable
(from the point of view of the long-term
variability study) to interrupt these space-time
Numerous attempts to estimate the long-term
trends in deep-sea fields of the North Atlantic
Ocean, where generally
the most intense
observational activity in XX century occurs, gave
few significant results. I’ll put attention to the
some of them. First, the positive temperature and
salinity trends in the deep North Atlantic Ocean
during recent decades were found and
documented by Antonov and Grojsman (1989),
Levitus (1989) and Parrilla et al. (1994).
Secondly, the warming of the subsurface
equatorial Atlantic during the summer and
autumn months of the 1980's in comparison with
previous decades was documented by Polonsky
et al. (1995). Thirdly, cooling and freshening of
high-latitude North Atlantic waters in the 1980's
were found by Read and Gould (1992). Besides,
the coupled nature of the decadal-scale variability
of the subtropical gyre was demonstrated recently
by Polonsky and Kuzmin (1999) using historical
data sets from 1969 to 1992. They showed that
the term W 
(where: W is the decadal
fluctuations of vertical Ekman velocity due to the
decadal change of horizontally-inhomogeneous
wind field over the subtropical Atlantic and
is the average vertical temperature gradient in the
subtropical thermocline) is very important in the
decadal heat balance of the Atlantic subtropical
thermocline. Moreover, the phase characteristics
of the decadal fluctuations of vertical Ekman
velocity over the eastern subtropical Atlantic and
of vertical temperature gradient in the subtropical
thermocline are similar one another. The
westward propagation of the decadal temperature
signal due to the linear Rossby waves (as
predicted by the standard theory) is absent in the
subtropical gyre because the Rossby waves are
modified by the eastward average currents.
It should be emphasized that all these results
were obtained using simple statistic processing
without assimilation procedure. It is not sure that
assimilation of recent global and/or regional
datasets in ocean or coupled models (or/and other
recent techniques, e.g., ensemble averaging) may
specify the above results or improve essentially
their statistical significance. In fact, uncertainty
of the surface boundary conditions and model
drift may obscure the real long-term ocean
variability. Let us analyze briefly these problems.
2.2 Surface fields
Both large-scale climatic-mean surface wind and
surface heat fluxes are estimated with a typical
error of 30 to 50% at best. For instance, Harrison
(1987) argued that wind stress of Hellerman and
Rosenstein (1983) is overestimated by about
40%.Large discrepancy between winds of
Hellerman and Rosenstein (1983) and of the
European Center for Medium Range Weather
Forecast (ECMWF) was found by Trenberth et
all (1989).
There are two sources of discrepancy in different
wind climatic sets. The first of these is due to
different techniques of wind observation and of
wind stress calculation (space averaging, drag
coefficient, etc.). The second is due to the
difference in the periods of averaged
observations. For example, Hellerman and
Rosenstein (1983) used the ship observations
collected during about century, while the
ECMWF wind stress was calculated using fourdimensional assimilation of the available global
information for about a decade (seven years). As
a result, the simulated ocean responses to two of
these wind fields differ essentially without any
changes of other parameters of the model
(Chervin and Semtner, 1993). It is not clear
whether this change of response is due to real
differences in the wind climate or not. Trenberth
et all (1990) analyzed the differences of zonally
integrated Sverdrup transports produced by
winds of ECMWF and of Hellerman and
Rosenstein (1983), while Landsteiner et all
(1990) analyzed the same differences produced
by three wind stresses over the tropical Pacific in
1979 to 1981, namely marine climatology, fourdimensional assimilation of all available
information into the GCM and satellite data.
Reference is further made to Trenberth et all
(1990). They argued "that in spite of the expected
reduction in wind stress due to the use of a drag
coefficient that is about 20% smaller, the added
variance captured by the ECMWF analyses has
apparently been compensated to some extent...
the 1980-86 period (period of the ECMWF wind
averaging - AP) may have been unusual."
Landsteiner et all (1990) intended but could not
check the existence of the Sverdrup balance in
the equatorial Pacific because the differences
between three their assessments were too large.
Reynolds et all (1995) recently compared the
simulation results concerning the sea level
variability in the equatorial Pacific using NMC
(U.S.National Meteorological Center), FSU
(Florida State University) and satellite (ERS-1)
wind forcing and TOGA-TAO data. They found
large differences between these results especially
in the Eastern equatorial Pacific. FSU wind
agreed with TOGA-TAO data best of all.
Systematic errors of definition of monthly wind
can be caused by the transition in the 1960's to
instrumental measurements instead of visual
estimation using the Beaufort scale. It results in
artificial positive trends (see for example,
[Isemer and Hasse, 1991]). Actually, wind speed
data, obtained using the Beaufort scale,
underestimate the wind speed. The instrumental
measurements began at the end of the 1940's,
their fraction in total number of observations has
increased in the 1950's. Approximately in the
middle of 1960's they already prevailed in the
magnitudes of monthly mean wind speed. This
may lead to an artificial trend of about 0.1 m/sec
per 10 years. However, the artificial linear trend
was not found in the North Atlantic by
Voskresenskaya and Polonsky (1994) using
comparison of measured wind and index of
circulation for 1957 to 1990. The positive wind
speed trend in the tropical and subtropical
Atlantic after the Second World War was
determined also in the independent estimates by
Bigg (1993) and Wolter and Hastenrath (1989)
using ship observations and measurements at the
coastal stations. Voskresenskaya and Polonsky
(1994) showed that the trend of wind speed in the
North Atlantic is even more statistically
significant than the trends of thermal
characteristics and the humidity deficiency. On
the other hand they showed that low-frequency
change in wind field is better approximated by a
nonlinear trend and that this change is actually a
quasi-periodical interdecadal variability which
strongly correlates with high-latitute temperature
change and hence with overturning intensity (see
also, [Polonsky and Voskresenskaya, 1996 a, b]).
However, information is insufficient to estimate
the multidecadal and century variability of the
wind field over entire ocean basins. Moreover,
high-frequency changes of the wind field should
also be taken into account for simulation of the
climate processes (Polonsky et al., 1992). This
means we should have at least daily global wind
fields for a long (about century) period if we
want to study the multidecadal changes in the
coupled ocean-atmosphere system or in the
Typical absolute errors of calculation of heat
gain using climatic monthly hydrometeorological
fields and bulk formula are more than 15 and
more than 50 Watt per square metre in the midlatitudes and in the tropical regions, respectively
(Gleckler, 1993; cf. also results by Budyko,
1963, and Bunker, 1976). Lack of precise longterm cloudiness observation is one of the main
causes. Therefore the current heat flux
assimilation may not improve essentially the
results of simulation of long-term oceanic
climatic change. However, some significant
results concerning a trend-like variability of the
sea surface heat fluxes were obtained using the
climatic monthly fields after the Second World
War and bulk formula. For instance,
Voskresenskaya and Polonsky (1994) found that
significant surface cooling of the North
equatorial Atlantic during the 1960's and 1970's
was accompanied by reduced interface humidity
contrast and air-sea temperature difference and
intensification of the trade winds. Then (in
1980’s), the sign of linear trend of the surface
heat flux has been changed. As a result, Polonsky
and Voskresenskaya (1996a) extracted the
significant parabolic trend of the total heat flux
over the most North Atlantic in 1957 to 1990. It
is due mostly to the parabolic trend of the
interface humidity contrast which is, in fact, the
manifestation of the 50-60yr variability in the
coupled system. Advective ocean processes
regulate this variability. Kushnir (1994) analyzed
the differences of SST, sea surface pressure and
wind between anomalously warm periods in the
North Atlantic (1950 to 1964) and anomalously
cold one (1970 to 1984). He showed that North
(extratropical) Atlantic warming is accompanied
by cyclonic circulation anomalies in mid-latitude
atmosphere. It seems that the recent techniques
(such as assimilation procedure) cannot specify
the above results. Certainly, the assimilation of
meteorological data is very effective for the
short-term weather
forecast. The
atmospheric models, such as ECMWF, are using
the assimilation during about 20 years. However,
parameterization, etc., lead to artificial changes
in the results of simulation if one deals with the
long-term atmosphere variability. Therefore, the
atmosphere fields produced by such models
without correction are useless for the detection of
interdecadal variability. Results of the reanalysis
from 1979 those are available now are suitable
to study the interannual-to-decadal (not longerterm) climatic fluctuations.
It seems, the use of the coupled models permits
to avoid the problems of the surface field
inadequacy. However, on the other hand, the
other serious problems arise if one uses the
interactive models.
2.3 Model inadequacy
Inadequacy of models include a lack of complete
parameterization of relatively small-scale and
high-frequency processes in the coupled oceanatmosphere system. Recent coupled oceanatmosphere models consist of a relatively highresolution atmospheric part and a relatively
coarse oceanic component (Gates et al., 1993;
Phillips, 1994). This difference is due to the
various Rossby scales in the atmosphere and
oceans. Therefore the typical space step of a few
degrees in the recent coupled models is eddyresolving for the atmosphere but not the oceanic
part. Certainly, the resolution of coupled models
may be improved essentially in the near future.
However, it is not clear whether a more detailed
resolution itself may improve essentially the
results of climate modeling. For example, a
comparison of the land surface climatology
produced by two versions (R15 and R42) of the
NCAR model showed only some regional
differences (Bonan, 1994). This is not the case
for the parameterization of cloudiness, smallscale sea-air interaction and especially mixing
processes in the oceans. Small-scale turbulence,
double diffusion and internal waves in the oceans
are the principal mechanisms of the highfrequency thermodynamics of the coupled system
which are responsible for the mixing and
dissipation. They cannot be simulated directly in
large-scale coupled models because of extremely
small space-time scales. However, they are the
crucial factors regulating the long-term
thermodynamics and properties of the coupled
ocean-atmosphere system. Accordingly, a
complete parameterization of mixing processes in
the oceans is extremely important. Unfortunately
this problem is not solved. Different
parameterizations of the upper mixed layer
processes and deep-sea mixing give very
different results even in the ocean-only models
(Polonsky, 1989; Hirst and Cai, 1994; Marotzke,
1997). For instance, Marotzke (1997) showed
that the meridional heat transport is proportional
to Kv½ (Kv is the coefficient of vertical mixing) if
Kv varies in the range permitting the steady
solution. At the same time, Weber (1998) clear
demonstrated that the strong sensitivity of the
interactive ocean-atmosphere model to the
reasonable change of the coefficient of the
vertical diffusion in the ocean occurs. Moreover,
ocean temperature and salinity in the coupled
model are more sensitive to this coefficient than
in the ocean-only model.
Another problem of the coupled system is the
large time scale of the ocean along with a poor
knowledge of its initial state. At least partly the
model drift is due to the above problems. In fact,
the "climate drift in coupled models is associated
primarily with systematic errors in the
component models which manifest themselves as
incompatibilities in the surface fluxes required by
the ocean to maintain the observed climatology,
and those surface fluxes which are predicted by
the atmospheric component of the model"
(Moore and Gordon, 1994). Flux correction is not
a principal solution of this because "although
such flux corrections are independent of any
anomalies in either the control or perturbation
runs, and have no interaction with the simulated
response, they may influence a model's ability to
portray longer-term climate change. It is hoped
that future improvements in coupled models will
make the use of such flux corrections
unnecessary" (Gates et. al., 1994). Recently
Dijkstra and Neelin (1999) clear showed that
multiple equilibria can strongly depend on the
flux correction procedure.
It seems that the assimilation of current
oceanographic data into the coupled models may
help to reduce the model drift. However on the
other hand, the initialization of large-scale
oceanic fields is necessary because otherwise the
artificial model drift may increase. Numerous
attempts of initialization of the oceanic fields
using high-resolution ocean models were not
quite effective especially in the tropical ocean
(see discussion of this problem in [Bulgakov et
al.,1990]). The changes in the oceanic fields
strongly depend on the model parameters and
time of initialization. Uncertainty of climate
deficiencies may lead to a wrong final state of the
coupled system after initialization. We should
also remember that the change of climate in the
coupled ocean-atmosphere models looks like a
nonlinear chaos. The state of such system
strongly depends on parameterization of smallscale processes (see above and [Fleming,1993;
Tziperman et al., 1995]).
So, we do not know reliably even the present
climatic state of the coupled system.
Accordingly, forecasts of climatic tendency using
coupled models, such as (Davies and Haat, 1994;
Rumstorf, 1995; Stouffer et al., 1994), may be
considered as very rough assessments. They
underestimate usually the natural variability in
the coupled system and overestimate the humaninduced changes. However, the models'
improvement may lead to more reliable results in
the future. Assimilation of precise oceanographic
data in such models may be a useful tool for the
study of the long-term variability of the coupled
system over a few years/decades. At the same
time, preliminary analysis of different data sets is
essential as a first step in the study of the longterm variability of the coupled system.
What should we do first of all during the next
few decades for the development the study of the
long-term climatic variability of the coupled
ocean-atmosphere system on the globe, taking
into account an uncertainty of natural climate
ocean state/boundary conditions, etc.? It is
clearly necessary to perform precise global
(including deep sea) observations and to develop
coupled models. However, a fast progress in the
study of the long-term climatic variability even
using the assimilation procedure, ensemble
averaging and improved models is not likely.
Duration of observations should be of the same
order as the typical time scale of processes to be
studied. This means we should continue to
support the global observational network at least
during the next few decades and to put attention
to analysis of different kinds of observations.
From the oceanic point of view the most
important long-term observations are as follows:
-the volunteer observing system including the
XBT program;
-the precise deep oceanographic observational
programs, such asOWS, WOCE and TOGA-TAO;
-long-term satellite observations.
In the best case all of these should be continued
and intensified especially in the region with poor
network as was recommended, for instance, by
the CLIVAR-GOALS Workshop (Melbourne,
Australia, 10-12April 1995) or by Godfrey et al.
(1995). Recent PIRATA program spreading the
TOGA-TAO-type observations at the tropical
Atlantic is a good example of such strategy. In
another case it is necessary to choose more
informative and less expensive program. First of
all this is VOS. However, the absence of deepsea observations in the VOS program does not
permit to restrict our ocean observations only to
observations are absolutely necessary also for the
study of low-frequency climatic change. At least,
the present repeated oceanographic sections
should be performed during a few decades. If we
change the sections' location we cannot detect
reliably the trend-like deep ocean variability
(even if we perform accurate observations). The
same problems are well-known in the analysis of
meteorological observations (Robeson,1995).
Another recommendation is to renew the
observations at the OWS or at least at the some
of them as soon as possible.
Antonov, D.I., and Grojsman,P.A.,1988: Temperature
change below active layer in the North Atlantic
Ocean (in Russian). Meteorology and
Hydrology, No.3, 57-63.
Bigg, G.R., 1993: Comparison of coastal wind and
pressure trends over the Tropical Atlantic:
1946-1987. Int. J.Climatol., 13, 411-421.
Birman, B.A., and Posdnjakova, T.G., 1992: Climatic
monitoring of the virtual heat fluxes at the
ocean surface (in Russian). Meteorology and
Hydrology, No.4, 102-104.
Bjerknes, J., 1964: Atlantic air-sea interaction. Adv.
Geophys., 10, No.1, 1-82.
Bonan, G.B. 1994: Comparison of the land surface
climatology of the National Center for
Atmospheric Research community climate
model 2 at R15 and R42 resolutions. J.
Geophys. Res., 99, 5D,10357-10364.
Budyko, M.I., 1963: Atlas of heat balance of the
World (in Russian). GGO. Leningrad.
Bunker, A.F., 1976: Computations of surface energy
flux and annual air-sea interaction cycle of the
North Atlantic Ocean. Mon. Weath. Rev., 104,
8, 1122-1140..
Bulgakov,N.P. et all 1990: Structure and seasonal
variability of the hydrophysical fields of
tropical and sub-tropical Atlantic as a result of
adaptation (in Russian). MHI press,
Sevastopol, 36p.
Chervin, R.M., and Semtner, A.J., 1993: Sensitivity of
a global ocean model to alternative wind stress
prescription. pp.13-14 in Preprints of 4th
Oceanography.(29 Mar.-2 Apr. 1993, Hobart,
Australia). AMS, Boston, MA.
Davies, H., and Haat, B., 1994: The problem of
detecting climatic change in the presence of
climatic variability. J. Meteor. Soc. Japan, 72,
5, 765-771.
Dijkstra, H.A., and Neelin, J.D. 1999: Imperfections
of the thermohaline circulation: multiple
equilibria and flux correction. J.Climate, 12, 5,
Dzhiganshin, G.F., and Polonsky, A.B., 1993:
Interannual variability of the oceanographic
fields in the tropical Atlantic (in Russian).
Oceanology, 33, 1, 654-660.
Fleming, R. J., 1993: The dynamics of uncertainty:
application to parameterization constants in
climate models. Climate Dynamics, 8, 3, 135150.
Gates, W. L.,et all 1993: An intercomparison of
selected features of the control climates
simulated by coupled ocean-atmosphere
circulationmodels.WCRP82,WMO/TD-No.574,46 p.
Gaysky, V.A. et all, 1992: Statistical structure of the
temperature field in the tropical Atlantic during
GATE (in Russian). Collected papers:
Automatized control of measurements in the
Oceans. Marine Hydrophy-sical Institute,
Sevastopol, 69-82.
Gleckler, P.J., 1993: Uncertaintly estimates in global
ocean surface heat fluxes. pp.93-98 in
Proceedings of 17th Annual Climate
Diagnostics Workshop. Norman, Oklahoma,
Oct. 18-23 1992.
Ghil, M., and Melotti-Rizotti, P., 1991: Data
assimilation in meteorology and oceanography.
Advances in Geophysics, 33, 141-266.
Godfrey, J.S. et all, 1995: The role of the Indian
Ocean in the global climate system:
recommendations regarding the Global Ocean
Observing System (OOSDP
Report No. 6): Ocean Observing System
Development Panel, February 1995, 89 p.
Harrison, D.E., 1987: Equatorial sea surface
temperature sensitivity to wind forcing. p. 413419 in Further progress in equatorial
oceanography., Nova University Press.
Hellerman, S., and Rosenstein, M., 1983: Normal
monthly wind stress over the World Ocean
with error estimates. J.Phys. Oceanogr., 13, 4,
Hirst, A.C., and Cai, W., 1994: Sensitivity of a World
Ocean GCM to changes in subsurface mixing
parameterization. J. Phys. Oceanogr., 24,
Kushnir, Y., 1994: Interdecadal variations in the North
Atlantic sea-surface temperature and associated
atmospheric conditions. J. Climate, 7, 141-157.
Isemer, H-J., and Hasse, L., 1991: The scientific
Beaufort equivalent scale: effects on wind
statistics and climatological air-sea flux
estimates in the North Atlantic Ocean.
J.Climate, 4, 819-836.
Landsteiner, all, 1990: On the sensitivity of
specification of wind stress forcing in the
tropical Pacific. J. Geophys. Res. 95, C2,16811691.
Levitus, S., 1982: Climatological atlas of the World
Ocean. NOAA Prof. Paper-13, U.S.
Government Printing Office., Washington, 174
Levitus, S., 1989: Interpentadal variability of
temperature and salinity in the deep North
Atlantic, 1970-74 versus1955-59. J. Geophys.
Res., 94, 16125-16131.
Levitus, S., and Gelfeld, R.D., 1992: National
Oceanographic Data Center inventory of
Department of Commerce, NOAA, USA, 242
Marotzke, J., 1997: Boundary mixing and the
dynamics of three-dimensional thermohaline
circulation. J. Phys. Oceanog., 27, 8. 17131728.
Moore, A.P., Gordon, H.B., 1994: An investigation of
climate drift in a coupled atmosphere-oceansea ice model. Climate Dynamics, 10, 81-96.
Parilla, G. et all, 1994: Rising temperature in the
subtropical North Atlantic Ocean over the past
35 years. Nature, 369, 48-51.
Phillips, T.J.,1994: A summary documentation of the
AMIP models. PCMDI Report No.18,
Livermore, 343 pp.
Polonsky, A.B., 1989: Upper mixed layer in
horizontally inhomogeneous ocean and its
modelling (in Russian). VNIIGMI-MCD,
Obninsk-Sevastopol, 234 p.
Polonsky, A.B. 1997: The problems in study of the
low-frequency variability in the coupled oceanatmosphere system due to the inadequacy of
historical datasets. Preprint volume of the 10 th
Conference on Applied Climatology, (20-23
Oct.1997, Reno). AMS, Boston, MA, 240-243.
Polonsky, A.B., Baev, S.A., Diakite, A., 1992: On the
response of the ocean upper layer to synoptic
variability of the atmosphere. Dyn. Atmos.
Oceans, 16, 225-248.
Polonsky, A.B. et all, 1995: Global manifestation of
ENSO events and long-term coupled
ocean-atmosphere variability. pp. 94-95 in
Abstracts of International Scientific Conference
ATMOSPHERE (TOGA 95), 2-7 April 1995,
Melbourne, Australia.
Polonsky, A., and Kuzmin A. (1999). What regulates
the decadal-scale variability in the North
Atlantic. Ocean. Meteorology and Hydrology,
Polonsky, A.B., and Voskresenskaya, E.N.,1996a:
variability in the coupled
ocean-atmosphere system in the North Atlantic
in 1957 to1990. GAOS, (submitted).
Polonsky, A.B., and Voskresenskaya, E.N., 1996b:
Low-frequency changes of meridional Ekman
transport in the North Atlantic, (in Russian).
Meteorology and Hydrology, No.7, 89-99.
Power, S., Tseitkin, F., and Dix, M. 1995: Stochastic
variability at the air-sea interface on decadal
time scales. Gephys. Res. Lett., , 22, 19,
Read, J.-F., and Gould, W.J., 1992: Cooling and
freshening of the subpolar North Atlantic since
the 1960's. Nature, 360, 55-57.
Reynolds, R.M., et al., 1995: Comparison of the
effects of different wind stresses on tropical
Pacific model sea level. WCRP/WMO No.717,
Robeson, S. M., 1995: Resampling of networkinduced variability in estimates of terrestrial air
temperature change. Climate Change, 29, 213229.
Rumstorf, S. 1995: Bifurcations of the Atlantic
thermohaline circulation in respons to changes
in the hydrological cycle. Nature, .376, 145149.
Stouffer R.J., Manabe S., and Vinnikov R.Ya.,
1994:Model assessment of the role of natural
variability in recent global warming. Nature,
367, 634-636.
Trenberth,K. E., Large, W. G., and Olson, J. G.,1989:
A global ocean wind stress climatology based
on ECMWF analyses. NCAR Tech. Note
NCAR/TN-338+STR, 93 p.
Trenberth, K.E., Large, W.G., and Olson, J.G., 1990:
The mean annual cycle inglobal ocean wind
stress. J. Phys. Oceanogr., 20, 1742-1760.
Tziperman, E., Cane, M.A., and Zebiak, S.E.,
1995:Irregularity and locking to the seasonal
cycle in an ENSO prediction model as
explained by the quasi-periodicity route to
chaos. J. Atmos. Sci.,52, 293-306.
Voskresenskaya, E.N., and Polonsky, A.B., 1994:
Low-frequency variability of sensible and
latent heat fluxes in the North Atlantic in 1957
to 1990 (in Russian). Meteorology and
Hydrology, No.9, 58-69.
Weber, S.L., 1998: Parameter sensitivity of a coupled
atmosphere-ocean model. Clim. Dyn., 14, 3,
White W.B., 1995: Design of a global Observation
System for Gyre-Scale Upper Ocean
Oceanography, 36, 3, 169-218
Wolter, K., and Hastenrath, S., 1989: Annual cycle
and long-term trends of circulation and climate
variability over the tropical oceans. J. Climate,
2, 1329-1351.
Wright, P.B., 1986: Problem in the use of ship
observations for the study of interdecadal
climate changes. Mon.Weather Rev., 114,