JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 106, NO. C10, PAGES 22,249-22,265, OCTOBER 15, 2001
Pacific
Ocean
wind
stress
and
surface
heat
flux
anomalies from NCEP reanalysis and observationsCrossestatistics and ocean model responses
GuillermoAuad, Arthur J. Miller, JohnO. Roads,and Daniel Cayan•
ScrippsInstitution of Oceanography,La Jolla, California
Abstract.
Wind
stresses and surface
heat fluxes over the Pacific
Ocean
from
the National
Center for EnvironmentalPrediction (NCEP) reanalysisand the comprehensive
Ocean-Atmosphere
Data Set (COADS) (blendedwith FSU tropical wind stresses)
are comparedover a commontime interval (1958-1997)in their statisticsand in
the responses
that they inducein seasurfacetemperature(SST) and heat storage
when
used to force an ocean model.
Wind
stress anomalies
from
the two data
sets are well correlated in the midlatitude extratropics, especially in the highly
sampled North Pacific. In the tropics and subtropics, low correlations were found
between the two wind stress data sets. The amplitudes of the stress variations of
the two data sets are similar in midlatitudes, but in the tropics NCEP wind stresses
are weakerthan the COADS/FSU stresses,especiallyon interannualtimescales.
Surface heat flux anomalies
from the two data sets are well correlated
on interannual
and shorter timescalesin the North Pacific Ocean poleward of 20øN, but they are
poorly correlated elsewhere and on decadal timescales. In the extratropics the
amplitudes of the heat flux variations of the two data sets are comparable, but
in the tropics the NCEP heat fluxes are weaker than those of COADS. Ocean
model hindcasts driven by both data sets are also compared. The midlatitude SST
hindcastswere superior when using the NCEP flux anomalies while tropical SST
hindcastswere equally skillful for the two hindcasts when consideringall climatic
timescales. The spatial and temporal sampling rates of the COADS observations
and their consequentimpacts on constrainingthe NCEP reanalysisappear to be
the main factors controlling the results found here.
1.
age, 1984, 1987; Barnett, 1984; Wright, 1986; Morris-
Introduction
Wind stress and surface heat flux fields over the ocean
are often usedfor diagnosingocean-atmosphereclimate
variations[e.g., Cayan, 1992b; Trenberthand Huttel,
1994; Iwasaka and Wallace, 1995; Tanimoto et al.,
1997; Deser et al., 1999; Parrish et al., 2000] and for
forcing and testing numerical models of ocean climate
sey,1990; Cayan,1992a; Ward andHoskins,1996]that
are employed in deriving the fluxes. The portion of
the observational error that is random may be reduced
by aggregatingover multiple flux estimatesin time and
spaceso that well-sampled areas of the ocean, e.g., parts
of the North Pacific and North Atlantic Oceans, have
more reliable flux estimates than those that are poorly
variations[e.g.,Harrisonet al., 1990;Miller et al., 1993, sampled,e.g., much of the tropics and Southern Ocean
1994; Xie et al., 2000; Stockdaleet al., 1998; Fevrier et
[Weare,1989; Cayan, 1992a]. More difficultproblems
al., 2000].
are those that produce constant or time-varying biases,
Surface heat and momentum
fluxes derived
from
di-
caused by uncertainties in the bulk formulae and by
rect observations, however, are usually sparsely saminstrumental practices such as changesin surfacewind
pled in space and time, especially in tropical, subtropestimation proceduresor by increasesin ship anemomeical, and Southern Hemisphereregions. These surface
ter height[Taylor, 1984].
flux estimates are also infected by uncertainties in the
Atmospheric analysescan provide relatively complete
bulk formulae[Blanc,1985; Weare,1989; Taylor,1984] spatial and temporal coveragebut they can be influ-
and by errorsin the weatherobservations
[e.g., RamThis paper is not subject to U.S. copyright. Published in
2001 by the American GeophysicalUnion.
Paper number 2000JC000264.
encedby modelerrors[Bettset al., 1996; Weare,1997;
Bony et al., 1997; Trenberth and Guillemot, 1998; Scott
and Alexander,1999; Yanget al., 1999],by inadequate
quantities of assimilateddata [Waliser et al., 1999;
Marshall and Harangozo,2000; Putman et al., 2000],
22,249
22,250
AUAD
ET AL.: MODEL
RESPONSE
and by the exclusion of data that are difficult to assim-
TO COADS AND NCEP FORCING
flux forcing fields for the Pacific for the period 1958-
ilate suchas surfaceheat fluxesand rainfall [Janowiak 1997 to determine the similarity of the two data sets
et al., 1998.]
over monthly, interannual, and interdecadal timescales.
In recent years, surfacefluxes from the National Cen-
We also use the two data sets to provide anomalous
ters for EnvironmentalPrediction/NationalCenterfor forcing for an ocean model of the Pacific and compare
Atmospheric
Research(NCEP/NCAR) reanalysis
have the results with observations of SST and heat content.
becomeroutinelyavailable[Kalnayet al. 1996]. Sur- These ocean hindcasts extend the Pacific Ocean decadal
face fluxes derived from direct observations associated
processes
studiesof Miller et al. [1994,1998],Cayanet
with the ComprehensiveOcean-AtmosphereData Set al. [1995]and Auad et al. [1998b]to longertimescales
(COADS) are also availablefor a similar time period with higher spatial resolution.
[Woodruffet al., 1987; Cayan•992a; da Silvaet al.,
1994]. The forcingfunctionsderivedfrom thesetwo
This comparison provides a general understanding
of the similarities
and differences
of the COADS
and
sourcesare ubiquitouslybeing usedto forceoceanmod- N CEP flux data sets and the character of oceanic reels for simulating and diagnosingobservedbasin-scale sponseto be expected when using either dataset as forcoceanclimate variations over long timescales.
ing. In summary, we find that the COADS and the
These ocean hindcasts have revealed numerous asNCEP reanalysis still require improvements, but that
pects of the physics of oceanic climate variations on they appear to be adequate in many regionsfor forcing
seasonalto interannual and decadaltimescales.Trop- long ocean model hindcasts.
ical ocean hindcasting has revealedthe sensitivity of
This paper is organized as follows. In section 2 we
the tropical ocean circulation to the interannual wind describe both surface flux data sets; in section 3, their
stressforcing and the damping effect of surface heat statistics are compared; section 4 compares responses
fluxes[e.g.,Harrison et al., 1990;Barnett et al., 1991; of the ocean model to forcing obtained from both data
Stockdaleet al., 1998]. In the midlatitudes,heat fluxes
appearto play the major role in drivingseasurfacetemperatureanomalies(SST) anomalieson seasonalto interannualtimescales[e.g.,Lukschandyon$torch,1992;
Miller et al., 1994; Battisti et al., 1995;Kleeman et al.,
1996;Halliwell, 1998; Gieseand Carton,1999;Seagetet
al., 2000]. However,horizontalcurrentscan contribute
substantiallyto midlatitude SST evolutionduring certain intervalsand on the longertimescales[Miller et
al., 1994; Giese and Carton, 1999], and especiallyin
westernboundarycurrentregions[Kleemanet al., 1996;
Halliwell,1998;Xie et al., 2000; Qiu, 2000].Subsurface
midlatitude adjustment processesdue to Rossbywaves
sets; section 5 contains a summary and conclusions.
2.
Data
Sets
The time spanof the COADS [Woodruffet al., 1987]
studied here is from January 1951 to December 1997
in a Pacific domain from 40øSto 60øN. Figure I shows
the global samplingdensity of COADS wind speedand
SST measurements.
Wind stress(momentumflux) anomalieswerederived
from COADS on a 2øX2ø grid followingCayan[1992a].
In the extratropics the COADS wind stress anomalies
are derivedfrom monthly meansof the product IVIV
[e.g.,Lysneet al. 1997;Miller et al., 1997]andsubduc- from individual wind speedobservations.Drag coeffition by meancurrents[Schneider
et al., 1999]havealso cientsweretakenfromIsemerandHasse[1987]andare
been identified
in these ocean climate
hindcasts.
These results of driving ocean models with surface
heat and momentum flux estimates, however, are impaired by inaccuraciesin the fluxesas well as by inadequaciesin the ocean models. The consistencyof model
physicscan be rigorouslytested usingerror statisticsof
the atmosphericforcing and of the validating oceanic
data sets [e.g., Frankignoulet al., 1989, 1995; Sennechaelet al., 1994;Bennettet al., 1998,2000],but the
weakly dependent upon wind speed and air-sea temperature difference, AT. We used this COADS anal-
ysis, rather than that of da Silva et al. [1994],becausethe unadjusted COADS observationshave low fre-
quencydrifts in primary variables,suchas wind speed,
which are thought to be spuriouseffectsof instrumental changes. The COADS analysis we used makes a
rough attempt to correct for changesin wind speed,
A T, and surfaceheat flux, A Q by removingbasinwide
computational requirementsof suchtests of full-physics
low-frequency drifts from these variables before fluxes
models
arecalculatedvia bulkformulae(seeMiller et al. [1994]
for additionaldetails).The da Silva et al. [1994]anal-
can be ominous.
Using observedflux variations to force ocean models
clearlyleadsto a better understandingof howthe ocean
can interact with the atmosphereto organize climate
variations. But it is unclear how the uncertainty in
the flux estimates
is transferred
to errors in the ocean
model physical balances. How similar are the COADS
and N CEP flux forcing data sets which are commonly
used to force ocean models? How differently would a
singlemodel respond to forcing by these two data sets?
Our goal is to answer these questionsas follows: We
first statistically analyze the NCEP and COADS surface
ysis doesnot addressthese time-varying problemsthat
could have substantial impact upon modeled decadal
variability.
Since the COADS wind stressobservationsare very
lightly sampledin the low latitudes (Figure 1), we use
Florida State University (FSU) subjectivelyanalyzed
wind stressanomalies[Goldenberg
and O'Brien, 1981]
in the region +20 ø latitude. In an overlap region of
5 ø latitude
at 20øN and at 20øS the COADS
and FSU
anomaly fields were smoothly merged. Unfortunately,
AUAD
ET AL.:
MODEL
RESPONSE
TO COADS
AND
NCEP
(a) COADS
2-DEG SST,TOTALNO. OF SHIP OBS(1/1955 - 12/1997)
CI-1000.,
FORCING
MIN•
0., MAX---
22,251
251216.
03
xl
2O
19
6DN
•
' ""••'
• .....•' ß......
?'•'•-•
.......
.. •-:• '•-
•••
"' :•'•/:•/•x
...........
5•.
.... ' - '
_ •:--•ff
.• ......
•:•'•• •.
.•
•..;•-:.:•'•
....
.•-.'3:•:':'"'"'•
•:•.:•:<...--,•;;•<•i.
•-" <"•'•T'?J
•:'"
•-:'-..""-""•::•;:..•
"•'•••'•'
,
--'.•,::•-'
•:-•.:--•:
'::-'-'
-•"-'•:• -'•,•:'".•:•
"'•:.•:'"•
•,•.-.
•
.
.....
...•..:
•.<•
•:•....
•,,,•::• ......
15
14
13
12
•..:4F
'•:•:: ::7•:::•
.'....
'•'•.........
:• ......
:.•.....
;2•?•t•
:•
11
•' '........
'.•,•:...:
....
"-• •-:•I ' •'•D
........
•, ..:•,.•.•,-.•'•.,,?• .•
10
---•-:.:--:•-v.
'"...•"'•:"•"•
-::::
.•,..
17
16
'•:'•
•...••. =•...•..•..
• ,,. ..:.:..,•
*
.
-•'•:'
.... ' ;.•.....•:•
......
•:"•
18
•..'"'•7.%,
..,• • - . .....
........
. .•:•:.•.•
•,.•.•.•
.....-:
.............
•-.•
9
:•..
8
.• ........ •:•:• •:•
7
6
5
4
3
6•S
B
9DE
1 8g
•:.'•'•
:•, , •
9•
(b) COADS
2-DEG
WIND
SPEED,
TOTAL
NO.OFSHIPOBS
(1/1955- 12/1997)
CI--
i
•..........•...... !....•
: ___•.,,.t,_
........}..... •. _,....t
.... t,..,..,__••_
lOO0..
MIN=
{........ :
0., MAX--
213050,
!..... ,.... ...s_.,.__•
i .~
2
1
0
03
xl
20
19
18
17
16
I ½.... . •:;•;:•
•.,•
...,
.•
.
,,.
.•'
.
."'•'½•••
','-•<•f-v4"...r
.
,.
• ......
•
..½ '
15
14
13
12
.-.-.--.--•--•••••••.•••••••,
..... "--'-•"•-•• .....ß<.
--•t:•?.
11
,•,•.•--.•:•:•,:'
10
9
8
7
6
i
'•"•
5
4
3
2
1
0
•
9•E
18•
9•W
Figure 1. Distributionof the total numberof observations
for (top) COADS SSTsand (bottom)
winds. Data plotted include observationsfrom 1955 through 1997.
FSU winds are only available since 1961. So, we creMissing data values in space and time were treated
ated pseudo-FSUwind stressstarting in 1951 as follows. as follows. We first linearly interpolated in time for
We computedempiricalorthogonalfunctions(EOFs) of thosemissingvalueswhich had nonmissingvaluesavailFSU wind stressfrom 1961 through 1997. The principal able in the previous and following months. We then
componentof the first EOF washighly correlatedto the linearly interpolated in space for any remaining missSouthernOscillationIndex. We regressedthis index for ing values which were surrounded by four grid points
1951-1960 onto spatial loading pattern for EOF-1 from with non-missingvalues. The remaining missing values
the period 1961-1997. We then computed the EOFs of were estimated by quadratic interpolation using subthe COADS wind stressesfor the period 1951-1960 in routines "blockmean" and "surface" from Generic Mapthe FSU region. The secondand third COADS EOFs ping Tools(GMT). The last step wasto map all of the
for 1951-1960 resemble the second and third FSU EOFs
fieldsto a commongrid, which for conveniencewas chofor 1961-1997. So we used the principal componentsof sen to be the ocean model grid (discussedin section
the second and third
COADS
EOF
modes as time
se-
ries for the 2nd and 3rd FSU EOF modes for the period
1951-1960.
The
three-EOF
mode
reconstruction
4), a roughly 1.5ø grid coveringthe Pacific Oceanfrom
Antarctica
2.1.
to the Arctic
COADS
Wind
Stress
explained about 60% of the FSU wind stressvariance
Heat flux anomalies were likewise computed on a 2ø
in the period 1961-1997. We therefore expect that the
reconstruction will explain a similar amount of wind by 2ø grid by Cayan [1992a]for the COADS observastress variance for the period 1951-1960.
tions poleward of the 10ø latitude. The convention is
22,252
AUAD ET AL.: MODEL RESPONSE TO COADS AND NCEP FORCING
that positive values correspondto heat entering the
2.3.
ocean.
The turbulent kinetic energy (TKE) flux into the
mixedlayeris neededas a forcingfunctionfor the bulk
mixedlayerformulationof the oceanmodeldescribedin
The COADS
latent
and sensible fluxes were
formed from monthly averagesof productsof individual observationsusing bulk formulae. Exchangecoefficientsweretaken from Isemer and Hasse[1987]and
are weakly dependenton wind speedand AT. During
the 1950smany observations
of SST and wind speed
were not accompaniedby humidity measurement,and
so heat fluxes were not calculated directly from bulk
formulae. Instead, heat flux was determined at those
points by a regressionwith SST and wind speed. Two
sets of regressioncoefficientswere computed,one for
Turbulent
Kinetic
Energy
section 4. The fields of TKE from COADS and NCEP
willnotbecompared
herebecause
itsestimation
isvery
uncertainwhenusingmonthlymeanobservations.
The
Weibullparameterization
of Pavia and O'Brien[1986]
for relatingmeanwind speedcubedto monthlymean
wind speedis invokedto estimate its variability as a
forcingfunctionfor both flux data sets(seeMiller et
al. [1994]
fordetails).
Theareawiththelargest
TKE
midlatitudes and another set for the tropics. It is im-
anomalies is the central North Pacific which shows an
portant to note that in the tropical band (:•10ø) the
COADS dataset has many gapsin spaceand time; for
increase
in its variabilityin the late 1970s.
conveniencethesewere filled in usingthe GMT subroutines mentioned previously
3.
2.2. NCEP/NCAR
COADS
and NCEP
Flux
Comparisons
Reanalysis
Our goal is to comparethe two differentflux data
The atmosphericreanalysiswas obtained with the setsthat are frequentlyusedin variousoceanmodelNCEP T62 (209km) globalspectralmodelof 28 vertical ing studies. It is important to note that the NCEP
levelswith 5 levelsin the boundary layer. The NCEP reanalysisassimilatesCOADS as well as other obserreanalysis
modelincludes
parameterizations
of all ma- vations. For this reason, areas with a high density of
jor physicalprocesses,namely, convection,large scale observationswill be more likely to agree. However,the
precipitation,shallowconvection,gravity wavedrag, ra- fluxeswerecomputedusingdifferentbulk formulaewith
waysfor estimating
inputvariables,
suchas
diation with diurnal cycle and interaction with clouds, different
boundary layer physics,an interactive surfacehydrol- SST [Kalnayet al., 1996; Cayan,1992a]. Determinare dueto the differogy,and vertical and horizontaldiffusionprocesses.The ing whetherthe main differences
usedin theCOADS/FSUandNCEP
details of the model physicsand dynamicsare given by entbulkformulae
data sets or to the different input variable estimates
Kalnay et al. [1996].
Flux fields are derived from the four times per day employedin computingthe respectiveheat fluxesis benear surfacefields produced by NCEP's global atmo- yond the scopeof this paper. It is important to highspheric analyses. These fields are convertedwith bulk light that eventhoughthe COADS and NCEP heat
formulae using a constant drag coefficient. The data fluxes are computedthrough different formulationswe
were then monthly averaged for use in comparing to want to comparethem asthey are, without usinga common bulk formulae framework, becausethis is the way
COADS and in forcing the ocean model.
ANALYZED
AREAS
120øE 140øE 160øE 180 ø 160øW140øW120øWlOOøW 80øW 600W
Figure 2. Key regionsusedin the correlation
and coherence
analysisin Tables1 and 2. The "N
20øN"regionin Tables1 and 2 is the entirebasinaveragednorth of 20øN.
AUAD
ET AL.:
MODEL
RESPONSE
TO COADS
AND
NCEP
FORCING
22,253
Table 1. Correlations,Standard Deviationsratios, Coherence,Amplitude
of Transfer Function and Lag for COADS and NCEP zonal wind stress
Coherence
Area
Corr
EEP
SD
0.23
WEP
(CCS
CNP
N 20øN
K.Ext.
WNP
2years
2.20
(0.60)
(0.89)
(0.89)
(0.81)
(0.87)
(0.75)
2.5years
0.40
(2.83)
(0.91)
(0.68)
(0.78)
(0.80)
(1.31)
(0.65)
(0.86)
(0.76)
(0.77)
(0.65)
(0.13)
3.3years
0.27
5years
0.28
0.25
(0.84)
(0.81)
(0.77)
(0.61)
(0.65)
(0.38)
10years
0.15
(085)
(062)
(076)
(085)
(080)
(0.82)
(0.68)
(0.86)
(0.82)
(0.78)
(0.62)
(0.77)
0.55
0.55
(0.78)
(0.69)
0.54
055
CORR and SD were computed using anomalous time series with no filter-
ing. Resultsfrom the cross-spectral
analysisare shownfor the five lowest(climate) bandsof the computation(2, 2.5, 3.3, 5 and 10 years). All ratios are
COADS/NCEP, and a negativelag impliesthat NCEP leadsCOADS. Valuesin
parenthesesare significantat the 90% level
Amplitude
2years
2.5years
1.30
1.00
3.3years
1.12
Lag (months)
5years
10years
1.10
0.74
2years
2.5years
-1.62
3.3years
-0.93
-1.06
5years
10years
-0.59
4.71
1.92)
(0.77)
(0.70)
(0.97)
(0.66)
(2.05)
(0.64)
(0.75)
(0.82)
(0.81)
(1.94)
(0.77)
(0.78)
(0.86)
(0.94)
(2.00)
(0.65)
(0.58)
(0.83)
(0.72)
(1.70)
0.40
0.48
(0.90)
(0.76)
(-0.05)
(0.39)
(-0.13)
(0.01)
(-0.25)
(0.27)
(0.29)
(0.09)
(-0.23)
(0.33)
(-0.37)
(0.04)
(-0.27)
(-0.34)
(0.04)
(-2.48)
(-1.78)
(0.20)
(-0.78)
(0.01)
(-4.00)
-1.56
2.56
(3.33)
(4.25)
0.73
(0.53)
(0.73)
0.63
0.96
-1.63
(1.63)
(3.43)
5.84
4.97
in which they are presently used by the community to
force ocean models.
With
these similarities
and differ-
there is a clear disagreementin amplitude in spite of
significantcoherency.In the tropics, NCEP zonal wind
encesin mind, we next analyze their statistics and cross stresses
are much weakerthan COADS/FSU [Putman
statisticsto provide an a priori, general idea of how the et al., 2000]. This differencedecreases
away from the
two data sets may differ when used as oceanic forcing tropics, suchas in the western subtropical North Pacific
functions.
3.1.
area which showsa muchbetter correspondence
(Table
1).
Wind
We next discussthe spatial structure of the zero-lag
Stress
correlation
coefficients
between
NCEP
and
Figure 2 showsthe regionsfor which area-averaged COADS/FSU wind stressanomalies. Figure 3 shows
These areas were selected
that both components of the wind stress field are well
becausethey coverkey dynamical regionsof the North correlated north of 20øN, in contrast to low correlaPacific, most of which have been previously studied tions, below 0.3, that occur in the tropical band. In the
time series were constructed.
[e.g., Stockdaleet al., 1998; Miller et al., 1994; Auad Southern Hemisphere the correlations increase relative
et al., 1998a,1998b]).
to the tropics, but they reach 0.6 only in a limited area
Table
I shows some basic statistics
between
NCEP
and COADS/FSU zonal wind stressin thesesevenkey
regionsof the equatorial and North Pacific. The meridional stressstatisticsshowa similar level of agreement,
although the phase discrepancyis much worse in the
tropics.
The highest correlationsand the best agreementsin
coherenceamplitude and phase occur in the midlati-
off the Australian
coast.
The area displayingthe lowestcorrelationsis the eastern tropical Pacific, where wind stress variables are
poorly sampled in space and time. Caution should
therefore be used when interpreting these maps because
a low correlation does not necessarily mean that the
NCEP model is not providing a good representation of
the wind field there
or that
the bulk formulae
used to
tudes. In the easternequatorialPacific (EEP) region obtain the COADS/FSU data setsare unrealistic.Low
the correlation and coherencyare weak and insignifi- correlations could simply mean inadequate sampling in
cant. In the westernequatorialPacific (WEP) region, COADS/FSU.
22,254
AUAD ET AL.' MODEL RESPONSE TO COADS AND NCEP FORCING
Correlation
Coefficients:
COADS
as the central North Pacific, lags are smaller than 4-1
vs. NCEP
month for the ENSO band (a 4% error) and smaller
than 4-3 monthsfor the decadalband (a 3% error). It
is importantto notethat the squaredcoherences
in Figure 5 have a spatial structuresimilar to the correlation
maps of Figure 3. Over the basinthe most coherent
6O
Heat
Flux
4•øN
20øN
I
band is the ENSO band which has a typical squaredco-
herencethat greatlyexceeds
the 90%confidence
levelof
40 øS
' • ' •"' • :•"'•' '•" •' "•' t
0.35 in the tropicsfrom 120øWwestward,in the western
North Pacific north of 20øN, and in the western South
Pacific middle latitudes. The eastern tropical Pacific
remainsproblematicas insignificantcoherences
are ob-
"'"'•" •
150øE 160øW 110øW
tained for all interannual frequencies.
o6ø
40 N ••
(b)
Similarresults(not shown)areobtainedfor the merid-
ional wind stress over the central North Pacific. Un-
TAU-xo
like the zonal stress coherences,maximum coherences
in midlatitudes,for all frequencies,are locatedfurther
eastby 200-30ø, whereanomalous
windsassociated
with
20øS
•
the Aleutian Low are oriented more meridionally. This
alsois a featureof the tropicswherethe easterntropical
Pacificshowssomesignificantcoherences
for the merid-
40øS
150øE 160øW IlOøW
60 øN
STANDARD DEVIATiON RATIOS: COADS vs. NCEP
40W
TAU-y
20øN
60øNJ•
40øN'"
....
'""•
o
HeatFluxø
20N
20øS
40øS
150øE 160øW 110øW
Figure3. Correlation
coefficients
between
theheat
20
S'.
40os
150øE 160øW 110øW
fluxesandwindstresses
ofthe COADS/FSUandNCEP
data sets. The spatiallyaveraged
confidence
levels,
60øN : ....
based
ontheDavis[1976]autocorrelation
timescale,
are
(topto bottom)
0.35,0.33,and0.36.Thefields
were
smoothedwith a 1000 km radius filter.
40 øN
TAU-x
Figure 4 showsthat the ratio of the standard deviations of the wind stress components is nearly unity
outside the subtropical latitudes. Along the equator,
COADS/FSU stressesare larger than NCEP stresses
(b)
20øS
•
40øS'
150øE 160øW 110øW
by a factor of up to 2.5 and 3.5 for the zonal and meridional components, respectively. The largest discrepancies again occur in the eastern tropics, which is an area
with low densityof observationsin COADS/FSU and
with
little
data
assimilated
into the NCEP
model.
In order to discriminate among different frequency
ue•ween
the NCEP
bands w• •u,•pu•u •u• •1•ucua
.... •'
'
and COADS/FSU flux time seriesover the entire Pacific basin. We focuson E1 Nifio/SouthernOscillation
(ENSO) timescales(2-8 years)and decadaltimescales
(>8 years)asthey are the target of manyclimatestudies. Figure 5 showsthe transfer function amplitudesand
phases and the squared coherency for the zonal wind
(a)
TAU-y
o
'"
(c)
2o
20
S
40øS
150øE 160øW 110øW
Figure 4. Ratios betweenthe standarddeviationsof
the heat fluxesand wind stressesof the COADS/FSU
stressbetweenthe COADS/FSU and NCEP fieldsfor
(numerator)and NCEP (denominator)
data sets.The
these two period bands. For the coherent areas, such
fields were smoothed with a 1000 km radius filter.
AUAD
ET AL.:
MODEL
RESPONSE
TO COADS
AND
NCEP
150 øE 160 øWl l OøW
(b)
FORCING
22,255
150 øE 160 øWl l OøW
(c)
150 øE 160 øWl l OøW
LAG (months)
(e)
150 øE 160 øWl l OøW
COHERENCE**2
Figure 5. Crossspectralanalysisbetweenthe COADS/FSU andNCEP zonalwindstresses.The
three columnscorrespondto amplitude, phase and squaredcoherenceof the transfer function.
The labelson the left denotethe frequency
bandin question,decadaland subdecadal
(>8 year)
and ENSO (2-8 years). The 40 year long time serieswas dividedinto four segments
which
overlapped50%. The 90% confidence
levelis 0.35. The contourintervalfor squaredcoherences
is 0.1 starting at 0.15 with contours<0.15 not plotted; the 0.35, 0.25 and 0.15 are includedsince
most of them are parallel to the significantcontours> 0.35. The contour intervals for transfer
function amplitudesare 1.5, 1.3, 1.1, 0.9, 0.7, 0.5, etc. The contour interval for LAGS are 2
months for the ENSO band and 3 months for the decadal and subdecadal band. The transfer
function amplitude is defined as NCEP over COADS.
ional component,unlike the zonal wind stresscompo- in the Kuroshio/OyashioExtensionarea, a regionwhich
nent.
is crucial to decadal midlatitude ocean-atmosphere in-
For interseasonal
frequencies(3, 6, and 9 month pe- teractions[Nakamuraet al., 1997; Miller et al., 1998;
riods) both the zonal and meridionalwind stressesare Deser et al. 1999; Qiu, 2000; Miller and Schneider,
significantlycoherent(not shown)only north of 20øN 2000].
and show patterns similar to Figure 5. The transfer
Figure 3 (top) showsthe correlation map of heat
function amplitude indicates that NCEP wind stresses fluxes from NCEP and COADS. As with the wind
are roughly10-30%smallerthan COADS/FSU in the stress correlations, minimum values are observed in
interseasonalband in that region.
the tropical band and increasetoward higher latitudes.
The Northern Hemisphere has larger correlations than
3.2.
Heat
Fluxes
the Southern Hemisphere while the maximum Southern Hemisphere correlations are seen off the Australian
Table 2 shows some basic statistics between NCEP
and COADS surfaceheatflux in sevenkeyregionsof the coast,mostlikely becauseof a higher sampling rate in
this region.
equatorial and North Pacific. Midlatitude areas exhibit
Figure 4 (top) showsthe standarddeviationratios of
a good correspondence
in both amplitude and phase.
As with the wind stressdata, the major discrepancies heat fluxesfor COADS and NCEP. Unlike wind stresses,
are in the eastern and western equatorial Pacific areas
the ratios for heat fluxes in the tropical band are much
(EEP and WEP areas). The NCEP heat flux in the closerto unity. In the midlatitude Northern Hemisphere
EEP area (not shown)has an interdecadaloscillation
the ratios
which seems to be absent from the COADS
ence(not shown)in the tropicsfor Qc -Q•v is 0(5 W
record. It
is interesting to note that in both data sets the maxi-
are between
0.9 and 1.0.
The
RMS
differ-
m-2), whilein the subtropical
NorthernHemisphere
it
mum variabilityof heat fluxes(not shown)is observed is 0(-5 to-10 W m-2). In the subtropicalSouthern
22,256
AUAD ET AL.: MODEL RESPONSE
TO COADS AND NCEP FORCING
Table 2. Correlations, Standard Deviations ratios, Coherence,Amplitude
of Transfer Function and Lag for COADS and NCEP surfaceheat fluxes
Coherence
Area
EEP
Corr
SD
(0.33)
(0.96)
WEP
0.12
CCS
CNP
N 20øN
K.Sxt.
WNP
2years
0.90
(0.86)
(0.81)
(0.66)
(0.79)
(0.72)
(1.10)
(1.26)
(1.16)
(1.39)
(1.18)
2.5years
3.3years
5years
10years
0.18
0.37
0.53
0.52
0.45
0.04
0.01
0.12
0.49
0.40
(0.65)
(0.64)
0.52
0.57
0.33
(0.69)
(0.77)
0.46
(0.72)
(0.39)
0.40
(0.73)
0.08
0.32
0.52
0.02
0.31
0.03
0.12
(0.75)
0.28
0.32
0.10
0.16
(0.77)
CORR and SD were computed using anomalous time series with no filtering. Results from the cross-spectralanalysisare shownfor the five lowest (climate) bands of the computation (2, 2.5, 3.3, 5 and 10 years). All ratios are
COADS/NCEP, and a negativelag impliesthat NCEP leadsCOADS. Valuesin
parenthesesare significantat the 90% level
Amplitude
2years
0.34
(0.12)
(0.64)
(0.81)
0.56
0.57
0.53
2.5years
0.54
0.03
(0.75)
(0.83)
0.41
(0.51)
0.61
3.3years
0.79
0.26
0.68
(0.67)
0.25
0.30
0.97
Lag (months)
5years
0.83
(0.32)
0.08
0.31
0.13
0.22
(1.06)
10years
2years
0.79
-0.49
(0.25)
0.27
0.60
2.22
(-1.36)
(0.05)
0.19
-3.0
0.30
(0.95)
-1.41
-0.63
Hemisphere RMS differences are smaller than in the
Northern Hemisphereand tend to zero on average in
the band 20øS-40øS.
The cross-spectral analysis between N CEP
and
COADS heat fluxesis shownin Figure 6 for the ENSO
(2-8 years)and decadaland subdecadal(>8 years)frequency bands. For regions where significant squared
coherencesare seen in the right the ratio of COADS
over NCEP is less than unity for all frequencies. As
with the wind stresscomparison,errorsin phase(Figure 6, middle) are small (no morethan 4% to 5%). The
sparsely sampled tropics and Southern Hemisphere do
not show significant coherences.The most coherentareas are the western subtropical North Pacific and some
highlatitude areas (Table 2). Similar to the analysisof
2.5years
3.3years
-0.52
2.31
11.2
-1.81
(-0.75)
-3.34
(-1.23)
-1.64
-8.5
0.46
(-1.80)
5years
3.48
(-16.7)
5.16
(-4.52)
10years
-1.59
(-43.2)
-9.98
10.06
-0.23
-1.14
-41.0
-0.67
0.31
3.88
(0.81)
-2.17
(0.69)
parable to those of the shortwave fluxes so the shortwave flux errors can degradethe quality of oceanmodel
summertime SST hindcasts. In the tropics, shortwave
fluxes
and the combined
effect of sensible and latent
heat fluxes are comparable year-round, but they normally act as a damping effect on SST anomaliesin that
region.
4. Ocean Model Response to
COADS/FSU
4.1.
OPYC
and NCEP forcing
Model
The primitive equation ocean model called the Ocean
isoPYCnal (OPYC) modelwasdevelopedby Oberhuber
wind stresses, the surface heat fluxes in the intersea- [1993]and appliedby Miller et al. [1994,1997, 1998],
sonalbands (3 to 9 months)exhibit significantcoher- Cayanet al. [1995],and Auad et al. [1998a,1998b]to
ences(not shown)only north of 20øN,with COADS and study monthly through decadal-scaleocean variations
N CEP heat fluxeshaving nearly the sameamplitudes. over the Pacific Basin. Here an updated version of the
It is important to note that the shortwave fluxes from model was used with higher resolution and a revised
the N CEP reanalysis are known to be inaccurate and schemefor forcing with monthly mean fluxes.
might contribute to errors in SST anomalies,especially
The model is constructed with 10 isopycnal layers
duringsummer[Scottand Alexander,1999].In winter, (eachwith nearly constantpotential densitybut varihowever, the combined effect of the latent and sensi- able thickness,temperature, and salinity) that are fully
ble heat fluxes is larger than the shortwaveby I order coupledto a bulk surfacemixed-layer model. The grid
in midlatitudes[Cayan, 19923]. In summerthe mid- extends from 119øE to 70øW and from 67.5øS to 66øN,
latitude latent and sensible fluxes together are corn- with periodic boundary conditions along the latitudes
AUAD ET AL.: MODEL
RESPONSE
of the Antarctic Circumpolar Current. The resolution
is 1.5ø in the midlatitude open ocean, with zonal resolution gradually increasedto 0.65ø resolution within
a 10ø band around the equator. Although the model
is not eddy resolving, equatorial instability waves occur spontaneously,and eddies occur in the west wind
drift of the midlatitudes. We only seekto study largescalepatterns in the responseand regard this intrinsic
variability as noise. The semi-implicit time step is 0.75
days.
Two versions of the surface forcing are employed:
(1) monthly mean seasonalcycle fields plus monthly
mean COADS/FSU anomaliesand (2) monthly mean
TO COADS AND NCEP FORCING
22,257
surface heat flux at each time step with bulk formulae
that useevolvingmodel SST with ECMWF-derived atmosphericfields(air temperature,humidity,cloudiness,
etc.); the daily mean seasonalcycleof heat flux is then
saved(averagedoverthe last 10 yearsof a 99-yearspinup) and subsequentlyused as specifiedforcingduring
the anomalously forced hindcasts.
Anomalousfields of monthly mean wind stress,total
surface heat flux, and TKE are then added to the respective monthly mean seasonal cycles. Because there
is no SST feedback to any of the anomalous forcing
fields, the model is not constrained to reproduce the
observedtemperature variations (as could be achieved
by adaptinga Haney[1971]or Barnier et al. [1995]formulation). Becauseof the lack of data in the tropicswe
usethe COADS/FSU blendwind stresses(and TKE eset al. [1994]. The monthlymean wind stressclima- timates) as describedin section2. A similar strategy is
tology is derived from a combination of monthly mean employedby Putman et al. [2000]to improvethe tropEuropean Centre for medium-RangeWeather Forecasts ical wind stressesof the NCEP fields, but here we use
in their originalform. Specifiedsur(ECMWF) midlatitude fields and monthly mean the NCEP stresses
Hellerman-Rosensteintropical climatology. The monthly face freshwater flux anomalies are excluded from both
mean seasonalcycle climatology of turbulent kinetic hindcasts becauseof inadequate rainfall observations in
energy (TKE) input to the mixed layer is estimated COADS and unreliable precipitation estimates from the
from the samedata sets [Oberhuber,
1993]. The sur- NCEP reanalysis[e.g.,Janowiaket al., 1998]. The conface freshwaterflux is representedas a combination of tinuous relaxation to annual mean Levitus salinity, howobservedmonthlymeanrainfall [Legatesand Willmort, ever, implies that freshwater flux anomaliesdo occur in
1992],evaporationcomputedby bulk formula,plusa re- the hindcasts; however they have a weak influence on
laxation to the annual mean Levitus salinity field over SST and mixed layer depth anomalies.
Near the equator the anomalousheat fluxes are poorly
30-day timescales. The monthly mean seasonalcycle
climatologyof total surfaceheat flux is computeddur- known because of the many gaps in ship weather reing spin-up(with no anomalousforcing)by determining ports. Moreover, tropical heat fluxes generally serveas
seasonalcycle fields plus monthly mean NCEP anomalies. The monthly mean seasonalcycle forcing is derived
from various sourcesand is the same as used by Miller
150øE 160øWllOøW
(a)
150øE 160øWllOøW
150øE 160øWllOøW
(b)
150øE 160øWllOøW
T.F. AMPLITUDE
(d)
150øE 160øWllOøW
LAG (months)
(e)
(c)
150øE 160øWllOøW
COHERENCE**2
(f)
Figure 6. As in Figure 5 but for surfaceheat fluxes.
22,258
AUAD ET AL.: MODEL RESPONSE
TO COADS AND NCEP FORCING
a dampingmechanism[Liu and Gautier, 1990; Cayan, by computingthe oceanmodel climatologiesoverthree
segmentsof the 1951-1997run from
1992b; Barnett et al., 1991], although Seaget[1989] separatesuccessive
shows these fluxes can sometimes excite SST anomalies
which anomalies are then computed.
_
in certain
locations.
Therefore
unless the wind-driven
model SST reproducedthe observedSST with nearly
perfectfidelity, heat flux anomaliesspecifiedfrom observations would try to damp an SST anomaly that "was
not there". This nonphysicalforcing can then lead to an
excitation of model SST anomalies with opposite sign as
the observed. Hence a physically motivated NewtonJan
dampingis employedwithin a 6ø e-foldingscalearound
the equator. The SST anomaliesthere are dampedback
to the modelSST climatology(averagedfrom the last 10
yearsof the spin-uprun) overtimescalesthat dependon
a damping coefficientdivided by the mixed layer depth
4.2.
Modeled-Observed
SST Comparisons
We next examine the performance of the two mod-
els runs, forcedwith NCEP and COADS/FSU anomalousforcing,in simulatingSST observedin the COADS.
The simple statistical analyses employed here include
monthly mean anomalies for all months of the year
and give a broad idea of the year-roundmodel performance. A more detailed look at the model performance
as a function of season,however, would show that winter month
SST
anomalies
are much better
simulated
than summermonths(asfoundby Miller el al. [1994]),
(followingMiller et al. [1994];seeBarnett et al. [1991]
mainly becauseof the strong forcing signalsin winter
for a map of the coefficient).
The monthlyforcingstrategyof Auad et al. [19984, and the high sensitivityof thin summermixed layersto
errors in the specifiedforcing.
1998b]is used to properly weight the monthly mean
Figure 7 showsthe correlation coefficientsbetween
observed and modeled monthly mean SST anomalies
forcing,followingKillworth [1996]. Miller et al. [1994]
for both runs. The patterns are fairly similar in that
employ simple linear interpolation of the monthly mean
forcing, which yields monthly mean model forcing that
is typically weaker than the true monthly mean obforcing anomalies,which are added to the seasonalcycle
servedforcing.The Killworth[1996]scheme
is a method
for increasing the input monthly mean forcing fields
Correlation
to allow linear interpolation in time to yield correct
monthly mean forcing.
The
ocean model
is run for two cases.
The
N CEP
run (forcedby anomalousNCEP fluxes)extendsfrom
January 1958 to December 1997. The COADS/FSU
run (forcedby anomalousCOADS fluxessupplemented
with FSU and reconstructedtropical stresses)extends
from January 1951 to December 1997. Initial conditions
for those runs are from the ninetieth year of the model
spin-up with climatologicalforcing. Becausethere is no
model SST feedbackto the surfaceheat fluxes(except
in a narrow equatorial band) there is a possibilityof
Coefficients
for SST
(a) COADS OBS. vs. COADS RUN
2;oN
:0o60
20•0
120øE
a drift in the model climatology becausethe model has
not reacheda complete equilibrium with mean flux forc-
160øE
160øW 120øW
80øW
•
(b) COADS OBS. vs. NCEP RUN
ing after 90-100yearsof spin-up. The NCEP run (and
a separaterun with zero forcinganomalies)indeedex-
o60øN•
hibits a small and apparently inconsequentialdrift due
to this effect. The COADS/FSU run, however,exhibits
a larger drift in the model SST climatology.
Its origin appears to be mainly associated with
COADS
heat flux anomalies
which have interdecadal
variations that are not present in the NCEP heat fluxes.
These
interdecadal
COADS
heat
flux
anomalies
are
likely erroneouslylarge becausethe "reduced thermal
damping" effectsof air-sea coupling should effectively
shutoffheatfluxesat very low-frequencies
[e.g.,Battisti
et al., 1995;Barsugliand Battisti, 1998]. Attempts to
o
20øS
40øS•
60ow
20ow
soow
remove these spurious trends and decadal variations a
Figure 7.
Correlation coefficientsbetween obpriori by trend removaland high-passfiltering[Kaylot, servedSST (COADS) and modelSST from the (top)
1977] the COADS heat flux forcingfieldsfail to pre- COADS/FSU-forced
runandfromthe (bottom)NCEP-
serve the true low frequency variability in the forcing forced run. The fields were smoothed with a 1000 km
fields. So their effect is instead removed a posterJori radius filter.
AUAD ET AL.: MODEL RESPONSE TO COADS AND NCEP FORCING
STANDARD
DœVIA TION RATIO$
(a) COAD$ S$T OB$, vs.
MODEL RUN (COAD$)
60 øN
40ø17
22,259
than those of Miller et al. [1994] and Cayan et al.
[1995]becauseof severalfactors. This time intervalis
longerthan the 1970-1988periodmodeledin theseprevious studiesand henceincludestime periodswith less
confidentlyobservedforcing and responsefields. For
example,the subset1970-1988winter midlatitudeSST,
from the previousmodel simulations,matchesthe cor-
20 øN
relationsdiscussed
by Miller et al. [1994]muchmore
o
closely. However,the 1970-1988year-roundSST correlationsare smallerthan thoseof Cayanet al. [1995]
in the midlatitudes,indicating the summermonth periods are modeled here with less fidelity. This seems
to be causedby the present model's previouslymen-
20øS
tioned tendency to produce eddies due to instabilities
40øS
120øE
160øE
160øW 120øW
80øW
(b) COAD$ $$T OB$. vs.
MODEL RUN (NCEP)
60 øN
40W
20øN
in the west xvind drift.
An ensemble
of hindcast
runs
could drive down this eddy noise to better establish the
forcedpart of the response,but that is beyondour computational means at this time.
The ratios of standard deviations(observed/model)
for SST anomalies are shown in Figure 8.
The
COADS/FSU run tendsto underestimateSST anomaly
variance in the tropical Pacific and in the subpolar region of the North Pacific. The NCEP run overestimates
SST anomalyvariancethroughoutthe Pacific exceptin
a small region in the central equatorial Pacific. Both
o
runs seriously overestimate SST anomaly variance in
the subtropicsof both hemispheres.
20øS
As was mentionedpreviously,errorsin both the forcing
and validating data may be large in these regions
40 øS
due to limited observations, and errors in the model
120øE 160øE 160øW 120øW 80øW
may be large due to the large mean model mixed layer
depth in this region.
Figure 8. Ratios of the standard deviations between
observations
of SST anomalies
from COADS (m•mera- A feature not evident in Figures 7 and 8 is that, on
tor) andthe modelSST anomalies
(denominator)
ob- interannual timescales,the NCEP run producesE1 Nifio
tained from the (top) COADS/FSU-forcedrun and and La Nifia eventsthat are roughly half the observed
from the (botttom) NCEP-forced r•m. The fields were amplitude. This occurs in spite of the NCEP fluxes
smoothed with a 1000 km radius filter.
containing stronger than observed interseasonal variations. The weak interannual
they show the best correlations in the central and eastern tropical Pacific and the central and eastern midlat-
wind stresses of the NCEP
analysismotivated Putman et al. [2000]to blend the
FSU tropical stresseswith the NCEP stressesto provide a more useful forcing dataset for future studies.
COADS
likewise
suffers from weak interannual
wind
itude North Pacificnorth of 30øN. The South Pacific, stress representation, hence motivating the use of a
wherethere are not as many observations
of SST, wind blended COADS/FSU wind-stressforcing dataset by
stresses,and surfaceheat fluxes,exhibitsmuch poorer Miller et al. [1994]and in this study.
correlations. The poor correlationsseen in the western North Pacific, centered at 20øN between 160øE and
180ø, also may be due to the lack of observationsin
4.3. Modeled-Observed
Heat Storage
Comparisons
that area. However,the modelbulk mixedlayeris very
deepin that region(exceeding
150 m in winter) which
We next examine the performance of the two modwasfound,in separatemodelsensitivityexperiments,to elsruns,forcedwith NCEP and COADS/FSU anomabe due to the combinedeffectsof Ekmandownwelling lousforcing,in simulatingheat storage,here definedas
andsurfacecoolingduringthe entraining
seasons
[De ocean temperature integrated over the upper 400 m of
Szoeke,1980]. The excessively
deepmixedlayermay the watercolumn(scaledby densityandheatcapacity).
distributethe surfaceforcingtoo deepinto the water Observedheat storageis takenfrom the White [1995]
column yielding poorer results.
The correlations between model and observed SST
analysis of available subsurfacetemperature observa-
tions since1955. Chepv.
rin and Carton [1999]discuss
(Figure 7) for the COADS/FSU-forcedrun are loxver potential deficitsof this analysis. The simple statisti-
22,260
AUAD
ET AL.: MODEL
RESPONSE
TO COADS AND NCEP
FORCING
CORRELATiON
COEFFICIENTS
FOR HEAT STORAGES
JEDAC
Obs. vs COADS
run
60W
40 øN
20W
o
20øS
40øS
120øE
160•E
JEDAC
160øW
120øW
80øW
Obs. vs NCEP run
60W
20W
o
20øS
40øS
120øE
160øE
160øW
120øW
80øW
Figure 9. As in Figure 7 but for heat storages.
cal analyseshere include monthly mean anomaliesfor much greater area than found by A uad et al.
all monthsof the year to give a synopsisof the year- during the 1970-1988 time interval for heat storageon
round model performance.Heat storageincludescom- interannual and decadal timescales. Thus, the interponentsof responselocally forcedby diabatic processes seasonalvariations of heat storage, which are inade(evidenced
mainlyasSSTanomalies)
andadiabaticpro- quately simulated, appear to be dominating the corcesses
(seenasthermocline
heave)asdiscussed
by Auad relation maps in Figure 9.
Figure 10 displays the ratios of standard deviations
et al. [1998a,1998b].Errorsin the estimationof wind
stresscurl forcing, which largely controlsthe thermo- of heat storage(observations/model).
Alongthe tropicline heave,may be large and may stronglyinfluence cal strip, where significantcorrelationsoccurredin both
the model responseduring periodsof limited observa- runs, heat storage variance is nearly the same as obtions.
servedin the western tropical Pacific but greater than
The correlations(zero time lag) betweenobserved observedin the eastern tropical Pacific. In the subpoand modeledheat storageare shownin Figure9. Max- lar region, which showedsignificantcorrelationsin the
imum zero-lag correlations are obtained in the warm NCEP run, the model seriouslyoverestimatesthe heat
pool of the western tropical Pacific and in the eastern storage variability.
tropical Pacific.
The NCEP run, unlike the
The model heat storage variations in the western
COADS/FSU run, exhibitssignificantcorrelationsfor tropical Pacific warm pool area are better correlated
heat storagein the subpolararea, north of 40øN. The to observations than are SST variations. Since this remidlatitude regionsof insignificantcorrelationscovera gion is dominated by local, rather than remote wind
AUAD
ET AL.:
MODEL
RESPONSE
TO COADS
AND
NCEP
FORCING
22,261
forcing[Schneider
et al. 1999],a reasonably
accurate the time period 1970-1988, which has the highest denrepresentationof the wind stresscurl is responsiblefor
the significant
model-datacorrelations.However,wind
stresscurl from COADS/FSU and NCEP show surprisinglylow correlationsin the warm pool area even
sity of subsurfacedata. Figure 9 showslow model-data
heat storagecorrelation in midlatitudes occurswhen including all climatic timescalesfrom 1958-1997. The reasonsfor this include the intrinsic variability of the ocean
thoughsimulatedheat storages
from both runsagree model, sparse subsurfaceoceanic data before 1970, inreasonablywell with observations.It appearsthat a accurate wind stresscurl forcing, and the inclusionof
fewenergetic
frequencies
of the windstress(associatedinterseasonaltimescales. Indeed, Figure 11 showsthat
with E1Nifio) in the westerntropicaland subtropical the correlation between COADS and NCEP wind stress
Pacificareadrivethemodeled
fieldsthat areresponsible curl in the North Pacific is significant but does not exceed 0.7.
for this agreement
with observed
heat storage.
In the centralNorthPacific,modelheatstoragevariations are poorly correlatedwith observations,while
modelSST anomaliesare significantlycorrelatedwith
observations.In this region, COADS and NCEP sur-
5.
Summary
and Discussion
A comparisonof COADS (blendedwith tropical FSU
facefluxesare alsowell correlatedand directlyforce wind stresses)and NCEP surfaceflux anomaliesover
modelSSTanomalies
throughvarious
processes
[Miller the Pacific Ocean on climatic timescales from 1958et al., 1994]. It is surprisingto find that modelheat 1997 was executed. The two data sets were then tested
storageresponseto thesetwo correlatedforcingsdoes as forcing functions in separate simulations of Pacific
not matchobservations
(exceptin the subpolargyrefor Ocean variability using an ocean model.
Wind stress anomalies from the two data sets are
the NCEPrun). Auadet al. [1998a,1998b]
showed
with
an earlier version of this model that model thermocline
well correlated in the midlatitude extratropics, more
dynamicsare adequateto simulateheat storagefluc- so in the highly sampled North Pacific than in the
tuations in midlatitudes that are significantly coherent
South Pacific. In the tropics and subtropics, low cor-
with observations if one focuses on the interannual and
relations
interdecadal timescales. But that study was limited to
sets;somewhat higher correlationsoccurredin the western part of these regions because of denser sampling
than in the eastern part. The amplitudes of the stress
STANDARD
DEVIATION
RATIO$
FOR HœA T STORAGES
JEDAC
Obs./COAD$
eun
variations
were found between
of the two data
the two wind stress data
sets are similar
in midlati-
tudes. In the tropics, NCEP wind stressesare weaker
than the COADS/FSU stresses,especiallyon interan-
øN
nual timescales, which is problematic for E1 Nifio simulations.
40 øN
Surface heat flux anomalies from the two data sets are
well correlated
20W
on interannual
and shorter
timescales
in
the North Pacific Ocean north of 20øN, but they are
poorly correlated elsewhere and on decadal timescales.
In the extratropics the amplitudes of the heat flux variations of the two data sets are comparable, but in the
tropics the NCEP heat fluxes are weaker than COADS.
o
20øS
40øS
Errors in the estimationof the COADS/FSU fluxes
120øE 160øE 160øW120øW
JEDAC
Obs./NCEP
80øW
run
60
due to limited data sampling cannot be ruled out as the
primary explanation for the low correlations found in
this study. Indeed, areas with a high density of observations usually exhibit significant correlation between
COADS/FSU and NCEP fluxes. This strongly suggests that data density controls the comparability and
usefulnessof the forcing fields. This idea is supported
40W
by a frequencydomainanalysis(includingperiodsfrom
3 monthsto 10 years) that showedcoherencyis independent of frequencyin the geographicalareas where
20W
o
COADS/FSUandNCEP surfacefluxesaresignificantly
20 øS
correlated.
Ocean simulations were then used to test the sensitiv40øS
120øE 160øE 160øW 120øW 80øW
Figure 10. As in Figure8 but for heat storages.
ity of the oceanicresponse
to the two differentforcing
data sets. One simulation used the surface flux anoma-
lies specifiedfrom NCEP (1958-1997),and the other
usedthoseof COADS,/FSU (1951-1997).
22,262
AUAD ET AL.: MODEL RESPONSE TO COADS AND NCEP FORCING
Wind Stress Curl COADS/NCEP
(a) StandardDeviationRatios
60 øN
40 øN
20W
o
20øS
40øS
120øE
160øE
160øW
120øW
80øW
(b) CorrelationCoefficients
60 øN
40W
20øN
o
20øS
40øS
120øE
160øE
160øW
120øW
80øW
Figure 11. Wind stresscurlcomparison.
(top) standard
deviation
ratiosof COADS/FSUto
NCEP windstresscurlsand (bottom)correlation
coefficients.
The fieldsweresmoothed
with
1000 km radius filter.
The midlatitude SST hindcasts were superior when
tions of modeled and observed tropical heat storage
usingthe N CEP fluxes,suggesting
that the N CEP data variationsincludingall climatictimescaleswerefound
are eminentlysuitablefor oceanhindcastprocessstud- for both hindcasts.However,only the NCEP simulation
correlations
with observed
midlaties in those regions. Tropical SST anomalieshad sim- exhibitedsignificant
ilar skill levels for the two hindcasts when consideritudeheat storage,andthat waslimited to a portionof
ing all climatic timescales. However, on interannual the North Pacificsubpolargyre and easternboundary.
timescalesthe NCEP tropical wind stressesare weaker SinceCOADS and NCEP fluxeswerecorrelated(r=-0.7)
in the two forcing
than COADS/FSU stresses,consequently
yieldingin- in this sameregion,minordifferences
terannual tropical SST anomaliesthat are muchweaker functionssets can lead to substantially different model
The maindiscrepancy
bethan observed.Adjusting NCEP tropical wind stresses oceanheatstorageresponses.
to alleviate this problem is presentlythe most feasible tweenmodeledand observedheat storagecan be traced
strategy,as Putnam et al. [2000]showedusingFSU to inadequatecorrelation(r=0.5) betweenNCEP and
stresses.
COADS wind stress curl anomalies which dominate the
variationsin the subpolarNorth
Heat storageanomaliesin the two simulationswere drivingof thermocline
Miller et al., 1998;
also comparedwith observations. Significant correla- Pacific[Auadet al., 1998a,1998b;
AUAD
ET AL.:
MODEL
RESPONSE
TO COADS
AND
NCEP
FORCING
22,263
Deseret al., 1999].Apparently,the consistentdynam- tional ScienceFoundation under grants OCE97-11265 and
ics of the NCEP reanalysismodelefficientlyorganizes OCE00-82543, and by the National Aeronautics and Space
the input observationsto yield a better estimateof wind
stress curl than is obtainable from COADS observations
alone.
Problemsarise in explainingdecadal-scaleCOADS
heat flux variationsand in removingtheir potentialeffects on oceanmodel response.Some of the fundamen-
Administration under grant NAG5-8292. The views expressedherein are those of the authors and do not necessarily reflect the views of NOAA or any of its sub-agencies.
We are grateful to Warren White for providing us with the
JEDAC dataset of subsurfacetemperature observationsthat
made the modeled-observedheat storage comparisonspossible.
We
thank
Emelia
Bainto
for
her
valuable
techni-
cal support. Josef Oberhuber generously provided modeltal COADS marineweatherobservations
(wind speed, ing advice and accessto his latest codes, for which we are
SST, etc.) that are usedto estimate the surfacefluxes grateful. Many important suggestionsthat greatly improved
are knownto containsuspicious
low-frequencyvariabil- the manuscript were provided in detailed reviews by Mike
ity due to changesin observationalprocedures,ship Alexander, an anonymous referee, and Mike Spall.
characteristics,
and instrumentation[Michaudand Lin,
1992; The main discrepancybetween modeled and observed heat storage can be traced to inadequate corre-
References
Auad, G., A. J. Miller, and W. B. White, Simulation of
lation (r=0.5) betweenNCEP and COADS wind stress
heat storages and associatedheat budgets in the Pacific
Ocean, 1, E1 Nifio-Southern Oscillation timescale, J. Geocurl anomalieswhich dominate the driving of thermophys. Res., 103, 27,603-27,620, 1998a.
clinevariationsin the subpolarNorth Pacific [Auad et
Auad, G., A. J. Miller, and W. B. White, Simulation of
al., 1998a,b;Miller et al., 1998;Deser et al., 1999]. Apheat storages and associatedheat budgets in the Pacific
parently the consistent dynamics of the NCEP reanalOcean, 2, Interdecadal timescale, J. Geophys. Res., 103,
27,621-27,635, 1998b.
ysis model efficiently organizesthe input observations
to yield a better estimate of wind stress curl than is Barnett, T. P., Long-term trends in surface temperature
over the oceans, Mon. Weather Rev., 112, 303-312, 1984.
Barnett, T. P., M. Latif, E. Kirk, and E. Roeckner, On
Problems arise in explaining decadal-scaleCOADS
ENSO physics, J. Clim., 4, 487-515, 1991.
heat flux variations and in removing their potential Barnier, B., L. Siefridt, and P. Marchesiello, Thermal forceffects on ocean model response. Some of the funing for a global ocean circulation model using a 3-year
climatology of ECMWF analysis, J. Mar. Syst., 6, 363damental COADS marine weather observations(wind
380, 1995.
speed,SST, etc.) that are usedto estimatethe surface
obtainable
from COADS
observations
alone.
fluxes are known to contain suspiciouslow frequency
variability due to changesin observationalprocedures,
Barsugli, J. J., and D. S. Battisti, The basic effects of
atmosphere-oceanthermal coupling on midlatitude variability, J. Atmos. Sci., 55, 477-493, 1998.
shipcharacteristics,
instrumentation[MichaudandLin, Battisti, D. S., U.S. Bhatt, and M. A. Alexander, A modeling study of the interannual variability in the wintertime
1992; Ward and Hoskins,1996]. The NCEP decadal-
North Atlantic Ocean, J. Clim., 8, 3067-3083, 1995.
Bennett, A. F., B. S Chua, D. E. Harrison, and M. J.
McPhaden, Generalized inversion of tropical Atmospherefrom coupledocean-atmosphere
modelstudies[Barsugli
Ocean data and a coupled model of the tropical Pacific,
J. Clim., 11, 1768-1792, 1998.
and Battisti, 1998;Bhatt et al., 1998].
The results of this study suggest that the NCEP Bennett, A. F., B. S Chua, D. E. Harrison, and M. J.
McPhaden, Generalized inversion of Tropical Atmospheresurfacefluxes overall yielded a better basinwide ocean
scaleheat fluxes had much smaller amplitude than those
of COADS which is consistentwith what is anticipated
hindcastthan the COADS/FSU fluxes,exceptfor the
Ocean (TAO) data and a coupled model of the tropical
Pacific, part II,: The 1995-96 La Nifia and 1997-98 E1
Nifio, J. Clim., 13, 2770-2785, 2000.
Betts, A. K., S. Y. Hong, and H. L. Pan, Comparison
of NCEP-NCAR reanalysis with 1987 FIFE data, Mon.
Weather Rev., 124, 1480-1498, 1996.
seriousproblems with weak interannual variability in
the tropics. Denser and more frequent observationsof
surfacevariablesfor deriving air-sea fluxes are therefore needed in many key areas of the Pacific Ocean,
Bhatt, U.S., M. A. Alexander, D. S. Battisti, D. D. Houghton,
especially the eastern tropical Pacific and the western
and L. M. Keller, Atmosphere-ocean interaction in the
subtropical Pacific. These data can then be used in atNorth Atlantic: Near-surface climate variability, J. Clim.,
mosphericanalysesto derive consistentlong-term forc11, 1615-1632, 1998.
ing functionsfor testing ocean models and diagnosing Blanc, T. V., Variation of bulk-derivedsurfaceflux, stability,
andM roughnessresultsdue to the useof differenttransfer
climate variations. Sustainedbasin-scalelong-term obcoefficient schemes,M J. Phys. Oceanogr. 15, 650-669,
serving programs using new techniquesof remote sens1985.
ing and in situ measurements,such as planned under Bony, S., Y. Sud, K. M. Lau, J. Susskind,and S. Saha,
the the Pacific BasinwideExtended Climate Study, are
Comparisonand satellite assessment
of NASA/DAO and
NCEP-NCAR reanalysesovertropical ocean: Atmospheric
the best strategy for achievingthis goal.
Acknowledgments.
This research is funded in part by the National Oceanic
AtmosphericAdministration, U.S. Department of Commerce,
under grants NA77RJ0453 (Experimental Climate Prediction Center), NA47GP0188 (Consortiumfor Ocean'sRole in
Climate), NA66GP0274(PaleoclimateProgram),by the Na-
hydrology and radiation, J. Clim., 10, 1441-1462, 1997.
Cayan, D. R., Variability of latent and sensibleheat fluxes
estimated using bulk formulae, Atmos. Ocean, 30, 1-42,
1992a.
Cayan, D. R., Latent and sensible heat flux anomalies over
the northern oceans: Driving the sea surfacetemperature,
J. Phys. Oceanogr., 22, 859-881, 1992b.
22,264
AUAD ET AL.: MODEL
RESPONSE
TO COADS AND NCEP FORCING
Cayan, D. R., A. J. Miller, T. P. Barnett, N. E. Graham,
J. N. Ritchie, and J. M. Oberhuber, Seasonal-interannual
fluctuations in surface temperature over the Pacific: Effects of monthly winds and heat fluxes, in Natural Climate
Variability on Decadal-to-Century Time Scales, 133-150
pp., edited by Nat. Acad. Press, Washington, D.C., 133150, 1995.
1996.
Chepurin, G. A., and J. A. Carton, Comparison of retrospective analyses of the global ocean heat content, Dyn.
Atmos. Oceans, 29,, 119-145, 1999.
da Silva, A.M., C. C. Young, and S. Levitus, Atlas of Surface Marine Data, Vol. 3, Anomalies of Heat and Momentum Fluxes, NOAA Atlas Set., NESDIS 7, Natl. Oceanic
and Atmos. Admin., Washington, D.C., 413 pp., 1994.
Davis, R. E., Predictability of sea surface temperature and
sea level pressureanomalies over the North Pacific Ocean,
J. Phys. Oceanogr., 6, 249-266, 1976.
Deser, C., M. A. Alexander, and M. S. Timlin, Evidence
for a wind-driven
Killworth, P., Time interpolation of forcing fields in ocean
models, J. Phys. Oceanogr., •, 136-143, 1996.
Kleeman R., R. A. Colman, N. R. Smith, and S. B. Power,
A recent change in the mean state of the Pacific basin
climate: Observational evidence and atmospheric and
oceanic responses, J. Geophys. Res., 101, 20,483-20,499,
intensification
of the Kuroshio
Current
Extension from the 1970s to the 1980s, J. Clim., 12, 16971706, 1999.
De Szoeke, R. A., On the effects of horizontal variability of
wind stresson the dynamics of the ocean mixed layer, J.
Phys. Oceano9r., 10, 1439-1454, 1980.
Fevrier, S., C. •¾ankignoul, J. Siryen, M. K. Davey, P.
Delecluse, S. Ineson, J. Macias, N. Sennechael, and D.
B. Stephenson, A multivariate intercomparison between
three oceanic GCMs using observedcurrent and thermocline depth anomaliesin the tropical Pacific during 19851992, J. Mar. Syst., 2•, 249-275, 2000.
Frankignoul, C., C. Duchene, and M. A. Cane, A statistical approach to testing equatorial ocean models with
observed data, J. Phys. Oceanogr., 19, 1191-1207, 1989.
Frankignoul, C., S. Fevrier, N. Sennechael,J. Verbeek, and
P. Braconnot, An intercomparison between four tropical
ocean models- Thermocline variability, Tellus, •7, 351364, 1995.
Giese, B. S., and J. A. Carton, Interannual and decadal variability in the tropical and midlatitude Pacific, J. Clim.,
12, 3402-3418, 1999.
Goldenberg, S. B., and J. J. O'Brien, Time and space variability of tropical Pacific wind stress, Mon. Wea. Rev.,
109, 1190-1207, 1981.
Halliwell, G. R., Simulation of North Atlantic decadal and
multidecadal winter SST anomalies driven by basin-scale
atmospheric circulation anomalies, J. Phys. Oceanogr.,
28, 5-21, 1998.
Haney, R. L., Surface thermal boundary condition for ocean
circulation models, J. Phys. Oceanogr., 1, 241-248, 1971.
Harrison, D. E., B. S. Giese, and E. S. Sarachick, Mecha-
nismsof SST changein the equatorialwave-guideduring
the 1982-83 ENSO, J. Clim., 3, 173-188, 1990.
Isemer, H., and L. Hasse, The Bunker Climate Atlas of
the North Atlantic Ocean, Vol. 2, Air-sea interactions,
Springer Verlag, New York, 1987.
Iwasaka, N., and J.M. Wallace, Large-scale air-sea interaction in the Northern Hemisphere from a view point of
variations of surface heat flux by SVD analysis, J. Meteorol. Soc. Jpn., 11, 781-794, 1995.
Janowiak, J. E., A. Gruber, C. R. Kondragunta, R. E.
Livezey, and G. J. Huffman, A comparison of the NCEPNCAR reanalysisprecipitationand the GPCP rain gauge-
Legates, D. R., and C. J. Willmort, A comparisonof GCMsimulated and observed mean January and July precipitation, Global Planet. Change, 97, 345-363, 1992.
Liu, W. T., and C. Gautier, Thermal forcing on the tropical
Pacific from satellite data, J. Geophys. Res., 95, 13,20913,217, 1990.
Luksch, U. and H. V. Storch, Modeling the low-frequency
sea surface temperature variability in the North Pacific,
J. Clim., 5, 893-906, 1992.
Lysne, J., P. Chang, and B. Giese, Impact of the extratropical Pacific on equatorial variability, Geophys. Res. Left.,
2•, 2589-2592, 1997.
Marshall, G. J., and S. A. Harangozo An appraisal of
NCEP/NCAR reanalysisMSLP data viability for climate
studies in the South Pacific, Geophys. Res. Left., 27,
3057-3060, 2000.
Michaud, R., and C. A. Lin, Monthly summaries of merchant ship surface marine observations and implications
for climate variability studies, Clim. Dyn., 7, 45-55, 1992.
Miller, A. J., and N. Schneider,Interdecadal climate regime
dynamics in the North Pacific Ocean: Theories, observations and ecosystemimpacts, Progr. Oceanogr., •7, 355379, 2000.
Miller, A. J., T. P. Barnett, and N. E. Graham, A comparison of some tropical ocean models - Hindcast skill and E1
Nifio evolution, J. Phys. Oceanogr., 23, 1567-1591, 1993.
Miller, A. J., D. R. Cayan, T. P. Barnett, N. E. Graham, and
J. M. Oberhuber, Interdecadal variability of the Pacific
Ocean: Model responseto observedheat flux and wind
stress anomalies, Clim. Dyn., 9, 287-302, 1994.
Miller, A. J., W. B. White, and D. R. Cayan, North Pacific thermocline variations on ENSO time scales,J. Phys.
Oceanogr., 27, 2023-2039, 1997.
Miller, A. J., D. R. Cayan and, W. B. White, A westwardintensified decadal change in the North Pacific thermocline and gyre-scale circulation, J. Clim., 11, 3112-3127,
1998.
Morrissey, M. L., An evaluation of ship data in the equatorial western Pacific, J. Clim., 3, 99-112, 1990.
Nakamura, H., G. Lin, and T. Yamagata, Decadal climate
variability in the North Pacific during the recent decades,
Bull. Am. Meteorol Soc., 78, 2215-2225, 1997.
Oberhuber, J. M., Simulation of the Atlantic circulation
with a coupled sea ice - Mixed layer isopycnal general
circulation model, part I, Model description, J. Phys.
Oceangr., 23, 808-829, 1993.
Parrish, R. H., F. B. Schwing, and R. Mendelsohn, Midlatitude wind stress: The energy sourceof climatic shifts
in the North Pacific Ocean Fish. Oceanogr., 9, 224-238,
2000.
Pavia, E., and J. J. O'Brien, Weibull statistics of wind speed
over the ocean, J. Clim. Appl. Meteorol., 25, 1324-1332,
1986.
Putman, W. M., D. M. Leglet, and J. J. O'Brien, Interannual variability of synthesized FSU and NCEP-NCAR
satellite combined dataset with observational error considreanalysis
pseudostressproducts over the Pacific Ocean,
erations, J. Clim., 11, 2960-2979, 1998.
J. Clim., 13, 3003-3016, 2000.
Kalnay, E., et al., The NMC/NCAR reanalysisproject, Bull.
Qiu, B., Interannual variability of the Kuroshio Extension
Am. Meteorol. Soc., 77, 437-471, 1996.
system and its impact on the wintertime SST field, J.
Kaylot R. E., Filtering and decimationof digital time series,
Phys. Oceangr., 30, 1486-1502, 2000.
Tech. Rep. BN 850, 42 pp., Inst. for Phys. Sc. and
Technol., Univ. of Md., 42 pp., 1977.
Ramage, C. S., Can shipboard measurementsreveal secular
AUAD ET AL.: MODEL RESPONSE TO COADS AND NCEP FORCING
22,265
changesin air-seaheat flux?, J. Clim. Appl. Meteorol., Waliser, D. E., Z. Shi, J. R. Lanzante, and A. H. Oort,
23, 187-193, 1984.
The Hadley circulation: AssessingNCEP/NCAR reanalRamage, C. S., Secularchangesin reported surfacewinds
ysis and sparsein situ estimates, Clim. Dyn., 15, 719-735,
1999.
over the oceans,J. Clim. Appl. Meteorol, 26, 525-528,
1987.
Ward, M. N., and B. J. Hoskins, Near-surface wind over the
Schneider,N., S. Venzke, A. J. Miller, D. W. Pierce, T.
Global Ocean 1949-1988, J. Clim., 9, 1877-1895, 1996.
P. Barnett, C. Deser, and M. Latif, Pacific thermocline Weare, B.C., Uncertainties in estimates of surface heat
bridge revisited, Geophys. Res. Left., 26, 1329-1332,
fluxes derived from marine reports over tropical and sub1999.
tropical oceans, Tellus, dl, 357-370, 1989.
Scott, J. D., and M. A. Alexander,Net shortwavefluxesover Weare, B.C., Comparison of NCEP-NCAR cloud radiative
the ocean,J. Phys. Oceangr.,2g, 3167-3174,1999.
forcing reanalyses with observations, J. Clim., 10, 2200Seager,R., Modeling tropical Pacific sea-surfacetempera2209, 1997.
ture- 1970-87, J. Phys. Oceanogr.,19, 419-434, 1989.
White, W. B., Design of a global observingsystem for gyreSeaget,R., Y. Kushnir, M. Visbeck, N. Naik, J. Miller, G.
scaleupper ocean temperature variability, Prog. Oceanogr.
Kralmann, and H. Cullen, Causesof Atlantic Ocean cli36, 169-217, 1995.
mate variability between1958 and 1998, J. Clim., 13,, Woodruff, S. D., R. J. Slutz, R. L. Jenne, and P.M. Steurer,
2845-2861, 2000.
A comprehensiveocean-atmosphere data set, Bull. Am.
Meteorol. Soc., 68, 1239-1250, 1987.
Wright, P. B., Problems in the use of ship observations for
J. Phys. Oceanogr.,2•, 2288-2305, 1994.
the study of interdecadal climate changes,Mon. Weather
Stockdale,T. N., A. J. Busalacchi,D. E. Harrison,and R.
Rev., 11•, 1028-1034, 1986.
Seaget,Oceanmodelingfor ENSO, J. Geophys.Res., 103, Xie, S. P., T. Kunitani, A. Kubokawa, M. Nonaka, and
14,325-14,355, 1998.
S. Hosoda, Interdecadal thermocline variability in the
Tanimoto,Y., N. Iwasaka,and K. Hanawa, Relationships North Pacific for 1958-1997: A GCM simulation, J. Phys.
betweensea surfacetemperature,the atmosphericcircuOceanogr., 30, 2798-2813, 2000.
lation and air-seafluxeson multiple time scales,J. Mete- Yang, S. K., Y. T. Hou, A. J. Miller, and K. A. Campana,
orol. Soc. Jpn., 75, 831-849, 1997.
Evaluation of the earth radiation budget in NCEP-NCAR
Taylor, P. K., The determination of surface fluxes of heat
reanalysis with ERBE, J. Clim., 12, 477-493, 1999.
Sennechael,
N., C. Frankignoul,and M. A. Cane, An adaptive procedurefor tuning a seasurfacetemperaturemodel,
and water by satellitemicrowaveradiomerry and in situ
measurements,in Large-scaleOceanographicExperiments
and Satellites, edited by C. Gautier and M. Fieux, D.
Reidel, NATO ASI SeriesC, Sciences,
vol. 128 pp., 223246, 1984.
Trenberth, K. E., and C. J. Guillemot, Evaluation of the atmospheric moisture and hydrological cycle in the
G. Auad, D. Cayan, A. Miller, and J. Roads, Climate Research Division, Scripps Institution of Oceanography, La Jolla, CA, 92093. (e-mail: guillo@ucsd.edu;
dcayan@ucsd.edu;
ajmiller@ucsd.edu;jroads@ucsd.edu)
NCEP/NCAR reanalyses,
Clim. Dyn., 1•, 213-231,1998.
Trenberth, K.E., and J.W. Hurrell, Decadal atmosphereocean variations in the Pacific, Clim. Dyn., 9, 303-319.
1994.
(ReceivedJanuary 28, 2000; revised January 17, 2001;
acceptedJanuary 18, 2001.)