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Low-Frequency Pycnocline Variability in the Northeast Pacific
ANTONIETTA CAPOTONDI AND MICHAEL A. ALEXANDERNOAA/CIRES Climate Diagnostics Center, Boulder, Colorado

CLARA DESER
National Center for Atmospheric Research,* Boulder, Colorado

ARTHUR J. MILLER
Scripps Institution of Oceanography, La Jolla, California(Manuscript received 14 January 2004, in final form 23 November 2004)
ABSTRACTThe output from an ocean general circulation model (OGCM) driven by observed surface forcing is usedin conjunction with simpler dynamical models to examine the physical mechanisms responsible for interannual to interdecadal pycnocline variability in the northeast Pacific Ocean during 1958–97, a period that
includes the 1976–77 climate shift. After 1977 the pycnocline deepened in a broad band along the coast andshoaled in the central part of the Gulf of Alaska. The changes in pycnocline depth diagnosed from themodel are in agreement with the pycnocline depth changes observed at two ocean stations in different areasof the Gulf of Alaska. A simple Ekman pumping model with linear damping explains a large fraction of
pycnocline variability in the OGCM. The fit of the simple model to the OGCM is maximized in the central
part of the Gulf of Alaska, where the pycnocline variability produced by the simple model can account for⬃70%–90% of the pycnocline depth variance in the OGCM. Evidence of westward-propagating Rossby
waves is found in the OGCM, but they are not the dominant signal. On the contrary, large-scale pycnocline
depth anomalies have primarily a standing character, thus explaining the success of the local Ekman
pumping model. The agreement between the Ekman pumping model and OGCM deteriorates in a large
band along the coast, where propagating disturbances within the pycnocline, due to either mean flow
advection or boundary waves, appear to play an important role in pycnocline variability. Coastal propagation of pycnocline depth anomalies is especially relevant in the western part of the Gulf of Alaska, where
local Ekman pumping-induced changes are anticorrelated with the OGCM pycnocline depth variations. The
pycnocline depth changes associated with the 1976–77 climate regime shift do not seem to be consistent with
Sverdrup dynamics, raising questions about the nature of the adjustment of the Alaska Gyre to lowfrequency wind stress variability.

1. Introduction
The oceanic pycnocline, a subsurface layer characterized by large vertical density gradients, can be viewed
as the interface between the surface ocean mixed layer
and the deep ocean. Changes in pycnocline depth may* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Antonietta Capotondi, NOAA/CIRES Climate Diagnostics Center, R/CDC1, 325 Broadway,
Boulder, CO 80305-3328.
E-mail: Antonietta.Capotondi@noaa.gov© 2005 American Meteorological Society

JPO2757

be indicative of changes in upwelling, a process that
influences the exchange of properties between the deep
and upper ocean. Large areas of the northeast Pacific
Ocean are characterized by a fresh and well-mixed surface layer, separated from the deeper ocean by large
salinity gradients, or halocline. At high latitudes, where
temperatures are low, salinity has a dominant influence
on density, particularly in winter, so that pycnocline
depth is very similar to winter mixed layer depth(Freeland et al. 1997). Thus, understanding the processes governing pycnocline variability can also help
understand the changes in mixed layer depth, a quantity that has a large influence upon biological productivity (Polovina et al. 1995; Gargett 1997).
What processes control pycnocline depth variability

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in the northeast Pacific? Using subsurface observations
over the period 1968–90, Lagerloef (1995) examinedinterannual and decadal variations of the dynamic
height field, which is closely related to pycnocline topography, over the Gulf of Alaska. The dominant modeof variability of the dynamic height field, identified
through empirical orthogonal function (EOF) analysis,
captured the 1976–77 climate shift and was well correlated with other climate indices. A simple model for theevolution of the dynamic height field, in which theforcing was supplied by the local Ekman pumping, anddissipation was modeled with a linear damping term,
appeared to reproduce a large fraction of the low-frequency dynamic topography variations. However, thehindcast of dynamic heights underestimated the observed variability in the western half of the gyre, a result that Lagerloef (1995) attributed to the presence of
westward propagating baroclinic Rossby waves.
Cummins and Lagerloef (2002, CL02 hereinafter)
used the same Ekman pumping model described by
Lagerloef (1995) to examine the structure of pycnocline
depth variability forced by the dominant patterns ofanomalous Ekman pumping over the northeast Pacific(30°–60°N, 180°–120°W) during 1948–2000. The evolution of pycnocline depth predicted by the simple Ekman pumping model reproduced well the observed
variations at Ocean Weather Station Papa (OWS P:
50°N, 145°W). However, CL02 were unable to test theperformance of the simple model at other locations in
the Gulf of Alaska and to verify the accuracy of thespatial patterns of pycnocline depth changes producedby the model over the entire northeast Pacific. Cummins and Lagerloef (2004, CL04 hereinafter) further
investigated the performance of the local Ekman
pumping model of CL02, by estimating its ability toreproduce observed SSH variability in the northeast
Pacific during 1993–2003, and concluded that interannual variability in the Gulf of Alaska is dominated by
the local response to wind forcing.
The success of the simple Ekman pumping model in
explaining pycnocline variability is somewhat surprising
given the role of Rossby waves in the adjustment of thelarge-scale ocean circulation to changes in surface forcing. Indeed, satellite altimetry shows westwardpropagating sea surface height (SSH) anomalies in thenortheast Pacific (Kelly et al. 1993; Fu and Qiu 2002).
Further, Qiu (2002) has shown that SSH variability
from the Ocean Topography Experiment (TOPEX)/
Poseidon (T/P) altimeter mission (1993–2001), areally
averaged over the offshore region of the Gulf of
Alaska, can be largely explained by first-mode baroclinic Rossby wave propagation.
The simplest dynamical paradigm to describe the

VOLUME 35

steady-state response of the large-scale ocean circulation to wind forcing is the Sverdrup relation (Sverdrup
1947), which expresses the linear vorticity balance between wind stress curl and meridional advection ofplanetary vorticity. Although the Sverdrup relation was
originally derived as a theory for the steady-state ocean
circulation, it has proven very useful to explain circulation changes associated with large-scale variations of
the wind forcing. Deser et al. (1999) have related theintensification of the Kuroshio Extension after the mid1970s to the strengthening of the westerlies in the central Pacific using Sverdrup dynamics. In the Gulf of
Alaska, the Alaskan Stream can be viewed as the western boundary current of the Alaska gyre, and it is conceivable that some of the changes in the upper-ocean
density structure and circulation associated with the
1976–77 climate shift can be explained in terms of Sverdrup dynamics. Qiu (2002) has interpreted the annual
modulation of the Alaska gyre intensity during 1993–
2000 in terms of time-dependent Sverdrup balance.
However, the dynamical balances governing the variations of the Alaska gyre at decadal time scales are still
unclear.
In this study we examine the physical mechanisms
responsible for low-frequency (interannual to interdecadal) pycnocline variability in the northeast Pacific during 1958–97. Both interannual and decadal time scales
will be examined, but our major focus will be on thedecadal–interdecadal variations and, in particular,
those associated with the 1976–77 climate shift. The
specific questions we ask are: What fraction of pycnocline variability can be explained by local Ekman
pumping in different areas of the Gulf of Alaska? If thesimple model of CL02 and CL04 fails in some areas,
what other processes control pycnocline changes inthose areas? What is the role of baroclinic Rossby
waves in pycnocline variability? Does Sverdrup balancehold at decadal time scales? To answer these questions
we will use the output from an ocean general circulation model (OGCM) driven by observed surface forcing. After testing the model’s performance at locations
where long-term observations are available, the
OGCM output is used to test the different dynamical
hypotheses. Our study will extend the work of CL02 by
testing the local Ekman pumping model over the entire
northeast Pacific. It will also extend and complement
the work of CL04 by examining the changes that took
place across the mid-1970s.
The OGCM used for this study is described in section
2, and in section 3 the performance of the OGCM is
examined by comparing the pycnocline depth changes
in the OGCM with those observed at two oceanographic stations in different areas of the Gulf of Alaska.

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The influence of local Ekman pumping upon pycnocline variability is examined in section 4, and in section
5 the role of baroclinic Rossby waves is considered. The
validity of the Sverdrup balance at decadal time scales
is examined in section 6, and the processes governing
the evolution of pycnocline depth anomalies along thecoast is analyzed in section 7. We conclude in section 8.

2. The OGCM
The OGCM used for this study is the National Center for Atmospheric Research ocean model (NCOM)
that has been described in detail by Large et al. (1997),
Gent et al. (1998), and Large et al. (2001). The specificnumerical simulation analyzed here is described in
Doney et al. (2003). In this section we only provide abrief summary of the basic model characteristics andinformation about the surface forcing used for thissimulation.
NCOM is derived from the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Modelwith the addition of a mesoscale eddy flux parameterization along isopycnal surfaces (Gent and McWilliams
1990) and a nonlocal planetary boundary layer parameterization (Large et al. 1994). The model is global,
with a horizontal resolution of 2.4° in longitude andvarying resolution in latitude ranging from 0.6° near theequator to 1.2° at high latitudes. The model version
used for this study includes an anisotropic viscosity parameterization (Large et al. 2001) with enhanced viscosity close to ocean boundaries and much weaker viscosity in the ocean interior.
The surface forcing includes momentum, heat, andfreshwater fluxes for the period 1958–97. The windstress is computed from the reanalyses fields produced
at the National Centers for Environmental Prediction(NCEP) (Kalnay et al. 1996) using bulk formulas. The
sensible and latent heat fluxes are computed from the
NCEP winds and relative humidity and the model’s
SSTs using standard air–sea transfer equations (Large
and Pond 1982; Large et al. 1997). Sensible and latent
heat fluxes depend on the difference between SST andsurface air temperature. Since SST and air temperature
closely track each other, when observed air temperatures are used in the bulk formulas, as in the present
model simulation, the model’s SST is relaxed toward
observations (Haney 1971). The relaxation time scale isrelatively short (30–60 days for typical mixed layer
depths), so the SST in the model can be expected to bestrongly constrained by the surface forcing rather thanby the interior ocean dynamics.
The numerical simulation is started from an initialcondition obtained from a preliminary climatologicalintegration, so the initial model state is not too different

FIG. 1. Pycnocline depth changes associated with the 1976–77
climate regime shift from the output of the NCAR OGCM. The
depth of the 26.4 ␴␪ isopycnal is used as proxy for pycnocline
depth. The positions of OWS P (P: 50°N, 145°W) and GAK1 (G:59°N, 149°W) and the location of Kodiak Island (K), Seward (S),Queen Charlotte Island (Q), the Alaska Peninsula, and the Aleutian Islands are indicated for reference.

from the mean state characteristic of the 40-yr experiment. Then the model was run for two 40-yr cycles, thesecond cycle starting from the conditions achieved at
the end of the first 40-yr segment. The mismatch between the model state and the forcing at the beginningof the second cycle did not seem to produce any longterm transient behavior, but some residual drift in temperature and salinity can be detected at depths larger
than approximately 500 m (Doney et al. 2003). Here weanalyze the output for the second 40-yr period using
monthly mean values.

3. How realistic is pycnocline variability in the
OGCM?
Lagerloef (1995) has shown that a significant fraction(28%) of the subsurface variance over the period 1960–
90 is associated with the 1976–77 climate regime shift.
Thus, we start by examining the pycnocline depth variations exhibited by the OGCM in association with the
1976–77 climate shift. The depth of the 26.4 ␴␪ isopycnal, which lies in the core of the main pycnocline, isused as proxy for pycnocline depth. The mean depth of
the 26.4 ␴␪ isopycnal ranges from ⬃100 m in the centerof the Alaska gyre to 150–180 m around the rim of thegyre. The changes in pycnocline depth are computed as
the difference between the pycnocline depth in the period 1977–97 (period 2) and the pycnocline depth in the
period 1960–75 (period 1). After the mid-1970s, the pycnocline was shallower in the central part of the Gulf of
Alaska and deeper in a broad band following the coast(Fig. 1). The deepening was more pronounced in thewestern part of the Gulf of Alaska, to the southwest of
Kodiak Island (K in Fig. 1), following approximately
the Alaska Peninsula. A similar pattern of pycnoclinechanges was found by Miller et al. (1994) in a coarserresolution (⬃4°) ocean model hindcast, using surface

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FIG. 2. Zonal section of mean potential density along 53.8°N for
the periods 1958–75 (solid contours) and 1977–97 (dot–dashedcontours). Contour interval is 0.4 ␴␪ units. The 26.4 ␴␪ isopycnal,used as proxy for pycnocline depth, is highlighted with a thickcontour for both periods.

forcing fields from the COADS dataset instead ofNCEP. The leading mode of a simple stochastic model
forced by Ekman pumping also displayed the spatial
structure and temporal evolution characteristics of the
1976–77 regime shift (Fig. 4 in CL02). Changes in thedensity structure are detectable to depths greater than⬃800 m, but appear to be more pronounced in theupper ocean as shown by a zonal density section along
53.8°N (Fig. 2). Above 400 m, the isopycnals deepen
toward the eastern and western edges of the gyre after
the mid-1970s, resulting in a more pronounced domingof the density structure.
Is the pattern of pycnocline variations in Fig. 1 realistic? Subsurface observations are generally sparse andsalinity data in particular are very limited. Since salinity
is an important factor controlling the potential density
distribution in the subarctic Pacific, the paucity of salinity data limits the possibility of testing the performance of the OGCM over the whole northeast Pacific.
However, there are a few oceanographic stations in the
Gulf of Alaska where measurements of both temperature and salinity are available over a long period of time
including the 1976–77 climate shift. We will use measurements from two stations, Ocean Weather Station
Papa (OWS P, P in Fig. 1), which is located in the area
where the pycnocline becomes shallower, and station
GAK1 (G in Fig. 1), which is in the band where thepycnocline deepens.
OWS P (50°N, 145°W) provides a long record ofbottle cast and CTD data over the period 1957–94. The
time series has been recently augmented by more recent observations from 1995 to 1999. Generally, thedensity of observations is higher in the earlier period,
1957–81, when OWS P was occupied on a regular basis

FIG. 3. Pycnocline depth variations (m) at (top) OWS P and(bottom) Station GAK1. Positive anomalies indicate deeper pycnocline, while negative anomalies correspond to a shallower
pycnocline. The dots connected by the dashed line are the winteraverages of observed values at the two stations (usually a few
values for each winter), while the solid line shows the winterpycnocline depth anomalies from the NCAR OGCM, based onmonthly averages. Correlation coefficients between the observed
and modeled winter values are 0.7 and 0.77 at Papa and GAK1,
respectively. The observed time series at Papa was kindly provided by P. Cummins.

by a weather ship. Freeland et al. (1997) have computed
pycnocline depths by assuming a two-layer representation of the density structure and using a least squares
approach to determine the thickness of the upper layerfrom the observed density profiles. The two-layer representation is appropriate for wintertime (December–
April) conditions, and it tracks the core of the main
pycnocline in winter. CL02 have used the additional
observations after 1994 to extend the time series ofmean winter pycnocline depth from 1994 to 1999. Thus,
the whole time series covers the period 1957–99, withgaps in 1985, 1988, 1990, and 1994. The resulting timeseries is compared in Fig. 3a with the winter pycnocline
depth from the OGCM. The correlation between the
two time series is 0.7, and the amplitude of the OGCM
pycnocline depth anomalies is also approximately correct (the standard deviation of the OGCM time series is
9.7 m and the observed time series standard deviation is11.3 m). Notice that the observed pycnocline depth
anomaly values are often averages of few data over the
winter months, while the winter pycnocline depths estimated from the OGCM are based on monthly aver-AUGUST 2005

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ages of potential density, which is available at eachmodel time step (of the order of one day). Thus, higherfrequency processes (e.g., mesoscale eddies and baroclinic tides), which are filtered out in the OGCM, may
contribute to some of the observed pycnocline variability. Both model and observations indicate a shoaling
trend of the main pycnocline, as discussed by Freeland
et al. (1997) and CL02.
Station GAK1 (59°N, 149°W) is located at the mouthof Resurrection Bay near Seward, Alaska, where the
OGCM indicates a deepening of the pycnocline after
the mid-1970s (Fig. 1). Profiles of temperature and salinity to a depth of 250 m have been measured startingin December 1970, and are still ongoing. The number ofobservations over the winter period varies from lessthan 5 measurements in 1972, 1980, 1981, and 1985 to a
maximum of 15 values in 1999. Starting from the individual density profiles, we have estimated the depth of
the 25.4 ␴␪ isopycnal, which is located close to the baseof the observed winter pycnocline. Winter values havethen been computed by averaging all the values between December and April. Standard deviations about
each winter value (not shown) can be as large as 50 m.
Station GAK1 is situated very close to shore, and there
is no model grid point available at the exact location of
the station. So we have estimated winter pycnocline
depths from the OGCM at the closest model grid point(58.6°N, 147.6°W). The comparison between the observed and simulated depth anomalies is shown in Fig.
3b. Although the observed time series exhibits interannual fluctuations of larger amplitude than the time series from the OGCM, which are based on monthly averages of values at each model time step, the two timeseries agree quite well (the correlation coefficient is0.77) on both interannual and longer time scales (apartfrom a discrepancy in 1979–81). In particular, the lowfrequency behavior is very similar in both time series
and indicates a deepening of the pycnocline after the
mid-1970s. Thus, the comparison with observations indicates that the pattern of pycnocline depth changes
produced by the OGCM is believable. Next we use the
OGCM to assess which fraction of the pycnocline variability can be explained by local Ekman pumping.

4. Where is Ekman pumping important?
The vertical velocity at the base of the Ekman layer
due to the divergence of the Ekman currents (Ekman
pumping) is defined as the vertical component of thecurl of the wind stress ␶ divided by the Coriolis parameter f and the mean density of seawater ␳␱:冋 冉 冊册

WE ⫽ ⵱ ⫻

␶
␳0 f

.

z

共1兲

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FIG. 4. Winter Ekman pumping changes associated with the
1976–77 climate regime shift. Contour interval is 0.1 ⫻ 10⫺4
cm s⫺1. Solid contours indicate positive values, while dashed contours are used for negative values. Anomalies larger than 0.3 ⫻10⫺4 cm s⫺1 are dark shaded, while values smaller than ⫺0.3 ⫻10⫺4 cm s⫺1 are light shaded. The mean Ekman pumping is positive over the Gulf of Alaska. Negative Ekman pumping anomalies
imply reduced upwelling and vice versa.

The changes in the Ekman pumping field associatedwith the 1976–77 climate shift are shown in Fig. 4 as thedifference between Ekman pumping during winter(December–April) in 1977–97 (period 2) and 1960–75(period 1). The resulting pattern shows negative WEchanges (anomalous downwelling) in a broad band that
follows the coast to the east of Kodiak Island (indicatedwith a K in Fig. 1), and positive changes (anomalous
upwelling) elsewhere in the Gulf of Alaska, with maximum values extending to the southeast of the Alaskan
Peninsula. The largest negative difference is achievedin the very northern part of the Gulf of Alaska, between approximately Seward (S in Fig. 1) and Queen
Charlotte Island (Q in Fig. 1). Since the time-averaged
Ekman pumping is positive (e.g., upwelling) over thewhole Gulf of Alaska, a positive (negative) difference
implies that upwelling is enhanced (diminished) after
the 1976–77 climate shift. The pattern of WE changes inFig. 4 is very similar to that computed by Lagerloef(1995) using an empirical orthogonal function approach.
The pattern of WE bears some similarity to the distribution of pycnocline changes in Fig. 1: areas of negative (positive) WE differences generally correspond toareas of deeper (shallower) pycnocline after 1977. The
major discrepancy between the two fields occurs in the
coastal band extending southwestward from Kodiak Island to the Aleutian Islands, where a positive change inWE from period 1 to period 2 is associated with adeeper pycnocline. The local correlation between Ekman pumping and pycnocline depth from the OGCM
(hOGCM) is shown in Fig. 5a using low-pass filtered Ekman pumping data to remove periods shorter than 2 yr
and finding the lag at which the correlation is maxi-

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To further examine the influence of the local Ekman
pumping upon pycnocline variability and understandthe phase relationships between the two fields, we consider the simple model used by Lagerloef (1995) and
CL02:dh⫽ ⫺WE ⫺ ␭h,dt

共2兲where h is the pycnocline depth, WE is the Ekman
pumping, and ␭ is a linear damping coefficient with theunits of the inverse of time. Equation (2) is forced withmonthly anomalies of Ekman pumping computed fromthe NCEP–NCAR reanalyses wind stress, the same
forcing which drives the OGCM. Equation (2) is solved
using a second-order accurate trapezoidal scheme asn⫹1Ⲑ2,
hn⫹1 ⫽ ␣1hn ⫹ ␣2WE

FIG. 5. (a) Maximum lag correlations between low-pass filtered
Ekman pumping (periods shorter than 2 yr are removed) andpycnocline depth anomalies. Pycnocline depth anomalies havebeen multiplied by ⫺1 to achieve positive correlations (a positive
depth anomaly indicates deeper pycnocline, and should be associated with negative WE anomalies, and vice versa). Values larger
than 0.5 are light shaded, while values larger than 0.7 are dark
shaded. (b) Lag (months) between WE and hOGCM at which the
maximum correlations in (a) are achieved. Positive lags indicatethat WE leads the changes in pycnocline depth.

mized. The correlation is highest in a band that extends
westward from the center of the Alaskan gyre (⬃50°N,
145°W) to the Aleutian Islands, with values of ⬃0.7
(Fig. 5a). The correlations decrease toward the Alaska
Peninsula where they drop to values very close to zero.
Along the coastal band to the east of Kodiak Island,correlations tend to be higher (0.3–0.5) than on thewestern side, with maximum values of ⬃0.5 between
Seward and Queen Charlotte Island. Figure 5b shows
the lags between Ekman pumping and pycnocline
depth that yield the maximum correlations. Ekman
pumping variations always lead pycnocline depth variations with lags ranging from 6–8 months over a large
area in the central part of the Gulf of Alaska to values
as large as 10–15 months in the northern portion of the
Gulf of Alaska and close to the southeastern corner ofthe domain. Lags are much smaller from the southernpart of the Alaska Peninsula to the Aleutian Islands
where Ekman pumping and pycnocline depth are only
weakly correlated.

共3兲

where ␣1 ⫽ (2 ⫺ ␭⌬t)/(2 ⫹ ␭⌬t) and ␣2 ⫽ (2⌬t)/(2 ⫹␭⌬t). We have used the OGCM pycnocline depth
anomalies at the initial time (January 1958) as initialconditions for the simple model.
A question remains concerning the value of thedamping time scale ␭⫺1. CL02 found that the time evolution of the pycnocline depth produced by the simple
model (h) is largely controlled by the damping timescale ␭⫺1. Longer damping time scales tend to emphasize the oceanic response at lower frequencies, and toincrease the lag between Ekman pumping and pycnocline depth variations. As shown by (3), ␣1 can be interpreted as the lag-1 autocorrelation of pycnocline
depth. Using the lag-1 autocorrelation of yearly pycnocline depth anomalies at OWS P, CL02 found a value of
1.4 yr for ␭⫺1, while the lag-1 autocorrelation of the
Pacific decadal oscillation (PDO) index [the leading
principal component of monthly SST anomalies over
the North Pacific, as computed by Mantua et al. (1997)]
yielded ␭⫺1 ⫽ 1.6 yr
CL02 used a single reference value of ␭⫺1 ⫽ 1.5 yr for
their entire North Pacific domain. However, if thedamping term ⫺␭h in (2) is a parameterization of oceanic processes, the damping time scale may be expected
to vary spatially depending on the strength of those
processes. Using the OGCM as a term of comparison,
we have determined the value of ␭⫺1 that maximizes
the instantaneous correlation with the OGCM time series at each grid point. The resulting distribution (Fig.
6a) shows indeed large spatial variability of the damping time scale, ranging from 14–16 months in the central
part of the Gulf of Alaska to values as large as 90
months along the coastal band to the east of Kodiak
Island. In the area along the Alaska Peninsula from
Kodiak Island to the Aleutian Islands ␭⫺1 is close to 12

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FIG. 6. (a) Spatial distribution of the damping time scales ␭⫺1(months), which maximizes the correlation between pycnocline
depth anomalies produced by the Ekman pumping model (2) andthe pycnocline depth anomalies in the OGCM. (b) Spatial distribution of the correlation between h and hOGCM obtained using thevalues of ␭ in (a). Light shading is for values larger than 0.7,
intermediate shading is for values larger than 0.8, and dark shading is for values larger than 0.9.

months, but correlations are very low in this area (Fig.
6b). The values we find for the damping time scale inthe central part of the Gulf of Alaska (14–18 months)
are in agreement with Lagerloef (1995) and CL02’s estimates.
The pattern of ␭⫺1 in Fig. 6a is generally consistent
with the spatial distribution of the lags between WE andpycnocline depth in Fig. 5a. However, it may be seenfrom (2) that the phase lag between h and WE is frequency dependent and given by ⌽ ⫽ atan (␻␭⫺1),
where ␻ is the angular frequency. Thus, ⌽ depends
upon the ratio between the frictional time scale ␭⫺1 andthe period of the forcing 2␲␻⫺1, varying from values
very close to 0° when the period of the forcing is much
longer than the frictional time scale to ␲/2 for forcing
periods much shorter than the frictional time scale.
Thus, at low frequencies Ekman pumping and pycnocline depth variations tend to be in phase, while at highfrequencies they are in quadrature. Comparison between Figs. 5b and 6a shows that the lag between Ekman pumping and pycnocline depth response is gener-

1409

ally larger in areas where ␭⫺1 is larger. In the central
part of the Gulf of Alaska ␭⫺1 ⬃15 months. Considering a period of 3 yr for the forcing (recall that the
Ekman pumping used for the correlations only contains
periods longer than 2 yr) the time lag is ⬃7 months,
very close to the lag between WE and OGCM pycnocline depth in the same area (Fig. 5b).
The pattern of correlations between h and hOGCM
obtained with the values of ␭⫺1 in Fig. 6a is shown in
Fig. 6b. The overall structure is similar to the correlation pattern between WE and hOGCM, but the correlations between h and hOGCM are higher than the correlations between WE and hOGCM because of a larger
level of high-frequency variability in the WE field withrespect to the h field. The correlations in Fig. 6b indicate that the simple model gives an excellent fit to the
OGCM in the central part of the Gulf of Alaska, andwest of ⬃145°W, with maximum correlations (0.9) atapproximately 50°–52°N, 155°–150°W. Correlations decrease onshore, especially toward the Alaska Peninsula
and to the south of ⬃45°N. To the east of Kodiak Island
values range from 0.5 south of Queen Charlotte Island
to 0.7 between Queen Charlotte Island and Kodiak Island, while southwestward of Kodiak Island correlations drop to near zero. Thus, the area to the southwestof Kodiak Island is the region where the simple model
completely fails to reproduce the OGCM pycnocline
depth changes. This is the region of the Alaskan
Stream, a strong western boundary current. Advection
processes can be expected to be important in this areaand be responsible for the inability of the simple model
to reproduce the OGCM pycnocline variability. Satellite observations have also shown the presence of pronounced anticyclonic meanders that develop along thestream as a result of the stream instabilities (Crawford
et al. 2000). These meanders, which are not captured by
the OGCM because of its coarse horizontal resolution,
will certainly produce large, low-frequency variability
in the local pycnocline that cannot be reproduced by
the simple Ekman pumping model.
To further examine the simple model fit to the
OGCM, we compute the difference between pycnocline variability in the OGCM and pycnocline variability produced by Eq. (2) using the spatially varying ␭⫺1that maximizes the correlation. The standard deviationof this residual field, which is a measure of the misfit of
the simple model to the OGCM, maximizes in the western part of the Gulf of Alaska (Fig. 7b) where the variance of hOGCM is also very large (Fig. 7a). In particular,
to the southwest of Kodiak Island the residual fieldaccounts for 80%–90% of the variance of hOGCM (Fig.
7c). The minimum of the residual standard deviation isfound in the central part of the Gulf of Alaska (cen-1410

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FIG. 7. (a) Standard deviation of pycnocline depth in the OGCM. Contour interval is 5 m.
Values larger than 10 m are shaded. (b) Standard deviation of the residual field computed as
the difference between pycnocline depth anomalies in the OGCM and pycnocline depth
anomalies from the Ekman pumping model (2). Contour interval is 5 m. Values larger than 10
m are shaded. (c) Percentage of the OGCM pycnocline depth variance accounted for by theresidual field. Values larger than 60% are light shaded, and values larger than 70% are dark
shaded.

tered around 52°N, 150°W), where (2) produces thebest fit to the OGCM. In this area the variance of theresidual field accounts for 10%–30% of the OGCM
pycnocline depth variance (Fig. 7c).

5. Rossby wave dynamics
Equation (2) emphasizes the importance of local Ekman pumping in driving thermocline variability. Satellite altimetry, on the other hand, has provided evidenceof westward propagating SSH anomalies, which appear
to be associated with first-mode baroclinic Rossby

waves (Kelly et al. 1993; Qiu 2002; Fu and Qiu 2002).
What fraction of pycnocline variability in the OGCM isdue to Rossby wave propagation? To answer this question we compare the performance of the local Ekman
pumping model (2) with that of a Rossby wave model.
A direct comparison is carried out along two latitudes,
40.5° and 49°N. As seen in Fig. 6b, the local Ekman
pumping model gives a good fit to the OGCM along
49°N with correlations of ⬃0.8 west of 140°W, but its
performance is worse along 40.5°N where the maximum correlations are less than 0.7.
Following CL04, we consider a linear, reduced-

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gravity, quasigeostrophic model to simulate upperocean variability that includes Rossby wave dynamics.
The wavelength of Rossby waves at interannual timescales is typically longer than ⬃2000 km (Chelton and
Schlax 1996; Fu and Chelton 2001), while the Rossby
radius of deformation is ⬃30 km around 40°N (Chelton
et al. 1998) and decreases poleward. Thus, the longwave approximation is valid poleward of 40°N. In thelong-wave limit the equation describing the evolutionof pycnocline depth is
⭸hR
⭸hR
⫹ cR
⫽ ⫺WE ⫺ ␭1hR,
⭸t
⭸x

共4兲

where hR is the pycnocline depth anomaly in the presence of Rossby wave propagation, cR ⫽ ⫺␤R2 is thephase speed of long baroclinic Rossby waves, where R
is the Rossby radius of deformation and ␤ is the latitudinal gradient of the Coriolis parameter, and ␭1 is aRayleigh friction coefficient. Following Killworth et al.
(1997), who took into account the influence of a background mean flow, we have chosen cR ⫽ 0.8 cm s⫺1along 49°N and 1.5 cm s⫺1 along 40.5°N. To optimize
the comparison with the OGCM ␭1 has been chosen tobe (4 yr)⫺1. Equation (4) can be solved by integrating
along Rossby wave characteristics in the x–t plane:hR共x, t兲 ⫽ hR共xE, t ⫺ tE兲e⫺␭1tE
⫹

冕

x

x

E

WE共␰, t ⫺ t␰兲 ⫺␭ t
e 1 ␰ d␰,
cR共␰兲

共5兲

where the solution at each point x and time t is obtained
as the superposition of the disturbances generated eastof point x at previous times. The first term on the rhs of(5) is the contribution of the signals originating at theeastern boundary xE, and reaching point x at time t witha transit time tE ⫽ 兰xxE [d␰/cR(␰)]. The second term on
the rhs of (5) is the contribution of the disturbances
generated by the Ekman pumping east of the target
point x with t␰ ⫽ 兰x␰ [ds/cR(s)]. Both boundary and windforced terms decay while propagating counterclockwisewith the frictional time scale 1/␭1.
Figures 8 and 9 show the comparisons between theevolution of pycnocline depth anomalies from the
OGCM and those obtained from the Ekman pumping
model (2) and Rossby wave model (4) along 49° and
40.5°N, respectively. The pycnocline depth variations in
the OGCM along 49°N (Fig. 8a) exhibit a clear change
in character after 1976 with positive anomalies west of⬃145°W before the shift and negative afterward. Pycnocline depth anomalies east of 145°W tend to be of
opposite sign to those farther west, consistent with Fig.

1. Westward propagation is evident at times: for example the positive anomaly centered at ⬃155°W in
1965 and the negative anomaly centered around thesame longitude in 1987. However, in most cases pycnocline depth anomalies exhibit a standing character. The
evolution produced by the Ekman pumping model (Fig.
8b) does not capture the westward propagation seen in
the OGCM but reproduces relatively well other aspectsof the variability. The Rossby wave model, on the other
hand, gives a good representation of the westwardpropagating anomalies but does not capture other features, for example the anomalies west of 170°W during
1958–66 and 1975–87 (Fig. 8c). It is worth noting thatthe period examined by Fu and Qiu in the altimeterdata (1993–2000) contains a prominent westwardpropagating signal. To quantify the goodness of the fitof the Ekman pumping and Rossby wave models to the
OGCM we show in Fig. 10a the instantaneous correlations of each model with the OGCM along 49°N. Overall, the Ekman pumping model produces a better fit to
the OGCM than the Rossby wave model, indicatingthat over the 1958–97 period Rossby wave propagation
is not the controlling dynamics along 49°N.
Along 40.5°N, the evolution of the pycnocline depth
anomalies in the OGCM does not show any clear indication of the 1976–77 climate shift. Westward propagation, as indicated by the slope of the phase lines, can benoticed for some events east of ⬃165°W. However, thelarge-scale anomalies appear to be primarily of standing nature, an aspect captured relatively well by the
Ekman pumping model. Along this latitude the performance of the two simple models is comparable, as indicated by the instantaneous correlation with the
OGCM (Fig. 10b).

6. Sverdrup dynamics
A classical model of the mean circulation of the Gulf
of Alaska would include a Sverdrup interior with the
Alaskan Stream as the western boundary current. Are
the changes in the Alaskan Stream that took place after
the mid-1970s consistent with Sverdrup dynamics? If
the Alaskan Stream does respond to the adjustment of
the Alaska gyre according to Sverdrup dynamics, this
could partially explain why its variability is only weakly
affected by the local wind stress curl.
Sverdrup balance (Sverdrup 1947) is derived fromthe vertical integral of the vorticity equation␤␷ ⫽ f

⭸w
,
⭸z

共6兲

where ␷ is the meridional velocity, w is the verticalvelocity, and z is the vertical coordinate. Equation (6)1412

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FIG. 8. Comparison between the evolution of pycnocline depth along 49°N in (a) the OGCM, (b) the one computed from the Ekman
pumping model, and (c) the one obtained from the Rossby wave model. The solution for the Ekman pumping model was obtained byusing the optimal values of ␭ in Fig. 6a along 49°N. Dashed contours and blue shading are for negative pycnocline depth anomalies(shallower pycnocline), while solid contours and orange shading are for positive anomalies (deeper pycnocline). Contour interval is 10
m in all three panels.

states that the potential vorticity changes associatedwith the vertical stretching of the water column must bebalanced by the changes in planetary vorticity due tomeridional motions. Integrating (6) from a level of nomotion to the base of the Ekman layer, we obtain theclassical form of the Sverdrup balance:␤Vg ⫽ fWE,

共7兲

where Vg is the vertically integrated geostrophic meridional velocity. The condition of nondivergence forthe vertically integrated geostrophic flow allows to introduce a streamfunction ␺g, such that
Vg ⫽

⭸␺g
.
⭸x

共8兲

Using the boundary condition that ␺g(xE) ⫽ 0 at theeastern boundary xE of the ocean basin, the geostrophic
streamfunction at longitude x along a given latitude canbe computed by integrating westward from the easternboundary:␺g共x兲 ⫽ ⫺

f
␤

冕

x

WE dx⬘.

共9兲

x

E

The time averaged barotropic streamfunction in themodel describes the cyclonic circulation of the Alaska
gyre (Fig. 11a). The mean transport of the model Alaskan Stream is ⬃7–8 Sv (Sv ⬅ 106 m3 s⫺1), a value
smaller than some observational estimates. Geostrophic transports relative to 1500 dbar are typically
12–18 Sv (Musgrave et al. 1992). However, intermittent
surveys of the Alaskan Stream yielded a broad range oftransport estimates with values as low as 2 Sv and aslarge as 20 Sv (Reed et al. 1980; Royer 1981). The
barotropic streamfunction difference between 1977–97
versus 1960–75 (Fig. 11b) shows a slight weakening of
the circulation in the eastern part of the basin and a

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FIG. 9. As in Fig. 8 but for 40.5°N.

strengthening of the circulation to the southwest of Kodiak Island. The intensification of the Alaskan Stream
after the mid-1970s is consistent, by geostrophy, withthe more pronounced doming of the isopycnals after
the mid-1970s (Fig. 2), and resulting larger zonal density gradients (Fig. 1).
The changes in barotropic streamfunction associatedwith the 1976–77 climate shift, as predicted by (9), havebeen computed using the Ekman pumping difference(period 2 ⫺ period 1) based on annual values. The
annual Ekman pumping changes have a spatial structure that is very similar to the winter difference (Fig. 5),
but the magnitude of the changes is generally smaller.
The result (Fig. 11c) shows significant differences withthe streamfunction changes found in the OGCM. The
circulation changes implied by the Sverdrup balance
include an intensification of the Alaskan Stream only
south of 54°N, while north of this latitude the flow
weakens appreciably, with transport changes as large as
2 Sv. Thus, the Sverdrup balance does not seem to provide a good description of the wind-forced circulation
changes in the Gulf of Alaska after the mid-1970s.

7. Coastal propagation of pycnocline depth
anomalies
We have seen in section 4 that the agreement between the Ekman pumping model (2) and the OGCM
deteriorates in a broad band along the coast, particularly in the western side of the Gulf of Alaska. Thisband is characterized by the cyclonic circulation of the
subpolar gyre, as shown in Fig. 12, where the OGCM
average velocities above the pycnocline (identified by
the 26.4 ␴␪ isopycnal) are displayed. The primary currents include a broad northward flowing eastern boundary current, the Alaska Current, which becomes narrower and stronger in the apex of the gyre and continues southwestward along the Alaska Peninsula as the
Alaskan Stream. Thus, it is conceivable that advection
processes may influence the evolution of pycnocline
depth anomalies in this area. The OGCM is too coarse
to resolve mesoscale variability, but in the real world
the presence of long-lived eddies and meanders (Crawford et al. 2000) can also produce low-frequency pycnocline variability that is not captured by the simple

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phase lines and has been computed by fitting a straightline through the points of maximum correlations. Using
different points along the path as reference points forthe lag correlations leads to different estimates of
propagation speeds, especially in the eastern part of thepath, approximately east of point C where propagation
appears to be faster when reference points east of point
A are considered. In all cases, propagation speeds appear to increase from west to east independently of thereference point chosen.
To account for the propagation of pycnocline depth
anomalies, (2) has been modified to include a propagation term in the form

FIG. 10. (a) Instantaneous correlations between the pycnocline
depth anomalies in the OGCM and those computed with the local
Ekman pumping model (solid line), and the Rossby wave model(dot–dash line) along 49°N. (b) As in (a) but along 40.5°N.

⭸hp
⭸hp
⫹␷
⫽ ⫺WE ⫺ Rhp,
共10兲
⭸t
⭸␨
where hp indicates the isopycnal depth displacement
obtained when propagation is included, ␷ is the propagation velocity, ␨ is the coordinate along the propagation path, and R is a Rayleigh friction coefficient. We
found that the correlations and regressions between hp
and hOGCM increase with decreasing values of R. So wehave chosen R ⫽ 1/100 yr⫺1. Equation (10) is solved byintegrating along the path in Fig. 13a:
hp共␨, t兲 ⫽ hp共␨s, t ⫺ ts兲e⫺Rts ⫹Ekman pumping model. To examine propagation phenomena along the coastal band we consider a curved
path that approximately follows the coast and lies along
the axis of the Alaska Current and Alaskan Stream, as
shown in Fig. 13a. Some points along the path (A, B, C,
D, and E) have been highlighted as reference points.
Figure 13b shows the time evolution of hOGCM at points
A, B, and C, smoothed using a three-point binomial
filter. The OGCM pycnocline variations at point A lag
the pycnocline variations at point B by 4 months, witha maximum correlation coefficient of 0.96, and lag thevariations at point C by ⬃6 months, with a maximum
correlation coefficient of 0.92. Thus, pycnocline variations along the path are strongly correlated at lags that
increase westward, suggesting a counterclockwise
propagation of the depth anomalies.
The lag correlations between pycnocline depth variations at point A and at all the points along the path (Fig.
14) show that the pycnocline depth anomalies are correlated over a very large distance along the coast andpropagate counterclockwise along the path with speeds
varying from approximately 16 cm s⫺1 at distances
greater than 2100 km (approximately east of point C) to⬃8 cm s⫺1 between 600 and 2100 km, and decreasing to⬃1.3 cm s⫺1 at the western end of the path, as indicatedby the slope of the dot–dash line in Fig. 14. The dot–dash line shows the approximate inclination of the冕␨

WE共␰, t ⫺ t␰兲 ⫺Rte ␰ d␰.
␷共␰兲
␨
s

共11兲
The form of the solution is similar to the one obtained
for the Rossby wave equation in (4), but in this case thesolution at each point ␨ and time t is obtained as thesuperposition of the disturbances generated upstreamof point ␨ at previous times. The first term on the rhs of(5), hPS, is the contribution of the signals present at thestarting point of the path ␨s, and reaching point ␨ attime t with a transit time ts ⫽ 兰␨␨s [d␰/␷(␰)]. The second
term on the rhs of (5), hPE, is the contribution of the
disturbances generated by the Ekman pumping east of
the target point ␨, with t␰ ⫽ 兰␰␨ [ds/␷(s)].
Using the estimates of propagation speed from thelag-correlations in Fig. 14 as a guideline, we have chosen ␷(␨) to increase from 3 cm s⫺1 at the western end of
the path to 7.5 cm s⫺1 approximately between points A
and C, 10 cm s⫺1 around point C, and reaching a maximum value of 22 cm s⫺1 upstream of point C. The fit ofEq. (10) to the OGCM does not seem to be very sensitive to the values of propagation speed used. Monthly
anomalies of pycnocline depth at point E (see Fig. 13a)in the OGCM have been used as boundary condition
hp(␨s, t) in (11).
The resulting correlations between hp and hOGCM
along the coastal path are shown in Fig. 15a, where they
are compared with the correlations between hOGCM andAUGUST 2005

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FIG. 11. (a) Mean model barotropic streamfunction over the Gulf of Alaska, describing thecyclonic circulation of the model Alaska Gyre. (b) Difference of barotropic streamfunction
between 1977–97 and 1960–75. (c) Difference in barotropic streamfunction implied by Sverdrup dynamics, computed as the zonal integral of the annual Ekman pumping difference.
Contour intervals are 2.5 Sv in (a) and 0.5 Sv in (b) and (c). Solid contours are for positive
values, and dashed contours are for negative values.

the pycnocline displacements h obtained with the local
Ekman pumping model without propagation. The correlations between h and hOGCM maximize (⬃0.7) just to
the west of point C along the path, but they drop tovalues close to zero south of point D (4000–5000 km
from the western end of the path), and southwest of
point B (Kodiak Island). On the contrary, when propagation is included, correlations are ⬃0.7–0.8 over mostof the path, and increase to larger values when approaching the boundary point E. Thus, over most of thepath, propagation appears to have a large influence on
pycnocline depth evolution. In particular, the pycnocline depth variations to the southwest of Kodiak Island(500–1500 km from the western end) result entirely
from the propagation into that area of pycnocline depth
anomalies originating further upstream, since the local
Ekman pumping would tend to produce anomalies ofthe opposite sign.

How much of the pycnocline variability along thecoastal path is due to the downstream propagation of
the displacements at point E (hps), and what fraction isforced by the Ekman pumping along the path? The

FIG. 12. Average upper-layer velocities (cm s⫺1) in the OGCM.
The upper layer is defined as the layer extending from the surface
to the depth of the 26.4 ␴␪ isopycnal, which is used as proxy for
pycnocline depth.

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FIG. 13. (a) Coastal path along which lag correlations of pycnocline depth anomalies in the OGCM are considered. The points
A, B, C, D, and E are highlighted for reference. (b) Time series ofhOGCM at point A (solid line), B (dot–dash line), and C (dottedline). The time series have been smoothed with a three-point
binomial filter. The pycnocline variability at point A lags the pycnocline variability at point B by ⬃4 months (correlation coefficient ⫽ 0.96), and the pycnocline variability at point C by ⬃6months (correlation coefficient ⫽ 0.92).

structure of the solution [Eq. (11)] allows to separate
the two contributions. Shown by the dotted line in Fig.
15a is the correlation between hps and hOGCM along thepath, indicating that the downstream propagation of
the signals at point E can account for most of the correlations along the path. However, the lower correlations of hps and hOGCM relative to hp and hOGCM downstream of ⬃3000 km from the western end of the pathsuggest that the Ekman pumping between the distancesof 1800 and 3000 km may influence the variability. In
this area the local Ekman pumping model also yields
correlations comparable to those obtained including
propagation. The area between 1800 and 3000 km fromthe western end of the path is where the largest negative Ekman pumping changes associated with the 1976–
77 climate shift are found (Fig. 5).
Although the inclusion of propagation considerably
improves the performance of the simple model, there
are still some discrepancies with the OGCM, especiallyin the amplitude distributions along the path, as shown

VOLUME 35

FIG. 14. Lag correlations between pycnocline depth variationsat point A in Fig. 13a and all the points along the coastal path.
Values larger than 0.7 are light shaded, and values larger than 0.9
are dark shaded. The dot–dashed line indicates the approximate
slope of the phase lines in three different areas along the path,corresponding to propagation speeds of ⬃1.3, 8.6, and 16 cm s⫺1from west to east. The lags yielding the maximum correlation ateach distance have been used as a guideline to draw the dot–dashlines. Distance is computed starting at the westernmost point. To
associate the distance with specific locations along the path, thepoints A, B, C, D, and E in Fig. 13a are indicated along the top
axis.

by the regression of hp upon hOGCM (Fig. 15b, solidline): regressions decrease from unity at point E, where
the pycnocline depth anomalies coincide with the
OGCM values, to ⬃0.5 at the western end of the path,indicating that the simple model (10) tends to underestimate the amplitude of the pycnocline displacements in
the OGCM. When only hps is considered (dotted line in
Fig. 15b), the amplitude of the OGCM pycnocline variability is further underestimated downstream of point
C. The regression between h and hOGCM is also shownfor comparison in Fig. 15b (dot–dash line): apart fromthe area between points B and C where the regressions
between h and hOGCM are comparable to those between
hp and hOGCM the local Ekman pumping model greatly
underestimates the amplitude of the OGCM pycnocline
variability, in particular upstream of point D and downstream of point B, indicating that in those areas propagation is essential. Thus, the correlation and regression
analyses suggest that the Ekman pumping forcing canplay a role in exciting pycnocline variability in the area

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1417

FIG. 16. Winter (Dec–Apr) upper-ocean speed as a function of
distance along the coastal path in Fig. 13a in the OGCM. The
upper-ocean speed has been estimated by vertically integrating
the model speed from the surface to the depth of the 24.6 ␴␪
isopycnal, assumed as proxy for pycnocline depth. The points A,
B, C, and D shown in Fig. 13a are indicated along the top axis fororientation purposes.

FIG. 15. (a) The solid line shows the correlation between isopycnal depth displacements obtained from the Ekman pumping
model with propagation (hp) and the isopycnal depth displacements in the OGCM (hOGCM) along the coastal path in Fig. 13a.
The dot–dashed line shows the correlation between the isopycnal
depth variations from the Ekman pumping model without propagation (h) and hOGCM along the path. (b) Same as (a) but regressions are displayed instead of correlations.

around point C, and the wind forced anomalies are thenadvected downstream.
Are the propagation speeds inferred from the lagcorrelations consistent with mean flow advection? The
velocities in the OGCM vary strongly with depth, andwe need to first determine what is the advective velocity of pycnocline depth. As a conceptual framework weconsider a two-layer quasigeostrophic model with layer
thicknesses H1 and H2 and mean velocities U1 and U2for the first and second layers, respectively (Pedlosky1996). For this system the advective velocity UA isUA ⫽U2H1 ⫹ U1H2.
H1 ⫹ H2

共12兲

Thus, if the two layers have equal depths (H1 ⫽ H2),
the advective velocity is simply the average velocity
0.5(U1 ⫹ U2). If, on the other hand, H1 Ⰶ H2, then UA⬃ U1. In winter the Gulf of Alaska can be viewed as a
two-layer system, where the two layers are separated by
the halocline. The upper-layer thickness is ⬃100–150 m,
and it is reasonable to assume that H1 Ⰶ H2. Thus, theadvective velocity in the model can be estimated byvertically averaging the model velocity from the surface
to the depth of the pycnocline. Figure 16 shows themodel speed, averaged from the surface to the depth of
the 26.4 ␴␪ isopycnal, which we have used as proxy for
pycnocline depth, along the coastal path in Fig. 13a.
The upper-layer speed increases from 0.5 cm s⫺1 atpoint D to a maximum value of ⬃6.5 cm s⫺1 between
point A and Point B (the model’s Alaskan Stream) andthen decreases again to ⬃3 cm s⫺1 at the western end of
the path. While the upper-layer speeds are comparablewith the propagation speeds estimated from the lagcorrelation analysis to the west of point B, they aremuch smaller than the propagation speed to the east of
point B. Thus, propagation through mean flow advection seems possible to the southwest of Kodiak Island(point B), but farther east the model mean velocity is
too small to explain the depth anomaly propagation.
Boundary waves, as described in several observational
studies (e.g., Enfield and Allen 1980; Chelton andDavis 1982; Qiu 2002) may be responsible for the
propagation of pycnocline depth anomalies in the eastern part of the basin.

8. Summary and discussion
In this paper we have examined the influence of local
Ekman pumping and horizontal propagation processes
upon pycnocline depth variability in the Gulf of Alaska,
using the output from an ocean general circulation
model forced with observed surface fields over the pe-

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riod 1958–97. The evolution of the OGCM pycnocline
depth has been compared with observations at two locations in the Gulf of Alaska, where long-term measurements of the upper-ocean density structure areavailable (Ocean Weather Station P at 50°N, 145°W,
and Station GAK1 at 59°N, 149°W). The winter pycnocline depth changes in the OGCM agree relatively
well with those observed at both interannual and longer
time scales, especially during periods with more measurements. The low-frequency evolution of the pycnocline depth is dominated by the 1976–77 climate regime
shift. After the mid-1970s, the model shows a deepening of the pycnocline in a broad band following thecoast and shoaling of the pycnocline in the central partof the Gulf of Alaska. These changes are consistent
with the low-frequency evolution of the pycnocline
depth at Station GAK1, where the pycnocline exhibitsa deepening trend, and at OWS P, where a shoaling
trend is observed.
To understand what fraction of the OGCM pycnocline depth variability can be driven by local Ekman
pumping, we have introduced a simple model for theevolution of pycnocline depth, where the forcing is supplied by the local Ekman pumping and dissipative processes are modeled with a linear damping term. Thismodel is very similar to the one used by Lagerloef(1995) and CL02. Solutions to the Ekman pumping
model have been computed for different values of thedamping time scale. Correlations between pycnocline
depth variations from the simple model and from the
OGCM maximize toward the center of the Alaska gyre,
where the solution of the simple model accounts for alarge fraction of the OGCM pycnocline depth variability. Correlations decrease along a broad coastal band
and, in particular, to the southwest of Kodiak Island
where they drop to values very close to zero. In thisarea the pycnocline deepens, while the Ekman pumping changes are indicative of enhanced upwelling. The
damping time scales that yield the maximum correlations between the OGCM and the simple model arecharacterized by a spatial pattern with values ranging
from 14–18 months in the central part of the Gulf of
Alaska to values as large as 90 months at some locations along the coast. The damping time scales in thecentral part of the Gulf of Alaska, where correlations
are higher, are very similar to those estimated by Lagerloef (1995) and CL02 using statistical methods.
Westward-propagating Rossby waves can be detectedin the evolution of the model pycnocline. Propagating
features cannot be reproduced by the local Ekman
pumping model, but can be modeled with the explicit
inclusion of a zonal propagation term. However, largescale pycnocline depth anomalies in the OGCM have a

VOLUME 35

predominantly standing character that is well capturedby the Ekman pumping model. This result is in agreement with the study of Cummins and Lagerloef (2004)
based on the comparison of the local Ekman pumping
model and a quasigeostrophic model with satellite dataover the period 1993–2003.
Pycnocline depth anomalies exhibits a counterclockwise propagation along the coastal band, clearly indicating that Ekman pumping alone cannot account forthe pycnocline variability in that area. This coastal
propagation may be due to either mean flow advection
or boundary waves. The inclusion of propagation, witha speed estimated from the OGCM, considerably improves the agreement with the OGCM along thecoastal path. The physical nature of the propagation
velocity remains an open question. Along the western
half of the path pycnocline depth anomalies appear to
propagate with a speed that is very similar to the
OGCM upper-layer mean speed, so propagation could
be consistent with mean flow advection. However, thepropagation speed increases eastward to values that aremuch larger than the upper-layer mean speed in the
OGCM. Thus, in the eastern part of the path boundary
waves appear to be a more likely cause of pycnocline
depth anomalies propagation. Inviscid boundary waves
have an offshore scale of the order of the first baroclinic
Rossby radius of deformation, which in the Gulf of
Alaska is only 15–20 km (Chelton et al. 1998), and one
may expect a severe distortion of these waves in amodel with coarse horizontal resolution. In the presence of lateral viscosity the offshore scale of the Kelvinwave increases while the alongshore phase speed decreases because of the combined effect of lateral viscosity and limited model resolution (Hsieh et al. 1983).
If the model resolution is fine enough to resolve thebroader offshore scale of the viscous wave, then thealongshore phase speed is essentially determined by
viscosity, but if the model resolution is still too coarse toresolve the wave structure then the propagation speed
is also dependent upon the model resolution. Given theviscosity used in the model, the offshore scale of the
waves may be marginally resolved by the model, but it
is unclear how realistic the model’s viscosity is and how
distorted boundary waves may be in this numericalsimulation. On the other hand, the agreement of the
OGCM pycnocline variations with the pycnocline
variations observed at Station GAK1 suggests that
propagation speeds in the model may not be too unrealistic. Further studies are needed to clarify the natureof the propagating signals in the OGCM and the degreeof distortion of these signals introduced by the model’sviscosity and resolution.
The large changes in upper-ocean density structure

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CAPOTONDI ET AL.

and circulation that took place after the mid-1970s do
not seem to result from the dynamical adjustment of
the Alaska gyre according to Sverdrup theory. The latter would imply a weakening of the circulation north of⬃54°N, while the circulation changes in the model exhibit an intensification of the Alaskan Stream starting
upstream of Kodiak Island. Sverdrup balance isachieved through Rossby wave propagation, but
Rossby wave propagation can account for only a part ofthe pycnocline variability in the OGCM (section 5). On
the other hand, propagation of pycnocline depth
anomalies along the coast seems to have a significant
impact on the pycnocline changes along the Alaska
Peninsula, and may be responsible for some of thechanges in the density structure and related circulation
changes. Further studies with high-resolution models
are needed to elucidate the processes responsible forthe adjustment of the Alaska gyre to variations in surface wind forcing at time scales ranging from seasonal
to decadal–interdecadal.
Acknowledgments. We thank the NCAR Oceanography Section for making the model output available tous. We also thank Dr. Patrick Cummins for providing
the observed time series of winter pycnocline depth at
Ocean Weather Station P, as well as useful commentson the manuscript, and the GLOBEC program forthe long-term measurements at station GAK1. Doctor
E. Firing and an anonymous reviewer have offered very
valuable comments and suggestions that have greatly
improved the manuscript. Author AC was supportedby the NOAA/CIFAR program, and AJM was supported by NOAA/CIFAR (NA17RJ1231), NSF(OCE00-82543), and DOE (DE-FG03-01ER63255).
The views expressed herein are those of the authorsand do not necessarily reflect the views of NOAA or
any of its subagencies.
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