Progress in
Oceanography
Progress in Oceanography 64 (2005) 1–29
www.elsevier.com/locate/pocean
The seasonal cycle of diabatic heat storage in the Pacific Ocean
Warren B. White a,*, Daniel R. Cayan a,b, Peter P. Niiler a, John Moisan c,
Gary Lagerloef d, Fabrice Bonjean d, David Legler e
a
Scripps Institution of Oceanography, UCSD, La Jolla, CA 92037, USA
b
US Geological Survey, La Jolla, CA 92037, USA
c
Goddard Space Flight Center, Greenbelt, MD 20771, USA
d
Earth and Space Research, Seattle, WA 98102, USA
e
US CLIVAR Office, Washington, DC 20024, USA
Received 21 January 2003; received in revised form 15 March 2004; accepted 14 June 2004
Abstract
This study quantifies uncertainties in closing the seasonal cycle of diabatic heat storage (DHS) over the Pacific Ocean
from 20S to 60N through the synthesis of World Ocean Circulation Experiment (WOCE) reanalysis products from
1993 to 1999. These products are DHS from Scripps Institution of Oceanography (SIO); near-surface geostrophic and
Ekman currents from Earth and Space Research (ESR); and air–sea heat fluxes from Comprehensive OceanAtmosphere Data Set (COADS), National Centers for Environmental Prediction (NCEP), and European Center for
Mid-Range Weather Forecasts (ECMWF). With these products, we compute residual heat budget components by
differencing long-term monthly means from the long-term annual mean. This allows the seasonal cycle of the DHS tendency to be modeled. Everywhere latent heat flux residuals dominate sensible heat flux residuals, shortwave heat flux
residuals dominate longwave heat flux residuals, and residual Ekman heat advection dominates residual geostrophic
heat advection, with residual dissipation significant only in the Kuroshio–Oyashio current extension. The rootmean-square (RMS) of the differences between observed and model residual DHS tendencies (averaged over 10 latitude-by-20 longitude boxes) is <20 W m2 in the interior ocean and <100 W m2 in the Kuroshio–Oyashio current
extension. This reveals that the residual DHS tendency is driven everywhere by some mix of residual latent heat flux,
shortwave heat flux, and Ekman heat advection. Suppressing bias errors in residual air–sea turbulent heat fluxes and
Ekman heat advection through minimization of the RMS differences reduces the latter to <10 W m2 over the interior
ocean and <25 W m2 in the Kuroshio–Oyashio current extension. This reveals air–sea temperature and specific humidity differences from in situ surface marine weather observations to be a principal source of bias error, overestimated
over most of ocean but underestimated near the Intertropical Convergence Zone.
2005 Elsevier Ltd. All rights reserved.
*
Corresponding author. Tel.: +1 858 534 4826; fax: +1 858 534 7452.
E-mail address: wbwhite@ucsd.edu (W.B. White).
0079-6611/$ - see front matter 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.pocean.2004.06.012
Keywords: Seasonal cycle; Pacific Ocean; Heat storage budget
1. Introduction
During planning for the World Ocean Circulation Experiment (WOCE) in 1985, an objective was proposed to measure the global circulation of the upper ocean well enough to close residual diabatic heat storage (DHS) budgets on annual and interannual period scales to within 10 W m2 (World Climate Research
W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
3
DHS component responds to residual horizontal heat advection and air–sea heat flux, independent of mass
(volume) conservation, and can be expected to play the principal role in coupling ocean and atmosphere
over the seasonal cycle (e.g., White, Cayan, Dettinger, & Auad, 2001; White, Cayan, & Dettinger, 2003).
We utilize residual air–sea heat fluxes from five sources; the Scripps Institution of Oceanography (SIO)
and Comprehensive Ocean-Atmosphere Data Set (SIO-COADS) reanalysis (Slutz et al., 1985; Cayan, 1992;
Woodruff, Lubker, Wolter, Worley, & Elms, 1993), the National Centers for Environmental Prediction and
National Center for Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al., 1996; Kistler et al.,
2001), the European Center for Mid-range Weather Forecasts (ECMWF) reanalysis (Uppala et al., 1999;
Beljaars & Kallberg, 2001), the ISCCP reanalysis of shortwave heat flux based on satellite cloud measurements (Zhang, Rossow, Lacis, Oinas, & Mishchenko, 2003), and the KM sensible-plus-latent heat flux
reanalysis based on satellite surface winds (Kubota & Mitsumori, 1997). We compute residual horizontal
heat advection from the estimation of geostrophic and Ekman current velocities in the Earth and Space
Research (ESR) reanalysis (Bonjean & Lagerloef, 2002). In the latter, geostrophic currents are computed
from gradients in the sea surface height (SSH) measured by TOPEX-Poseidon satellite altimetry, referenced
to the annual mean dynamic height from Levitus, Burgett, and Boyer (1994a), Levitus and Boyer (1994b),
while Ekman currents are computed from surface winds measured by the satellite Special Sensor Microwave Imager (SSM/I) (Atlas, Hoffman, Bloom, Jusem, & Ardizzone, 1996). Earlier, a scale analysis conducted by Gill and Niiler (1973) had indicated that residual horizontal heat advection has little influence
on the seasonal cycle of upper ocean heat storage over the interior ocean. Here we find its magnitude comparable to that of the turbulent and radiative heat flux residuals nearly everywhere over the Pacific Ocean.
Our objective is to estimate the RMS of the differences between observed and model residual DHS tendencies in the seasonal cycle of the Pacific Ocean from 20S to 60N. If theses RMS differences are found to
be significantly less than the RMS of the observed residual DHS tendency, then we can determine the dominant heat balance. Initially, we minimize RMS differences to estimate the cross-isopycnal heat flux at the
top of the main pycnocline (i.e., the dissipation) in the residual DHS budget. Subsequently, we suppress
random noise by conducting a 10 latitude-by-20 longitude box-average on all the fields. We focus discussion on the RMS of the differences between observed and model DHS tendencies averaged over eight regional 10 latitude-by-20 longitude ‘‘boxes’’ (a, Fig. 1). Boxes A, B, and C focus on the residual DHS
budget underneath the Westerly Winds in the mid-latitude North Pacific Ocean (Tanimoto, Iwasaka, Hanawa, & Tobe, 1993), contrasting the Kuroshio–Oyashio current extension (Box A), the Subarctic Frontal
Zone in the central ocean (Box B), and the mid-latitude eastern ocean (Box C). Boxes D, E, and F focus
on the residual DHS budget underneath the northeast and southeast Trade Winds. Boxes G and H focus on
the residual DHS budget on the equator under the southeast Trade Winds, contrasting the Warm Pool in
the western ocean (Box G) with the Cold Tongue in the eastern ocean (Box H). Finally, we minimize RMS
differences to correct for biases in the residual air–sea turbulent heat flux and Ekman heat advection in the
COADS, NCEP, ECMWF, and ESR reanalysis products.
2. Defining budgets for diabatic and adiabatic heat storage residuals
We start by formulating the budget equation for residual DHS tendency in the upper ocean. Residual DHS is defined as the component of heat storage in the layer above the top of the main pycnocline
arising from the vertical-average temperature residual in that layer (Moisan & Niiler, 1998). The algorithm for finding the top of the main pycnocline begins with the definition of the near-surface mixed
layer depth; i.e., the depth where temperature changes 0.1 C from that at the sea surface. This coeon
differs from the 0.3 to 0.8 C temperature change used by Kara, Rochford, and Hurlburt (2003) and
may have underestimated the residual DHS. Regardless, we have applied this definition whether temperatures decrease or increase with depth, the former (latter) indicative of a near- surface mixed layer
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W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
Fig. 1. (a) Location of eight boxes, designated ÔAÕ through ÔHÕ, within which residual DHS budgets of the Pacific Ocean are examined
for the seasonal cycle averaged over the 7 years from 1993 to 1999. Each box has dimension of 10 latitude-by-20 longitude, which is
consistent with the 10 latitude-by-20 longitude box-average applied to all of the fields in this study. (b) Distribution of the RMS of
the observed residual DHS tendency over the seasonal cycle in units of W m2. These data were originally interpolated onto 2
latitude-by-5 longitude grid (White, 1995), but then averaged over a 10 latitude-by-20 longitude box in the present study. Contour
intervals are 10 W m2 and hatching is for effect.
dominated by a seasonal thermocline (seasonal temperature inversion). We have also applied it whether
a barrier layer exists within the near-surface mixed layer due to shallow vertical salinity gradients
resulting from rainfall (Maes, Picaut, & Belamari, 2002; Sato, Suga, & Hanawa, 2004). This may have
over-estimated the residual DHS in parts of the western tropical and subtropical Pacific Ocean. The
DHS algorithm defines the top of the main pycnocline to be the depth of an isotherm (at each grid
point) defined by the deepest penetration of the near-surface mixed layer over the 7-year record (Moisan & Niiler, 1998). This choice of isotherm assures that no near-surface mixed layer penetrated deeper
than this isotherm over the 7-year record. It assumes negligible cross-isopycnal mixing at the top of the
main pycnocline across this isotherm. However, in realistic ocean general circulation model (OGCM)
simulations of observed interannual variability in the Pacific Ocean (Auad, Miller, & White, 1998), realistic DHS anomalies were achieved only by simulating significant cross-isopycnal mixing at the top of
the main pycnocline via dissipation. Thus, we relax this assumption by allowing dissipation in the residual DHS budget.
With the DHS defined in this manner, its residual budget can be derived from the conservation of the
residual heat storage in the near-surface layer above the top of the main pycnocline, displayed in flux form;
i.e.,
scale depth of 70 m has been assumed. Subsequently, multiplying this error estimate by the frequency of the
annual cycle yields a standard error for the residual DHS tendency of 10 W m2. This monthly grid-point
error reduces conservatively to 4 W m2 when formed into long-term monthly mean residuals about the
long-term annual mean. It reduces again to 2 W m2 when averaged over the 10 latitude-by-20 longitude boxes. Yet, it is the sampling bias determined by White et al. (2001, 2003) that is of most concern,
indicating that the amplitude of the seasonal cycle in DHS is underestimated by 10%. Furthermore, systematic biases may arise from two additional sources: (1) using the 0.1 C temperature difference criterion
to determine the depth of the near-surface mixed layer above the top of the main pycnocline; and (2) the
inability to take into account the presence of the barrier layer (Maes et al., 2002), which may confine the
residual DHS tendency to a layer shallower than the top of the main pycnocline defined by the 0.1 C temperature-change criterion.
We find the residual horizontal heat advection in Eq. (2.2) dominated by the residual Ekman advection
of mean heat (see below). Bonjean and Lagerloef (2002) have estimated the interpolation errors associated
with residual VE to be 0.02 m s1 on a 1 latitude-by-1 longitude grid. Propagating this through the computation for residual Ekman advection of mean heat [i.e. (30 m)(qOCPO) (0.02 ms1)(1.0 · 105 C m1)]
according to Young (1962) yields a standard error of 12 W m2. In this computation, the Ekman scale
depth of 30 m from Bonjean and Lagerloef (2002) is used and the scale horizontal temperature gradient
of 1.0 · 105 C m1 is assumed. These random errors, initially on a 1 latitude–longitude grid, reduce
to 4 W m2 when interpolated onto the 2 latitude-by-5 longitude grid. They reduce again to
2 W m2 when formed into erm monthly mean residuals out the annual mean. Finally,
they reduce to 1 W m2 when averaged over the larger 10 latitude-by-20 longitude boxes. Yet, again, it
is the bias that is of most concern. The estimation of Ekman flow depends on imperfect knowledge of the
air–sea exchange coefficient for the turbulent transfer of momentum, which is a function of wind speed and
stability of the atmospheric planetary boundary layer (e.g., Greenhut, 1982; Smith, 1988; Friehe et al., 1991;
Xie, Ishiwatari, Hashizume, & Takeuchi, 1998; White & Annis, 2003). Moreover, Ekman currents may
overestimated since the nd-driven flow averaged over the upper layer (i.e., from the ce to the
of the main pycnocline) generally extends deeper than 30 m depth (Bonjean & Lagerloef, 2002). Here, we
estimate the overall bias in the residual Ekman heat advection by conducting least-squares minimization of
the RMS of the differences between observed and model residual DHS tendencies (see below).
The seasonal cycle of sensible and latent heat fluxes, and shortwave and longwave radiative fluxes, display similar patterns regardless of the source or the bulk formulae used (Esbensen & Kushnir, 1981;
Hsiung, 1986; Isemer & Hasse, 1985). However, Weare and Strub (1981), Taylor (1984), Hanawa and Toba
(1987), Weare (1989), Kent and Taylor (1991), Kent, Truscott, Taylor, and Hopkins (1991), Cayan (1992),
Moisan and Niiler (1998), and White (2001) found significant differences in magnitude, deriving from both
random noise and bias. All of the air–sea products utilize the same surface marine weather observations from the volunteer observing ship (VOS) network. But significant sampling biases exist in this
VOS network. Most important is the under-sampling of synoptic storms, which leads to underestimation
in the transfer of heat, moisture, and momentum across the air–sea interface. Sampling bias also derives
from seasonal changes in the geographical coverage by the VOS network. Whilst absolute total air–sea heat
flux can have uncertainties ranging from 30 to 60 W m2
2
on
the 2 latitude–longitude grid. When the residual total air–sea heat flux is interpolated onto the standard 2by-5 latitude–longitude grid and formed into long-term monthly mean residuals, the random noise is
reduced to 5-to-10 W m2. And reduced again to 3-to-5 W m2 when averaged onto the 10 by 20 lati-
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W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
5. Relative magnitudes of the residual DHS budget components
Closing the residual DHS budget requires the observed residual DHS tendency on the left-hand-side of
Eq. (2.2) to be balanced by the model residual DHS tendency on the right-hand-side due to the sum of
residual horizontal heat advection, air–sea heat flux, and dissipation. We begin by examining the distribution of the root-mean-square (RMS) of the observed residual DHS tendency determined from upper ocean
temperature measurements (b, Fig. 1), where the 10 latitude-by-20 longitude box-average has been applied. This finds largest values (170 W m2) in the Kuroshio–Oyashio extension current off the east coast
of Japan, decreasing eastward and equatorward to smaller magnitudes off the west coast of North America
(30–90 W m2), decreasing to minimum values along the Intertropical Convergence Zone (<20 W m2),
and increasing to larger estimates (40 W m2) in the eastern tropical South Pacific Ocean.
The distribution of the RMS of residual horizontal heat advection on the right-hand-side of Eq. (2.2) (a,
Fig. 2a) has largest values in the Kuroshio–Oyashio current extension (80 W m2) and in the Cold Tongue in the central/eastern equatorial Pacific Ocean (20 W m2), decreasing to <20 W m2 over the remainder of the Pacific Ocean. The distribution of the RMS of the total air–sea heat flux residuals from COADS,
NCEP, and ECMWF on the right-hand-side of Eq. (2.2) (b, Fig. 2a) is largest in the Kuroshio–Oyashio
current extension (140–160 W m2) decreasing to minimum magnitude along the ITZC (<20 W m2),
and increasing to larger estimates in the tropical South Pacific Ocean near 15S (40 W m2). These three
patterns of QT from COADS, NECP-I, and ECMWF differ little from one another qualitatively, with
ECMWF estimates at mid-latitudes larger by 20 W m2 than those of COADS and NCEP. Quantitative
differences can be seen more readily when the ratio of the RMS of residual horizontal heat advection to the
RMS of air–sea heat flux residuals is computed (c, Fig. 2a). The RMS of the residual horizontal heat advection can be seen ranging from 20% to 60% of the RMS of the total air–sea heat flux residual over the extra-
Fig. 2a. (a) Distribution of the RMS of the residual horizontal heat advection over the seasonal cycle in units of W m2, the same in
each panel. (b) Distributions of the RMS of the residual total air–sea heat flux over the seasonal cycle in W m2, from COADS (left),
NCEP (middle), and ECMWF (right). (c) Distributions of the ratio of the RMS of the residual horizontal heat advection to that of the
residual total air–sea heat flux in units of percentage, for COADS (left), NCEP (middle), and ECMWF (right). Here, the 10 latitudeby-20 longitude box-average has been applied to all the fields. Contour intervals are 20 W m2 in (a) and (b), and 20% in (c); hatching
is for effect.
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W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
the Pacific Ocean. The distribution of the RMS of residual Ekman heat advection (b, Fig. 2b) is largest in
the Kuroshio–Oyashio current extension (>60 W m2) and in the vicinity of the Cold Tongue (>20 W m2),
smaller over the remainder of the Pacific Ocean. When the ratios of the RMS of the residual geostrophic
heat advection to the RMS of residual Ekman heat advection are computed (c, Fig. 2b), the former are
found to be 20–70% of the latter over the Pacific Ocean.
The residual horizontal heat advection can also be partitioned into the mean advection of residual heat
and the residual advection of mean heat (d and e, Fig. 2b). The distribution of the RMS of the mean advection of residual heat (d, Fig. 2b) is largest near the Kuroshio–Oyashio current extension (10 W m2) and
on the equator (5 W m2), but <5 W m2 over the remaining Pacific Ocean. The distribution of the RMS
of the residual advection of mean heat (e, Fig. 2b) is largest in the Kuroshio–Oyashio current extension
(80 W m2) and in the central/eastern equatorial Pacific (15 W m2), but <10 W m2 over the remaining
Pacific Ocean. When the ratios of the RMS of the mean advection of residual heat to the RMS of residual
advection of mean heat are computed (f, Fig. 2b), the former are found to be <20% of the latter in the extratropics, but of comparable magnitude or larger in the Trade Winds.
The residual air–sea heat flux on the right-hand-side of Eq. (2.2) depends on the difference between
shortwave-minus-longwave heat flux residuals and sensible-plus-latent heat flux residuals from COADS,
NCEP, and ECMWF (Fig. 2c). The distribution of the RMS of sensible-plus-latent heat flux residuals from
the three products (a, Fig. 2c) has them largest in the Kuroshio–Oyashio current extension (90–
100 W m2), decreasing to minimum value along the ITCZ (<10 W m2), and increasing in the tropical
Fig. 2c. (a) Distribution of the RMS of the residual sensible-plus-latent heat flux over the seasonal cycle in units of W m2 from
COADS (left), NCEP (middle), and ECMWF (right). (b) Distribution of the RMS of the shortwave-minus-longwave radiative heat
flux residuals over the seasonal cycle in W m2 from COADS (left), NCEP (middle), and ECMWF (right). (c) Distribution of the ratio
of the RMS of the residual sensible-plus-latent heat flux to that of the residual shortwave-minus-longwave radiative heat flux in units of
percentage from COADS (left), NCEP (middle), and ECMWF (right). Here, the 10 latitude-by-20 longitude box-average has been
applied to all the fields. Contour intervals are 10 W m2 in (a) and (b), and 20% in (c); hatching is for effect.
W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
13
South Pacific Ocean to peak values of 20 W m2. The distribution of the RMS of shortwave-minus-longwave heat flux residuals from the three products (b, Fig. 2c) has them increasing poleward from minimum
value along the ITCZ (10 W m2) to maximum value in the high-latitude North Pacific Ocean (60–
80 W m2), larger in ECMWF estimates than in COADS and NCEP-1 estimates. When the ratios of the
RMS of sensible-plus-latent heat flux residuals to the RMS of shortwave-minus-longwave heat flux residuals are computed (c, Fig. 2c), the former are found to be 100–300% of the latter in the western Pacific
Ocean (particularly along the Kuroshio–Oyashio current extension and along the ITCZ), but 20% to
100% in the eastern Pacific Ocean. The ratios from the three products are similar in the mid-latitude North
Pacific Ocean, but those from NCEP and ECMWF are smaller than those from COADS throughout the
tropics.
The residual radiative heat flux on the right-hand-side of Eq. (2.2) depends on the difference between
residual shortwave and longwave heat flux from COADS, NCEP, and ECMWF (Fig. 2d). The distribution
of the RMS of longwave heat flux residuals from the three products (a, Fig. 2d) has minimum value near
the equator (<5 W m2), increasing with latitude to maximum in the Kuroshio–Oyashio current extension
(10–20 W m2). The distribution of the RMS of shortwave heat flux residuals from the three products (b,
Fig. 2d) has minimum value along the ITCZ (<10 W m2), increasing with latitude to maximum value near
60 latitude (55 to 75 W m2). The ECMWF shortwave heat flux residuals achieve larger maxima in the
mid-latitudes than those of COADS and NCEP. When the ratios of the RMS of the longwave heat flux
residuals to the RMS of the shortwave heat flux residuals are computed (c, Fig. 2d), the former are found
to be 10% to 30% of the latter over the Pacific Ocean in each product.
The residual turbulent heat flux on the right-hand-side of Eq. (2.2) depends on the sum of sensible and
latent heat flux residuals from COADS, NCEP, and ECMWF (Fig. 2e). The distribution of the RMS of
Fig. 2d. (a) Distribution of the RMS of the residual longwave heat flux over the seasonal cycle in units of W m2 from COADS (left),
NCEP (middle), and ECMWF (right). (b) Distribution of the RMS of the residual shortwave heat flux over the seasonal cycle in
W m2 from COADS (left), NCEP (middle), and ECMWF (right). (c) Distribution of the ratio of the RMS of the residual longwave
heat flux to that of the residual shortwave heat flux in units of percentage from COADS (left), NCEP (middle), and ECMWF (right).
Here, the 10 latitude-by-20 longitude box-average has been applied to all the fields. Contour intervals are 5 W m2 in (a) and (b), and
5% in (c); hatching is for effect.
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W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
Fig. 2e. (a) Distribution of the RMS of the residual sensible heat flux over the seasonal cycle in units of W m2 from COADS (left),
NCEP (middle), and ECMWF (right). (b) Distribution of the RMS of the residual latent heat flux over the seasonal cycle in W m2
from COADS (left), NCEP (middle), and ECMWF (right). (c) Distribution of the ratio of the RMS of the residual sensible heat flux to
that of the residual latent heat flux in units of percentage from COADS (left), NCEP (middle), and ECMWF (right). Here, the 10
latitude-by-20 longitude box-average has been applied to all the fields. Contour intervals are 5 W m2 in (a) and (b), and 20% in (c);
hatching is for effect.
sensible heat flux residuals from the three products (a, Fig. 2e) has minimum value in the tropics
(<5 W m2), increasing to maximum value in the Kuroshio–Oyashio current extension (25–35 W m2).
The distribution of the RMS of latent heat flux residuals from the three products (b, Fig. 2e) has minimum
value along the ITCZ (<15 W m2), increasing poleward to maximum in the Kuroshio–Oyashio current
extension (60–70 W m2). When the ratios of the RMS of sensible heat flux residuals to the RMS of latent
heat flux residuals are computed (c, Fig. 2e), the former are found to be 20% of the latter over the tropical
Pacific Ocean, but comparable in magnitude along the eastern and northern boundaries of the North Pacific Ocean. Along the eastern boundary, the NCEP and ECMWF sensible heat flux residuals dominate the
latent heat flux residuals, the opposite of that in the COADS estimates.
The residual dissipation on the right-hand-side of Eq. (2.2) is computed by choosing K at each grid point
that minimizes the RMS of the differences between observed and model residual DHS tendencies. The distribution of the RMS of residual dissipation for the three products ( Fig. 2f) has them maximum in the Kuroshio–Oyashio current extension (80–100 W m2), but <10 W m2 over the remaining Pacific Ocean. Thus,
residual dissipation is largest where residual turbulent air–sea heat fluxes and DHS tendencies are largest.
6. RMS differences between observed and model residual DHS tendencies
The distributions of the RMS of the model residual DHS tendencies on the left-hand-side of Eq.
(2.2) for COADS, NCEP, and ECMWF products (a, Fig. 3) are qualitatively similar to one another
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W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
Fig. 3. (a) Distribution of the RMS of the model residual DHS tendencies on the left-hand-side of Eq. (2.2) in units of W m2 from
COADS (left), NCEP (middle), and ECMWF (right). (b) Distribution of the RMS of the differences between the observed residual
DHS tendencies (b, Fig. 1) and model residual DHS tendencies in (a) from COADS (left), NCEP (middle), and ECMWF (right). (c)
Same as in (b), but where the residual shortwave heat flux has been replaced by that of Zhang et al. (2003). (d) Same as in (b), but where
the residual turbulent heat flux has been replaced by that of Kubota and Mitsumori (1997). Here, the 10 latitude-by-20 longitude
box-average has been applied to all the fields prior to computation of the RMS of the residuals and their differences. Contour intervals
are 10 W m2 in (a), and 5 W m2 in (b), (c), and (d); hatching is for effect.
satellite-derived winds, the RMS differences between observed and model residual DHS tendencies perform better in the Kuroshio–Oyashio current extension but marginally worse everywhere else (d, Fig.
3). This indicates that internal consistency among the set of radiation and turbulent heat flux residuals
from COADS, NCEP, and ECMWF is at least as important as improving individual flux estimates by
incorporating satellite data into their computation.
7. Analysis of the residual DHS budget in regional boxes over the Pacific Ocean
Now we identify the dominant terms in the residual DHS budget in Eq. (2.2) in the 10 latitude-by-20
longitude boxes labeled ÔAÕ through ÔHÕ (a, Fig. 1). In each box, we overlay time sequences of the observed
residual DHS tendency and the model residual DHS tendency computed from the sum of terms on the
right-hand-side of Eq. (2.2) extending over one seasonal cycle (a, Figs. 4a, 4b, and 4c). We also display
the residual air–sea turbulent heat flux, air–sea radiation heat flux, horizontal heat advection, and dissipation on the right-hand-side of Eq. (2.2). from COADS, NCEP, and ECMWF.
We begin with residual DHS budgets in boxes ÔAÕ, ÔBÕ, and ÔCÕ (Fig. 4a) located in the mid-latitude North
Pacific Ocean under the influence of the Westerly Winds (Fig. 1). The RMS of the differences between observed and modeled residual DHS tendencies in the Kuroshio–Oyashio current extension (Box A) is 33, 43,
and 51 W m2 for the COADS, NCEP, and ECMWF air–sea heat flux residuals, respectively (Fig. 4a). This
is reduced in the central mid-latitude North Pacific Ocean (Box B) to 16, 21, and 28 W m2 for the three
products, respectively, and reduced even more in the eastern mid-latitude North Pacific Ocean (Box C) to 8,
4, and 15 W m2, respectively (Fig. 4a). In the three mid-latitude sub-regions, the residual shortwave heat
flux dominates the residual longwave heat flux, and the residual latent heat flux dominates the residual
sensible heat flux, consistent with Figs. 2d and 2e. Moreover, in all three sub-regions, residual horizontal
heat advection is comparable with residual latent heat flux and shortwave heat flux.
In the Kuroshio–Oyashio current extension (Box A), the observed residual DHS tendency fluctuates in
phase with the model estimate in all three products (Fig. 4a). In this sub-domain, the amplitudes of residual
latent heat flux, shortwave heat flux, horizontal heat advection, and dissipation are comparable. In the central mid-latitude North Pacific Ocean (Box B), the observed residual DHS tendency lags the model estimate
by 0.5 month in all three products (Fig. 4a), with the amplitudes of residual latent heat flux, shortwave
heat flux, and horizontal heat advection comparable, with dissipation much less. In the eastern midlatitude
North Pacific Ocean (Box C) the observed residual DHS tendency fluctuates in phase with the model estimate from COADS, but lags by 0.5 months for NCEP and ECMWF products, with residual shortwave
heat flux dominating the other terms (Fig. 3). In all three mid-latitude sub-regions, residual latent heat flux
tends to fluctuate in phase with the residual horizontal heat advection because both terms are proportional
to zonal wind speed residuals in the Westerly Winds. That is, an increase in Westerly Winds produces larger
latent heat flux out of the ocean, whilst increasing the Ekman advection of cool water equatorward.
Next, we examine the residual DHS budgets in boxes ÔDÕ, ÔEÕ, and ÔFÕ (Fig. 4b) of the tropical Pacific
Ocean under the influence of the Trade Winds (Fig. 1). The RMS of the differences between observed
Fig. 4c. Same as Fig. 4a, but fcare ffGffiffiao
Fig.
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Sam2 n7.8(lss)-297
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shortwave heat flux dominates residual longwave heat flux and residual latent heat flux dominates residual
sensible heat flux, consistent with Figs. 2d and 2e. In the two western sub-regions, the residual latent heat
flux and shortwave heat flux are comparable to the residual horizontal heat advection, with residual dissipation negligible.
In the eastern tropical North Pacific Ocean (Box D), the observed residual DHS tendency is misaligned
with the model estimate by 0.5 months or so (Fig. 4b). On the other hand, in the western tropical North
and South Pacific oceans (Box E and Box F), the observed residual DHS tendency leads the model estimate
by 1 month in all three products (Fig. 3). In these three sub-regions, residual latent heat flux fluctuates out
of phase with the residual horizontal heat advection because both terms are proportional to residual zonal
wind speed in the Trade Winds. That is, an increase in Trade Winds produces larger latent heat flux out of
the ocean, whilst increasing the Ekman advection of warm water poleward.
Finally, we examine residual DHS budgets in boxes ÔGÕ and ÔHÕ (Fig. 4c) straddling the equator
in the Warm Pool and Cold Tongue, respectively (Fig. 1). The RMS of the differences between
observed and modeled residual DHS tendencies in the western equatorial Pacific Ocean (Box G) is
6, 6, and 4 W m2from COADS, NCEP, and ECMWF, respectively, whilst in the eastern equatorial
Pacific Ocean (Box ÔHÕ) it is 15, 8, and 6 W m2respectively. In the eastern equatorial Pacific
Ocean, COADS air–sea heat flux residuals perform significantly worse than those of NCEP and
ECMWF.
W.B. White et al. / Progress in Oceanography 64 (2005) 1–29
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Fig. 5. (a) Distribution of the minimized RMS of the differences between observed and model residual DHS tendencies in Eq. (8.1)
from COADS (left), NCEP (middle), and ECMWF (right). Here, RMS differences have been minimized by a least-squares estimation
of coefficient ÔaÕ modifying the residual air–sea turbulent heat flux and coefficient ÔbÕ modifying the residual Ekman heat advection in
Eq. (8.1). (b) Distribution of the best-fit coefficient ÔaÕ that modifies the residual air–sea turbulent heat flux. (c) Distribution of the bestfit coefficient ÔbÕ that modifies the residual Ekman heat advection in Eq. (8.1). Here, the 10 latitude-by-20 longitude box-average has
been applied to all the fields prior to computation of the RMS of residuals and their differences. Contour intervals are 5 W m2 in (a)
and 0.2 in (b) and (c); hatching is for effect.
Kuroshio–Oyashio current extension and to <10 W m2 in the interior ocean (a, Fig. 5), compared with
<100 W m2and <20 W m2, respectively, prior to minimization (b, Fig. 3).
The distributions of the best-fit coefficient ÔaÕ (modifying residual sensible-plus-latent heat flux) display
similarities and differences among the COADS, NCEP, and ECMWF products (b, Fig. 5). In the western
and central mid-latitude North Pacific Ocean, residual sensible-plus-latent heat fluxes in all three products
are determined to be too large by factors ranging from 0.4 to 0.8. Similarly along the equator and in the
western tropical South Pacific Ocean, residual sensible-plus-latent heat fluxes in all three products are