4582

JOURNAL OF CLIMATEVOLUME 18

Estimation of the Surface Heat Flux Response to Sea Surface Temperature Anomalies
over the Global Oceans
SUNGSU PARK*
Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado

CLARA DESER
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado

MICHAEL A. ALEXANDERNOAA–CIRES, University of Colorado, Boulder, Colorado(Manuscript received 4 October 2004, in final form 21 February 2005)
ABSTRACTThe surface heat flux response to underlying sea surface temperature (SST) anomalies (the surface heat
flux feedback) is estimated using 42 yr (1956–97) of ship-derived monthly turbulent heat fluxes and 17 yr(1984–2000) of satellite-derived monthly radiative fluxes over the global oceans for individual seasons. Net
surface heat flux feedback is generally negative (i.e., a damping of the underlying SST anomalies) over the
global oceans, although there is considerable geographical and seasonal variation. Over the North Pacific
Ocean, net surface heat flux feedback is dominated by the turbulent flux component, with maximum values(28 W m⫺2 K⫺1) in December–February and minimum values (5 W m⫺2 K⫺1) in May–July. These seasonalvariations are due to changes in the strength of the climatological mean surface wind speed and the degree
to which the near-surface air temperature and humidity adjust to the underlying SST anomalies. Similarfeatures are observed over the extratropical North Atlantic Ocean with maximum (minimum) feedback
values of approximately 33 W m⫺2 K⫺1 (9 W m⫺2 K⫺1) in December–February (June–August). Although
the net surface heat flux feedback may be negative, individual components of the feedback can be positive
depending on season and location. For example, over the midlatitude North Pacific Ocean during late spring
to midsummer, the radiative flux feedback associated with marine boundary layer clouds and fog is positive,
and results in a significant enhancement of the month-to-month persistence of SST anomalies, nearly
doubling the SST anomaly decay time from 2.8 to 5.3 months in May–July.
Several regions are identified with net positive heat flux feedback: the tropical western North Atlantic
Ocean during boreal winter, the Namibian stratocumulus deck off West Africa during boreal fall, and theIndian Ocean during boreal summer and fall. These positive feedbacks are mainly associated with thefollowing atmospheric responses to positive SST anomalies: 1) reduced surface wind speed (positive turbulent heat flux feedback) over the tropical western North Atlantic and Indian Oceans, 2) reduced marine
boundary layer stratocumulus cloud fraction (positive shortwave radiative flux feedback) over the Namibian stratocumulus deck, and 3) enhanced atmospheric water vapor (positive longwave radiative flux feedback) in the vicinity of the tropical deep convection region over the Indian Ocean that exceeds the negative
shortwave radiative flux feedback associated with enhanced cloudiness.

1. Introduction* Current affiliation: Department of Atmospheric Sciences,
University of Washington, Seattle, Washington.

Corresponding author address: Dr. Sungsu Park, Department ofAtmospheric Sciences, University of Washington, Box 351640,
Seattle, WA 98195-1640.
E-mail: sungsu@atmos.washington.edu© 2005 American Meteorological Society

JCLI3521

Surface heat fluxes generate sea surface temperature(SST) anomalies, and SST anomalies, in turn, canmodulate surface heat fluxes. Understanding how surface heat fluxes respond to underlying SST anomalies—
surface heat flux feedback—is essential to understanding the nature of the coupled atmosphere–ocean sys-

1 NOVEMBER 2005

4583

PARK ET AL.

tem. Physically, the surface heat flux feedback is expected to be strongly negative (i.e., damping theunderlying SST anomalies) due to explicit enhancements of upward turbulent heat and longwave radiative
fluxes over warm SST anomalies. However, atmospheric responses can either weaken or strengthen thisexplicit negative feedback. In general, moisture andtemperature of the near-surface air increase over warm
SST anomalies, resulting in a weakening of the negative
turbulent feedback (e.g., the concept of reduced thermal damping; Barsugli and Battisti 1998). Further
weakening of the negative feedback can occur in association with the following atmospheric responses towarm SST anomalies: reduced surface wind speed(positive turbulent flux feedback), reduced boundary
layer cloud fraction (positive shortwave radiative feedback), and enhanced atmospheric water vapor (positive
longwave radiative feedback). On the other hand, thenegative feedback can be strengthened in association
with enhanced surface wind speed or increased cirrus
and stratiform clouds.
Conventionally, surface heat flux feedback has been
estimated from the response of atmospheric general circulation models (GCMs) to prescribed SST anomalies(e.g., Frankignoul et al. 1998). However, as discussed byFrankignoul and Kestenare (2002, hereafter FK02),
surface heat flux feedback can be different dependingon the origins of the SST anomalies, requiring the useof the coupled ocean–atmospheric GCM simulations
for reliable feedback estimation. Because simulated
surface heat flux feedback differs between GCMs
(Frankignoul et al. 2004), however, the surface heat
flux feedback in nature should be estimated from observations. The problem is how to separate surface heat
flux feedback and atmospheric forcing (independent of
surface heat flux feedback) solely from the observed
surface flux data.
Frankignoul et al. (1998) and FK02 suggested amethod to estimate surface heat flux feedback over the
North Pacific and Atlantic Oceans using observed
monthly anomalies of SST and surface heat fluxes.
After filtering out remote ENSO influences, FK02 decomposed the downwelling surface heat flux anomaly(Q⬘) into the sum of a stochastic atmospheric forcing
and oceanic feedback term (⫺␭ · T⬘, where ␭ is a constant feedback factor in watts per meters squared per
kelvin and T⬘ is the underlying SST anomaly). Given
that the SST anomaly is essentially uncorrelated withstochastic atmospheric forcing at lags longer than theatmospheric persistence time scale (⬃1–2 weeks), feedback strengths of individual surface heat flux components were estimated as␭共0兲 ⬅ ⫺⭸Q⬘
Cov关T⬘共⫺1兲, Q⬘共0兲兴,
共0兲 ⫽ ⫺
⭸T⬘
Cov关T⬘共⫺1兲, T⬘共0兲兴

共1兲

where Cov is covariance, (⫺1) and (0) are the values atthe previous and present month, and a prime denotes
the monthly anomaly. FK02 found that observed surface heat flux feedbacks are negative (positive ␭) over
the North Pacific and most of the Atlantic Oceans andare dominated by the turbulent heat flux feedback.
However, significant uncertainties exist in FK02’s
estimation of radiative heat flux feedback. In FK02,
the net downwelling shortwave (SW) and longwave(LW) radiation at the sea surface were estimated using
the conversion formulas of Reed (1977) and Budyko(1974) using the Comprehensive Ocean Atmosphere
Data Set (COADS; Woodruff et al. 1987). However,these two conversion formulas greatly simplified spatial
and temporal variations of clear sky and cloud opticalproperties, with the total cloud fraction as the only
cloud variable explicitly considered. Recently, Zhang
and Rossow (2004) calculated net downwelling SW
and LW radiation by combining the International
Satellite Cloud Climatology Project (ISCCP; Rossow
and Schiffer 1991) satellite-derived cloud optical properties, observed clear-sky properties, and a radiativetransfer model during July 1983–December 2000. Thismethod explicitly incorporates spatial and temporalvariations of clear-sky and cloud optical properties andis likely to be less affected by random observational
error and sampling noise than the COADS ship observations.
In this study, we estimate radiative feedback using
the ISCCP satellite-derived radiative flux data, unlike
FK02 who used indirect estimates based on COADS
cloud cover and empirical formulas. In addition, we
examine the relative contributions of SW and LW radiation to net radiative feedback and each SW and LW
feedback is further decomposed into the contributionsof clear- and cloudy-sky radiation. Turbulent heat flux
feedback is also analyzed in more detail than in FK02
by decomposing it into the contributions of moisture,temperature, and wind speed adjustments of the nearsurface air to underlying SST anomalies.
A description of the data and analysis methods are
given in section 2. In section 3, we discuss estimatedturbulent (section 3a), radiative (section 3b), and net
surface heat flux feedbacks (section 3c) including acomparison of the ship-derived and satellite-derived radiative fluxes (section 3b) and the role of surface heat
flux feedback in the month-to-month persistence of the
SST anomalies (section 3c). A summary and discussion
are given in section 4.

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2. Data and analysis methods
We follow the analysis methods employed by FK02.
However, instead of using monthly data, individual shipobservations are used to calculate surface heat fluxes.
Using individual ship observations obtained from the
Extended Edited Cloud Report Archive (EECRA;
Hahn and Warren 1999), we constructed 5° latitude ⫻10° longitude (also 5° latitude ⫻ 5° longitude for Figs. 4
and 5) monthly data of SST, latent and sensible heat
fluxes, net downwelling SW and LW fluxes at the sea
surface during 1956–97. Latent and sensible heat fluxes
are calculated from the bulk formulas using the transfer
coefficients suggested by Isemer and Hasse (1985) andnet downwelling SW and LW radiation at the sea surface are calculated from the formulas suggested byReed (1977) and Budyko (1974), respectively. Shipderived radiation data are used only for comparison
with those of satellite products (e.g., Figs. 4 and 5) on
which our estimation of radiative feedback is based.
Details of bulk and conversion formulas used in thisstudy are summarized in the appendix.
To reduce the effects of random observational error
and sampling noise in the ship observations, a minimumof 20 observations per month were required in eachmonthly 5° latitude ⫻ 10° longitude grid box; grids notsatisfying this criteria were given missing values. Then,monthly climatologies were calculated requiring a minimum of 30 yr for each month in the 42-yr record andmonthly anomalies were subsequently calculated. Missing values were filled by spatiotemporal interpolations
using the nearby nonmissing grid values and then thedata were detrended using a third-order polynomial. To
minimize interpolation effects, only the grids with aminimum of 30 yr of nonmissing monthly values foreach month before interpolations in the 42-yr record
were used for the subsequent analysis. The fraction of
missing values filled by the interpolations (mainly concentrated in the tropical Pacific, Indian, and South Atlantic Oceans) is about 5%. As a check, we repeatedthe same feedback estimates 1) by filling the missing
gaps using empirical orthogonal functions (EOFs) instead of spatiotemporal interpolations, and 2) without
filling the missing gaps. Both methods produced results
similar to the ones shown here.
To use Eq. (1) to obtain unbiased feedback estimates, any low-frequency variations in the atmosphericforcing with decorrelation times longer than a monthshould be removed prior to the analysis. One source of
low-frequency atmospheric variations is ENSO, which
influences the global atmosphere and oceans via atmospheric teleconnections throughout the year (Lau andNath 1996; Klein et al. 1999; Alexander et al. 2002,

VOLUME 18

2004; Park and Leovy 2004; Park 2004). The seasonallydependent ENSO influence was removed from the surface heat flux estimates as follows. First, ENSO indiceswere obtained from a covariance-based EOF analysis
of monthly SST anomalies over the tropical Pacific andIndian Oceans between 20°N and 20°S using the “Kaplan” dataset (Kaplan et al. 1998) during January 1900–
December 2002. The first three principal components(PCs), which explain 44.5%, 11.6%, and 7.3% of total
variance, respectively, and are well separated from theother modes by the criteria of North et al. (1982), wereused to remove the ENSO signal from the surface heat
flux and SST by subtracting the simultaneous regression values of the monthly anomalies on the PC timeseries using three consecutive monthly values centeredon each calendar month. This ENSO filtering was applied to the global ocean areas including tropical Pacificand Indian Oceans.
As a last step, in order to further reduce random
observational error and sampling noise, we reconstructed monthly anomalies of SST and surface heat
fluxes over the North Pacific, Atlantic, and tropical Pacific and Indian Oceans, respectively, by combining the
EOF modes explaining 90% of the remaining ENSOfiltered variance in each domain.
In addition to ship observations, we analyzed
monthly 5° latitude ⫻ 10° longitude net and clear-sky
downwelling SW and LW radiation data from ISCCP
during January 1984–December 2000 (Zhang and Rossow 2004). Similar to the ship observations, we filteredout linear trends and ENSO signals but did not performthe EOF reconstruction because the satellite data arelikely to be less affected by random observational error
and sampling noise than are the ship observations. The
17-yr record of radiative fluxes may not be long enoughto obtain statistically stable estimates of radiative feedback. However, it is expected that random observational error and sampling noise in the ISCCP-derived
radiative heat flux data are much smaller than the ship
derivations, compensating somewhat for the disadvantage of the short period of record.
To increase the number of realizations and thus reduce sampling noise, seasonal values of the damping
coefficient ␭ are estimated using three consecutive
monthly anomalies following the methodology ofFK02. For example, ␭ during June–August (JJA) is estimated by correlating monthly heat flux anomalies in
JJA with monthly SST anomalies in May, June, andJuly:
共May, Jun, Jul兲,冋SST⬘
Q⬘共Jun, Jul, Aug兲 册
␭共JJA兲 ⫽ ⫺.
SST⬘共May, Jun, Jul兲,
Cov冋 SST⬘共Jun, Jul, Aug兲 册Cov

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PARK ET AL.

TABLE 1a. Average heat flux feedback parameters over the North Pacific Ocean. Area mean values (area-weighted averages ofindividual 5° lat ⫻ 10° lon grid boxes) of seasonal heat flux parameters for the North Pacific Ocean (25°–55°N, 140°E–120°W). Values
in parentheses are monthly climatologies of SST ⫺ Ta (K), RH [relative humidity of near-surface air (%)], and U (m s⫺1). Units of
␭(qs–q␷)⬘ [Eq. (2)], ␭(SST–Ta)⬘ [Eq. (2)], ␭RH, and ␭U⬘ [Eq. (3)] are g kg⫺1 K⫺1, K K⫺1, % K⫺1, and m s⫺1 K⫺1, respectively, and ␭a ⬅ ␭LHF
⫹ ␭SHF ⫹ ␭SW ⫹ ␭LW. The units of the last two rows in Tables 1a,b are month and the others have units of W m⫺2 K⫺1. Turbulent,
radiative, and net feedback parameters are signified by bold characters.

␭LHF ⫹ ␭SHF
␭QT
␭WS
⫺␭(qs–q␷)⬘⫺␭(SST–Ta)⬘
⫺␭RH⬘
⫺␭U⬘

␭SW ⫹ ␭LW
␭SW
␭SW,cloud
␭LW
␭LW,clear␭a␭LHF ⫹ ␭SHF ⫺1
␳ · Cp · H⫺1
␭a
␳ · Cp · H

冉
冉

冊

冊

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Yr

27.0
22.4
3.1
0.39
0.29
(1.9)
⫺1.1
(79.5)
0.15(10.3)
1.0
⫺0.6
⫺0.8
1.6
1.3
28.0

23.9
19.4
2.8
0.37
0.25
(1.6)
⫺1.1
(79.5)
0.17(10.0)
0.6
⫺0.5
⫺0.6
1.0
0.7
24.5

23.3
17.9
3.2
0.38
0.26
(1.1)
⫺1.2
(79.5)
0.24
(9.4)
0.7
⫺0.7
⫺0.8
1.3
1.4
24.0

15.9
12.5
1.7
0.31
0.19
(0.5)
⫺1.0
(80.1)
0.19
(8.5)
ⴑ2.5
⫺3.7
⫺3.9
1.2
1.3
13.4

11.4
9.6
0.8
0.29
0.14
(⫺0.1)⫺0.9(82.2)
0.14
(7.6)
ⴑ3.9
⫺6.6
⫺6.8
2.6
1.1
7.5

9.0
8.5
0.5
0.30
0.12
(⫺0.3)⫺0.8(84.3)
0.10
(6.9)
ⴑ4.3
⫺6.8
⫺7.1
2.5
0.7
4.7

10.2
9.7
0.3
0.37
0.14
(⫺0.3)⫺1.0(85.0)
0.10
(6.5)
ⴑ2.5
⫺5.9
⫺6.3
3.3
1.6
7.7

12.1
10.8
0.6
0.40
0.16
(⫺0.1)⫺0.9(84.0)
0.11
(6.6)
ⴑ2.2
⫺4.7
⫺5.2
2.5
0.8
9.9

15.6
13.3
1.5
0.41
0.19
(0.4)
⫺0.8
(81.5)
0.17
(7.4)
ⴑ2.6
⫺4.2
⫺4.7
1.7
⫺0.2
13.0

18.7
15.7
2.4
0.39
0.20
(0.9)
⫺0.7
(79.8)
0.17
(8.5)
ⴑ2.4
⫺2.7
⫺2.9
0.3
⫺1.2
16.3

21.8
19.2
2.1
0.40
0.24
(1.5)
⫺0.9
(78.8)
0.13
(9.5)
ⴑ1.1
⫺1.5
⫺1.5
0.4
⫺0.6
20.7

22.9
19.8
1.8
0.37
0.24
(1.8)
⫺0.9
(79.0)
0.07(10.1)
ⴑ0.1
⫺0.8
⫺0.9
0.7
0.0
22.8

19.1
16.0
1.6
0.41
0.22
(0.7)
⫺1.2
(81.1)
0.15
(8.4)
ⴑ2.4
⫺3.8
⫺4.2
1.4
0.1
16.7

5.4

6.3

5.1

5.2

3.6

2.8

2.0

1.8

2.1

2.5

2.8

4.3

3.7

5.1

6.1

4.8

6.1

5.1

5.3

2.9

2.3

2.6

2.9

3.0

4.2

4.4

Area mean values of seasonal and annual heat flux
feedback parameters are summarized in Tables 1a–c forseveral geographical domains. For the estimation of theannual feedback parameters in these tables, we first
calculated ␭s for three different lags of 1, 2, and 3

months using all 12 monthly values in the above equation, and the annual ␭ was obtained by averaging thethree ␭s.
Although the ENSO signals were removed, our feedback estimation over the tropical Pacific and Indian

TABLE 1b. Same as in Table 1a but for the extratropical North Atlantic Ocean (20°–60°N, 80°W–0°).

␭LHF ⫹ ␭SHF
␭QT
␭WS
⫺␭(qs–q␷)⬘⫺␭(SST–Ta)⬘
⫺␭RH⬘
⫺␭U⬘

␭SW ⫹ ␭LW
␭SW
␭SW,cloud
␭LW
␭LW,clear␭a␭LHF ⫹ ␭SHF ⫺1
␳ · Cp · H⫺1
␭a
␳ · Cp · H

冉
冉

冊

冊

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Yr

29.5
22.6
4.6
0.46
0.30
(1.7)
⫺0.7
(77.4)
0.28
(9.0)
3.4
1.3
1.8
2.1
3.3
32.9

26.5
20.5
3.9
0.44
0.27
(1.5)
⫺0.8
(77.4)
0.24
(8.9)
2.4
⫺0.2
0.3
2.6
3.2
28.9

18.1
14.9
1.9
0.37
0.19
(1.0)
⫺0.7
(77.8)
0.12
(8.5)
2.3
1.6
2.2
0.7
1.6
20.4

17.2
13.2
2.2
0.38
0.17
(0.6)
⫺0.9
(78.1)
0.15
(7.6)
ⴑ1.0
⫺0.2
0.4
⫺0.9
⫺1.3
16.2

15.7
11.8
1.7
0.42
0.15
(0.1)
⫺1.1
(79.7)
0.13
(6.8)
ⴑ1.8
⫺1.1
⫺0.5
⫺0.7
⫺1.6
13.9

14.8
12.1
0.9
0.49
0.15
(⫺0.1)⫺1.4(81.4)
0.11
(6.3)
ⴑ2.8
⫺3.6
⫺2.9
0.8
⫺1.6
12.0

12.2
11.0
0.0
0.46
0.13
(⫺0.1)⫺1.0(81.7)
0.03
(6.2)
ⴑ2.8
⫺4.3
⫺3.6
1.5
⫺0.8
9.4

15.1
12.7
1.0
0.52
0.14
(0.1)
⫺1.1
(80.9)
0.10
(6.2)
ⴑ0.8
⫺1.6
⫺0.9
0.8
⫺0.7
14.3

19.1
14.7
2.3
0.52
0.17
(0.5)
⫺1.1
(79.7)
0.16
(6.6)
ⴑ0.1
0.1
1.0
⫺0.2
⫺0.2
19.0

23.7
18.5
3.6
0.56
0.20
(1.0)
⫺1.3
(78.4)
0.25
(7.4)
1.8
1.6
2.8
0.2
0.7
25.5

25.8
20.6
3.7
0.50
0.25
(1.4)
⫺0.9
(77.3)
0.24
(8.2)
1.5
1.0
2.3
0.5
1.5
27.3

29.0
22.7
4.4
0.48
0.28
(1.7)
⫺0.7
(77.2)
0.27
(8.9)
1.8
0.2
1.1
1.6
2.1
30.8

22.3
17.1
2.0
0.49
0.22
(0.8)
⫺1.0
(78.9)
0.17
(7.6)
1.1
⫺0.1
0.9
1.2
1.7
23.3

5.1

6.2

7.6

4.6

2.3

1.4

1.4

1.4

1.8

2.2

2.9

3.9

3.4

4.4

5.3

6.4

5.0

2.8

1.9

1.7

1.5

1.9

2.1

2.8

3.6

3.3

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JOURNAL OF CLIMATEVOLUME 18

TABLE 1c. Same as in Table 1a but for the tropical Atlantic Ocean (20°S–20°N, 80°W–10°E).

␭LHF ⫹ ␭SHF
␭QT
␭WS
⫺␭(qs–qv)⬘⫺␭(SST–Ta)⬘
⫺␭RH⬘
⫺␭U⬘
␭SW ⫹ ␭LW
␭SW
␭SW,cloud
␭LW
␭LW,clear
␭a

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Yr

3.4
9.6
⫺5.7
0.40
0.10
⫺0.1
⫺0.31
(6.3)
ⴑ1.4
⫺1.0
0.3
⫺0.5
⫺2.4
2.0

3.7
9.6
⫺5.4
0.41
0.09
⫺0.2
⫺0.30
(6.2)
1.8
1.8
2.7
0.0
⫺1.3
5.5

3.5
8.9
⫺5.2
0.39
0.10
0.1
⫺0.28
(6.1)
ⴑ0.1
⫺0.4
1.1
0.3
⫺1.2
3.4

2.8
7.7
⫺4.3
0.33
0.09
0.3
⫺0.23
(6.2)
ⴑ1.2
⫺0.9
0.5
⫺0.2
⫺2.3
1.6

3.7
8.3
⫺3.8
0.33
0.09
0.3
⫺0.21
(6.3)
0.0
⫺1.6
⫺0.4
1.6
⫺0.7
3.7

6.4
7.9
⫺1.7
0.30
0.07
0.1
⫺0.10
(6.5)
ⴑ1.0
⫺3.9
⫺2.4
2.9
0.3
5.5

7.2
8.6
⫺2.4
0.33
0.07
0.1
⫺0.12
(6.4)
0.2
⫺3.2
⫺1.2
3.4
1.3
7.4

9.1
8.2
⫺0.2
0.32
0.05
⫺0.1
0.00
(6.3)
ⴑ2.0
⫺5.2
⫺2.1
3.2
0.0
7.1

8.4
8.9
⫺1.0
0.36
0.06
⫺0.1
⫺0.05
(6.1)
ⴑ2.7
⫺5.9
⫺1.8
3.3
⫺0.9
5.7

10.6
11.4
⫺1.2
0.46
0.10
⫺0.5
⫺0.06
(6.0)
ⴑ6.1
⫺10.4
⫺5.4
4.2
⫺0.7
4.5

7.3
11.0
⫺3.5
0.43
0.11
⫺0.2
⫺0.18
(6.1)
ⴑ7.2
⫺9.6
⫺5.5
2.4
⫺2.0
0.1

5.6
10.7
⫺4.6
0.42
0.11
⫺0.2
⫺0.24
(6.2)
ⴑ5.1
⫺5.8
⫺3.1
0.7
⫺2.5
0.5

6.5
8.9
⫺2.5
0.35
0.08
0.1
⫺0.14
(6.2)
1.1
⫺2.7
0.1
3.8
1.3
7.6

Oceans may still be biased due to the potential persistence of the atmospheric forcing in the remaining dynamic coupling variation modes between the atmosphere and ocean. In addition, over the regions where
ENSO effects dominate the total variances, the remaining covariances between the ENSO-filtered SST andsurface fluxes may be too weak to produce reliable
feedback estimates. Thus, only qualitative interpretations of the results are valid in these regions.

3. Resultsa. Turbulent heat flux feedback
Figure 1 shows estimated turbulent heat flux feedback for the four seasons. Domains used for the EOF
reconstructions (North Pacific, Atlantic, and tropical
Pacific and Indian Oceans) are indicated by thick blacklines. If the SST autocorrelation at 1-month lag does
not differ from zero at the 95% confidence level from

FIG. 1. Turbulent heat flux feedback (␭LHF ⫹ ␭SHF, the sum of latent and sensible heat flux feedbacks) for eachseason estimated from the EECRA ship observation. The EOF reconstruction domains for the North Pacific,
Atlantic, and tropical Pacific and Indian Oceans are indicated by thick black lines. Grids where SST autocorrelation at 1-month lag does not differ from zero at the 95% confidence level from the two-sided Student’s t test
assuming independent samples (e.g., the East China Sea, west of Taiwan in JJA) are indicated by yellow shading.

1 NOVEMBER 2005

PARK ET AL.

the two-sided Student’s t test assuming independent
samples, ␭ was not estimated (indicated by yellow shading in the figures).
Over the North Pacific and extratropical North Atlantic Oceans, turbulent heat flux feedback damps SST
anomalies (positive ␭) in all seasons, with maximum(minimum) values during winter (summer). During December–February (DJF), regions of strong negative
turbulent heat flux feedback with magnitudes over 40
W m⫺2 K⫺1 occur over the far western subtropicalNorth Pacific, central North Pacific (north of the Hawaiin Islands), central and northeastern Atlantic, andthe Mediterranean Sea. Over the tropical Atlantic
Ocean, turbulent heat flux feedback is generally
weaker than the North Pacific and extratropical North
Atlantic Oceans. Regions of positive turbulent heat
flux feedback (negative ␭) occur over the western tropical Atlantic during DJF and over the central tropicalAtlantic during March–May (MAM). Over the tropical
Pacific and Indian Oceans, turbulent heat flux feedback
generally damps SST anomalies. Over the Indian
Ocean, negative turbulent heat flux feedback is strongest during the boreal spring but weak during the boreal
fall while a reverse pattern is observed over the western
tropical Pacific Ocean. In general, except in the tropical
Pacific and Indian Oceans, overall patterns and magnitudes of estimated turbulent heat flux feedback aresimilar to FK02.
To obtain insights into the mechanisms responsible
for the observed turbulent heat flux feedback, we repeated the same lag-covariance analysis using (qs ⫺ q␷)⬘
[qs: saturation specific humidity at SST with a correction of salinity effect (see the appendix), q␷: specifichumidity of surface air], (SST ⫺ Ta)⬘ (Ta: surface airtemperature), and surface scalar wind speed anomaly(U⬘). After calculating ␭ for each (qs ⫺ q␷)⬘, (SST ⫺Ta)⬘, and U⬘ from Eq. (1) (note that the units of ␭ forthis case is not watts per meters squared per kelvin but
grams per kilograms per kelvin, kelvins per kelvin, andmeters per second per kelvin, respectively), independent contributions of moisture–temperature adjustment and wind speed adjustment to turbulent heat flux
feedback were estimated as␭QT ⫽ 关L␷ ␳Cq ␭共qs⫺q␷兲⬘⫹ Cp ␳Ch ␭共SST⫺Ta兲⬘兴 U,

共2兲

␭WS ⫽ 关L␷ ␳Cq 共qs ⫺ q␷兲⫹ Cp ␳Ch 共SST ⫺ Ta兲兴 ␭U⬘,

共3兲

where the overbar denotes the monthly climatology ateach grid point, ␭QT and ␭WS are the turbulent heat flux
feedbacks associated with moisture–temperature andwind speed adjustments of near-surface air, respec-

4587

tively, and definitions of the other variables are in the
appendix. Note that ␭WS represents a dynamics feedback that was not considered in the classical Hasselmann-type stochastic model (Frankignoul 1985). Although Cq and Ch are functions of SST ⫺ Ta and U andthe contribution of ␭(qs⫺q␷)⬘(SST⫺Ta)⬘ is neglected, thesum of ␭QT and ␭WS roughly corresponds to turbulent
heat flux feedback (not shown but see Tables 1a–c).
Figure 2 shows the turbulent heat flux feedback associated with moisture–temperature adjustment of thenear-surface air, ␭QT [Eq. (2)]. Comparison of Fig. 2
with Fig. 1 indicates that over the extratropical oceans,strong negative turbulent heat flux feedback (highly
positive ␭) stems mainly from relatively weak moisture–temperature adjustment of near-surface air to underlying SST anomalies. In these regions, annual cyclesof turbulent heat flux feedback are largely explained by
the annual cycles of ␭QT, about half of which is from theannual cycle of moisture–temperature adjustment, ␭QT/
U, and the remaining is from the annual cycle of climatological surface wind, U (Tables 1a,b). Note that overthe extratropical oceans, relative humidity of the nearsurface air generally decreases over warm SST anomalies (⬃⫺1% K⫺1; Tables 1a,b), implying that cloudbase height (i.e., lifting condensation level) rises as SST
increases.
Figure 3 shows the turbulent heat flux feedback associated with the wind speed adjustment of nearsurface air, ␭WS [Eq. (3)]. The contribution of ␭WS toturbulent heat flux feedback is generally weaker than␭QT especially over the extratropical oceans but ␭WS
shows nontrivial temporal and spatial variations withsignificant sign reversals. In particular, positive turbulent heat flux feedback over the western and central
tropical Atlantic Ocean in Fig. 1 are due to reduced
surface wind speeds over warmer SST anomalies. Thisfeature, often referred to as “wind–evaporation–SST
feedback,” has been interpreted as the results of thevariations of climatological Atlantic trade winds in responses to regional SST anomalies: hydrostatic reduction of local sea level pressure (SLP) over warm SST
anomalies induces anomalous cross-equatorial pressure
gradient forces, which results in the weakening of local
trade wind speed over warm SST anomalies (e.g.,
Chang et al. 2001). Because this region is strongly influenced by tropical Pacific SST anomalies (Alexander
and Scott 2002; Wang and Enfield 2003), which canreinforce positive heat flux feedback in this region (Saravanan and Chang 2000), parts of observed positive
feedback may be associated with the imperfect removalof nonlinear remote ENSO influences (Hoerling et al.
1997).
Reduction of surface wind speed over warm SST

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FIG. 2. Same as in Fig. 1 but for turbulent heat flux feedback in association with moisture–temperatureadjustment of near-surface air to underlying SST anomalies estimated from EECRA ship observations [␭QT in
Eq. (2)].

anomalies also occurs over the equatorial Indian Ocean
in September–November (SON) contrasting to the enhanced surface wind speed in MAM. Similar featureswith opposite polarities are also observed over thewestern tropical Pacific Ocean. Note that this windspeed adjustment significantly contributes to the annual
cycle of turbulent heat flux feedback in these tropicalregions (see Fig. 1 and Table 1c) in contrast to midlatitude areas where the annual cycle of moisture–temperature adjustment is the dominant contributor.
Regions of strong negative turbulent heat flux feedback (highly positive ␭) over the North Pacific and extratropical North Atlantic Oceans in autumn and winter (Fig. 1) are in part associated with enhanced surface

FIG. 3. Same as in Fig. 1 but for turbulent heat flux feedback in association with wind speed adjustment ofnear-surface air to underlying SST anomalies estimated from EECRA ship observations [␭WS in Eq. (3)].

1 NOVEMBER 2005

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4589

FIG. 4. Interannual correlation coefficients between ISCCP satellite-derived and EECRA
ship-derived net downwelling (a) SW, (b) LW, and (c) SW ⫹ LW radiation during JJA basedon the period 1984–97.

wind speed over warm SST anomalies (Fig. 3). Usingsatellite data, Nonaka and Xie (2003) found a similar
increase of surface wind speeds over warm SST anomalies over the Kuroshio and its extension. Over the
North Pacific and extratropical North Atlantic, maximum ␭QT roughly coincides with maximum ␭WS. In
these areas, the adjustments of T⬘a and q⬘␷ are weak andrelative humidity of surface air shows strong reduction(up to ⫺3% K⫺1) over warm SST anomalies (notshown). This implies that cooling and drying processes
tend to counteract the surface flux forcing. As discussed
by Nonaka and Xie (2003), this is likely to reflect enhanced incorporation of dry and fast-moving upper air
into the marine boundary layer (MBL) due to enhanced strength of boundary layer turbulence overwarm SST anomalies. However, it is not clear how thismechanism alone can induce cooling anomalies of nearsurface air given a statistically stable layer across the
top of atmospheric MBL. It is speculated that nonlocal
or diabatic processes involving atmospheric circulation
changes may play roles here.

b. Radiative heat flux feedback
In this section, we first compare ship- and satellitederived radiation fields to show that radiative feed-backs estimated from the two different datasets can besignificantly different. Then, radiative feedback estimated from the satellite derivation will be discussed indetail in each geographical domains.
Figure 4 shows interannual correlation coefficients
between satellite- and ship-derived net downwelling
SW, LW, and SW ⫹ LW radiation at the sea surface
during JJA. SW radiation from Reed’s formula is wellcorrelated with those from ISCCP over the northeastern Atlantic and subtropical western Pacific Oceans(correlation coefficient, r ⫽ 0.7⬃0.9). In contrast, LW
radiation is essentially uncorrelated in the two datasets
over most of the global ocean except the far northeastern Atlantic Ocean. The resulting net radiation shows
relatively high correlations over the northeastern Atlantic and subtropical western North Pacific Oceans but
over most of the remaining ocean areas, correlations
are very weak. Figure 5 shows a function of the ratio of
the interannual standard deviations during JJA derived
from ship versus satellite estimates. Not only weakly
correlated, they also show systematic differences in themagnitude of variance. Significantly, the underestimated variances of LW radiation by Budyko’s formulaare ⬃50%–100% of the ISCCP variances over most of
the oceans. The same analysis during DJF shows that

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JOURNAL OF CLIMATEVOLUME 18

FIG. 5. Ratio of ship-derived to satellite-derived standard deviations of net downwelling (a)SW, (b) LW, and (c) SW ⫹ LW radiation, expressed as the fraction [100 · (␴EECRA/␴ISCCP ⫺ 1)],
where ␴ is the interannual std dev during JJA. Red (blue) shading indicates that the ship(satellite-) derived values are larger than those from satellites (ships). Climatologies of interannual standard deviations of ISCCP radiation during JJA are plotted as solid lines in eachfigure with a contour interval of 2 W m⫺2.

over the midlatitude oceans, net radiation is less wellcorrelated than in JJA even though differences of interannual standard deviations are reduced due partly to
the weak incoming SW radiation at the top of the atmosphere during DJF (not shown). Both the weak correlations and difference in standard deviations arelikely to result in different estimates of radiative flux
feedback [Eq. (1)].
The sum of the SW and LW feedback estimated fromthe satellite observations is shown in Fig. 6, and theindividual components in Fig. 7 (SW) and Fig. 8 (LW).
To get insights into the mechanisms responsible for theobserved radiative feedback, we also analyzed clear-sky
radiative feedback using clear-sky radiation data at thesea surface. The cloud contribution to radiative feedback was estimated by subtracting clear-sky radiative
feedback from the net sky radiative feedback. The results of clear-sky and cloud radiative feedbacks areshown in Fig. 9 (clear-sky SW) and in Figs. 10–11 (LW).
As a whole, SW feedback is largely dominated by thecloud component, which is similar to Fig. 7. In contrast,
LW feedback is mostly from the clear-sky component

but the contribution of cloud LW feedback is not negligible especially over regions where MBL stratocumulus clouds are abundant.

1) NORTH PACIFIC OCEAN
Over the North Pacific, radiative feedback is generally positive (negative ␭) during midspring–fall but isnegative (positive ␭) during winter centered to thesouth of Alaska, with maximum values ⬃8–16 W m⫺2
K⫺1 (Fig. 6 and Table 1a). During summer, regions ofpositive radiative feedback with magnitude ⬃8–16 W
m⫺2 K⫺1 occurs over the far western and eastern Pacific, and central North Pacific along the 37.5°N. Duringfall, positive radiative feedback weakens over the central North Pacific but strengthens over the eastern subtropical North Pacific west of California.
Over the eastern and central North Pacific, most of
positive radiative feedback comes from strong positive
SW feedback (slightly cancelled by weaker negative
LW feedback) in association with MBL stratocumulus
clouds that are highly reflective to SW radiation andhave low cloud-top height (Fig. 7). Parts of positive SW

1 NOVEMBER 2005

PARK ET AL.

4591

FIG. 6. Same as in Fig. 1 but for radiative heat flux feedback (␭SW ⫹ ␭LW) estimated from ISCCP satellite data.

feedback over the western and central North Pacificnorth of 40°N during summer are likely to be associatedwith the fog that is favored by colder water and stable
atmospheric surface layer (see Table 1a) under northward advection (Norris 1998a,b). Magnitudes of cloud
SW feedback in these regions are 6–20 W m⫺2 K⫺1 andcan be up to 28 W m⫺2 K⫺1 over the California stra-

tocumulus deck during boreal fall. Because the variations of California stratocumulus clouds are strongly
influenced by the variations of upstream boundary conditions in association with strong trade winds (Klein
1997; Park et al. 2004), Eq. (1) may underestimate thisnonlocal feedback effect. Over the central North Pacific in JJA, strong positive SW feedback is distributed

FIG. 7. Same as in Fig. 1 but for SW feedback (␭SW) estimated from ISCCP satellite data. Solid lines in eachfigures are seasonal climatologies of stratocumulus plus fair-weather stratus amount obtained from EECRA shipobservations during 1956–95.

4592

JOURNAL OF CLIMATEVOLUME 18

FIG. 8. Same as in Fig. 1 but for LW feedback (␭LW) estimated from ISCCP satellite data. Solid lines in eachfigure are seasonal climatologies of cumulonimbus frequency obtained from EECRA ship observations during
1956–95.

along a zonal band where meridional gradients of climatological stratocumulus cloud fraction and SST arestrongest. This is consistent with Norris (2000) who
showed that a poleward shift of the SST gradient zone
provides unfavorable conditions for stratocumulus formation, which in turn can strengthen the local warm
SST anomalies. A similar analysis using ship-observed

stratocumulus cloud fraction indicates that strong SW
feedback over the eastern and central North Pacific islargely associated with the variations of MBL stratocumulus cloud fraction [not shown but see Norris andLeovy (1994)]. Over the California stratocumulus deck,warm SST anomalies are likely to promote vertical
deepening and decoupling of the MBL and so limit

FIG. 9. Same as in Fig. 7 but for clear-sky SW feedback.

1 NOVEMBER 2005

PARK ET AL.

4593

FIG. 10. Same as in Fig. 8 but for clear-sky LW feedback.

moisture transport from the sea surface to MBL stratocumulus clouds, resulting in the reduction of MBL
cloud fraction (Bretherton and Wyant 1997; Park et al.
2004). Over the central North Pacific, weakened cold
advection associated with reduced activity of synoptic
storms over warm SST anomalies may further contribute to the reduction of stratocumulus cloud fraction(Norris 2000; Park and Leovy 2004).
Over the subtropical western North Pacific, modula-tion of clear-sky LW radiation also contributes to thepositive radiative feedback (Fig. 8). Similar but muchstronger positive LW feedback is observed over thetropical Pacific and Indian Oceans where deep convection frequently occurs. It is speculated that positive LW
feedback is partly associated with enhanced atmospheric water vapor over warm SST anomalies, which
enhances downwelling LW radiation enough to compensate for enhanced upwelling LW radiation over

FIG. 11. Same as in Fig. 8 but for cloud LW feedback.

4594

JOURNAL OF CLIMATE

warmer SST anomalies (Manabe and Strickler 1964).
Contrasting behavior is observed over the far northeastern Pacific south of Alaska during winter–spring,where mean SST and atmospheric water vapor are
much lower than over the western subtropical North
Pacific.

2) ATLANTIC OCEAN
Radiative feedback over the Atlantic Ocean showscomplex seasonal and geographical variations. One of
the most significant features is strong positive radiative
feedback (negative ␭) west of South Africa in the
Namibian stratocumulus deck region. This positive
feedback is especially large during SON with magnitudes up to 24–32 W m⫺2 K⫺1, strong enough to compensate for negative turbulent heat flux feedback in thisregion (Fig. 1). Note that maximum positive SW feedback tends to be located over the maximum gradient ofstratocumulus cloud fraction on the equatorward flankof the stratocumulus deck (Fig. 7). Positive SW feedback associated with MBL stratocumulus clouds is also
observed over the stratocumulus deck in the eastern
subtropical North Atlantic Ocean during JJA and
MAM. However, negative LW feedback significantly
cancels the positive SW feedback especially during
summer.
Except in MAM, positive LW feedback tends to occur over the western tropical and subtropical North Atlantic Ocean where deep convection frequently occurs.
During MAM when the Atlantic ITCZ is well developed and at its southernmost position, positive LW
feedback is also observed. This positive LW feedback ismainly associated with the variation of clear-sky radiation, suggesting that the increase of atmospheric water
vapor over warmer SST anomalies is responsible. In
contrast, SW feedback in the vicinity of these deep convection zones varies in a complex way depending on
season and geographical location. The resulting net radiative feedback is positive over the Caribbean Sea andnorthern coast of South America during SON and DJF.
In combination with the positive turbulent heat flux
feedback, the positive radiative feedback in this region
can significantly enhance the persistence of SST
anomalies during the boreal winter. The Gulf of
Mexico and nearby western Atlantic Ocean exhibit
negative radiative feedback during winter and positive
radiative feedback during MAM.

3) TROPICAL PACIFIC AND INDIAN OCEANS
In the vicinity of tropical deep convection regions,
there is a general tendency for strong negative cloud
SW feedback to cancel strong positive clear LW feed-

VOLUME 18

back. Some of the positive LW feedback are associatedwith cloud feedback but are much weaker than theclear-sky feedback. A similar lag-covariance analysis
using precipitation data during 1979–2001 (Xie and Arkin 1997) and outgoing LW radiation (OLR) data at the
top of the atmosphere during 1974–99 (Waliser andZhou 1997) indicates that warm SST anomalies are followed by enhanced precipitation and reduced OLR in
this feedback region (not shown). Thus, a simple plausible explanation for this feedback is that both cloud
fraction [i.e., cirrus anvil clouds in the vicinity of updraft regime and precipitating stratiform clouds in thedowndraft regime (Houze 1993)] and atmospheric water vapor increase over warm SST anomalies in association with enhanced deep convection. Both precipitating stratiform and cirrus anvil clouds are likely toreflect incoming solar radiation and thereby cool theunderlying sea surface (Ramanathan and Collins 1991)
while enhanced atmospheric water vapor and enhanced
cirrus anvil clouds may increase downwelling LW radiation and warm the sea surface (Manabe and Strickler 1964).
Opposite feedbacks are also observed. Over thewestern tropical Pacific warm pool region centered at⬃150°E on the equator, warm SST anomalies decrease
net downwelling LW radiation (mostly clear LW) while
increasing net downwelling SW radiation (mostly cloudSW) especially in MAM and JJA. Precipitation amount(OLR) in this area tends to have near-zero or weaknegative (positive) correlations with the previous
month’s SST anomalies (not shown). Our analysis implies that warm SST anomalies in this region do not
trigger deep convection directly but atmospheric dynamic forcings counteract the effects of enhanced SST
anomalies. Previous studies indicate that the tropicalatmospheric intraseasonal oscillation with periods of
40–50 days [also known as the Madden–Julian oscillation (MJO; Madden and Julian 1971] has large influences on the variations of tropical deep convection (andso corresponding clouds and atmospheric water vapor)in this region (Lau and Chan 1986; Maloney and Hartmann 1998).
In spite of the strong cancellation between SW and
LW feedback, net radiative feedback can be significant.
Notable are strong positive radiative feedback in the
Indian Ocean centered over the Bay of Bengal in JJASON and west of Australia in SONDJF, and strong
negative radiative feedback in the central Indian Ocean
in DJFMAM and the East China Sea extending to theeast in JJA. Other notable features are strong positive
SW feedback over the central South Pacific and theeastern tropical Pacific Oceans in SON.

1 NOVEMBER 2005

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PARK ET AL.

FIG. 12. Same as in Fig. 1 but for net surface heat flux feedback (␭LHF ⫹ ␭SHF ⫹ ␭SW ⫹ ␭LW).

c. Net surface heat flux feedback
Figure 12 shows the sum of the turbulent and radiative heat flux feedback. Area mean values of seasonalheat flux feedback parameters are summarized inTables 1a–c for the North Pacific (25°–55°N, 140°E–
120°W), extratropical North Atlantic (20°–60°N, 80°W–0°), and tropical Atlantic Oceans (20°S–20°N, 80°W–
10°E), respectively.
Over the North Pacific and extratropical North Atlantic Oceans, surface heat flux feedback is strongly
negative during winter, with maximum values of 28 and
33 W m⫺2 K⫺1 during January for the North Pacific andextratropical North Atlantic Oceans, respectively. The
annual cycle of surface heat flux feedbacks roughly follows a single sinusoidal pattern with minimum values of
5 W m⫺2 K⫺1 during June for the North Pacific and 9 W
m⫺2 K⫺1 during July for the extratropical North Atlantic. Although weak, regions of positive feedback areobserved over the midlatitude North Pacific during
summer and over the Californian stratocumulus deck
during fall. Net surface heat flux feedback over thetropical Atlantic Ocean is much weaker than the North
Pacific and extratropical North Atlantic Oceans, with amaximum value of 7 W m⫺2 K⫺1 during July and aminimum value of 0 W m⫺2 K⫺1 during November.
Regions of strong positive heat flux feedback occur
over the tropical western North Atlantic Ocean during
winter and over the Namibian stratocumulus deck during SON. Net heat flux feedback over the western tropical Pacific Ocean is negative but positive feedback occurs over the Indian Ocean during JJASON. Over the

far eastern tropical Pacific Ocean, there is a hint of net
positive feedback during SON.
To obtain insights into the role of surface heat flux
feedback on the evolution of SST anomalies, we divided the net heat flux feedback (␭a ⬅ ␭LHF ⫹ ␭SHF ⫹␭SW ⫹ ␭LW) by the heat capacity (␳ Cp) multiplied by
the monthly climatology of ocean mixed layer depth(H ) obtained from the dataset of Monterey and Levitus(1997; Fig. 13 in per monthly units). This quantity [␭a/(␳ Cp H )] represents the inverse of the e-folding time of
SST anomalies in association with surface heat flux
feedback in the stochastic climate model of Frankignoul and Hasselman (1977), as can be seen in the following anomalous heat budget equation of ocean mixed
layer:

␳ Cp Hd
SST⬘ ⫽ ⫺共␭a ⫹ ␭0兲 SST⬘ ⫹ F⬘,
dt

共4兲

where ␭0 is an oceanic feedback parameter and F⬘ areforcings not associated with local heat flux feedback.
Even though negative surface heat flux feedback isstrongest during winter (Fig. 12), damping of SST
anomalies associated with surface heat flux feedback in
the North Pacific and extratropical North Atlantic
Oceans tend to occur most rapidly during late summer
and fall, with minimum SST e-folding times of 2.3
months in the North Pacific and 1.5 months in the extratropical North Atlantic (see Tables 1a,b).
One of the most interesting features is the significant
enhancement of SST persistence by positive radiative
feedback over the North Pacific during late spring and4596

JOURNAL OF CLIMATEVOLUME 18

FIG. 13. Inverse of SST anomaly e-folding time associated with net surface heat flux feedback (month⫺1)[(␭LHF ⫹ ␭SHF ⫹ ␭SW ⫹ ␭LW) / (␳ Cp H )]. Inverses of the values, indicated by red numerals below the color bar,
represent the e-folding time of SST anomalies due to net surface heat flux feedback.

summer. Without radiative feedback, SST e-folding
time associated with turbulent heat flux feedback ismaximum during February (6.3 months) and minimum
during August (1.8 months) over the North Pacific(Table 1a). During winter, negative radiative feedback
slightly decreases the persistence of the SST anomalies.
However, during later spring and early summer, positive radiative feedback significantly increases themonth-to-month persistence of the SST anomalies, witha maximum increase of e-folding time during June(from 2.8 to 5.3 months) when positive radiative feedback is up to ⬃50% of negative turbulent heat flux
feedback (cf. with ⬃2% during winter; see Table 1a).
This result is qualitatively consistent with Zhang et al.
(1998) and Norris et al. (1998) who argued that strong
persistence of the leading mode of summer SST anomalies in the North Pacific is due to the positive radiative
feedback associated with MBL stratiform clouds. However, we note that modulation of MBL clouds by remote ENSO forcing can also have a influence on summer SST anomaly persistence (Park and Leovy 2004;
Alexander et al. 2004; Park et al. 2005, manuscript submitted to J. Climate).

4. Summary and discussion
Following the method described by Frankignoul and
Kestenare (2002), we estimated surface heat flux feedback using 42 yr of ship-derived monthly turbulent heat

flux data and 17 yr of satellite-derived radiative flux
data over the global ocean for individual seasons. To
obtain insights into the mechanisms responsible for theobserved surface heat flux feedback, we separately examined the responses of moisture, temperature, andwind speed of the near-surface air to the underlying
SST anomalies. We also decomposed the radiative
feedback into the contributions from SW and LW components, and each SW and LW feedback was further
decomposed into the clear-sky and cloud components.
Over the North Pacific and extratropical North Atlantic Oceans, turbulent heat flux feedback damps SST
anomalies, with maximum (minimum) negative feedback during winter (summer) when the climatologicalsurface wind speed is strong (weak) and moistening andwarming of near-surface air over warm SST anomalies
is relatively weak (strong). Patches of strong negative
turbulent heat flux feedback in these regions result
from enhanced surface wind speed and relatively weakmoisture–temperature adjustments of near-surface air
over warm SST anomalies. Enhancements of MBL turbulence and incorporation of dry and fast-moving upper air into the lower MBL may be responsible forthese responses. Over the western tropical Pacific andIndian Oceans, turbulent heat flux feedback is generally negative. On the other hand, positive turbulent
heat flux feedback is observed over the western andcentral tropical Atlantic Oceans especially over the Caribbean Sea during winter. A plausible mechanism for1 NOVEMBER 2005

4597

PARK ET AL.

this is the hydrostatic reduction of SLP and the associated weakening of the Atlantic trade winds over warm
SST anomalies. However, imperfect removal of nonlinear remote ENSO influences may also contribute tothis positive feedback.
Positive radiative feedback over the central and eastern North Pacific Ocean during late spring through fallis due to strong positive SW feedback mainly associatedwith variations of MBL stratocumulus cloud fraction.
Deepening and vertical decoupling of the MBL fromthe sea surface and associated reduction of MBL stratocumulus cloud fraction over warm SST anomalies islikely to be responsible. In the central North Pacific,
weakening of synoptic storm activity and accompanying
cold advection over warm SST anomalies may also contribute. Strong positive radiative feedback associatedwith MBL stratocumulus cloud is also observed overthe southeastern subtropical Atlantic Ocean (Namibianstratocumulus deck) during SON.
Positive radiative feedback associated with the variations of clear-sky LW radiation occurs in the vicinity of
deep convection regions: the western tropical North
Atlantic Ocean during SONDJF and the eastern part of
the Atlantic ITCZ during MAM. This is presumably
due to enhanced atmospheric water vapor over warm
SST anomalies. Positive clear-sky LW feedback is also
observed over the western tropical Pacific and Indian
Oceans with weak contributions from the cloud component, but these are significantly cancelled by negative
cloud SW feedback. Increases of upper-level cirrus andlow-level precipitating stratiform clouds in association
with enhanced deep convection are likely to be responsible for the negative SW feedback. However, we also
found the opposite effects over the western Pacificwarm pool region during MAMJJA. In spite of thestrong cancellation between SW and LW feedbacks, thenet radiative feedback can be significant.
In summary, negative net surface heat flux feedback(i.e., damping of the underlying SST anomalies) dominates over the global oceans. However, several regions
are identified with positive net heat flux feedback: thetropical western North Atlantic Ocean during DJF, theNamibian stratocumulus deck during SON, and western (eastern) Indian Ocean during JJA (SON). Over
the North Pacific and extratropical North Atlantic
Oceans in summer, surface heat flux feedback is weakly
negative in general, although patches of positive feedback are also observed. In these regions, the negative
heat flux feedback is strongest during winter but theassociated SST e-folding time is shortest during latesummer and fall due to the seasonal variation in ocean
mixed layer depth. Our analysis indicates that positive
radiative feedback associated with MBL stratocumulus

clouds can significantly enhance month-to-month persistence of the SST anomalies in the North Pacific
Ocean during late spring through summer.
Our observational estimates of surface heat flux
feedback can be used to assess the realism of simulated
air–sea heat flux feedback in coupled ocean–atmosphere general circulation models (see Frankignoul etal. 2004). In addition, our results can provide valuable
information for simple stochastic climate modelingstudies aimed at simulating statistical properties of observed SST anomalies (e.g., Deser et al. 2003; Coetlogon and Frankignoul 2003). With our estimates of theradiative feedback parameters, it may be possible toquantitatively assess the contribution of MBL clouds tothe persistence of extratropical SST anomalies (Park et
al. 2005, manuscript submitted to J. Climate).
Acknowledgments. We express our thanks to Profs.
Conway B. Leovy, C. Frankignoul, Joel R. Norris, andRobert Wood for valuable comments on this work. We
also thank Adam Phillips for help in preparing the figures. Our thanks also go to the anonymous reviewers
for their remarks, which helped to improve the draft.
Sungsu Park is supported by the Advanced Study Program. Clara Deser and Michael Alexander gratefully
acknowledge support from the NOAA/CLIVAR Program.

APPENDIX
Estimation of Net Downward Surface Heat Fluxes
from Individual Ship Observations
Downward latent (QLE in units of watts per meterssquared) and sensible (QSH in units of watts per meterssquared) heat fluxes are calculated using the following
bulk formula:
QLE ⫽ ⫺␳L␷ Cq U 共0.98 qso ⫺ q␷兲,

共A1兲QSH ⫽ ⫺␳ CpChU 共SST ⫺ Ta兲,

共A2兲where ␳ is the density of surface air {␳ ⫽ p/ [RdTa(1 ⫹0.61 q␷)], where p is sea level pressure and Rd is gas
constant of dry air (⫽ 287 J K⫺1 Kg⫺1)}; L␷ is the latent
heat of vaporization (⫽ 2.5 ⫻ 106 J Kg⫺1); Cp is the
specific heat of surface air at constant pressure (⫽ 1004
J K⫺1 Kg⫺1); U is the surface wind speed; Cq and Ch are
the bulk transfer coefficients of moisture and heat, respectively; qso is the saturation specific humidity at SST
with zero salinity; q␷ is the specific humidity of surface
air (kg kg⫺1); and Ta is the surface air temperature. In
Eq. (A1), 0.98 is multiplied to qso by considering a reduction of saturation vapor pressure by salinity effects(Kraus 1972). It is assumed that Ch ⫽ 0.94 Cq and Cq are

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JOURNAL OF CLIMATE

given as functions of U and SST ⫺ Ta [see Table 4 ofIsemer and Hasse (1985)]. In general, Cq is an increasing function of U and SST ⫺ Ta. In the above equations,
U should be the wind speed at 10-m height but we
simply used a ship-reported surface wind speed as U.
Net downwelling SW radiation at the sea surface(QSW in units of W m⫺2) is calculated using the following empirical formula of Reed (1977):
QSW ⫽ QSW,CLR共1–6.2 ⫻ 10⫺3N ⫹ 0.0019␣兲,

共A3兲

where QSW,CLR is the net downwelling daily mean SW
radiation at the sea surface for clear sky (W m⫺2) asfunctions of latitude and time of day of the year [Eq. (1)of Reed (1977)] assuming a uniform atmospheric transmission coefficient of 0.7 over the global ocean; N is thedaily mean total cloud cover (%); and ␣ is the noon
solar elevation angle (°). Equation (A3) was derived for
application to daily mean cloud cover but we applied
Eq. (A3) for individual observations assuming all the
ship observations are made at local noon.
Net downwelling LW radiation at the sea surface(QLW in units of W m⫺2) is calculated using the following empirical formula of Budyko (1974):
QLW ⫽ ⫺␦␴T 4a 共0.254–4.95 ⫻ 10⫺3e兲
共1–10⫺2cN兲 ⫺ 4␦␴T 3a 共SST ⫺ Ta兲,

共A4兲

where ␦ is the emissivity (⫽ 0.95); ␴ is the Stefan–
Boltzmann constant (⫽ 5.6696 ⫻ 10⫺8 W m⫺2 K⫺4); e is
the water vapor pressure of surface air (hPa); and c is acoefficient increasing with latitude [⫽ 0.5⬃0.82, see
Table 9 on p. 59 of Budyko (1974)].
In calculation of QSW and QLW, the observations executed under poor illumination conditions are prefiltered following a screening criteria developed by Hahn
et al. (1995). After calculating downward surface fluxes(QLE, QSH, QSW, QLW) using individual ship observations for the respective local daytime (0600–1800) andnighttime (1800–0600) observations, a daily average iscalculated by giving the same weighting to the respective daytime and nighttime average. If either the daytime or nighttime average value is missing, the otherwas used as the daily average.
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