Climate Dynamics (2004) 22: 205–222
DOI 10.1007/s00382-003-0376-7
A. Gershunov Æ R. Roca
Coupling of latent heat flux and the greenhouse effect by large-scale
tropical /subtropical dynamics diagnosed in a set of observations
and model simulations
Received: 19 February 2003 / Accepted: 14 October 2003 / Published online: 28 January 2004
Springer-Verlag 2004
Abstract Coupled variability of the greenhouse effect
(GH) and latent heat flux (LHF) over the tropical –
subtropical oceans is described, summarized and compared in observations and a coupled ocean-atmosphere
general circulation model (CGCM). Coupled seasonal
and interannual modes account for much of the total
variability in both GH and LHF. In both observations
and model, seasonal coupled variability is locally 180
out-of-phase throughout the tropics. Moisture is
brought into convergent/convective regions from remote
source areas located partly in the opposite, non-convective hemisphere. On interannual time scales, the
tropical Pacific GH in the ENSO region of largest interannual variance is 180 out of phase with local LHF
in observations but in phase in the model. A local source
of moisture is thus present in the model on interannual
time scales while in observations, moisture is mostly
advected from remote source regions. The latent cooling
and radiative heating of the surface as manifested in the
interplay of LHF and GH is an important determinant
of the current climate. Moreover, the hydrodynamic
processes involved in the GH–LHF interplay determine
in large part the climate response to external perturbations mainly through influencing the water vapor feedback but also through their intimate connection to the
hydrological cycle. The diagnostic process proposed here
can be performed on other CGCMs. Similarly, it should
be repeated using a number of observational latent heat
flux datasets to account for the variability in the
A. Gershunov (&)
Climate Research Division,
Scripps Institution of Oceanography,
La Jolla, CA 92093-0224, USA,
E-mail: sasha@ucsd.edu
R. Roca
Laboratoire de Météorologie Dynamique,
Ecole Polytechnique,
91128 Palaiseau, France
different satellite retrievals. A realistic CGCM could be
used to further study these coupled dynamics in natural
and anthropogenically altered climate conditions.
1 Introduction
Water vapor is the most important greenhouse gas in the
atmosphere. The radiative properties of water vapor are
central to the response of the climate system to perturbations. The water vapor feedback is usually considered
as a strong positive feedback on sea surface temperature
(e.g., Manabe and Wetherald 1967; Held and Soden
2000). While substantial debate has taken place about
the functioning of this feedback in the past decade (e.g.
Lindzen 1990; Pierrehumbert 1995; Zhu et al. 2000), the
processes at play are still poorly understood. The classical view, inherited from global radiative-convective
one dimensional model studies, links the atmospheric
water vapor content to atmospheric temperature by
simple thermodynamics (the Clausius-Clapeyron law) as
well as to global surface evaporation acting as a source
of moisture for the atmosphere (Ramanathan 1981).
One feature lacking in this classical view concerns the
role of large-scale dynamics and water vapor transport
from surface sources to atmospheric sinks. In the
intertropical belt, evaporation maxima are located over
the subtropical regions, which act as sources of moisture
for the atmosphere. The low-level Hadley/Walker circulations transport this moisture from the subtropical
sources towards the meteorological equator and warm
pools (Cornejo-Garrido and Stone 1977; Gershunov
et al. 1998) where deep convection injects moisture into
the free troposphere. The advected water vapor and liquid water (a product of convection) form regions of
strong greenhouse effect in tropical convergence zones
while subtropical high pressure regions are cooled by
releasing latent heat of evaporation.
The non-local nature of the GH–LHF coupling was
investigated by Gershunov et al. (1998, hereafter G98),
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Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
who used a combination of satellite and in situ observations as well as analyzed surface wind fields and lowlevel moisture convergence to show that the clear-sky
GH (GHcs) is locally anticorrelated with LHF while
strong positive temporal correlations between GHcs and
LHF exist in remote regions of surface moisture sources
and atmospheric sinks. These remote links are imbedded
in seasonally and interannually varying Hadley and
Walker circulations. On seasonal time scales, much of
the moisture feeding convergence zones in the summer
hemisphere is advected across the Equator from the
subtropical high-pressure zones of the winter hemisphere. This non-local relationship between GH and
LHF shown on climatic time scales throughout the
tropics indicates that LHF can not provide either a local
source for the greenhouse, or a local control on greenhouse warming. Understanding the energy balance of
the tropics involves the consideration of radiative and
thermodynamic variables in the context of the entire
atmospheric circulation as it varies on all relevant climatic time scales.
The mechanisms involved in maintaining the spatial
distribution and variability of GH and LHF may influence natural climate variability through their potential
to influence the circulations in which they are imbedded.
[NB As a basic example, consider the following illustration. GH promotes atmospheric and surface warming
in the western Pacific warm pool region, enhancing zonal temperature and pressure gradients which promote
the pacific trades enhancing subsidence, divergence,
evaporation, and latent surface cooling in the eastern
Pacific subtropical highs. This works to further enhance
horizontal temperature and pressure gradients. Consequently, water vapor is more efficiently transported
across the Pacific where it feeds GH and convection over
the warm pool further enhancing the Walker and Hadley
circulations (G98).] These same mechanisms play a
paramount role in determining the nature of anthropogenic climate change by controlling the water vapor
feedback to external climate perturbations, thought to
be the largest positive feedback in the climate system
(e.g., Manabe and Wetherald 1967; Held and Soden
2000). In the present study, the results of G98 are reinvestigated using a novel dataset and observational period as well as a statistical technique dedicated to the
analysis and efficient description of the coupling between
LHF and GH. In order to account for the longwave
radiative effect of atmospheric moisture in all its phases,
total sky GH is considered instead of the clear sky
greenhouse effect previously investigated in G98. Furthermore, the coupling is examined in a fully coupled
global ocean-atmosphere model (CGCM) to test whether the CGCM can be used to better understand the
mechanisms that control the distribution and co-variability of GH and LHF, especially at time scales longer
than those covered by the observational record, i.e.,
decadal variability and climate change experiments.
The work is organized as follows. Section 2 introduces the different datasets used to perform the analysis.
A brief description of the CGCM simulations is also
included. Section 3 provides a broad statistical description of the observations and model output, i.e., means,
variances and local temporal relationships between GH
and LHF, revealing the necessity for a coupled non-local
analysis. In Sect. 4, the seasonal coupling between LHF
and GH is assessed via canonical correlation analysis.
The behavior of the CGCM is compared to observed
reality. Section 5 addresses modeled and observed GH–
LHF variability and coupling on interannual time scales.
Finally, discussion and conclusions are provided in
Sect. 6.
2 Data
2.1 Observations
The total sky greenhouse effect is computed using the Reynolds
optimum interpolation sea surface temperature (SST) analysis
(Reynolds 1988) and total sky outgoing longwave radiation (OLR)
obtained from NOAA (Smith et al. 1996). GH is only computed
over the oceans, which are assumed to emit like a blackbody. This
introduces an uncertainty of less than 1% in the computations
(Inamdar and Ramanathan 1998). The OLR is derived from the
operational NOAA satellites, which measure narrow band radiation, which is converted into broadband. When compared to the
Earth Radiation Budget Experiment (ERBE) data and to the most
recent ScaraB-1, -2 and CERES flux, the NOAA product shows a
cold bias of 8 W/m2 (e.g., Duvel et al. 2001). While such a data set
is not well suited for analyzing long-term climate trends, the seasonal and interannual variability, of interest in the present study, is
in good agreement with the other satellite measurements. The
NOAA products were preferred to the time-limited ERBE estimates of TOA radiation in order to span the observed LHF more
recent time period.The resulting 8 W/m2 GH bias towards higher
values does not influence our results on variability and the coupling
between GH and LHF, but it must be taken into account when we
consider the mean GH field.The period of study spans January
1992 to December 1996. Monthly means are used at a 2.5 · 2.5
resolution.
The latent heat flux (LHF) observations are provided by the
Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite
Data (HOAPS) products (Grassl et al. 2000). It consists of monthly
mean estimates of LHF at a 2.5 · 2.5 resolution. To estimate the
LHF, a bulk formulation is followed where each term is evaluated
individually. The SSTs are retrieved from the AVHRR sensor of
the NOAA/NASA Pathfinder Ocean Program (Smith et al. 1996).
The wind speed u and the atmospheric specific humidity q are
determined from SSM/I satellite measurements while the transfer
coefficient is determined empirically (Schulz et al. 1993; Schluessel
et al. 1995). Schulz et al. (1997) compared the results to in-situ
measurements during TOGA-COARE and indicated that in the
tropical conditions of interest, the uncertainty in LHF reaches
15 W/m2 for monthly mean time scale. Despite this bias, the time
variability of the satellite product was shown to agree reasonably
well with in situ observations. In the present study we focus on the
most stable period of the HOAPS climatology spanning January
1992 to December 1996 when a single SMM/I satellite was operational, avoiding issues of intercalibration and long-term inhomogeneities. The three datasets (SST, OLR and LHF) suffer from
small estimated biases, which should be borne in mind when direct
comparisons are made with the CGCM output. Recent intercomparison between different satellite-based climatologies of LHF indeed indicates that the HOAPS climatology underestimates the
mean LHF in the tropics with respect to other available products
(Kubota et al. 2003). However as discussed later, the methodology
employed here mainly relies upon correlations and hence the
analysis of the coupled GH–LHF variability is not corrupted by
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
these systematic biases in the mean fields. A thorough intercomparison of the variability structure in the different available data
sets of OLR and LHF should shed more light on the discrepancies
between each data set. Such an analysis is outside the scope of the
present study. We proceed, bearing in mind the limitations of the
present observational data.
In addition to the listed thermodynamic variables, we used the
850 hPa re-analyzed winds from the National Center for Environmental Prediction (NCEP) (Kalnay et al. 1996) to document the
large-scale low-level flow.
2.2 CGCM
The Community Climate System Model (CCSM) is one of the most
advanced coupled models currently available. Being the first climate model developed and applied by the scientific community
using pooled resources from many institutions, it relies on and
benefits from the involvement of the broad climate research community for its advancement. The CCSM includes interactive
atmosphere, ocean, land, and sea ice models whose details and
coupling are described by Blackmon et al. (2001).
One of the long-term goals of the CCSM project is ‘‘to make the
model readily available to, and usable by, the climate research
community, and to actively engage the community in the ongoing
process of model development’’ (Blackmon et al. 2001). The source
code with documentation and output from the primary CCSM
simulations have both been made available on the CCSM Web site
(http://www.ccsm.ucar.edu/models). Another stated goal is ‘‘to use
the CCSM to address important scientific questions about the climate system, including questions pertaining to global change and
interdecadal and interannual variability’’. Moreover, the develop-
Fig. 1 a Mean greenhouse
effect (GH) in over 5 years of
observations and b 10 years of
CCSM. The colors and contours
represent GH trapping in
W/m2. The color scale is the
same on each panel and the
contours are drawn at 25 W/m2
intervals starting with
125 W/m2. Vectors represent
mean 850 hPa wind direction
and velocity (a reanalysis,
b CCSM) with the reference
maximum velocity magnitude
shown over Asia. Here, as in
other figures, winds vectors are
interpolated on a 5 · 5 grid, for
easier visualization
207
ers encourage the climate community to ‘‘diagnose and suggest
possible avenues to improve the component models so that the full
CCSM can better simulate coupled atmosphere–ocean variability
on intraseasonal, seasonal and interannual time scales’’.
Researchers should be able to use the CCSM for ‘‘predictability
and sensitivity studies to further understanding of the nature and
predictability of interannual variations of the climate system and
their importance in the dynamics of longer-term climate variations
and climate response to external forcing’’ (Blackmon et al. 2001).
Plentiful data from a 300-year control simulation that has reproduced stable surface temperatures without artificial flux adjustments as well as a climate change run have been made freely
available on the Internet. We therefore chose the CCSM for its
quality, availability and openness to scrutiny by the wider climate
research community. From the 300-year control simulation, we
chose the last 10-year period, which showed pronounced interannual variability. As in the observations, we use modeled SST and
OLR to compute GH, and analyze it together with modeled LHF
and 850 hPa winds.
3 Means, variances and local correlations
Mean observed and modeled patterns of GH are
remarkably similar (Fig. 1). Tropical GH is strongest
over the warm pool and the inter-tropical and south
Pacific convergence zones (ITCZ and SPCZ, respectively). In general, the strongest GH follows convection
in regions of wind (and moisture) convergence. Low
values of GH and strong winds are found in the
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Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
subtropical highs. The modeled GH is too low over the
Indian Ocean and in the Atlantic ITCZ as well as in the
equatorial central and eastern Pacific. The modeled
SPCZ appears to extend too far into the tropical southcentral Pacific suggesting the existence of a double ITCZ
in the model. Overall, however, the qualitative and
quantitative agreement between CCSM and observations is within allowable limits. The major discrepancy
between model and observations is that the mean model
greenhouse appears to be of roughly equal magnitude in
both hemispheres, while in the observations, Northern
Hemisphere oceans are marked with a stronger mean
GH. This observed result is consistent with precipitation
observations (see e.g., Gershunov and Michaelsen 1996,
their Fig. 4a).
Mean observed and modeled LHF fields are displayed in Fig. 2. The top panel suggests that regions of
strong GH are typically regions of weak evaporation
and LHF while the largest sources of moisture are found
in subsidence zones characterized by strong winds.
Strong LHF is also observed in the western boundary
currents, e.g., the northward branches of the Kuroshio
and the Gulf Stream. The weakest LHF is in the eastern
margins of the equatorial Pacific and Atlantic Oceans.
Modeled mean LHF (Fig. 2b) only roughly resembles
the observed. The largest regions of evaporative cooling
and moisture sources are located away from the equator
Fig. 2a, b Same as Fig. 1, but
for latent heat flux (LHF).
Contours start at 25 W/m2
and the region of lowest LHF is the equatorial Pacific
cold tongue, which appears to be too long in the model.
The Gulf Stream and the Kuroshio Currents are marked
with remarkably high LHF, higher than observed.
Modeled winds are also stronger then observed in the
northwestern Pacific. The model is characterized by too
much evaporation in general, especially in the equatorial
western Pacific and Indian oceans as compared with
observations. Moreover, extensive tropical Pacific
moisture sources tend to be located in the central and
western tropical Pacific and, therefore, are not as clearly
spatially de-coupled from regions of strong GH in
CCSM as they are in the observations. From the
broadest point of view, observations show that, on
average, the Southern Hemisphere produces more
moisture as evaporation. In the model, however, both
hemispheres are marked with comparably high LHF.
Despite being small, the observed interhemispheric gradient is a robust feature of various available LHF
observational data sets (Kubota et al. 2003), especially
in the Pacific. The model, however, displays the opposite
interhemispheric LHF gradient in the Pacific, e.g., less
evaporation in the Southern than in the Northern
Hemisphere.
Figures 3 and 4 quantify the standard deviation in
observed and modeled GH and LHF total fields. The
largest GH variability occurs in the Asian – Australian
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
monsoon regions and, by extension, around the western
Pacific warm pool, the most spatially extensive region of
strong GH. Other regions of strong variability are found
around central America, equatorial Atlantic, the north
and south Atlantic convergence zones, and the Guinea
Basin. A distinct region of high observed GH variability
is located in the central equatorial Pacific east of the date
line (Fig. 3a). Modeled GH variability is less representative of reality than the modeled mean GH. Modeled
variability is too strong in the tropical seasonal convergence zones. This high variability extends all the way
around the tropics on both sides of the Equator, even in
the central and eastern Pacific, further suggesting the
presence of a double seasonal ITCZ in the model.
Equatorial GH variability is lower, as in the observations, but still too high everywhere except the central–
eastern tropical Pacific where it is too low.
Curiously, while mean GH is reproduced better by
CCSM than the GH variability, LHF variability is better
reproduced than the mean field. Both in the observations
and the model, the strongest LHF variability is found in
the northward branches of the Kuroshio and the Gulf
Stream Currents (Fig. 4). High LHF variance is also
apparent in the tropical North Atlantic in a band
stretching southwestward from West Africa to Brazil.
There is a gradient of increasing variability away from
Fig. 3 a Standard deviation of
GH in observations and
b CCSM. The color scale is the
same on each panel. Contours
are drawn at 5 W/m2 intervals.
Vectors represent the standard
deviation of 850 hPa wind u
and v components. Reference
maximum standard deviation
magnitude is shown over Asia
209
the Equator and also westward across ocean basins. The
Northern Hemisphere oceans are marked with appreciably stronger LHF variability then the Southern
Hemisphere oceans. All these features of LHF variability appear in the observations and model alike. The
model differs from observations mainly in having too
much LHF variability in the equatorial western Pacific
and Indian Oceans and around Japan; and too little in
the southeast tropical Atlantic and Pacific, the southwestern Indian Ocean and the central equatorial Pacific.
To see that GH and LHF are inversely related in
time, consider Fig. 5a, which shows the observed local
temporal correlation between monthly means of GH
and LHF over the 5-year observational period. Correlations are negative everywhere except for the north- and
south-eastern Pacific, regions of low average GH as well
as low variability in both GH and LHF fields; and in the
central equatorial Indian Ocean, around Indonesia, the
far eastern Pacific and Atlantic Oceans, regions of low
mean and variance in the observed LHF field. These
areas are geographically small and the largest positive
correlations there are 0.3. Most of the tropics show
negative correlations reaching below –0.75 in the
southwestern Indian Ocean, northwestern Pacific, central Pacific and western Atlantic, just south of the
equator. However, strong negative correlations are
210
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
Fig. 4 Same as Fig. 3, but for
LHF
found practically everywhere the standard deviation of
both GH and LHF is high and where the mean values
are considerable as well. In most other areas, correlations are lower, but predominantly negative. Modeled
GH–LHF correlations (Fig. 5b) are negative in regions
where either GH or LHF variability is very strong.
However, these regions are less geographically extensive,
while regions of positive correlations are more extensive
and feature stronger correlations than in the observations. This propensity to more positive correlation is
clearly visible in the contrast between the observed and
modeled local correlation histograms or empirical
probability density functions (PDFs: Fig. 5c). On first
approximation however, the general modeled patterns
are not too un representative, except in the central
equatorial Pacific where modeled correlations tend to be
positive, while the observed ones are strongly negative.
The general picture of GH and LHF means and
variances gives a sense of spatial de-coupling between
LHF and GH, especially in the observations. In the
CCSM, the mean GH is well reproduced, the variability
is not; LHF presents the opposite story. However, to
first approximations, the comparison between modeled
and observed means and variances largely validates the
model. Local anti-correlation points to strong but nonlocal coupling between the sources of moisture and its
atmospheric sinks, as discussed by G98. The observational results validate the results of G98, obtained using
different data sources on a shorter and different period
of record (July 1987–February 1990). Model-derived
local correlations tend to be less negative, but generally
display correct large-scale patterns, except in the equatorial central and eastern Pacific region. Based on these
routine diagnostics, nothing further can be said about
the ‘‘coupled’’ nature of GH and LHF. Clearly, a
thoroughly non-local analysis is needed to describe this
coupled variability.
4 Coupled GH–LHF variability
4.1 Analysis procedure
Canonical correlation analysis (CCA: Barnett and
Preisendorfer 1987; Bretherton et al. 1992) is a multivariate statistical technique used to summarize patterns
of coupled variability in two fields of variables. Here,
locations in the spatial fields (grid cells) are the relevant
variables. The ordering of variables is not weighted, so,
for geographically distributed fields, no preference is
given to local coupling above temporal relationships
over great distances. Since there are many more
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
211
Fig. 5 Local temporal
correlations between GH and
LHF in a the observations and
b CCSM. Positive contours are
solid, negative are dashed, the
zero contour is heavily
thickened and the ±0.5 and
±0.75 contours are lightly
thickened. The thin contour
interval is 0.1. c Shows the
contrast between observed and
modeled local correlations (see
text for details)
variables in space than observations in time (3496 observed ocean grid cells and 60 months of observations,
2830 grid cells and 120 months of CCSM data) and since
each geographical field is highly spatially autocorrelated,
we reduce the dimensionality of each data set by prefiltering it separately with principal component analysis
(PCA). Ten leading PCs are retained to summarize the
variability in each field. Ten leading PCs account for
82.5% and 72.4% of the total variability in observed
GH and LHF, respectively; 83.1% and 74.1% for
modeled GH and LHF. CCA then looks for pairs of GH
and LHF patterns (i.e., linear combinations of these sets
of 10 GH PCs and 10 LHF PCs) whose temporal evolution is maximally correlated. After finding the leading
canonical correlation pair, CCA searches for the next
pair orthogonal to the first, and so on. For reasons that
will become obvious, we only consider the leading
canonical correlation pair (CC1: the leading coupled
mode). Exactly the same analysis is performed for
observations and for model data.
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Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
4.2 Seasonal cycle
The leading coupled GH–LHF mode in observations is
displayed in Fig. 6. Its time evolution is the seasonal
cycle with a maximum in January and a minimum in
July. The spatial patterns of the coupled seasonal mode
in GH (Fig. 6b) and LHF (Fig. 6c) are presented in
units of W/m2, with the same color scale and contouring
convention as the standard deviation in Figs. 3a and 4a,
respectively. Phase information is revealed via solid (in
Fig. 6 The leading coupled
mode (CC1) of observed GH
and LHF. Time series of CC1
evolution for the GH (solid) and
LHF (dashed) components of
the coupled leading mode.
Vertical long- (short-) dashed
lines are drawn to mark
January (July) positions. GH
and LHF time series are then
correlated with their respective
raw fields to obtain a
meaningful representation of
the CC1 spatial patterns. The
spatial patterns of CC1 b GH
and c LHF are displayed. The
squared correlations
representing the proportion of
local variance explained by CC1
are multiplied by the local
standard deviation and
displayed on the same color
scale as the standard deviations
in Figs. 3a and 4a for GH and
LHF, respectively. The contours
represent the same information
but are drawn solid where the
original correlations are
positive, and dashed where they
are negative. Phase information
is thus represented by contour
line type. As in Figs. 3 and 4,
contours are drawn at 5 W/m2
intervals. The zero contour is
thickened. Arrows represent
anomalous winds associated
with the CC1 of GH and LHF
represented as correlations
between a CC1 temporal
evolution and the u and v
components of the 850 hPa
winds. In the present situation,
these vectors can be interpreted
as January–July winds
phase) and dashed (180 out of phase) contours. This
spatial quantification of the seasonal cycle can be
interpreted with respect to the total variability of the
individual raw fields as explained next. The seasonal
cycle (CC1) explains most of the variance in the centers
of variability in raw GH (compare Figs. 6b and 3a) and
LHF (compare Figs. 6c and 4a) fields everywhere except
for the equatorial Pacific. The proportion of GH and
LHF standard deviation explained by CC1 in highly
variable areas away from the equatorial Pacific is at least
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
half and much more in the primary centers of variability
outlined in Figures 3 and 4. Thus, for example, over
80% of the total GH standard deviation is due to the
seasonal cycle in the Bay of Bengal, Timor and Arafura
Seas (North of Australia), South China Sea, Sea of Japan and around Central America. In LHF, the seasonal
cycle accounts for over 80% in the Kuroshio and Gulf
Stream Current regions as well as the north and south
tropical Atlantic Ocean centers of action. Somewhat
less, but still impressive proportions of variability are
explained by CC1 in most other regions of high variability in GH and LHF (e.g., eastern Pacific 315–20N
and S). The equatorial Pacific is an exception, this area is
considered in detail in the following section. Phase
information (contours) on Fig. 6b, c clearly shows that
moisture sources in the subtropical highs are enhanced
equatorward in the winter hemisphere when they provide moisture for the opposite summer–convective,
enhanced-GH hemisphere.
The seasonal cycles of GH and LHF are locally out
of phase with each other. Contours on Fig. 6 show that
GH is enhanced (diminished) in the summer (winter)
hemisphere while the LHF seasonal cycle behaves in the
opposite fashion. At first approximation, the intertropical belt is so partitioned into a convective (summer)
hemisphere and an evaporative (winter) hemisphere.
Anomalous winds tend to blow from the regions of high
LHF (moisture sources) to the regions of high GH
(atmospheric moisture sinks). The moisture evaporated
from the Kuroshio Current region probably mostly fuels
midlatitude cyclones. However, some of the moisture
originating in the tropical northwestern Pacific may
partially fuel convection in the SPCZ. Anomalous crossequatorial winds transport moisture from the tropical
northwestern Pacific (Philippine Sea and further east) as
well as from the seasonally weakened southeastern Pacific subtropical high to converge in the SPCZ in boreal
winter. Conversely, in boreal summer, the enhanced GH
in the northwestern Pacific convergence zone (NPCZ) is
transported there from the northeastern Pacific high as
well as by cross-equatorial flow from the winter hemisphere (to picture the boreal summer anomalous flow,
mentally reverse the direction of the wind vectors). The
northeastern Pacific moisture source (around Hawaii,
see Fig. 2a) does not vary strongly with the seasons. The
anomalous wind field over that region suggests that this
moisture source is efficiently tapped by midlatitude
cyclones in winter and by the NPCZ in summer.
A mechanism involving large-scale atmospheric motion, the seasonally varying Hadley and Walker circulations, could explain much of the observed non-local
coupling in GH and LHF. In the tropical-subtropical
belt, evaporation in seasonal convergence zones is
diminished by low-level convergence of moisture, which
fuels convection and enhances the greenhouse effect.
Away from areas of upward motion, GH is diminished
and evaporation is enhanced by subsidence drying (Betts
and Ridgway 1989), while evaporated moisture is removed via surface divergence. This moisture is carried
213
toward seasonal convergence zones by the seasonally
varying trade winds.
We next apply CCA to summarize the seasonal cycle
in CCSM and find that the leading coupled GH–LHF
mode in the coupled model also describes the seasonal
cycle (Fig. 7a). This modeled seasonal cycle lags reality
by one month with maxima typically in February and
minima in August. As in the observations, regions of
largest total GH and LHF variability (Figs. 3b and 4b)
are largely explained by CC1. Zonally elongated regions
of tropical off-equatorial Pacific and Atlantic GH variability are clearly seen to represent the seasonal migration of the modeled ITCZ into the summer hemisphere
(Fig. 7b). The moisture sustaining this seasonal GH
migration and warming originates in the evaporating
and cooling waters of the winter hemisphere. However,
the clean inter-hemispheric nature of CC1 in the observations is somewhat compromised in CCSM. The LHF
seasonal cycle is not as inter-hemispheric in the model as
it is in the observations. The meteorological equator is
not as well defined in LHF CC1 (compare Figs. 6c and
7c). Moreover, only the centers of LHF variability are
represented by the seasonal cycle with other extensive
areas, especially in the Southern Hemisphere, exhibiting
weakly opposite or no phase relationship with the
dominant southern hemispheric seasonal cycle. In the
observations, however, the seasonal cycle, to first
approximation, is an inter-hemispheric seesaw. Most of
the CCSM LHF variability is not accounted for by a
single harmonic seasonal cycle. CCSM winds, however,
show a consistent anomalous cross-equatorial transport
from the winter to the summer hemisphere. In the Pacific, this transport is strongest and most zonally consistent. This is certainly related to the highly seasonally
variable zonally elongated moisture source in the tropical Pacific 310N that obviously feeds the elongated
zone of strong seasonal GH variability (modeled summer ITCZ) in the south tropical Pacific Ocean.
It is worth noting here that the Gulf Stream is not as
seasonally variable in both model and observations as
the Kuroshio Current, both regions of very high total
variability. These two regions exhibit very high annual
mean LHF in reality and in the model. CCSM LHF
values show a global maximum in the Gulf Stream:
mean values above 200 W/m2, i.e., considerably higher
than what is observed. Interestingly, LHF in both these
intensely evaporating and highly variable regions, the
Gulf Stream and the Kuroshio Current, is much less
seasonally variable in the CCSM as it is in reality. The
second canonical mode (CC2) in both observations and
model (not shown) represents a seasonal cycle in quadrature with CC1 and explains the rest of seasonal variability with high loadings in low-variability regions that
were not accounted for by CC1.
Some of the dissimilarities between model and
observations, especially the discrepancies in the spatial
signatures of the seasonal cycle, stem from the differences in modeled and observed mean fields as well as
differences in the uncoupled variability structure of the
214
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
Fig. 7a–c Same as Fig. 6, but
for CCSM
individual fields. These dissimilarities lead to fundamentally similar coupled variability relative to the
respective modeled and observed mean and variance
fields. Other differences, such as the one-month setback
in the modeled seasonal cycle and the generally weaker
seasonal cycle in the modeled LHF field, could stem
from thermodynamic problems in the model and can
potentially lead to fundamental problems in moisture
cycling and dynamics resulting in discrepancies between
the modeled and observed global energy budgets. Notwithstanding these differences, on the whole, the leading
coupled mode in the model and in the observations is the
seasonal cycle characterized by generally similar behavior: a largely convective, enhanced GH, summer hemisphere and a moisture-supplying winter hemisphere.
On the whole, CCSM does a fine job reproducing this
general picture.
5 Interannual variability
Beyond the seasonal cycle, the observations and CCSM
data can be used to examine space-time scales of coupled
interannual variability in GH and LHF. To do this, we
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
follow a similar approach as the one described to diagnose the seasonal cycle.
5.1 Data processing
Before investigating interannual variability, we first removed the seasonal cycle from observed and modeled
data. The double harmonic (annual and semiannual)
cycles were fitted via least squares regression locally and
subtracted from the GH, LHF and 850 hPa wind fields
at each grid cell. In observations, interannual analysis
including CCA was performed exactly as in the previous
section on thus de-seasoned data.
In the model, it was necessary to process the data
further in order to focus on modeled interannual variability. The four-month exactly periodic cycle had to be
removed from all model fields. This was done by locally
fitting and subtracting a harmonic cycle. Because strong
high-frequency variability still remained, the model deseasoned fields were then smoothed using a method of
running medians known as 4(3RSR)2H (Tukey 1977).
Smoothing was performed at each grid cell twice by
smoothing once, computing the residuals from the
smooth results, smoothing these and adding the two
smoothed series together. Interannual signals are readily
apparent in this super-smoothed model data.
Fig. 8a, b Same as Fig. 3, but
for standard deviation of the
interannual GH and wind data
215
5.2 Variability and local correlations
Before presenting the CCA results, we first briefly describe interannual variability of GH and LHF individually as well as their local temporal correlations in the
de-seasoned observations and super-smoothed model
data. Figures 8 and 9 present the interannual standard
deviation of GH and LHF, respectively, for observations and CCSM. Figure 10 presents the local correlations.
The observed GH is most variable in the areas of
ENSO-related convection variability, i.e., the central
and far western equatorial Pacific Ocean (Fig. 8a). This
pattern is prominent in spite of the fact that ENSO
activity was weak during 1992–1996, the observational
period. An Indian Ocean center of interannual variability is also apparent. Modeled interannual variability
largely resembles the observed, but only a single stronger
center of action is found in the western equatorial Pacific. The Indian Ocean variability maximum is well
reproduced in the model, but modeled variability is
lower than observed in the central-eastern equatorial
Pacific and in the equatorial Atlantic Oceans. Interestingly, the magnitude of interannual GH variability in the
super-smoothed model data is roughly the same as that
in the de-seasoned observations. We note again that the
observational period was marked by particularly low
216
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
ENSO activity, while the model period was chosen for
its prominent interannual swings. The magnitude of
ENSO SST variability, however, is known to be underestimated in CCSM (Blackmon et al. 2001).
The level of modeled interannual LHF variability, in
contrast, is considerably lower than the observed
(Fig. 9). The magnitude of wind standard deviation is
also much lower in the super-smoothed model data
compared to observations. In the observations, aside
from a variable region in the central equatorial Pacific,
most of the variability is concentrated away from the
Equator in loosely defined zonal bands between about
10 and 30N and S as well as in the Kuroshio and
Gulfstream regions. The Hawaiian region and the
southeastern Pacific Ocean are prominent centers of
interannual LHF variability. The model also produces
interannual variability in the northward branches of the
Kuroshio and the Gulf Stream Currents as well as just to
the south of the SPCZ, but elsewhere, model variability
looks less realistic. A prominent horseshoe shaped region of enhanced variability extends from the western
Pacific to the northeast and southeast. The observations
of LHF could suffer from an overestimation of the
variability in the high flux regions owing to the limitations of the satellite technique to account for cold air
Fig. 9a, b Same as Fig. 4, but
for standard deviation of the
interannual LHF and wind data
outbreaks and associated stratification effects on the
fluxes (Bentamy et al. 2003). To which extent this might
apply to the HOAPS climatology is unclear but we remind the reader that model – observations discrepancies
are not only caused by the model and that possible
limitations of the current data set should be borne in
mind.
Interannual wind variability in the observations certainly is stronger than that in the super-smoothed model
data. Interestingly, while observed interannual wind
variability is predominantly zonal at the equator (except
in the Indian Ocean) and mostly meridional in the higher
latitudes, the interannual modeled wind varies everywhere in both u and v directions on interannual time
scales.
Observed interannual GH–LHF local temporal correlations (Fig. 10a) show one coherent region of strong
inverse relationship in the central equatorial Pacific
stretching and diminishing towards the southeast. A
large region around the Maritime continent is marked
with blotches of strong negative correlation. Weakly
positive correlations are observed in the eastern Pacific
cold tongue and the tropical eastern Atlantic. Elsewhere
in the tropics, correlations are predominantly negative
but modest in the entire analysis domain with some
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
exceptions that lack spatial coherence. The modeled interannual GH–LHF local temporal correlations
(Fig. 10b) also show predominantly negative values
throughout the analysis domain, but their spatial distribution is noisier than in the observations. The most
coherent spatial pattern of strong correlations is a swath
of positive values along the equatorial Pacific band with
well-defined maxima in the cold tongue and in the
western equatorial Pacific Ocean.
5.3 Coupled variability
Even during the observational period of weak ENSO
activity, CCA efficiently isolates the ENSO signal in deseasoned GH–LHF co-variability. The temporal evolution of the coupled mode closely follows NINO3.4
(Fig. 11a). The spatial signature of CC1 is clearly the
ENSO signal in GH (Fig. 11b). Enhanced GH in the
central and eastern equatorial Pacific (lower in the far
western Pacific) follows the typical El Niño signal in
convection and precipitation (e.g., Wallace et al. 1998,
Gershunov and Michaelsen 1996). Anomalous wind
convergence into this area of enhanced GH can also be
seen in Fig. 11b. Considered on a larger scale, the large
Fig. 10a, b Same as Fig. 5, but
for interannual GH and LHF
data
217
coherent pattern of weakly positive and negative GH
phases resembles Walker’s Southern Oscillation (Walker
1924). The LHF signature of this El Niño-related leading interannual coupled mode is a strong decrease in the
central equatorial Pacific coincident with the strong local increase in GH there. Furthermore, a tail of decreased LHF stretches southeastward from this center of
action during El Niño with weak decreases almost
everywhere else in the central tropical Pacific. However,
weak increases in LHF are observed in the equatorial
Pacific cold tongue, where usually low LHF increases
possibly due to warmer SST, as well as around the
Maritime Continent and in the far western, off-equatorial Pacific tropical seasonal convergence zones, where
usually low evaporation must increase during El Niño as
a result of anomalous subsidence drying and low-level
divergence. G98 obtained very similar ENSO signals in
GH and LHF using different data covering part of one
ENSO cycle (mostly the cold portion July 1987–1989). A
similar mechanism can explain much of this locally inverse coupling observed on seasonal and interannual
time scales. Low-level moisture convergence reduces
anomalous LHF but enhances GH by fueling convection. This, in turn, promotes anomalous subsidence
resulting in drying that reduces GH remotely and
218
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
Fig. 11a–c Same as Fig. 6, but
for observed interannual data.
The green curve in a shows the
observed NINO 3.4 SST
evolution for reference
together with low-level divergence enhances LHF. This
remotely evaporated moisture is advected into the central equatorial Pacific Ocean during El Niño.
Together, observed coupled patterns largely explain
the equatorial structure of total interannual uncoupled
variability in the GH and LHF (compare Figs. 11b, c
with Figs. 8a and 9a, respectively). Of course, most of
the central equatorial Pacific standard deviation in both
GH and LHF is due to ENSO and is explained by CC1.
Over 70% of the global maximum of GH standard
deviation located in the central equatorial Pacific is explained by CC1. CC1 also explains most of the
interannual GH variability in the entire Pacific region as
well as considerable portions of LHF variability around
the globe. Strong interannual LHF variability in the
south-central Pacific, the southwestern Indian Ocean
near Madagascar, and in the Gulf Stream remains largely unexplained by CC1. However, interannual LHF
variability in many regions, even those far removed from
the tropical Pacific, i.e., ENSO’s center of action, are
related to CC1. Thus, substantial portions of the interannual LHF variability are explained by CC1 in the
following highly variable regions: the southeastern Pacific (south of the cold tongue), the Kuroshio Current,
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
southeastern Indian Ocean (west of Australia), and parts
of the tropical off-equatorial Atlantic Ocean. These
apparent teleconnections are peripheral to the focus of
this work, but they are interesting nonetheless. Admittedly, beyond the tropical Pacific, the significance of
these relationships is questionable. The mechanisms of
these LHF apparent teleconnections cannot be explored
with 5 years of weak-ENSO activity data and are, in any
case, beyond the scope of the current study. We suspect
that, if these patterns are more than just sampling variability, they are mainly related to global circulation
changes associated with ENSO.
CC1 also explains most of the local correlation
structure between GH and LHF (compare Figs. 11 and
10) in the equatorial Pacific. Throughout the ENSO
cycle, GH and LHF are strongly inversely coupled in the
central equatorial Pacific – the center of ENSO activity.
Their coupling is weaker in the warm pool where GH
decreases with decreased convergence and convection
during El Niño and the LHF signal is split between an
increase to the east and a decrease to the west. Positive
in-phase coupling exists in the cold tongue where GH
and LHF both increase with warmer SST during El
Niño. Of course, it would help to consider an ENSOactive observational period to scrutinize and quantify
the ENSO signal in GH–LHF co-variability, but the
signal summarized here (Fig. 11) is certainly strong and
coherent enough to make a comparison with the coupled
model.
The leading coupled mode of the interannual GH–
LHF in the coupled model also evolves in line with
CCSM’s ENSO (Fig. 12a). The ENSO pattern in modeled SST covers almost the entire equatorial Pacific
stretching broadly from the south American west coast
in a narrowing pattern to the western Pacific (see
Blackmon et al. 2001). The CC1 signature in GH is
weakly apparent in this entire region. However, the
center of action in modeled GH CC1 is in its westernmost extremity, where a warm SST anomaly superimposed on climatologically warm SST enhances
(decreases) modeled convection during warm (cold)
phases of the modeled ENSO cycle. This ENSO pattern
in GH appears to be out-of-phase with the central
equatorial Indian Ocean GH. The GH signature of
modeled CC1 is weaker than that in the observations,
but still explains about 50% of total modeled interannual variability in its western equatorial Pacific center of
action (compare Figs. 8b and 12b). The leading coupled
mode signature in LHF is still weaker, but certainly
coherent enough to describe a positive, in-phase relationship with GH in the western equatorial Pacific.
Therefore, the strong positive local temporal correlations between modeled GH and LHF (Fig. 9b) result in
large part from an incorrect local coupling of GH and
LHF on ENSO time scales. When GH increases in the
model with increased SST and convergence in the western equatorial Pacific, LHF also increases there, contrary to observed reality. A local source of moisture thus
appears to exist in the model for the ENSO-enhanced
219
GH, whereas in observations, moisture sources are
clearly remote.
On longer than seasonal time scales, therefore, we
have reason to believe that the CCSM does not correctly
reproduce the observed GH–LHF dynamics. This means
that we cannot use the current version of the CCSM for a
detailed study of the interannual and longer time scale
dynamics responsible for greenhouse warming and
evaporative cooling of the ocean surface for natural
or anthropogenically perturbed conditions. Several
improvements planned for the CCSM may lead to a more
realistic depiction of the GH–LHF interplay. These
planned improvements include a more realistic parametrization of tropical convection; improved physical
linkages between the parametrizations of convection,
stratiform clouds, radiation and turbulence; addressing
the ocean model’s sensitivities to representations of deep
convection and boundary layer dynamics; and a more
physically based specification of material fluxes through
the sea surface (Blackmon et al. 2001). It is important to
check whether the next version of the CCSM, as well as
up-to-date versions of other available models, reproduce
the observed GH–LHF coupling.
6 Summary and conclusions
The aim here was to diagnose efficiently and quantify the
coupled variability of the total (mainly water vapor and
cloud) greenhouse effect and latent heat flux in observations and a coupled GCM. Low-level winds responsible for the transport of water vapor from surface
sources to atmospheric sink areas were also considered.
Due to the non-local nature of coupled relationships,
diagnostic methods had to be applied in a non-local
context. CCA was used to efficiently describe and summarize the salient modes of observed and modeled GH–
LHF co-variability. We began with a general description
and comparison of the observed and modeled GH and
LHF mean fields, variability and local temporal correlation. Then, CCA results were presented in such a way
as to quantify the proportion of total observed variability in the individual fields due to coherent and
meaningful coupled modes. The nature of observational
and model data allowed for the investigation of two
modes: the seasonal cycle and ENSO. These modes were
summarized in the context of coupled GH–LHF variability in observations and in a coupled model, the
CCSM.
Coupled modes of GH and LHF, derived to maximize correlation between temporal evolutions of patterns in the two fields, explain much of the total
(uncoupled) variability in each field. This is somewhat
less true in the CCSM than in the observations. Moreover, observations and model data clearly show that the
coupling of GH and LHF is very strong on seasonal and
interannual time scales and that seasonality and ENSO
are responsible for much of the observed total variability
in both GH and LHF (less so for the model). The time
220
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
Fig. 12a–c Same as Fig. 6, but
for CCSM interannual data.
The green curve in a shows the
modeled NINO 3.4 SST
evolution for reference
and space scales involved also clarify the mechanisms of
the GH–LHF relationship and suggest the processes
responsible for the interplay of these fields can, for the
most part, be explained by large-scale atmospheric motion involving Hadley and Walker cell dynamics. There
are important similarities between these mechanisms in
observations and in the CCSM, especially on seasonal
time scales, however there are also important differences
between model and reality, especially due to processes
operating on interannual (i.e., ENSO) time scales.
Throughout the tropics and subtropics, remote
moisture source regions sustain the greenhouse effect in
regions of strongest GH via large-scale atmospheric
dynamics. In fact, the spatial distributions of GH and
LHF appear to be imbedded in the Hadley and Walker
circulations and might reinforce these circulations by
enhancing large-scale horizontal temperature and pressure gradients through local thermodynamics. The nonlocal nature of the GH–LHF coupling is manifested on
at least seasonal and interannual time scales. For
example, on seasonal time scales, when greenhouse
warming intensifies in the summer hemisphere, evaporational cooling decreases there but increases in the
opposite, i.e., winter, hemisphere. The model does a
Gershunov and Roca: Coupling of latent heat flux and the greenhouse effect by large-scale
reasonable job simulating this seasonal interplay even
though modeled climatologies of GH and LHF are
roughly symmetric around the equator; in the observations, on average, the northern tropics are more convective and are marked by higher GH, while the
Southern Hemisphere evaporates more moisture per unit
area. This mean interhemispheric gradient is especially
clearly observed in the Pacific sector, where the model
produces the opposite gradient in LHF.
On interannual time scales, during El Niño, when
strong convection and GH usually located over the
western Pacific warm pool move eastward to the central
Pacific, observed LHF decreases locally and in the
subtropical highs, but increases almost everywhere else.
Although the five observed years (1992–1996) were
characterized by especially weak ENSO activity (Goddard and Graham 1997), our coupled analysis procedure
was sensitive enough to pick out the ENSO pattern as
the main mode of non-seasonal coupled variability. In
contrast to the observations, the model provides a local
source of moisture for ENSO-enhanced GH. A more
interannually active observational time period is needed
for a more detailed study of ENSO-related GH-LHF
dynamics. Then again, a CGCM that correctly reproduces these dynamics can be used in a detailed study of
interannual and longer time scale GH-LHF dynamics in
the contexts of natural and anthropogenically perturbed
climate variability. As far as the model ENSO goes, it is
possible that incorrect LHF dynamics could be part of
the reason why CCSM does not correctly simulate
ENSO variability. In particular, the magnitude of ENSO
SST variability in the CCSM is much too small
(Blackmon et al. 2001). In the central and eastern
equatorial Pacific, ENSO SST variability can be accounted for by heat advection and evaporative cooling
(Niiler et al. submitted 2003). Evaporative cooling of the
surface incorrectly positioned in the anomalous central
Pacific warm pool during El Niño should dampen
ENSO-related SST anomalies. The effect of this mechanism deserves further investigation, but again, a more
suitable (i.e., ENSO-active) observational period is
required for comparison with the model.
A caveat of the present comparison concerns the
observational data sets which are currently used. Indeed,
the LHF data appears to suffer from underestimation of
the mean in the tropics with respect to other recent satellite products (Kubota et al. 2003). While the main
conclusions of this study stem from strong, clear signals,
signals that were also reproduced in independent GH and
LHF data covering a different time period (G98), one still
needs to exercise caution when looking at the details of
the discrepancies between the model integration and the
present observational data set. In the near future, the
expected better characterization of the observations’
limitations and the increase in their quality and degree of
confidence will increase the usefulness of the present
diagnostic and its ability to unravel models’ discrepancies
in fine detail. Other available climatologies can be used to
complement the HOAPS data set, e.g., the Goddard
221
Satellite-based Surface Turbulent Flux (Chou et al. 1997)
and the Japanese Ocean Flux Data Sets using remote
sensing observations (Kubota et al. 2002). These products could help bracket the uncertainty in the satellite
observations of the LHF. Similarly, the use of recent
CERES OLR observations could provide an alternative
computation of the total sky GH allowing a stronger,
more detailed characterization of models’ errors.
After making these points, the clear and strong discrepancies between model and observations do not
necessarily stem from an incorrect simulation of the
individual fields of GH or LHF. Important discrepancies exist in the model relative to model climatology and
variability. Such a fundamental mismatch between
model and reality can lead to serious errors/biases in the
modeled energy budget variance due to natural as well
as to anthropogenic climate variability. Investigating
these two highly integrated variables reveals the model’s
limitations and points to a need for further detailed
investigations of specific issues in the model. For instance, an analysis of the interannual variability in the
wind-LHF relationship in both model and observations
is one perspective for future work. Further study could
partially explain the presently revealed discrepancies by
isolating the respective contribution of the surface wind
to the LHF variability with respect to the role of SST,
transfer coefficients and surface moisture deficit.
In the current analysis and interpretation of the CCSM
results, it must be borne in mind that deviations from
reality are model-specific. Similar investigations can and
should be carried out with other available CGCMs as part
of the coupled model intercomparison project. We hope
to embark on such a comparison in the near future.
Acknowledgements This work was realized during a visit of the first
author (A.G.) to the Laboratoire de Météorologie Dynamique,
Palaiseau, France under the auspices of visiting grants from both
the Centre National de la Recherche Scientifique an the Institut
Pierre Simon Laplace, Paris, France. Funding from National Science Foundation Grant ATM 99-01110 partially supported this
work. The authors thank Drs. R. Kandel and M. Desbois for
constructive discussions as well as J.-L. Monge and B. Bonnet for
technical support. Two anonymous reviewers provided excellent
comments and suggestions which improved the original text.
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