shortwave radiative cloud forcing in the tropical pacific including the 1982-1983 and 1987 el niÑos

INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 16, 1-13 (1996)

SHORTWAVE RADIATIVE CLOUD FORCING IN THE TROPICAL PACIFIC INCLUDING THE 1982-1983 AND 1987 EL NmOs

LIHANG ZHOU, R. T. PrNKER* AND 1. LASZLO Deparfment of MeteorologJ9 Universiw of Maryland, College Park, MD 20742, USA

Received 20 September 1994 Accepted I4 April I995

ABSTRACT The ‘cloud radiative forcing’ concept has been used extensively to study radiative effects of clouds on the earth-atmosphere system. Until now it has been applied primarily to the top of the atmosphere (TOA). It is of interest to apply it to the surface, where the absorbed radiant energy regulates the surface temperature. In this study we derived and compared the shortwave cloud forcing (SWCF) at the surface of the tropical Pacific during two El Niiio years (1982-1983 and 1987) with two regular years (1 984 and 1985). The surface SWCF during an El Niiio year averaged over the entire tropical Pacific region was found to exceed that of a normal year by 10 W m-’. The areas with strongest SWCF in El Niiio years are located in the ‘warm pool’ region, where they can exceed those of normal years by 40 W m-’. To evaluate the range of possible error in these estimates, the SWCF was derived from several available sources of information, such as: satellite methods driven with the International Satellite Cloud Climatology Project (ISCCP) C1 data; Earth Radiation Budget Experiment (ERBE) information at the TOA; and the National Meteorological Center (NMC) model output, both at the surface and at the TOA. Because the clear-sky component of the radiative flux that reaches the surface is affected by the amount of precipitable water in the atmosphere, a comparison was made between different sources of such information (e.g. European Centre for Medium Range Weather Forecasts (ECMWF); TIROS-N Operational Vertical Sounders (TOVS); and the Special Sensor Microwave/Imager (SSM/I)). The impact of such differences on the SWCF was estimated using LOWTRAN 7. We were also able to examine two widely discussed ‘greenhouse effects’ hypotheses during El Nifio conditions. One was proposed by Lindzen, suggesting possible decrease of the atmospheric greenhouse effect as a result of drying of the upper atmosphere during intensified direct thermal circulation. The other was proposed by Ramanathan and Collins, regarding the ‘thermostat’ effect of clouds, preventing the rise of ocean surface temperature above 305 K. During the April 1987 peak El Nifio period, Lindzen’s hypothesis was found to be valid, and a relationship between the sea-surface temperature and the surface SWCF was obtained similar to that found by Ramanathan and Collins for the SST and TOA SWCF.

KEY WORDS: tropical Pacific; remote sensing; satellite retrievals; shortwave cloud forcing; tropical tropospheric humidity

1. INTRODUCTION

The effects of a hypothetical increase in atmospheric trace gases are generally assessed with numerical climate models. The results of several such experiments have indicated a possible surface temperature rise of 2”-5”C (Cess et al., 1988). The largest uncertainty in predicting such climate change is associated with the unknown effects of clouds on the modulation of both shortwave (SW) and longwave (LW) radiative fluxes, known as ‘cloud radiative forcing’ (Coakley and Baldwin, 1984; Charlock and Ramanathan, 1985; Hartmann et al., 1986; Ramanathan, 1987; Arking, 1991). Small changes in cloud radiative forcing can have a large effect on the climate feedback mechanism (Charlock, 1982). Because the effects of clouds on the outgoing LW radiation (i.e. the greenhouse effect) and the absorbed SW radiation (i.e. the albedo effect) are opposing, the net effect may be small and difficult to estimate. Recently, it has been speculated that the Earth’s climate has the ability to regulate itself. Ramanathan and Collins (1991) (thereafter RC) hypothesized that clouds can act as a natural thermostat that will keep the sea-surface temperature (SST) from rising above 305 K.

Author to whom correspondence should be addressed.

0 1996 by the Royal Meteorological Society CCCO899-84 18/96/01 000 1-1 3

2 LIHANG ZHOU, R. T. PINKER AND I. LAZLO

Based on their analyses of the ‘greenhouse effect’ and cloud forcing variations during both El Niiio years, RC proposed that the ‘greenhouse effect’ increases dramatically with SST increase. This unstable situation continues until the bright cirrus clouds capping the cumulus towers, which are created from the surface warming, shield the ocean from further solar heating. Numerous discussions have followed. Wallace (1992) suggested that the ‘thermostat’ mechanism proposed by RC is not a necessary mechanism to prevent SSTs from exceeding 305 K. When extensive greenhouse warming occurs, increasing evaporation, resulting from modifications of large-scale circulation due to SST increase, would smooth out the ‘hot patches’ of SST. Similarly, Fu et al. (1992) argued that because the average evaporative flux during 1987 was larger than that for 1985 by 1 1.5 W m-2, the evaporation should be considered as an important factor in determining the SST distributions. Stephens and Slingo (1992) pointed out that to test the RC hypothesis, parameters such as ‘longwave cloud forcing, the precipitation and evaporation as well as dynamical influences on cloud cover’ have to be taken into account simultaneously. Arking and Ziskin (1994) presented a data analysis of 4 years of radiative fluxes at the top of the atmosphere (TOA), SSTs, and cloud parameters such as cloud fraction, cloud temperature, and cloud optical depth. They found that the warming of the tropical Pacific can change the cloud parameters significantly, although clouds have little, or no influence on SSTs. They expressed an ‘alternative hypothesis’, namely, that indirectly the high-level clouds do play a ‘thermostat’ role on SST, by weakening the large-scale circulation when the clouds ‘reach the maximum development’. To some extent, this is similar to Lindzen’s (1990) hypothesis, namely, that the deeper cumulus convection associated with surface warming can actually lead to drying of the upper atmosphere due to increased water vapour condensation and subsidence. The subsidence that is required to compensate the rising motion of the atmosphere will fill the atmosphere located above 3-5 km with dry air. This implies that the atmospheric greenhouse effect will decrease with the intensification of the direct thermal circulation.

Whereas most previous investigations have focused on cloud effects at the TOA, it is of interest to study cloud effects at the surface, where the regulation of SST by clouds occurs. Over the tropical oceans, the shortwave cloud forcing (SWCF) is of key interest because the LW cloud forcing is relatively small. For instance, Ardanuy et al. (1991) estimated the global LW cloud forcing distribution from Nimbus 7, and found that the SWCF is dominant, especially over low-albedo oceans. Zhi and Harshvardhan (1993) used a hybrid technique for computing the monthly mean net LW surface radiative forcing over oceanic areas by blending information from a combination of cloud radiative forcing derived from a general circulation model, the TOA cloud radiative forcing from the Earth Radiation Budget Experiment (ERBE), TIROS-N Operational Vertical Sounders (TOVS) profiles and SST. They obtained the mean monthly distribution of the LW cloud radiative forcing for April, July, and October 1985 and January 1986, and found that regions with small surface LW cloud forcing are concentrated in the tropical and subtropical oceanic areas. This is due to the moist boundary layer, which is radiatively opaque, even for clear skies. They claim that in the central Pacific Ocean regions, LW cloud radiative forcing below 20 W m-’ dominate during the year.

In this study, the surface SW cloud forcing in the tropical Pacific during two El Niiio years (1982-1983 and 1987) was compared with those of two regular years (1984 and 1985). The relationship between the SW radiative cloud forcing at the surface and the SSTs also has been investigated. The SW cloud radiative forcing was derived fiom satellite-based estimates of clear-sky and all-sky SW fluxes at the surface and at the TOA. The approach of Pinker and Laszlo (1992), driven with International Satellite Cloud Climatology Project (ISCCP) C1 data (Schiffer and Rossow, 1985), was used to obtain these radiative fluxes. Comparison was made with other available sources of information on radiative forcing, such as E W E at the TOA, and the National Meteorological Center (NMC) model output at the surface and at the TOA (K. Campana, pers. comm., 1993). Because the clear-sky component of the radiative flux that reaches the surface is affected by the amount of precipitable water in the atmosphere, different sources of such information were used (e.g. European Centre for Medium Range Weather Forecasts (ECMWF); TOVS; Special Sensor Microwavefimager (SSM)), and the impact of such differences on the SW cloud forcing was evaluated. The vertical distributions of moisture during normal and El Niiio years were also compared in order to test the Lindzen (1990) hypothesis about drying of the upper troposphere associated with warming. The data sources used will be presented in section 2; procedures and results will be described in section 3; and discussions will be outlined in section 4.

SHORTWAVE CLOUD FORCING 3

2. DATA SOURCES

2. I. Shortwave fluxes

The primary data on SW radiative fluxes, both at the surface and at the TOA, are produced from the International Satellite Cloud Climatology Project (ISCCP) C1 data (Schiffer and Rossow, 1985), using the inference method of Plnker and Laszlo (1 992). Under ISCCP, a uniform global climatology of satellite-measured radiances from an international network of operational weather satellites in the visible and thermal infrared is available (Schiffer and Rossow, 1985). The network consists of five geostationary satellites. The Global Processing Center (GPC) at NASNGISS produces the ISCCP C1 data received from both geostationary and polar orbiting satellites to obtain a complete global coverage every 3 h. The nominal resolution is 250 km; each cell represents an area equal to that of a 2.5" latitude by 2.5" longitude cell at the equator. Most of the radiance data over the central Pacific used in this study come from the Japanese geostationary satellite (GMS) and are supplemented with observations from the polar orbiting satellite.

Shortwave fluxes from the Earth Radiation Budget Experiment (ERBE) (Barkstrom, 1984) at the top of the atmosphere for 1985 are available from the Earth Observing System (EOS) Data Information System (EOSDIS) at the Langley Distributed Active Archive Center (DAAC) (Baum and Barkstrom, 1993). Shortwave fluxes at the surface and at the TOA are available from the National Meteorological Center (NMC) 18-layer global spectral model (Kalnay et al., 1990). Initial data from each monthly forecast have been obtained by reanalysing the beginning of each period using current analysis techniques (Derber et al., 1993). During the model forecast, radiation calculations are made every 3 h using clouds diagnosed from model variables similar to Slingo (1987). The SW fluxes are updated every time-step, at every point, by weighting them with the actual cosine solar zenith angle in order to produce the diurnal cycle.

2.2. Blended SST data

The sea-surface temperature data used in this study were produced by NOkAJNational Meteorological Center (NMC) (Reynolds, 1988), and are a blend of in situ observations and estimates derived from the Advanced Very High Resolution Radiometer (AVHRR). They are global monthly means at a 2" latitude by 2" longitude spatial grid. These data sets were interpolated to a 2.5" latitude by 2.5" longitude grid, in order to make them compatible with the other data sets.

2.3. Precipitable water

The major source of data on precipitable water used in this study and available for the entire study period comes from the ISCCP TIROS-N Operational Vertical Sounders (TOVS) observations made from polar orbiting satellites, as provided with the ISCCP data (Schiffer and Rossow, 1985). They consist of five values for the layer mean precipitable water amount (1000-800 hPa, 800-680 hPa, 680-560 ma, 5 6 W O hPa, 44&3 10 ma). For selected periods, data from other sources were also available, such as the precipitable water data derived from the Special Sensor MicrowaveDmager (SSWI) from 9 July to 31 July 1987 (P. Schlussel, pers. comm., 1993). They were used for comparison with the ISCCP C1 TOVS data, in the region from 40"s to 40"N and 100.25"E to 60"W. The resolution of the fields is 2.5" latitude by 2.5" longitude. To facilitate comparison with ISCCP C1 data, only co- located grids have been used. Humidity profiles from the European Centre for Medium Range Weather Forecasts (ECMWF) were obtained from Purdue University (Vincent and Ramsey, pers. comm., 1993). The original data file provides specific humidity for 55"N-55"S, 120"E-120"W at 10 levels between 1000 and 100 hPa, twice a day. Monthly mean values of precipitable water were derived and interpolated to the same grids as those of ISCCP C1 data, by using vertical interpolation (F. Misckolszi, pers. comm., 1993).

3. PROCEDURES AND RESULTS

The model used to derive the SW surface and TOA radiative fluxes from the ISCCP C1 satellite visible radiances is described in Pinker and Laszlo (1 992). In this model, the radiative fluxes at both boundaries of the atmosphere are


3 -c/

a, U 3

42

Surface SWCF (W/M**2). Ju ly / l983

Ju ly / l984

July/ l985

Figure 1 . Surface SWCF overthe tropical Pacific (1O0S-10"N, 14O0E-9O"W), derived from the ISCCP CI data with the SW model of Pinker and Laszlo (1992): (a) July 1983, @) July 1984, and (c) July (1985)

derived separately for clear (Acl,), cloudy (A,), and all-sky (A) conditions, and therefore enable the computation of SW cloud forcing, defined as:

both at the TOA and at the surface. The ERBE data also provide the clear (Acl,) and cloudy (A,) SW radiative fluxes at the TOA separately.

Upon request, the NMC off-line model output was also stored in a similar format (K. Campana, pers. comm., 1993).

To estimate the cloud effects during the 1982-1983 and the 1987 El Niiio episodes, the surface SWCF for 1983, 1984, 1985, and 1987 were derived. The first available month of ISCCP C1 data, July 1983, is used to represent the 1982-1 983 El Niiio. However, it is past the peak El Niiio event. April was chosen for the 1987 El Niiio because it is


Table I. Average surface SW cloud forcing (W M-2)

Global Tropical TOGA COARJ?

July 1983 -44.1 - 49.9 - 60.0 July 1984 - 44.0 - 38.7 -47.1 July 1985 -40.0 - 39.1 -44.6 July 1987 - 38.0 - 49.2 - 65.8 April 1984 - 44.8 -42.9 -43.4 April 1985 - 44.1 - 44.0 -45.8 April 1987 -41.3 - 52.3 - 55.2

considered to represent the peak of the episode. In Table I the 'global' (55"N-55"S), 'tropical' (140°W-90"E, 10"s-10"N) and 'TOGAKOARE' (14OoW-180", 5OS-5"N) averages of the surface SWCF for July 1983, 1984, 1985, and 1987 and April 1984, 1985, and 1987 are presented. The latter is in an area known as the 'warm pool', where surface temperatures exceed 28°C. Regions of such high water temperatures cover about 3 M O per cent of the Tropical Ocean surface and are believed to have a strong effect on global climate. It can be seen that in the tropical Pacific the surface SWCF during the El Niiio years is much stronger than that of the normal years, especially in the TOGNCOAFE area. However, in the 'globally' averaged values, the effect of the El Niiio are not seen. This would indicate that some compensating changes occurred at other locations.

In Figure 1 the distribution of the surface SWCF in the tropical Pacific for July 1983, 1984, and 1985 is presented. The largest negative values of SWCF occur in the north-eastern tropical Pacific, with values of about -110 W m-' for July 1983, -90 W m-* for July 1984 and -80 WmP2 for July 1985. The other large negative values appear in July 1983 in the western tropical Pacific and extend to the central Pacific. The

Surface SWCF April/ l987

Q) -0 3 - U .- a -

Surface SWCF April/ l987- 1985

Figure 2. Same as Figure 1 for April: (a) April 1987 and (b) difference between April 1987 and April 1985


[ - 37.01

[-41.4]

(-72.41 Figure 3. TOA SWCF for October 1985. The numbers in the lower right corners are the area averaged values: (a) from ISCCP/CI, (b) from

ERBE, and (c) from NMC output


surface SWCF is smallest in the central-eastem tropical Pacific, corresponding to the relatively low cloudiness of this area. Figure 2(a) shows the distribution of the surface SWCF in the tropical Pacific in April 1987, the peak episode of 1987 El Niiio. It can be seen that the largest surface SWCF occurred in the central tropical Pacific, with values of -80 W m-', and values exceeding -50 W m-' can be found over most of the tropical Pacific area. In Figure 2(b) the differences of the surface SWCF between April 1987 and April 1985 are presented. Negative values characterize the whole area, namely, the surface SWCF is stronger in April 1987 than in April 1985 (normal year). The largest differences are found in the central tropical Pacific, where the surface SWCF is larger by -40 W mP2 in April 1987 than in April 1985.

The comparison between the TOA SWCF produced from ISCCP C1, ERBE, and NMC show that the results from ISCCP C1 and ERBE are very similar in pattern and magnitude, but both differ from the NMC results. For October 1985, as shown in Figure 3, the area mean difference in the tropical Pacific between TOA SWCF from ISCCP C1 and ERBE is about 5 W mP2; the mean difference between ISCCP C1 and NMC is about 35 W m-'. The TOA SWCF is found to be highly correlated with that at the surface in the tropical Pacific, as was also found by Laszlo and Pinker (1993). The correlation coefficient is larger than 0-99. Therefore, the TOA comparison can be indicative of similar relationships at the surface.

Numerous studies have been done on the SST and cloud feedback over the tropical Pacific region. In this study, we calculated the correlations between SST and the surface SWCF. The correlation coefficients between SST and SWCF from the least-squares fit is 0.58 for July 1983, with a slope of -12 W mP2 K-'; 0.49 for April 1987, with a slope of about -10 W m-' K-I; and only around 0.3 for both April and July 1985, with slopes of -4 and -3 W m-' K-', respectively. The correlations between the variation of SST and the surface SWCF were also calculated in the same way that RC (1991) did for TOA SWCF. As shown in Figure 4, the correlation coefficient for the peak episode of the 1987 El Niiio is higher than 0.8, the slope of dC,/dT(i.e. the ratio of the product of dTand dC, to (dT)') is about -17 W m-' K-', which means that 1 K increase of SST can be accompanied by 17 W m-' decrease of the solar energy absorbed at the surface. These results are close to those of RC, who found -19 W m-'K-' for TOA, based on ERBE information.

h

I 3 M ul

a v

c n

E4

M v

a

50

0

450

-1 00

-1 50

-200

4 6 0 10 -2 0 2

d(SST) *d(SST) Figure 4. The scatter plot of d(SST) x d(SWCF) against d(SST) x d(SST) for the region of 1O"S-1OoN, 14O"E-9O0W, for April of 1987

and 1985


400 u)

300 c 0

2 200 3 C

100

0

However, the correlation for the case of July 1983 is lower, only about 0.45. This might be due to the fact that the peak of the 1982-1983 El Niiio preceded July 1983.

To estimate the reliability of the ISCCP C1 TOVS based precipitable water data, comparison was made with limited information from the S S M data (P. Schliissel, pers. comm., 1993). For three chosen cases (29,30, and 31 July 1987), the correlation coefficients between the independently estimated precipitable water exceeded 0.88, the area mean differences ranged from 0.02 to 0.06 cm, the root-mean-square (RMS) was about 0.7 cm. The results for the case of 29 July are presented in Figure 5 . It seems that in the lower range of precipitable water values, ISCCP C1 TOVS based data give higher estimates than S S M , whereas in the upper range of precipitable water the opposite is true. The comparison between the monthly mean precipitable water from ISCCP C1 and the ECMWF model (P. Ramsey, pers. comm., 1993) was also made. For July 1987, as shown in Figure 6, the correlation coefficient between the two data sets is about 0.96, the area mean difference is about 0.19 cm, the RMS is 0.44 cm. The maximum difference in precipitable water from the various sources is about 2.5 cm. Using LOWTRAN 7 for the tropical atmosphere standard profile, computations suggest that this can lead to an error less than 5 W m-' in surface SWCF.

To test Lindzen's hypothesis that surface warming is associated with drying of the upper troposphere, we compared the vertical distribution of water vapour of the El Niiio years with those of normal years. In Figure 7 the difference of precipitable water in the selected layers during April 1987 and 1985 is presented. In the lower layers (1000-680 hPa) the values for April 1987 are higher than those for April 1985 everywhere in the tropical Pacific. In the upper layers (680-310 hPa), the precipitable water in April 1987 is less than that in April 1985 over


0 1 2 3 4 5 6 lSCCPrr0VS

800

600

400

200

0

t

k 4

1

-2 -1 0 1 2 3 PW (cm)

Monthly Mean Column PWT o f Ju ly 1987 10s-ION

1

Figure 6. Comparisons between precipitable water (F'W) from ISCCP/TOVS and ECMWF output in the region of 55"S-55"N, 14OoE-120"W (a) scatter plot of PW from ISCCP/lDVS against PW from ECMWF output, @) bar plot of the difference, and (c) PW averaged in a 1O0S-10"N

belt

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the warm pool area and over part of the western Pacific (140”W-11 OOW), and the average difference over the entire tropical Pacific region is also negative.

4. DISCUSSION

There is a need for information on downwelling surface solar radiation (DSSR) over the oceans and on the modification of this parameter by clouds, known as SWCF. Information on DSSR will enable an improvement in estimates of latent and sensible heat fluxes over large areas, currently based on bulk parameterizations. Moreover, current climate models, which attempt to estimate SSTs, tend to overestimate these temperatures, in particular in the western Pacific, by several degrees Kelvin (Ji et al., 1994). This is caused by errors in estimated DSSR and by differences in the modelling of radiation penetration into the ocean (Lewis et al., 1990). Presently, several remote sensing inference methods are available to estimate DSSR at the surface from satellite observations at large or global scales (Whitlock et al., 1995). These methods are believed to be accurate within 10-20 W m-’ on monthly time-scales (Whitlock et al., 1993). Some of these methods evaluate the clear and all-sky conditions independently, and, as such, allow the estimation of the SWCF (Laszlo and Pinker, 1993), as performed in this study for selected months during the period 1983-1987. This allows the study of the effect of increased surface temperature (as might occur due to C 0 2 increase) as manifested during an El Niiio episode on the SWCF. We have found that the SWCF is stronger in the El Niiio years over the tropical Pacific region when compared with normal years. For instance, over ‘the warm pool’, the surface SWCF for April 1987 can be 40 W m-’ larger than that for April 1985. This implies that during an El Niiio, more clouds were developed and the SW effects of clouds strongly reduced the solar irradiance that reached the surface.

One source of uncertainty in these estimates is related to the values of precipitable water used. Possible errors were evaluated by performing sensitivity studies with LOWTRAN 7 using maximum differences in precipitable water as estimated from several sources. It was found that errors induced by differences in precipitable water on the surface SWCF are less than 5 W m-’. Liu et al. (1992) also compared global fields of precipitable water available from SSM/I and the ECMWF model. They found good agreement between the two data sources over most ocean areas, with differences less than 0.5 cm. They did find large differences in the dry air masses over the eastern tropical and subtropical oceans, where the ECMWF estimates can be 2 cm higher than those of SSM/I; but in the moist areas of the intertropical convergence zone (ITCZ) and the warm pool region, ECMWF estimates were lower than those of SSM/I. They pointed out that ECMWF estimates are influenced by TOVS, which can explain why we obtain similar results for the differences between ISCCP TOVS based estimates and SSM/I. Gutzler et al. (1993) also addressed the uncertainties in climatological tropical humidity profiles. They found that clear sky radiation uncertainties are small relative to uncertainties associated with variations of infra-red absorption due to clouds.

It has been suggested that in order to understand cloud feedback mechanisms, it is necessary to investigate simultaneously several aspects of the problem, such as clouds, the Earth’s radiation budget, and other climate parameters (Stephens and Greenwald, 1991). We have used diverse sources of information to study the vertical structure of humidity during normal and disturbed conditions as well as the relationship between clouds and surface temperature. The vertical distribution of moisture has important implications for the greenhouse effects, as pointed out by Elsasser (1984) and Lindzen (1990). The smaller amount of water vapour at higher altitudes is important for understanding the energy balance at the top of the tropical atmosphere. For instance, Gutzler (1 993) has shown that uncertainties of 2 per cent in precipitable water are responsible for 1-1.5 per cent change in outgoing LW clear-sky radiation at the tropopause level and that the latter is comparable to the decrease in clear- sky radiation caused by doubling of C02. We have found that the precipitable water for April 1987 (El Niiio) is lower than that of April 1985 (regular) in the upper layers (680-310 Ma), indicating that there is less water vapour, which is a very important greenhouse gas, accounting for more than 65 per cent of the IR absorption of the atmosphere. Because the greenhouse effect is more important in the upper atmosphere than near the surface, the drying in the upper atmosphere can diminish significantly the heating due to an El Niiio.

Recent observations have shown a direct effect of SW radiation on sea-surface temperature in the tropics. Lukas (1991) illustrated for the ‘warm pool’ region the existence of large temperature peaks on days of strong radiation


and light winds. Ravier-Hay and Godfiey (1993) show the importance of SW radiation on the observed diurnal cycle of the SST as well as on modelled temperatures at several depths of the mixed layer. We have investigated the relationship between SSTs and the surface SWCF. It was found that, generally, stronger surface SWCF corresponds to regions with higher SSTs, however, a simple linear relationship between SST and surface SWCF was not found to exist. Further studies are needed on this aspect of the problem.

ACKNOWLEDGEMENTS

L. Z. was supported under grant NA27 WC0275 fiom NOAA/NESDIS under the Cooperative Institute for Climate Studies (CICS) at the University of Maryland. The work of R.T.P. and I.L. has been supported by grant NAG5-914 from the National Aeronautics and Space Administration, Earth Science and Applications Division, Climate Research Program, and grant NA 16RC045 1-01 fiom the NOAA Climate and Global Change Program, Operational measurements. We wish to thank Dr P. Schliissel for providing the SSMA precipitable water data; P. Ramsey for providing the ECMWF humidity information; Dr Miskolczi for helping to develop computer software for vertical interpolation of the ECMWF humidity data, Larry Marx for providing the software for horizontal interpolation of the SST, and Brian Dotty for the use of the Grads plotting software package.

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