11 12 Local Time, hour

Figure 23. Enlargements of the results shown in Fig. 22. The left panels are from the same simulation in Fig. 22, which takes a midlevel relative humidity of 75%, whereas for the right panels the relative humidity is increased by 5%. Top two panels show the evolution of relative humidity (solid lines) and relative acidity in the Wanli aerosol (dashed lines) and NTU aerosol (dotted—dashed lines) scenarios. The middle and bottom panels show the evolution of aerosol number concentration in the Wanli and NTU aerosol scenarios, respectively.

scenario, but just the opposite in the NTU aerosol scenario. This result indicates that the variation of Sa is a stronger controlling factor than that of Sw.

The above example shows that the source of sulfuric acid for binary nucleation may come from not only the gas phase photochemical production but also the outgassing from existing aerosols. This leads to an interesting question: whether in-cloud aqueous phase production of sulfate (see Subsec. 3.1) may enhance the above mechanism. On the other hand, there may be other cloud processes that may enhance the gas phase photochemical production of sulfuric acid,

Figure 24. Schematics of the complicated aerosol and cloud processes (light blue), their interactions (dark blue), and their impacts on atmospheric radiation (brown).

such as the lightning activity which may produce NOk and involve in the photochemical reactions.

4. Summary

In the final diagram (Fig. 24), we summarize the main processes regarding the interactions between aerosols and clouds. It reveals the convoluted nature of this complex aerosol-cloud coupling system. This system not only plays very important roles in the Earth's climate change and hydrological cycle but also interacts strongly with the biosphere and influences the biogeochemical cycles.

The discussions given in the previous sections may lead us to believe that we already know plenty about the important mechanisms involved. But, in fact, a lot of details are absent, such as how the internal mixing of different chemical compositions alters the properties of aerosols, what is the precise rate of each micro-physical and chemical process, and what are the spatial and temporal variations of different kinds of CCNs and natural ice nuclei. Worse yet, there is no comprehensive understanding of the combined effects on either the regional or the global scale. The most versatile and effective tools for studying the aerosol and cloud effects on such scales are probably numerical models. But from the standpoint of our current knowledge (which is, by the way, far from complete), their treatments of aerosol-cloud interactions are very primitive. One of the main difficulties lies in the inability to link microscale processes of aerosols and clouds with the large-to-global-scale dynamic and radiative processes.

The third and fourth IPCC assessment reports (2001, 2007) listed the effect of aerosols and clouds on global climate as potentially important, with their radiative forcing nearly canceling out the CO2 greenhouse forcing (which accounts for about two-thirds of the total greenhouse forcing). However, the levels of scientific understanding of these aerosol and cloud effects are quite low. Although the level of scientific understanding of the aerosol direct effect has improved from very low (IPCC, 2001) to median low (IPCC, 2007), and that of both the first and the second indirect aerosol effect (through interaction with clouds) has improved from very low to low, they still give the largest uncertainties in our estimation and projection of climate change. As summarized by Lohmann et al. (2007), future studies should emphasize "synergetic approaches involving modeling and observational evidence at different spatial and temporal scales."

Besides the climate change issue, the influence of aerosol-cloud interactions on the hydrological cycle is also poorly understood. Among the various meteorological parameters, precipitation is perhaps the least accurate prognostic variable in global climate models. Even with mesoscale meteorological models using the explicit (but still highly parametrized) cloud-microphysical scheme, quantitative precipitation forecasts are very often far from satisfactory compared to the prediction of other parameters. One of the common causes of these models' deficiency in precipitation calculation is the inability to include cloud-aerosol interactions properly. Furthermore, it is hard to believe that the microphysical processes can be simulated correctly when very little CCN observation and essentially no IN observation is available for model initialization. Thus, many tasks remain to be accomplished in understanding the detailed mechanisms of microscale physics and chemistry via diligent observational, laboratory and theoretical research. It is equally important to convert obtained knowledge into mathematical formulas and aggregate these into a robust, physically based model. With unrelenting efforts, the uncertainties in our estimation of climate change or precipitation forecasts may be reduced to an acceptable level.

[Received 17 April 2007; Revised 19 December 2007; Accepted 23 December 2007.]


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Application of Satellite-Based Surface Heat Budgets to Climate Studies of the Tropical Pacific Ocean and Eastern Indian Ocean

Ming-Dah Chou, Shu-Hsien Chou* and Shuk-Mei Tse Department of Atmospheric Sciences, National Taiwan University, Taipei,, Taiwan [email protected] ntu. edu. tw

In tropical oceanic regions, surface heat fluxes influence the climate at all spatial and temporal scales, ranging from diurnal variations of the sea surface temperature (SST) and clouds to intraseasonal variations of atmospheric disturbances, as well as interannual variations of the El Nino/Southern Oscillation. In the past, research on the impact of sea surface heat fluxes on climate was limited by the lack of quality global data sets with high temporal and spatial resolutions. More recently, good quality sea surface heat flux data sets with a temporal resolution of 1 day and a spatial resolution of 1° x 1° latitude-longitude have been produced, based on satellite radiation measurements. We have applied these data sets to study climate in the tropical Pacific Ocean and eastern Indian Ocean. These studies include the climatology of surface heat budgets (SHB's), the role of SHB's in the regulation of Pacific warm pool temperature, the correlation between local SHB's and the rate of change of the SST, and the correlation between the interannual variations of the tropical Pacific basin-wide mean SHB and SST. We review the results of those studies in this article.

1. Introduction

The Earth-atmosphere system reflects ^30% of the incoming solar (or shortwave, SW) radiation back to space, and absorbs ^20% in the atmosphere. The rest, ^50%, is absorbed at the Earth's surface (Kiehl and Trenberth, 1997). Most of the SW radiation absorbed at the surface occurs over the tropical ocean, and is then transferred to the atmosphere primarily in the form of latent heat. The latent heat released in the atmosphere is a major force driving atmospheric circulation. Atmospheric circulation, in turn, provides wind stress for driving the ocean circulation. Thus, the heat exchanges at the tropical sea-air interface are among the most important mechanisms affecting Earth's climate.

The warm pool of the tropical western Pacific Ocean and Indian Ocean, with a monthly mean temperature >28°C, is a climatically important region, characterized by the warmest sea surface temperature (SST), frequent heavy rainfall, strong atmospheric heating, and weak mean winds with highly intermittent westerly wind bursts (Webster and Lukas, 1992). The large amount of heat contained in the warm pool drives the global climate, and plays a key role in the El Nino-Southern Oscillation (ENSO) and the Asian-Australian monsoon (Webster et al., 1998). Small changes in the SST of the Pacific warm pool by ~1°C, associated with the eastward shift of the warm pool during the ENSO events, have been shown to affect the global climate (Palmer and Mansfield, 1984).

* Deceased.

In order to enhance our understanding of the air-sea interaction and other tropical dynamical and physical processes in the warm pool, the Tropical Ocean-Global Atmosphere (TOGA) Coupled Ocean-Atmosphere Response Experiment (COARE) Intensive Observing Period (IOP) was conducted from November 1992 to February 1993 (Webster and Lukas, 1992).

The SST undergoes diurnal variation especially on clear days with weak surface winds due to strong diurnal SW radiation, weak evaporative cooling, and weak mixing of water in the surface layer. Webster et al. (1996) investigated the diurnal SST variation in the tropical western Pacific using a one-dimensional ocean mixed layer model. They suggested that because of the nonlinear relationships between clouds, winds, surface fluxes, and the SST, information on the SST diurnal variation may be important for simulation of large scale cloud systems and feedback to the large scale atmospheric dynamics. Using data collected from different observation platforms during the COARE IOP, Sui et al. (1997) investigated the diurnal variations of cumulus convection. They found that the tropical oceanic rainfall can be classified into three stages: morning cumulus rainfall, afternoon con-vective showers, and nocturnal rainfall. These stages of rainfall are related to the diurnal SW heating of the ocean and the infrared (or longwave, LW) cooling at the cloud top.

During El Niño events, the central equatorial Pacific and the eastern Pacific are anomalously warm, and the convection center in the western Pacific warm pool shifts eastward by ^40° latitude to the central equatorial Pacific, which can be seen from the satellite measurements in the 11 ¡m IR window channel (Chou et al., 2001). Also associated with the El Nino events are more frequent and intensive eastward propagating intraseasonal disturbances, known as the Madden-Julian Oscillation (MJO), with a period of 30-60 days. These disturbances are often followed by strong westerly wind bursts (Sui and Lau, 1992). Lau and Sui (1997)

investigated the mechanisms of short term SST variation associated with the MJO using data measured during the TOGA COARE IOP. Their study demonstrated the importance of the MJO in regulating the short term SST through the induced changes in surface heat fluxes. They suggested that the MJO-induced SST variation might feed back to influencing the MJO. In a study of the mechanisms of the 1997-98 El-La Niña, Picaut et al. (2002) found that successive westerly wind bursts excited equatorial Kelvin waves and advected warm water eastward, which triggered the strong 1997 El Ninño. They also found that the existing theories of the ENSO were all legitimized at various stages during the development of the 1997 El Ninño. In an analysis of the heat sources and sinks of the 1986-87 El Ninño, Sun (2000) suggested that the ENSO system behaves like a heat pump: the equatorial ocean absorbs heat during the cold phase, and transports that heat out of the tropics. It implies that the heat fluxes at the ocean surface may be a driving force for El Ninño.

Surface heat fluxes over global oceans can best be derived from satellite-inferred atmospheric and surface parameters. Currently, there are several satellite-based sea surface heat fluxes available for studying the relationships between SST, convection, cloud, and atmospheric and oceanic circulation. These data sets include the GSSTF2 (Chou et al., 2003), HOAPS (Grassl et al., 2000), and J-OFURO (Kubota et al., 2002) for turbulent heat fluxes and GSSRB (Chou et al., 2001) and NASA SRB Rel2 (available from pre-cipitation/gssrb.shtml) for radiative fluxes. We have applied these satellite-based data sets to study the interactions between the surface heat fluxes, SST, and climate in the tropical Pacific and eastern Indian Ocean. The results were published in a series of papers, which included the study of the region in the TOGA COARE IOP (Chou et al., 1998; Chou et al., 2000), the tropical western Pacific Ocean and Indian Ocean (Chou et al., 2001; Chou et al., 2004;

Chou et al., 2005a), and the entire tropical Pacific (Chou et al., 2005b). Those studies are reviewed in this article.

2. Data Sources and Satellite

Retrievals of Surface Heat Fluxes

Data used in this study include SST, surface wind, surface humidity, high level clouds, outgoing longwave radiation (OLR), and the surface radiative and turbulent heat fluxes. All the data have a spatial resolution of 1° x 1° latitude-longitude and a temporal resolution of

I day except for the SST, which has a temporal resolution of 1 week. The SST was taken from the National Centers for Environmental Prediction (NCEP) data archive (Reynolds and Smith, 1994), and the surface wind was retrieved from radiance measurements (Wentz, 1997) of the Special Sensor Microwave Imager (SSM/I). The high level cloud cover was inferred from the brightness temperature measured in the

II ¡m channel of Japan's Geostationary Meteorological Satellite-5 (GMS-5) using a threshold brightness temperature of 260 K. The GMS pixels have a spatial resolution of 5 km and a temporal resolution of 1 hr. The high level cloud cover was inferred at these high spatial and temporal resolutions, and then averaged to a spatial resolution of 1° x 1 ° latitude-longitude and a temporal resolution of 1 day (Chou et al., 2001). The OLR inferred from the NOAA Advanced Very High Resolution Radiometer (Gruber and Winston, 1978) was also used as a proxy for cloudiness in the tropical Pacific. The surface latent and sensible heat fluxes were taken from the GSSTF2 data archive of Chou et al. (2003) and the HOAPS-II data archive of Grassl et al. (2000). Two surface radiation data sets were used. One was the GSSRB of Chou et al. (2001), which covers the tropical western Pacific Ocean and eastern Indian Ocean, and the other was the NASA SRB Rel2 global data archive (available from srb/tablejsrb.html).

2.1. The GSSRB surface radiation

A common method for deriving surface radiation budgets is to couple satellite retrievals of cloud, surface, and atmospheric parameters to radiation model calculations. This method can be grouped into two types. One is to derive cloud parameters from satellite radiation measurements, and to compute fluxes using a radiation model (e.g. Chou, 1994; Zhang et al., 1995). The other is to derive a predetermined relationship between the radiation at the top of the atmosphere and the surface (e.g. Pinker and Laszlo, 1992; Li and Leighton, 1993). The predetermined relationship is derived from radiation model calculations with climatological temperature, humidity, clouds, and aerosols as input to the radiation model. Both approaches are attractive because the physical processes that affect the radiative transfer are explicitly included. Nevertheless, the accuracy of the calculated fluxes is inherently limited by inadequate information on the absorption and scattering properties of atmospheric gases and particulates (clouds and aerosols), as well as on the temporal and spatial distributions of clouds and aerosols. One cannot with absolute certainty define either the accuracy of cloud and aerosol parameters retrieved from satellite observations, or the accuracy of radiative transfer models in computing fluxes in both clear and cloudy atmospheres. Another method for deriving the downward surface SW and LW fluxes is to empirically relate the surface flux measurements to the satellite radiance measurements. This method reduces errors introduced by the uncertainties in cloud and aerosol retrievals, and in radiative transfer models.

Based on GMS radiance measurements and surface SW and LW radiative flux measurements at six sites on islands, research ships, and a buoy during the TOGA COARE IOP from November 1992 to February 1993, Chou et al. (1998) derived the relationship between the GMS radiance measurements and the surface SW and LW radiative fluxes. The surface

SW radiative flux was empirically related to the satellite-measured radiance in the visible channel and the solar zenith angle, whereas the surface LW radiative flux was related to the temperature and humidity near the surface and the satellite-measured radiance in the 11 ¡m window channel.

Chou et al. (2001) applied these relationships to empirically derive surface SW and LW radiative fluxes from the GMS-5 radiance measurements in the tropical western Pacific and eastern Indian Ocean for the period of October 1997-December 2000. The resultant surface radiation was validated against the observations at the US Department of Energy's Atmospheric Radiation Measurement (ARM) site on Manus Island (2.06°S, 147.43°E) (Mather et al., 1998). Figure 1 shows daily variations of the observed (solid curve) and retrieved (dashed curve) downward SW radiation at the Manus site during the period of December 1999-November 2000. The retrieved SW radiation is in agreement with the measured SW radiation. Averaged over the entire GSSRB period (October 1997-December 2000), the retrieved daily-mean downward surface SW flux has a positive bias of 6.7 Wm~2 and a standard deviation (SD) error of 28.4 Wm~2. In the GSSRB data archive, this small bias at the Manus site was assumed to be universal and subtracted from the retrievals over the entire domain. The error of the retrieved LW radiation is small.

The surface radiation was originally retrieved hourly with a spatial resolution of 25 km and subsequently averaged to daily values with a spatial resolution of 0.5° x 0.5° latitude-longitude. The GSSRB covers the domain 40° S-40°N, 90°E-170°W and the period of October 1997-December 2000. There is also a subset of GSSRB that covers the TOGA COARE IOP from November 1992 to February 1993. We used the GSSRB surface radiation to study the relationship between SST and surface heat budget (SHB) in the tropical western Pacific and

Figure 1. Daily variations of the downward surface SW flux measured at the ARM Manus site (2.06° S, 147.43° E) and retrieved from GMS-5 albedo measurements. Comparison is shown only for the period of December 1999—November 2000. Day 600 corresponds to December 1, 1999. Units of flux are Wm~2. (After Chou et al.., 2004.)

Figure 1. Daily variations of the downward surface SW flux measured at the ARM Manus site (2.06° S, 147.43° E) and retrieved from GMS-5 albedo measurements. Comparison is shown only for the period of December 1999—November 2000. Day 600 corresponds to December 1, 1999. Units of flux are Wm~2. (After Chou et al.., 2004.)

eastern Indian Ocean. The GSSRB data archive is available at

2.2. The NASA SRB_Rel2 sur

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