Diurnal sea surface temperature variation and its ... - Springer Link

Journal of Oceanography, Vol. 63, pp. 721 to 744, 2007

Review

Diurnal Sea Surface Temperature Variation and Its Impact on the Atmosphere and Ocean: A Review Y OSHIMI KAWAI1* and AKIYOSHI WADA2 1

Institute of Observational Research for Global Change, Japan Agency for Marine-Earth Science and Technology, Natsushima-cho, Yokosuka 237-0061, Japan 2 Meteorological Research Institute, Japan Meteorological Agency, Nagamine, Tsukuba 305-0052, Japan (Received 6 November 2006; in revised form 2 April 2007; accepted 14 April 2007)

The importance of the diurnal variability of sea surface temperature (SST) on air-sea interaction is now being increasingly recognized. This review synthesizes knowledge of the diurnal SST variation, mainly paying attention to its impact on the atmosphere or the ocean. Diurnal SST warming becomes evident when the surface wind is weak and insolation is strong. Recent observations using satellite data and advanced instruments have revealed that a large diurnal SST rise occurs over wide areas in a specific season, and in an extreme case the diurnal amplitude of SST exceeds 5 K. The large diurnal SST rise can lead to an increase in net surface heat flux from the ocean of 50–60 Wm–2 in the daytime. The temporal mean of the increase exceeds 10 Wm–2, which will be non-negligible for the atmosphere. A few numerical experiments have indicated that the diurnal SST variation can modify atmospheric properties over the Pacific warm pool or a coastal sea, but the processes underlying the modification have not yet been investigated in detail. Furthermore, it has been shown that the diurnal change of ocean mixing process near the surface must be considered correctly in order to reproduce SST variations on an intraseasonal scale in a numerical model. The variation of mixed-layer properties on the daily scale is nonlinearly related to the intraseasonal variability. The mixed-layer deepening/shoaling process on the daily scale will also be related to biological and material circulation processes.

1. Introduction Sea surface temperature (SST) is the most important factor in air-sea interaction. The sea surface is the lower boundary of the atmosphere, and SST influences weather and climate. On the other hand, SST is also controlled by atmospheric conditions. Accurate SST data are indispensable for climate monitoring, prediction and research. SST is also important in chemical and biological oceanography. In order to clarify the mechanism of the global climate system we need a high-quality SST dataset and knowledge of air-sea interaction processes. SST data have been collected for more than a century, and form the most abundant dataset in oceanography. Nowadays several in situ or analytical long-term global SST datasets are produced and released to the public

Keywords: ⋅ Sea surface temperature, ⋅ diurnal variation, ⋅ intraseasonal variation, ⋅ air-sea interaction, ⋅ surface flux.

(e.g., Reynolds et al., 2002; Rayner et al., 2003; Worley et al., 2005). Although these high-quality datasets enable us to investigate long-term and large-scale variations, the spatial and temporal resolution of these SST data is too poor to resolve eddies and temporal high-frequency variation. Several researchers have shown that the imposition of an hourly surface forcing is essential to reproduce diurnal and intraseasonal SST variations in a numerical model (e.g., Weller and Anderson, 1996; Sui et al., 1997b; Shinoda and Hendon, 1998; Bernie et al., 2005). Furthermore, Li et al. (2001) and Clayson and Chen (2002) indicated that the diurnal SST variation can have an impact on the atmosphere over the western Pacific warm pool. The air-sea interaction on the daily scale may play an important role in the Madden-Julian Oscillation (MJO), and in turn the El Niño and Southern Oscillation (ENSO) and climate (cf. Slingo et al., 2003; Dai and Trenberth, 2004).

* Corresponding author. E-mail: [email protected] Copyright©The Oceanographic Society of Japan/TERRAPUB/Springer

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Diurnal variation, which is caused by solar radiation and the earth’s rotation, is one of the dominant variations in SST. The existence of the diurnal variation in SST was known at least one century ago (cf. Sverdrup et al., 1942; Roll, 1965). Sverdrup et al. (1942) indicated that “the diurnal variation of sea temperature in general is so small that it is of little importance to physical and biological processes in the sea, but knowledge of the small variations is essential to the study of the diurnal exchange of heat between the atmosphere and the sea.” The diurnal amplitude of SST is O (0.1 K) on average, but often reaches a few degrees and can exceed 5 K in extreme cases (e.g., Flament et al., 1994; Yokoyama et al., 1995). The satellite remote sensing community has clearly recognized that the diurnal SST variability has to be adequately considered to provide better accuracy of satellite-derived SST (e.g., Hepplewhite, 1989; Wick et al., 2002; Donlon and the GHRSST-PP Science Team, 2005; Notarstefano et al., 2006). However, the diurnal variation has often been neglected in analytical SST datasets or numerical modeling, and the effects of air-sea interaction on the daily scale have not yet been widely recognized nor adequately revealed. Many researchers have recently become interested in the diurnal SST variation, and increasing numbers of studies pay attention to air-sea interaction on the daily scale. This review synthesizes knowledge of the diurnal SST variation, focusing on why and how the diurnal variation is important for the atmosphere and the ocean. Soloviev and Lukas (2006) gave a detailed, comprehensive account of the structure and dynamics of the nearsurface ocean. Furthermore, previous studies of the skin effect (see Section 2) were reviewed or introduced by Katsaros (1980), Robinson et al. (1984), and Ward et al. (2004a), for example. In this paper we focus on the diurnal sea surface warming and its effect on the atmosphere, with less emphasis on the skin effect. We do not take up the diurnal variation caused by the tide here. First, the vertical temperature structure near the surface and the definition of SST are explained in Section 2. Section 3 summarizes previous observational studies that showed large diurnal variations in SST. Section 4 introduces several models used to simulate the diurnal SST variation. Sections 5 and 6 discuss how the diurnal variability of SST affects the surface flux estimation and the atmosphere. Some main issues on modeling and observation of the diurnal SST variation are mentioned in Section 7. The paper is summarized in Section 8. 2.

Vertical Thermal Structure near the Sea Surface and SST Terminology Under clear and calm conditions, thermal stratification is formed within the top few meters of the ocean due to strong insolation. This diurnal stratified layer is often

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called “the warm layer” (Fairall et al., 1996). The thermocline near the surface that develops only in the daytime is referred to as “diurnal thermocline”. Furthermore, at the top of the ocean, a very thin cool layer, which is usually called the “thermal skin layer”, “cool skin layer” or simply “skin layer”, almost always exists (e.g., Saunders, 1967; Katsaros, 1980; Robinson et al., 1984; Ward and Donelan, 2006). The thickness of this layer is usually the order of 0.1–1 mm, and the temperature at the top of the skin layer is generally several tenths of a degree colder than the temperature below it because eddy diffusion becomes less than molecular diffusion just close to the surface. The phenomenon of the temperature drop occurring in this thin layer is called the “skin effect”. While the diurnal thermocline vanishes by sunrise next morning, the skin layer usually exists in both the daytime and nighttime, even in windy conditions (Donlon et al., 2002). In the daytime, the temperature difference across the skin layer becomes smaller due to the absorption of shortwave radiation in this layer. The temperature at the top can theoretically even become higher than that at the bottom when the air temperature is very much higher than that of the water, as has been confirmed by a tank experiment (Ward and Donelan, 2006). The skin layer thickness and the diurnal thermocline depth vary with surface heat and momentum fluxes (e.g., Fairall et al., 1996; Ward and Donelan, 2006). A better knowledge of SST demands consideration of both the diurnal warming near the surface and the effect of the skin layer. A sharp temperature gradient sometimes appears above 1-m depth in the daytime (e.g., Yokoyama et al., 1995; Soloviev and Lukas, 1997; Ward, 2006; see Figs. 1–3 in the present paper). The large temperature difference between the sea surface and about 1-m depth, where ships and buoys usually measure the seawater temperature as “SST”, has been recognized as one of the major sources of error in satellite-derived SST. The satellite infrared sensor, microwave sensor, and in situ sensors observe “different sea surfaces”, i.e., skin, subskin, and a depth of one or a few meters, respectively. Hence it was indispensable to define SST exactly and consider careful treatment of different SSTs when producing an accurate SST dataset. The Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP) Science Team has been doing work on coordinating a new generation of global, multi-sensor, high-resolution SST products for the benefit of the operational and scientific community (Donlon et al., 2007). This Science Team has defined five kinds of SST: interface SST (SSTint), skin SST (SSTskin), subskin SST (SSTsubskin), sea temperature at depth (SSTdepth), and foundation SST (SSTfnd) (Donlon and the GHRSST-PP Science Team, 2005). A schematic picture of the vertical temperature profile is shown in Fig. 1, and the definitions of

Table 1. Definitions of the five kinds of SST proposed by Donlon and the GHRSST-PP Science Team (2005) and Donlon et al. (2007). See also Fig. 1. Name

Abbreviation Temperature represented

Instrument to measure it

Interface SST

SSTint

Theoretical temperature at the precise air-sea interface

None

Skin SST

SSTskin

Temperature within the conductive diffusiondominated sub-layer at a depth of approximately 10−20 µ m

Infrared radiometer operating in the 3.7−12 micrometer spectral waveband

Subskin SST

SSTsubskin

Sea temperature at depth

SSTdepth

Temperature at the base of the conductive laminar Microwave radiometer operating in the 6−11 GHz sub-layer frequency range, high-performance autonomous profiler (SkinDeEP, Ward et al., 2004b) In situ temperature measured below the Traditional in situ sensor (thermistor, CTD, XBT, conductive laminar sub-layer, which is etc.)

Foundation SST

SSTfnd

Temperature of the water column free of diurnal temperature variability or equal to the SSTsubskin in the absence of any diurnal signal

the SSTs are explained in Table 1. In actuality, we cannot know SSTint even with current technology (Donlon et al., 2002), and SSTskin is usually utilized as a substitute for SSTint on the assumption that SSTskin is close enough to the true SSTint. The in situ SST measured at about 1-m depth or deeper has been called “bulk” SST. The Science Team recommends using “SSTdepth” rather than the conventional term “bulk SST” referring to an in situ SST measurement made at 1-m depth as SST1m, for example. This terminology is introduced to encourage reporting of the measurement depth along with the temperature, because, as depicted in Fig. 1, the temperature can change drastically with depth when the diurnal thermocline is formed. The new concept of “foundation SST” is introduced as a more precise, well-defined quantity instead of previous, loosely-defined “bulk” SST, which is affected by the diurnal warming. SST fnd will be similar to a nighttime minimum or pre-dawn value at depths of ~1–5 m, but note that this depth is only a rough estimate and can deviate from this range in some cases. This paper basically adopts the terminology proposed by Donlon and the GHRSST-PP Science Team (2005). When referring to the temperature near the surface in a general sense, loosely, the authors simply use “SST”. While a satellite sensor sees SSTskin or SST subskin, in situ SST observed from ships and buoys is the temperature at about 1-m depth or deeper. Algorithms of satellite-derived SST are conventionally tuned by using buoyobserved SST, i.e., SSTdepth , as the truth. Hence the average of the satellite SST agrees with that of SSTdepth. However, it is expected that the variability of the satellite SST reflects that of SSTskin or SST subskin, rather than SST depth (cf. Kearns et al., 2000; Kilpatrick et al., 2001; Wick et al., 2002; Stuart-Menteth et al., 2003; Dong et al., 2006). Some researchers call such satellite SST “pseudo-bulk

″

SST” (Notarstefano et al., 2006). We should note that the satellite SST tuned against SSTdepth has the above characteristics. For the recent satellite SST products from the Along Track Scanning Radiometer (ATSR) (Mutlow et al., 1994) and the Tropical Rainfall Measurement Mission (TRMM) satellite’s Microwave Imager (TMI) (Gentemann et al., 2004), the algorithms were developed based on the radiance simulated by a radiative transfer model in order to derive SSTskin or SST subskin exactly. The atmosphere senses only the exact interface between the atmosphere and the ocean. Hence we have to know SSTint (or SSTskin practically) and its diurnal variation for accurate estimation of air-sea heat and gas fluxes (e.g., Sarmiento and Sundquist, 1992; Fairall et al., 1996). If the temperature at a few meters depth is used as SSTint in a flux calculation, the atmosphere will receive incorrect heat and water vapor from the ocean. This impact is not always negligible, as discussed in Sections 5 and 6. 3.

Observational Facts Concerning Diurnal SST Variation—When and How can the Diurnal Variation Become Large?

3.1 In situ observation Sverdrup et al. (1942) and Roll (1965) introduced some early observational studies of diurnal SST variation. These early studies used research vessel SST data, which correspond to SSTdepth, and the depths of the measurements were not mentioned. For example, according to them, a 1923 report based on data of German and British research vessels obtained during 1872–1903 stated that mean diurnal amplitude of SST in the low latitude was about 0.3–0.4 K, and the amplitude decreased to 0.26 K at 45–55°S and 0.15 K at 55–60°S. Koizumi (1956) analyzed ocean station data at Extra (39°N, 153°E) and Diurnal SST Variation and Its Impact

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Tango (29°N, 135°E) in the northwestern Pacific. He showed that the diurnal amplitude of SST was smallest (0.15 K at Extra and 0.36 K at Tango) in winter and largest in summer (0.52 K at Extra and 0.65 K at Tango) on a monthly average. Koizumi (1956) also indicated that in summer the daily maximum occurred later (1500 LST) than in winter (around 1300 LST), although the seasonality of the time of the daily minimum was not obvious. Stommel and Woodcock (1951) and Stommel et al. (1969) reported examples of large diurnal SST rise reaching 1–1.5 K in the Gulf of Mexico and south of Bermuda in spring. As mentioned above, in the early and mid twentieth century it was indicated that the diurnal amplitude of SST was about 0.2–0.6 K on average, varying with latitude and season. It was also known that the diurnal amplitude depended on cloudiness and wind speed, and could reach about 1.5 K on clear, calm days. Stronger winds induce stronger turbulent mixing in the ocean and prevent thermal stratification. Furthermore, stronger turbulence in the atmosphere draws heat from the ocean. These two functions mean that the diurnal amplitude of SST decreases as the wind becomes stronger. On the other hand, stronger insolation causes a greater diurnal SST rise due to the absorption of radiation near the sea surface. About 60% of incoming shortwave radiation is absorbed within the upper 1 m of the ocean (cf. Soloviev and Lukas, 2006). In relatively recent years, larger diurnal SST variations have been reported, using advanced instruments. Bruce and Firing (1974) showed an example of a diurnal temperature rise exceeding 3 K in the layer of 0~1-m depth, using slow-sinking Expendable Bathythermograph (XBT) probes. Other researchers also observed diurnal amplitudes of SST depth or SST skin reaching 2–3 K or more under calm and clear conditions with buoys, profiling floats, and infrared radiometers on vessels (e.g., Price et al., 1986, 1987; Yokoyama et al., 1995; Weller and Anderson, 1996; Webster et al., 1996; Soloviev and Lukas, 1997; Kawai and Kawamura, 2002; Ward, 2006). In particular, diurnal SST variations were minutely observed in the Pacific warm pool during the Tropical Ocean Global Atmosphere (TOGA)/Coupled Ocean-Atmosphere Response Experiment (COARE) (cf. Godfrey et al., 1998). For example, Soloviev and Lukas (1997) observed many diurnal thermocline profiles using a special instrument called a free-rising profiler during COARE (Fig. 2). The depth of the diurnal thermocline tends to be shallower as the gradient of the thermocline increases, and the formation of a very sharp diurnal thermocline is restricted within 0~1-m depth. Ward (2006) also showed temperature stratification of up to 2.7 K formed above 1-m depth using an autonomous profiling float called the “Skin Depth Experimental Profiler (SkinDeEP)” (Fig. 3). This large temperature difference across the warm layer has a non-neg-

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ligible influence on air-sea heat flux estimation (see Subsection 5.1). According to the results of Soloviev and Lukas (1997) and Donlon et al. (2002), the diurnal thermocline almost disappears when wind speed exceeds about 5 m s–1. Furthermore, Webster et al. (1996) and Soloviev and Lukas (1997) indicated that the shallow halocline caused by precipitation also affects the diurnal SST variation. Infrared radiometers are sometimes operated on research vessels or an oil derrick to measure SSTskin (e.g., Schlüessel et al., 1987, 1990; Fairall et al., 1996; Donlon et al., 1998; Kearns et al., 2000; Barton et al., 2004; Niclòs et al., 2004). SSTskin is now urgently required for satellite SST tuning/validation and the study of air-sea fluxes and interaction, rather than SSTdepth. SSTskin shows larger diurnal variations than SSTdepth under calm and clear conditions (e.g., Fairall et al., 1996; Donlon et al., 1998; Clayson and Chen, 2002). During windy conditions, the variation of SSTskin is almost the same as that of SSTdepth, and SSTskin is a little cooler than SSTdepth and SSTsubskin due to the skin effect and the absence of the diurnal thermocline. Several kinds of infrared radiometer for in situ SSTskin observations have been developed, such as MAERI, ISAR, SISTeR, CIRIMS, etc. (cf. Barton et al., 2004). However, infrared radiometers that can measure SSTskin with high accuracy are generally so expensive and complicated that the radiometric SST observations are now restricting their usage to a limited number of research vessel cruises. Wide spread usage of tough, low-cost radiometers will be also necessary (cf. Donlon et al., 1998). 3.2 Satellite observation Operational satellite SST observations started in the 1980s, and have provided the ability to investigate diurnal SST variation over a wide region. Deschamps and Frouin (1984) studied the diurnal heating of the sea surface in the Mediterranean Sea using the SST observed by the Heat Capacity Mapping Radiometer (HCMR) satellite. They indicated that the day-night SST difference clearly depended on wind speed, and exceeded 3 K in very calm cases. Cornillon and Stramma (1985) and Stramma et al. (1986) used the SST derived from the National Oceanic and Atmospheric Administration (NOAA) satellite/Advanced Very High Resolution Radiometer (AVHRR) observation to show that the SST often became much higher in the daytime than the nighttime in the northern Atlantic. The large diurnal warming occurred around the ridge of the Azores-Bermuda high pressure in spring and summer, and the maximum day-night difference reached 3–4 K. Flament et al. (1994) reported an example of diurnal SST amplitude reaching 6.6 K locally off California using the AVHRR SST. Diurnal SST variation is also affected by biological process, and it can be

Fig. 1. Schematic showing (a) idealized nighttime vertical temperature deviations from the foundation SST and (b) idealized daytime vertical temperature deviations from the foundation SST in the upper ocean. From Donlon and the GHRSST-PP Science Team (2005). Courtesy of C. J. Donlon.

Fig. 2. Vertical temperature profiles in the TOGA COARE domain obtained by a free-rising profiler during different wind speed conditions taken at approximately the same afternoon time on different days. Reprinted from Soloviev and Lukas (1997), Copyright 1997, with permission from Elsevier.

detected by satellite observation. Kahru et al. (1993) reported that the AVHRR SST increased locally by up to 1.5 K in the daytime in the southern Baltic Sea, corresponding to surface accumulations of cyanobacteria. Large diurnal SST variations also frequently occur in the marginal seas around Japan from spring to summer (Yokoyama et al., 1995; Kawai and Kawamura, 1997, 2000, 2002; Kawai et al., 2006a). Figure 4 shows an example of the extremely large SST difference between

Fig. 3. Temperature-depth measurements from SkinDeEP at 22.52°N, 109.59°W on 10 Oct. 1999 (graph I). Wind speed (u) and downwelling shortwave radiation (Q sw) (graph II). Temperature differences: SST skin-SST subskin (blue) and SST subskin-SSTdepth (red) (graph III). Heat loss differences: Q(SST skin )-Q(SST subskin ) (blue) and Q(SST subskin )Q(SST depth) (red) (graph IV). Q(SSTskin ), Q(SSTsubskin ), and Q(SST depth) are the surface net heat flux calculated by using SST skin, SST subskin, and SST depth as SSTint, respectively. SST skin was measured with an infrared radiometer. From Ward (2006), Copyright 2006 American Geophysical Union. Reproduced by permission of American Geophysical Union.

morning and afternoon in the Japan Sea. The regional satellite SST dataset produced by Sakaida et al. (2000) was used here. In this case the diurnal SST rise exceeded 3 K off the coast of northern Japan, and clearly corresponded to the weak wind. In the Japan Sea the diurnal warming often becomes quite large under a ridge of high pressure or behind the high mountains of Japan. Stuart-Menteth et al. (2003) first showed the global climatological distribution of the day-night SST difference using six years of AVHRR SST data (see their figure 9). From spring to summer the difference becomes larger in the Mediterranean Sea, the Bay of Bengal, the Diurnal SST Variation and Its Impact

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Fig. 4. NOAA/AVHRR 0.01°-grid SST images at (a) 0645 LST (2145 UT) and (b) 1501 LST (0601 UT) on 27 July 1999. (c) SST difference between (a) and (b). (d) Daily mean 0.25°-grid surface wind speed observed with QuikSCAT/SeaWinds on the same day.

Fig. 5. Seasonal mean day-night difference of the AMSR-E ver. 5 SST produced by Remote Sensing Systems during June 2002– May 2006. Original grid size of the SST is 0.25°, and the seasonal mean is calculated in 1° grids. Nominal observation time is approximately 0130/1330 LST. 726


Arabian Sea, the seas around Japan, the north Pacific off North America, and the Azores-Bermuda high pressure belt. Basically, the diurnal warming is large in the tropics through the year. The diurnal SST variation was also studied using geostationary satellite data (Wu et al., 1999; Tanahashi et al., 2003). These authors also reported cases when the day-night SST difference reached a few degrees in the Azores-Bermuda high or the seas around Japan. Gentemann et al. (2003) showed that the TMI SST data could detect the diurnal variation, which clearly depended on solar radiation and wind speed. The microwave sensor has a great advantage in studying SST variations because it can observe both SST and wind speed synchronously through clouds. On the other hand, the infrared sensor on the geostationary satellite has another advantage in that it measures SST hourly with a higher spatial resolution, although infrared radiation cannot penetrate cloud. The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) on the Aqua satellite has been operating since June 2002 (e.g., Dong et al., 2006). AMSR-E is the first microwave sensor that can observe SST all over the oceans. We calculated seasonal mean day-night differences from the AMSR-E version-5 SST dataset produced by Remote Sensing Systems. The characteristics of the spatial distribution and seasonal variation of the day-night difference shown in Fig. 5 are basically consistent with the results of Stuart-Menteth et al. (2003). The spatial and seasonal variations of the daynight difference shown by Stuart-Menteth et al. are not as clear as those in our Fig. 5, especially in the southern hemisphere, mainly due to the lack of the AVHRR SST sampling hindered by clouds. In boreal spring before the Indian monsoon, the daynight SST difference is greater than 0.5 K over the northern Indian Ocean. A large day-night difference in the high latitude of the northern hemisphere, and in the subtropical high pressure belt of the southern hemisphere in summer is also clearly captured. Around 45°S in the Indian Ocean and the Pacific Ocean sections the mean day-night difference is less than 0.2 K throughout the year due to the strong westerlies. Interestingly, in the high-latitude region around 60°S the mean day-night difference reaches 0.3–0.5 K in austral summer. The diurnal SST variability around the Antarctic has rarely been reported. This needs to be investigated in future study. In the narrow zonal area in the eastern equatorial Pacific west of the Galapagos Islands, the day-night difference becomes notably large, reaching a maximum in boreal spring and a minimum in boreal autumn. Deser and Smith (1998) and Clayson and Weitlich (2005, 2007) also indicated that the diurnal SST amplitude has a local maximum over the cold tongue in the eastern equatorial Pacific. The diurnal amplitude in this area becomes larger

from boreal winter to spring. This is consistent with the analysis of buoy data by Cronin and Kessler (2002). According to Clayson and Weitlich (2005, 2007), this pattern disappeared in the mature phase of the 1997–98 ENSO event. An ENSO event changes the spatial and temporal variations of the diurnal SST amplitude as a result of the characteristically different surface conditions associated with ENSO in this region (Cronin and Kessler, 2002; Kawai and Kawamura, 2005). Using satellite data, Kawai and Kawamura (2005) and Clayson and Weitlich (2007) also indicated that the diurnal SST amplitude in the western equatorial Pacific varies in association with MJO. 4. Modeling Diurnal Variations near the Surface Many kinds of numerical or empirical models have been used to investigate the diurnal variations of the upper ocean. However, no model can simulate the diurnal variations perfectly (Soloviev and Lukas, 2006). In general, numerical models concerned with simulations of the diurnal variations are roughly categorized as diffusiontype, bulk- or slab-type, and transilient models. Soloviev and Lukas (2006) also provided general information about the modeling of the diurnal variations. In addition, they showed a simulated diurnal variation in the Pacific warm pool by a transilient model. This section reviews numerical models for the simulation of the diurnal variations, except the transilient model described by Soloviev and Lukas (2006). 4.1 Diffusion models Diffusion models are subdivided into the following categories: (1) which parameterize the turbulent mixing and eddy-diffusion directly in empirical or semi-empirical ways; (2) which estimate turbulence quantities by turbulent closure at each level. The former, based on our knowledge of the Monin-Obukhov similarity theory in the surface boundary layer, are the models proposed by Kondo et al. (1979) and Large et al. (1994). The latter are those of Mellor and Yamada (MY) (1982), and modified version of MY (e.g., Kantha and Clayson, 1994). Kondo et al. (1979) proposed a simple one-dimensional model to reproduce observed behavior of the surface current and daily sea temperature within 10-m depth near the sea surface. Their model specializes in simulating the surface boundary layer. Results of the numerical experiment suggested that the diurnal range of upper-layer temperature was certainly dependent on wind speed. The diurnal amplitude of simulated SST reached its peak of nearly 2 K when the 10 m-height wind speed was 2.5 ms–1 on a clear equinox day at 35°N. They also indicated that the more stable the sea surface layer was, the faster the surface drift current, due to inhibition of downward momentum transfer.

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Kawai and Kawamura (2000) modified Kondo et al.’s model by replacing dimensionless shear functions to make turbulent transfer larger for stable cases. Kawai and Kawamura (2000) were able successfully to simulate a very sharp diurnal thermocline within 1-m depth observed in Mutsu Bay (Yokoyama et al., 1995) by using both the modified version of Kondo et al.’s model and the second-order turbulence-closure model developed by MY (Fig. 6). 4.2 Bulk or slab models Modeling under the assumption of a constant profile of sea temperature, salinity and current within a mixed layer is in general categorized as a bulk or slab model (hereafter, bulk model). The bulk model is subdivided into integral and layer types. Integral models represent turbulent mixing at the base of the mixed layer by entrainment. Some of the entrainment rate parameterizations are derived from the kinetic energy equation under the condition that the energy equation is conserved. In general, a velocity jump has to be assumed at the bottom of the mixed layer in the integral models. Layer models use a modeling methodology that estimates turbulent kinetic energy in every vertical layer. Simplified versions of the latter models have been developed recently. We here divide bulk models into multilayer type and simplified single layer type. We do not refer to the integral type in the present review. a. Multilayer model A bulk model proposed by Price et al. (1986) has been frequently applied to studies of the diurnal SST variation. The model is often called the “Price-Weller-Pinkel (PWP)” model. Its numerical scheme is based on a dynamic instability model (DIM) (Price et al., 1978). The concept of DIM is that the rate of change of potential energy during deepening of the mixed layer balances that rate of energy released from the mean flow by the reduction of vertical shear. The PWP model is a modified version of the DIM by Price et al. (1978), including a mixing process in the stratified part below the mixed layer. It is worth noting, though, that the diurnal SST amplitude simulated by the PWP model tends to be too large when compared with observations (Kantha and Clayson, 1994; Large et al., 1994). Large et al. (1994) indicated that this disagreement of the PWP model could be caused by insufficient vertical mixing. SST is more sensitive to the strength of the vertical thermal diffusion when the surface stratified layer becomes less than 1-m thick under calm and clear conditions. The representation of the vertical mixing in the stable stratified layer is one of the crucial keys to predict diurnal SST variation accurately, but is still imperfect. The mixed layer models mentioned here do not take the turbulence generated by wave breaking and Langmuir circulation into consideration. These

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effects strengthen the vertical mixing. Recently, efforts have been made to incorporate these effects into the mixed layer model (e.g., Noh et al., 2004), which will improve the reproduction of the near-surface temperature variation. b. Simplified model If we desire to simulate sharp diurnal thermocline and diurnal SST variations in numerical models, we need a fine vertical grid interval near the surface (Bernie et al., 2005). This requires an enormous computational load. In order to avoid it, some simplified models have been proposed that specialize in simulating the diurnal variation near the surface. Fairall et al. (1996) proposed a greatly simplified form of the PWP model that ignores full mixed-layer dynamics in order simply to predict the diurnal variation of the near-surface temperature only. They assumed that the temporal integrals of surface heat and momentum fluxes are confined within the warm layer, and the temperature profile in the warm layer is linear (Fig. 7(a)). The depth of the warm layer is determined by requiring that the bulk Richardson number is no greater than a critical number. Their warm layer model is wellknown and is often utilized for air-sea flux estimation in combination with their cool-skin model. The Fairall et al. model is simple and convenient, but the linear temperature profile does not always agree with observations (e.g., Ward et al., 2004a). Zeng and Beljaars (2005) developed another simple scheme to estimate SSTskin by assuming a more realistic profile shape (Fig. 7(b)). The temperature profile in the warm layer T(z) is given by the following formula: ν

 z −δ  T ( z ) = SSTsubskin −   SSTsubskin − T ( DT ) , (1)  DT − δ 

[

]

where z is the depth, δ is the depth of the skin layer, and DT is the depth of the warm layer. An empirical parameter ν is assumed to be 1 in the Fairall et al. model and 0.3 in the Zeng-Beljaars model. Zeng and Beljaars (2005) mentioned that the diurnal variation of ocean temperature usually becomes small enough at 2~4-m depth, which corresponds to SSTfnd mentioned in Section 2. They determined D T in advance empirically within this depth range. The eddy diffusion coefficient in the surface layer was determined on the basis of the Monin-Obukhov similarity theory. For the purpose of reproducing the diurnal cycle of SST in ocean general circulation models (OGCM), Schiller and Godfrey (2005) proposed a simple method that incorporates an extra variable-depth diurnal sublayer in the top model layer (Fig. 7(c)). The sublayer exists only when the total buoyancy received in the top layer is positive. Their sublayer model is based on the concept of

Fig. 6. Vertical temperature profiles in Mutsu Bay at 1430 LST on 7 July 1992. Solid line and open circles represent observed values. Broken line and asterisks represent temperature simulated by the second-order turbulence-closure model of Mellor and Yamada (1982). Chain line and pluses represent those simulated by Kawai and Kawamura’s (2000) model. Reproduced from Kawai and Kawamura (2000).

DIM, like that of Fairall et al. (1996). Schiller and Godfrey’s model could well reproduce the observed diurnal cycle of 2.5-m-depth temperature. However, note that their model does not calculate SSTskin, and the temperature in the sublayer is assumed to be independent of depth. Hence the SST simulated by their model is expected to be a little different from SSTskin. Furthermore, we need to bear in mind that the simplification of the models introduced in this subsection would neglect some dynamical processes in the upper ocean, which may affect airsea interaction. 4.3 Empirical parametric models The diurnal SST variation depends primarily on wind speed and solar radiation, so the diurnal SST amplitude can be estimated from these meteorological data. Price et al. (1987) proposed an empirical model to evaluate the diurnal amplitude of 0.6-m-depth temperature. They related the amplitude of SST0.6m with surface stress and surface heat flux. Another simple model proposed by Webster et al. (1996) included precipitation rate as well as wind speed and solar radiation based on mixed-layer model simulations during COARE. Their empirical model of the diurnal amplitude of SSTskin (∆SSTskin) has the following form:

∆SSTskin = f + a( PS) + b( P) + c[ln(U )] + d ( PS) ln(U ) + e(U ),

(2 )

where PS is the daily peak surface solar radiation (W m–2), P is the daily mean precipitation rate (mm h–1), and U is the daily mean wind speed (m s –1). a, b, c, d, e, and f are the coefficients (Table 2). The estimated ∆SSTskin is shown in Fig. 8. If there is precipitation, ∆SSTskin becomes higher due to the saline stratification. The ∆SSTskin estimated by Webster et al.’s empirical model cannot exceed 3 K, even in the extremely calm and strong insolation case. This upper limit seems too small in comparison with the observational studies mentioned previously. They produced the model (2) based on the simulation results of Kantha and Clayson’s (1994) mixed layer model under various forcing conditions. The reason for the smaller upper limit of model (2) may be that the sensitivity of the mixed layer model to the forcing was imperfect. The effects of precipitation and latent heat flux will certainly be needed for better estimation of the diurnal amplitude of SST (e.g., Price et al., 1987; Soloviev and Lukas, 1997; Kawai and Kawamura, 2003). However, they have a secondary effect on the diurnal SST variation, and the diurnal amplitude can even be approximately evaluated from wind speed and solar radiation only (Kawai and Kawamura, 2002; Gentemann et al., 2003; Clayson and Weitlich, 2005). In reality it is not easy to know daily precipitation rate accurately over a wide ocean region. Stuart-Menteth et al. (2003) estimated the diurnal amplitude of SST over the globe using Kawai and Kawamura’s (2002) empirical formula, which uses only wind speed and solar radiation, and confirmed that the spatial distribution of the estimated diurnal amplitude agreed well with that of the day-night satellite SST difference. While the above models can estimate only the amplitude, which is defined as the daily maximum-minimum difference, Zeng et al. (1999) and Gentemann et al. (2003) proposed simple empirical models that can estimate the diurnal SST rise from the morning minimum at each hour. Clayson and Curry (1996) also developed a method to estimate the diurnal SSTskin cycle. They assumed a cosine-shaped diurnal cycle with the amplitude estimated by Webster et al.’s empirical model (2), and added it to daily predawn SSTskin, which was determined from satellite-derived SSTskin data by linear interpolation. Furthermore, Li et al. (2001) proposed a simple technique to estimate SSTskin at each hour in a numerical model by applying Webster et al.’s model. Li et al. (2001) assumed that the variation of SSTskin was determined by the surface wind and solar radiation one hour previously, and the hourly anomaly of SSTskin from its daily mean at the i-th hour on the j-th day (∆T i,j) could be expressed in the following form:  1 24  12 − i + 1  ∆Ti, j = Γi −1, j −  ∑ Γi ′, j −1  1 − ,   12 12  i ′ =1  Diurnal SST Variation and Its Impact

(3) 729

Fig. 7. Schematics of vertical temperature profile near the surface assumed in (a) Fairall et al.’s (1996) model, (b) Zeng and Beljaar’s (2005) model, and (c) Schiller and Godfrey’s (2005) sublayer scheme. DT is the depth of the warm layer (sublayer). Z k=1 in (c) is the thickness of the top layer of an ocean mixed model. δ is the depth of the skin layer. T top and T bot(z) in (c) are the temperatures in the sublayer, and the layer under the sublayer, respectively. Tbot(z) may depend on depth, but is not solved in the scheme. In (a) and (c) DT is time-dependent, while it is given in advance empirically in (b).

( )  ( ) + d ( Si, j ) ln(Ui, j ) + e(Ui, j ),

Γi, j = f + a Si, j + c ln Ui, j Γi, j = 0,

Si. j > 0

( 4)

Si. j ≤ 0,

where Si,j and Ui,j is the hourly surface solar radiation and wind speed at the i-th hour on the j-th day, respectively. The precipitation is ignored here. The time of lo24

cal sunset is defined as i = 1, and

∑ Γi ′, j −1 is the sum of

Table 2. Coefficients for the empirical formula (2). Reproduced from Webster et al. (1996). Coefficient

U > 2 m s–1

U ≤ 2 m s–1

f a b c d e

0.262 0.00265 0.028 –0.838 –0.00105 0.158

0.328 0.002 0.041 0.212 –0.000185 –0.329

i ′ =1

Γ on the previous day. They showed that their parameterization could well reproduce diurnal SST skin variations. Since these simple schemes introduced here use daily mean meteorological values and/or previousday information, it may be difficult to utilize these schemes for discussing simultaneous air-sea interaction processes in a coupled model. However, they will be useful to test the sensitivity of the atmosphere to the diurnal variation of SST. 5.

Effect of Diurnal Thermocline on Air-Sea Flux Estimations

5.1 Air-sea heat flux estimation Sverdrup et al. (1942) suggested the importance of the diurnal SST variability on air-sea heat exchange. Since the atmosphere contacts the sea skin, not the water at a few meters depth, evaluating the temperature difference across the warm layer and skin layer is indispensable for accurate air-sea heat flux estimation. An error of 1 K in SSTskin can lead to an error of 27 W m –2 in net surface heat flux in the tropical western Pacific (Webster et al., 1996). Furthermore, Cornillon and Stramma (1985) 730


showed an example in the north Atlantic where monthly mean SST was higher by about 0.2 K in the case that diurnal SST variations were included than in the case that they were ignored. This difference reduces the net heat flux of 5 W m–2 entering the ocean. Fairall et al. (1996) showed that the cool skin decreased the net heat flux from the ocean by about 11 W m–2 and the warm layer increased it by about 4 W m–2 on average over 70 days during COARE. The effect of the warm layer may seem to be fairly small, but this was the mean value including the cases when the warm layer did not develop. Fairall et al. (1996) and Ward (2006) indicated that the net heat flux from the ocean can increase 50–60 W m–2 in the daytime under calm and clear conditions due to the effect of the warm layer (Fig. 3). Clayson and Curry (1996) estimated surface turbulent heat fluxes during COARE from satellite data and compared them with in situ measurements. They showed that determining the fluxes every 3 h from interpolated satellitederived input variables, i.e., including diurnal cycles in SSTskin and atmospheric variables, improved the estimate

Fig. 8. Diurnal amplitude of SSTskin estimated by Webster et al.’s (1996) empirical model as a function of daily peak solar radiation for different values of daily mean wind speed and precipitation. Solid, broken, and dotted lines represent 0, 1, and 5 mm h–1 of daily mean precipitation rate, respectively. Black, magenta, red, blue, and green lines represent 0.1, 1, 3, 5, and 10 m s–1 of daily mean wind speed, respectively.

of daily-mean surface fluxes. However, they did not evaluate the effect of the diurnal variation of SSTskin on the fluxes. Zeng and Dickinson (1998) investigated the impact of the diurnal variation of SSTskin on surface fluxes over the equatorial Pacific using TOGA Tropical Atmosphere Ocean (TAO) buoy hourly data from 95°W to 137°E in 1990–1996. The surface latent and sensible heat fluxes showed clear diurnal variability, and the average diurnal amplitudes of the latent and sensible heat fluxes were 19.7 and 5.6 W m–2, respectively. They also calculated the heat fluxes by replacing hourly SSTskin with daily or monthly mean SSTskin. Figure 9 shows the examples of the differences between the heat fluxes with the hourly SSTskin and those with the daily or monthly SSTskin. Evidently the diurnal cycle in SSTskin is the main cause of the diurnal variability of latent and sensible heat fluxes. They suggested that numerical modeling may require the inclusion of the diurnal SSTskin variation. Parsons et al. (2000) also showed that the average diurnal amplitudes of surface sensible and latent heat fluxes were about 4 and 35 W m–2, respectively, within the inner intensive flux array of the COARE experiment in mid-November 1992. Schiller and Godfrey (2005) examined the effect of the diurnal variability of SST on surface latent heat flux during COARE using a one-dimensional coupled model with their sublayer scheme (see Subsection 4.2). Use of

this scheme increased the latent heat flux during the daytime by 10–20 W m–2 and reduced it in the nighttime by 0–5 W m–2. The increase in time-mean net heat loss of the ocean was about 10 W m–2. Zeng and Beljaars (2005) reported that incorporating their SSTskin scheme (see Subsection 4.2) into an operational forecasting model changed ensemble annual mean surface latent heat flux by more than 10 W m–2 over several regions in the north Atlantic. The authors also checked the impact of the warm layer on the air-sea heat transfer by a simple numerical experiment. The test data used here were obtained with a moored buoy of the Triangle Trans-Ocean buoy Network (TRITON) in the western tropical Pacific at 2.07°N, 138.06°E during 3–13 March 2004. The authors simulated near-surface temperature using the buoy-observed meteorological data and Kawai and Kawamura’s (2000) one-dimensional model. The details of the observations and the model simulation are reported in Kawai et al. (2006b). The diurnal temperature variation was large on 3, 7 and 9 March, and the model could reproduce the variation of SST0.3m approximately (Kawai et al., 2006b). This simulation result of SSTskin is shown in Fig. 10(a) with a solid line, and is called the “control run”. The layer between the sea surface and 1.5-m depth was then forcibly mixed by setting the minimum of the eddy diffusion coefficients to 5.0 × 10 –4 m2s–1 above 1.5-m depth. This simulation is referred to as the “no-warm-layer run” (broDiurnal SST Variation and Its Impact

731

ken line in Fig. 10). In the no-warm-layer run the diurnal thermocline was destroyed and the temperature became vertically homogeneous within 0–1.5 m depth, even in the daytime (Fig. 10(b)). The diurnal amplitude of SSTskin decreased by about 1.5 K in the no-warm-layer run in the daytime of 7 and 9 March. The no-warm-layer run almost corresponds to the case that a model applies a coarse vertical grid of 1.5 m, which cannot resolve the shallow warm layer. If the warm layer cannot be reproduced correctly, the surface net heat flux from the ocean decreases by 20– 40 W m–2 in the daytime (Fig. 11). The integrated difference of the net heat flux through a day exceeds 0.9 MJ m –2 on 7 and 9 March (Table 3). About 64% of the total heat difference is due to latent heat. The effect of the warm layer is to retain the incident solar energy near the sea surface and increase the heat transfer back to the atmosphere, mainly as latent heat. The heat of 0.9 MJ m –2, which can raise the temperature of the 10-mthick water column by only 0.02 K, may not be important for the ocean, but it will not be negligible for the atmosphere because the thermal capacity of the air is much smaller and such an amount of excessive heat from the ocean can destabilize the lower atmosphere. Recently several kinds of global surface flux datasets have been developed using satellite and/or reanalysis data (cf. Kubota et al., 2003; Curry et al., 2004). In some of these datasets, the SST produced by optimum interpolation (e.g., Reynolds and Smith, 1994; Reynolds et al., 2002) is used for the flux calculation. However, such SST data lack diurnal and day-to-day variability. Yu et al. (2004) suggested that the absence of the high-frequency variations in SST appeared to cause the degradation in the accuracy of air-sea temperature and specific humidity differences. Consideration of the high-frequency SSTskin variations will be necessary to improve the quality of surface flux data. 5.2 Air-sea gas flux estimation The solubility of gases in seawater depends on temperature. Hence diurnal increases in near-surface temperature can change the concentrations of CO2 and O2 and modify the air-sea gas flux. Researchers working on the air-sea gas exchange have been paying attention to the diurnal warming and the skin effect. For example, Soloviev et al. (2001) reported an observed example that dissolved oxygen concentration just near the surface (shallower than 0.1-m depth) was lower by about 5 ml l–1 than that at 0.5-m depth or deeper in association with the formation of the diurnal thermocline. McNeil and Merlivat (1996) revealed the diurnal variations of water temperature and dissolved CO2 fugacity (fCO2) observed at 2-m depth in the Mediterranean Sea, and indicated that the fCO2 predicted from observed SST2m and an fCO2-tem732


Fig. 9. Mean diurnal cycles of surface flux differences at 0°N, 156°E (a–b), and 2°S, 95°W (c–d). Latent (a and c) and sensible (b and d) heat flux differences using hourly versus daily (monthly) mean SSTskin are denoted by solid (dotted) lines. Abscissa represents local time. From Zeng and Dickinson (1998), Copyright 1998 American Geophysical Union. Reproduced by permission of American Geophysical Union.

perature dependence of 4.23% K–1 for constant total CO 2 was similar to the observed fCO2 (Fig. 12). This means that fCO2 near the surface can vary widely in association with diurnal warming. The diurnal amplitude of the calculated fCO2 was 10–20 µatm. Ward et al. (2004a) evaluated the effects of the skin layer and the warm layer on the air-sea CO2 exchange in the eastern equatorial Pacific during the GasEx-2001 cruise. Basically, the skin effect consistently suppresses the CO2 emission from the oceans through a day, while the formation of the warm layer increases it only in the daytime. According to their evaluation, the mean decrease of the surface CO2 flux due to the skin effect was about 2% with a maximum of almost 4%. The increase of the CO2 flux due to the warm layer could exceed 6% temporarily, although the mean value throughout the observation period was only about 0.7%. They implied that the effect of the warm layer may be less important for the air-sea CO2 exchange than the skin effect, because while the warm layer forms only in the daytime of a calm and clear day, the skin layer exists even in windy conditions (Donlon et al., 2002). The relative increase due to the

Fig. 11. Difference of net air-sea heat flux between the two runs (control run minus no-warm-layer run, upward is positive) at 2.07°N, 138.06°E in March 2004. Shortwave radiation is not included.

Fig. 10. (a) Time series of the model-simulated SSTskin in the no-warm-layer run (broken line), and that in the control run (solid line) at 2.07°N, 138.06°E in March 2004. (b) Their vertical temperature profiles at 1600 LST on 9 March.

warm layer effect shown by Ward et al. (2004a) was certainly small, but this can correspond to a large absolute increase in the regions such as the eastern tropical Pacific where the absolute value of the CO2 flux is large. They also indicated that the estimated difference of fCO2 across the warm layer of a few meters thickness can reach 20 µatm or more (Fig. 13). In the eastern equatorial Pacific, the diurnal SST amplitude becomes larger during La Niñas and lower during El Niños. Hence it is expected that the impact of the warm layer on the surface CO2 flux will vary with the ENSO cycle (Cronin and Kessler, 2002). The skin effect on the global CO2 budget has also been assessed by several researchers (e.g., Sarmiento and Sundquist, 1992; Robertson and Watson, 1992; Van Scoy et al., 1995; Wong et al., 1995), but this issue is beyond the scope of this paper, and we do not review it here. The studies mentioned in this subsection indicated that a careful consideration of the ocean surface boundary layer and its high-frequency variability will be significant in studies of the global material circulation and climate change.

6. Impact on the Atmosphere The diurnal variation of SST is strongly affected by meteorological conditions. Can the diurnal SST variation really significantly affect the properties of the atmosphere? As yet, we do not know the impact of the diurnal SST variation on physical properties or processes in the atmosphere, especially in the boundary layer. Large diurnal SST variations must be accompanied by a weak wind, which suppresses the turbulent heat transfer from the ocean to the atmosphere. Zhang (2005) pointed out that “the atmosphere does not see SST; it only senses it through surface fluxes.” From this viewpoint, paradoxically, the atmosphere near the surface may sense that the sea surface is cool in calm and clear conditions. Chen and Houze (1997) indicated that the diurnal cycle in the surface air temperature was not completely dependent on that in SSTskin, although they suggested the importance of the diurnal heating of the sea surface on cloud convection over the Pacific warm pool. On the other hand, as mentioned in Subsection 5.1 it has been reported that the diurnal SST rise in the equatorial Pacific can increase the surface net heat flux by the order of 10 Wm–2, which is probably non-negligible for atmospheric physical processes. Several researchers assume that the diurnal SST variability may have some impact on the atmosphere. For example, Dai and Trenberth (2004), using a fully coupled climate system model without the diurnal SST variation, indicated that simulated diurnal cycles in surface air temperature, pressure and precipitation over the oceans were much weaker than the observed values. They inferred that the lack of the diurnal cycle in SST was a significant deficiency. The diurnal variability of SST in the tropics is basically large, Diurnal SST Variation and Its Impact

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Table 3. Daily-integrated differences of the surface heat fluxes between the control run and the no-warm-layer run. Unit is MJ m –2.

Net Sensible heat Latent heat Longwave radition

3 March

7 March

9 March

0.649 0.080 0.417 0.152

0.912 0.112 0.590 0.210

0.990 0.117 0.631 0.242

and has been intensively investigated since COARE. In this section we first review studies and hypotheses on the effect of the diurnal SST variation on the tropical atmosphere. Other topics in the mid and high latitudes are then summarized. 6.1 Tropics The diurnal variability of cloud convection over the ocean has been observed, although its mechanism has not yet been completely solved. In general, the diurnal variations of cumulus convections over the ocean and land are different from each other. The convective activity is enhanced in the late afternoon to evening over continents and large islands, while it attains its maximum in the early morning over the ocean (e.g., Gray and Jacobson, 1977; Janowiak et al., 1994; Yang and Slingo, 2001; Dai and Trenberth, 2004). Various kinds of mechanisms have been proposed for the formation of diurnal deep convection over the ocean: the direct interaction between radiation and convection (Randall et al., 1991); the effect of the horizontal distribution of clouds on radiation (Gray and Jacobson, 1977); the radiative interaction between surface and clouds (Chen and Houze, 1997); the diurnal variation of available precipitable water due to the diurnal radiative cooling/heating cycle (Sui et al., 1997a); and gravity wave forcing induced by the nearby continental diurnal cycle of convection (Mapes et al., 2003). Analyses of the COARE observation results revealed a more detailed aspect of the diurnal variation of convection over the warm pool. During the convectively active phase of MJO, the large, deep convective systems tended to reach a maximum before dawn, as mentioned above. On the other hand, during the convectively suppressed phase of MJO, when the diurnal SST variation was large, shallow precipitating clouds were most abundant (Fig. 14). Many of these shallow clouds occurred in the afternoon near the time of maximum SST, unlike the diurnal variation of the deep convection in the active phase (e.g., Chen and Houze, 1997; Sui et al., 1997a; Johnson et al., 1999). Parsons et al. (2000) showed from COARE sounding data that there was a detectable diurnal cycle in convective inhibition (CIN), with a minimum in the late afternoon just preceding the maximum in convective activ734


Fig. 12. Observations during 5 days in August 1995 of 2-mdepth water temperature (T, dashed line) and fugacity of CO2 (fCO2, bold solid line) at a mooring site between Nice and Corsica in the Mediterranean Sea (43.42°N, 7.87°E). Thin solid line is the predicted fCO2 from the observed T and an fCO2-temperature relation of 4.23% K–1 for constant total CO2. From McNeil and Merlivat (1996), Copyright 1996 American Geophysical Union. Reproduced by permission of American Geophysical Union.

ity. Parsons et al. (2000) and Chen and Houze (1997) claimed that the absorption of shortwave radiation in the atmospheric boundary layer will be as important (or more important) in the diurnal variation in CIN as (than) the diurnal cycle in SST and the resulting changes in the fluxes. Slingo et al. (2003) suggested the possibility that the diurnal SST rise may act as a trigger for the shallow cloud convection. The basic paradigm proposed by them is as follows: the diurnal cycle in SST leads to a triggering of convection in the inactive phase of MJO. In turn the cumulus congestus clouds gradually moisten the free troposphere and prepare a favorable condition for deep convection. This preconditioning may set the time scale for the following active phase of MJO. These hypotheses have not yet been corroborated. Li et al. (2001) performed numerical experiments using a global atmospheric model with or without the diurnal variation of SSTskin. They indicated that the phases of the intraseasonal variations of surface flux and precipitation simulated with diurnal SSTskin variations were closer to those of the observations in the warm pool during COARE, compared to those simulated without the diurnal SSTskin variations. The diurnal SSTskin variation brought the increase of precipitation especially over the tropical Indian Ocean and the western tropical Pacific, although the ratio of the increase to the mean value was not more than 10%. Woolnough et al. (2007) also examined the impact

Clayson and Chen (2002) investigated the sensitivity of the atmosphere to SST as the lower boundary condition in the tropical Pacific using a coupled single-column model. They showed that use of the simulated SSTskin as the interfacial temperature rather than SSTdepth resulted in large differences in the atmospheric profiles of temperature, moisture, and cloud amount. Figure 15 shows the cloud amount simulated with the SSTskin (baseline simulation), and that with the SST4.5m (4.5-m temperature simulation). The SST4.5m was higher by 0.25 K than the SSTskin on average due to the skin effect, and showed less diurnal variability. The baseline simulation has lower low-level cloud amounts during periods 1 and 3, and higher amounts during period 2, compared with the 4.5m temperature simulation. The cloud amount differences in the low level are larger than that in the mid level. One of their interesting indications is that both the skin effect and the diurnal variability are important for the atmosphere, and which of the effects is dominant varies with the periods. Clayson and Chen (2002) also indicated that the model atmosphere was sensitive to the scheme of surface turbulent heat fluxes. Deser and Smith (1998) indicated from TAO buoy data that the mean diurnal amplitude of SST showed a local maximum over the cold tongue in the eastern equatorial Pacific. They proposed a hypothesis that this large diurnal SST variation over the cold tongue may affect the zonally symmetric diurnal cycle of equatorial wind divergence. Fig. 13. Series of SkinDeEP temperature profiles acquired on 8 Feb. 2001 in the eastern equatorial Pacific (GasEx-2001) and the extrapolation of the aqueous fCO 2 to the surface from an fCO2-temperature dependence of 4.23% K–1 for constant total CO2. The local times are indicated for each plot. From Ward et al. (2004a), Copyright 2004 American Geophysical Union. Reproduced by permission of American Geophysical Union.

of the diurnal mixing of the upper ocean on MJO by coupled model experiments. They compared the result of constant-SST simulation with the result simulated by coupling to a full dynamical ocean model of 10-m vertical resolution in the upper ocean, or that by coupling to a one-dimensional ocean mixed layer model of 1-m resolution. This 1-m resolution mixed layer model could reproduce large diurnal and intraseasonal SST variations. The experiment with the mixed layer model showed the best improvement in prediction skill, especially for the phase of the MJO over the Indian Ocean and the western Pacific. They indicated the role of the ocean in determining the propagation characteristics of the MJO, and the significance of the representation of the diurnal cycle in the upper ocean.

6.2 Mid and high latitudes Flament et al. (1994) found streaks of large diurnal sea surface warming that occurred in spring off California from satellite data. These streaks were at least 50 km long and 4–8 km wide. They hypothesized that these streaks may interact with roll-like circulations in the atmospheric boundary layer, and proposed two kinds of hypotheses to explain this phenomenon. These hypotheses are interesting, but nobody has verified them yet. Kawai et al. (2006a) investigated the impact of diurnal sea surface warming on a local atmospheric circulation over Mutsu Bay, which is located at the northern end of Honshu Island in Japan around 41°N, 141°E, on a clear summer day. Over this region small sea breeze circulations and mountain up-slope wind circulations are combined during the daytime, and a unique atmospheric circulation is formed. The result of their simple model experiment showed that the SSTskin rise in the daytime weakens this atmospheric circulation due to the decrease of the land-sea temperature difference. This results in the increase in surface air temperature over coastal areas. The effect of the diurnal sea surface warming on the sea breeze circulation is expected to obtain over other coastal areas throughout the world, especially in the tropics. Yang and


735

Fig. 14. Daily-average number of low or cumulus (0–4 km), middle or congestus (5–9 km), and high or cumulonimbus (11–16 km) radio-echo tops for a cruise of R/V Vickers during COARE, and SST observed with the Improved Meteorological instrument (IMET) buoy at 1.75°S, 156°E. In the upper three panels, solid curve segments refer to periods when convective echoes organized on the Mesoscale Convective System (MCS) scale (>100 km) and dotted segments to periods when only sub-MCS or isolated cells existed. Reproduced from Johnson et al. (1999).

Slingo (2001) indicated that the strong diurnal signals of convection over land were spread out many hundreds of kilometers away over the Bay of Bengal and the coastal seas in the Maritime Continent. They suggested that this phenomenon is affected by complex land-sea and mountain-valley breezes. The diurnal cycle in SST may affect this phenomenon through modification of the land-sea breeze circulation. Yang and Slingo (2001) and Slingo et al. (2003) point to the need for a proper parameterization of the effect of land-sea breezes in a numerical model for better simulation of the diurnal cycle in convection. The effect of the diurnal SST variation should also be included in the parameterization or model. Stuart-Menteth et al. (2003) and Kawai and Kawamura (2005) showed that the diurnal SST variation

736


became large in the Bay of Bengal and the South China Sea during the transition period of the Asian monsoon in boreal spring. It was also reported from in situ observations that the diurnal SST cycle in the South China Sea was dominant before the onset of the monsoon, and decreased thereafter (D. Wang, the presentation in the 11th Ocean Observations Panel for Climate, 2006). A suggested possibility was that the diurnal variability of SST may affect the monsoon onset, but no corroboration has yet been obtained. Kawai and Kawamura (2005) indicated that the diurnal amplitude of SST also became large, even in the Sea of Okhotsk from spring to summer. Low-level clouds and fog frequently cover this region in summer. Tachibana et al. (2004) pointed out that the appearance and disappearance of fog in the Sea of Okhotsk will be controlled by air-sea interaction. They reported from ship observations that when warm air was advected in the lowest level, the air cooled while the upper ocean warmed. In this case the atmospheric mixed layer could not develop and fog did not occur, which led to strong insolation at the sea surface. Therefore, thermal stratification was formed within a few meters depth. The large diurnal rise of SST may, in turn, change the stability of the lowest atmosphere. Furthermore, the formation of the shallow diurnal stratification may affect the variations of SST and the ocean mixed layer on a longer time scale (see Subsection 7.1). 7.

Issues in Modeling and Observation of Diurnal SST Variation

7.1 Numerical modeling a. Surface forcing In order to reproduce realistic diurnal variation of SST using an ocean model, accurate surface forcing is required as a boundary condition. In particular, insolation with a diurnal cycle is important in reproducing the diurnal variation. Recently many model researchers have investigated the effect of diurnal variability of surface forcing on modeling of SST and ocean mixed layer (e.g., Sui et al., 1997b; Shinoda and Hendon, 1998; McCreary et al., 2001; Schiller and Godfrey, 2003; Bernie et al., 2005; Lee and Liu, 2005; Shinoda, 2005; Danabasoglu et al., 2006). These studies indicate that the diurnal variation of surface forcing, especially that of insolation, plays an important role in reproducing diurnal and intraseasonal variations, or a mean state in the upper ocean. The diurnal change of the mixing process of the upper layer is not only related to the diurnal SST variability, but is also essential for the intraseasonal SST variation. The inclusion of the diurnal cycle of insolation enables one to simulate a more realistic temperature and depth of mixed layer. The temporal mean of tropical SST simulated with the

Fig. 15. Time series of (a) daily-averaged mid-level (400–700 hPa) cloud amount, and (b) low-level (below 700 hPa) cloud amount for the baseline simulation (solid line) and the 4.5-m temperature simulation (dashed line) with a coupled atmosphere-ocean single-column model during COARE. Reproduced from Clayson and Chen (2002).

diurnal cycle of insolation tends to be higher than without it. Figure 16 shows the result of a numerical experiment performed by Bernie et al. (2005), who simulated mixed layer depth and temperature with hourly surface fluxes and with daily mean fluxes, respectively. The amplitude of the intraseasonal SST variation clearly decreases if the diurnal variations of the surface fluxes are neglected. The mixed-layer depth simulated with the hourly fluxes becomes a little deeper than that with the daily fluxes. Sui et al. (1997b) insisted that, because of the asymmetric heating rate of the diurnal cycle, the variation of mixed-layer properties on the diurnal timescale is nonlinearly related to the intraseasonal variability. The diurnal change of the ocean mixed layer will affect the phase of intraseasonal atmospheric variation through the amplification of the intraseasonal SST variation (cf. Li et al., 2001; Woolnough et al., 2007). The inclusion of the mixed-layer deepening/shoaling process on the diurnal scale cannot be neglected in biological modeling (McCreary et al., 2001). b. Solar extinction modeling Unlike longwave radiation, sensible and latent heat fluxes, shortwave radiation can penetrate the ocean. Determination of vertical distribution of warming by shortwave radiation in the upper ocean is critical to reproducing accurate diurnal and intraseasonal variations of SST (e.g., Kantha and Clayson, 1994; Sui et al., 1997b; Shinoda, 2005; Wick et al., 2005; Ward, 2006; Clayson and Weitlich, 2007). Sui et al. (1997b) pointed out that realistic diurnal and intraseasonal SST variations in the

tropics cannot be reproduced if an inappropriate parameterization for shortwave radiation is used. An approximate formula of a polynomial exponential function (e.g., Paulson and Simpson, 1977) considering only the simple classification of water type (e.g., Jerlov, 1968) is convenient and is often used to calculate the downward shortwave radiation flux in seawater. However, this parameterization is not always satisfactory (e.g., Sui et al., 1997b), and cannot consider the interaction between physical and biological processes. Recently a significantly improved parameterization that depends on upper-ocean chlorophyll-a concentration, cloud amount, and solar zenith angle has been proposed (Ohlmann and Siegel, 2000). Fairall et al. (1996) originally adopted shortwave-radiation parameterizations that depended on only depth for their skin-layer and warm-layer models. Ohlmann and Siegel (2000) and Wick et al. (2005) replaced them with Ohlmann and Siegel’s parameterization, and compared the temperature and surface fluxes simulated by this modified version of Fairall et al.’s model with those by the original model. If Ohlmann and Siegel’s parameterization is adopted, due to the reduction of the absorption of insolation, the skin effect is strengthened and the temperature difference across the warm layer is decreased. As a result, SSTskin and the net heat flux from the ocean can be reduced by about 0.2 K and 5 Wm–1 under calm and clear conditions, respectively. As reviewed here, use of an improved parameterization for shortwave radiation in the ocean is important to simulate SST and surface fluxes in a numerical model accurately. It is known that biological procDiurnal SST Variation and Its Impact

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esses such as increase of phytoplankton and cyanobacteria can significantly affect SST (e.g., Kahru et al., 1993). Siegel et al. (1995) found that in the warm pool during COARE, the penetration depth of shortwave radiation decreased substantially after westerly bursts. This was due to the upward mixing of nutrients and subsequent phytoplankton growth. Godfrey et al. (1998) suggested that plankton dynamics may have a significant influence on SST in the warm pool too. It has also been reported that the chlorophyll-a concentration in the upper ocean significantly increases in response to tropical cyclones (e.g., Subrahmanyam et al., 2002; Siswanto et al., 2007). The formula proposed by Ohlmann and Siegel will be useful when considering the physical-biological interaction in relation to air-sea interaction. c. Time step and vertical resolution Restricted computational resources make it sufficiently difficult to refine the temporal and vertical resolution of an OGCM so that a sharp diurnal thermocline can be reproduced. Although even a model with a coarse vertical resolution of 10–15 m in the upper ocean can simulate the diurnal variation to a certain degree (Schiller and Godfrey, 2003; Danabasoglu et al., 2006), more realistic simulations need a much finer vertical resolution in the upper ocean. Bernie et al. (2005) discussed the temporal resolution of forcing flux and the vertical resolution required to accurately reproduce the diurnal variation of SST by a numerical model. They indicated that a temporal resolution of less than 3 h and a vertical resolution of less than 1 m should be specified in order to reproduce more than 90% of the amplitude of the diurnal SST variation. d. Other model parameterizations Modeling methodology remains affected by other problems, one of which is uncertainty in the bulk coefficients used to estimate air-sea fluxes (e.g., Clayson and Chen, 2002). Another is concerned with schemes of an atmospheric model. A coupled GCM (CGCM) with a short coupling interval of 1–3 h between an atmospheric GCM and an OGCM can produce diurnal ocean variations (Danabasoglu et al., 2006). However, even if correct diurnal SSTskin variations are supplied to an atmospheric model as a lower boundary condition, the model atmosphere cannot correctly respond to the diurnal variations of SSTskin without appropriate parameterizations. Present CGCMs still have deficiencies in the dynamics for lowlevel atmospheric convergence and in their physical parameterizations for the planetary boundary layer, cloud and precipitation formation, moist convection—especially at its initiation, as well as the deficiency that simulated diurnal SST variations are too small (Dai and Trenberth, 2004). These atmospheric parameterizations also need to be improved in order to study air-sea interaction on a diurnal time scale.

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Fig. 16. (a) Sample SST and (b) turbulent boundary layer depth time series from the control integration with hourly fluxes (Run CTL, solid line), and the sensitivity experiment with daily mean fluxes (Run 24HR, dotted line). IMET buoy data at 2°S, 156°E during COARE and a one-dimensional mixed layer model were used for the simulations. Dashed line shows the daily mean SST from the control integration to emphasize the intraseasonal variability. Reproduced from Bernie et al. (2005).

7.2 Observations Satellite observations, especially by such microwave sensors as TMI and AMSR-E, are very effective, indeed indispensable for research into high-frequency SST variation. In order to resolve the global diurnal cycle, flights of at least two satellites with microwave sensors are desirable. High-quality, sustainable satellite observations must be continued. However, uncertainty in satellite-derived SST is unavoidable. High-quality, continuous in situ observations of near-surface temperature are also necessary in order to minimize satellite SST errors and to determine the vertical temperature profile. At present moored buoys are densely arranged only in the tropics and some coastal areas. As mentioned above, the diurnal variability of SST will be also important in the extratropics, and we need a dense in situ observation system to observe the diurnal change of the upper ocean even in the mid- and highlatitudes. In situ SST measurement also has some problems. For ship SST data, it is well known that the SSTs reported from voluntary observing ships are noisier than buoy SSTs, and can have a warm bias due to the heating in the engine room (e.g., Saur, 1963; Reynolds and Smith, 1994; Emery et al., 2001; Reynolds et al., 2002). The SSTs observed with buoys are usually considered to be more reliable, and are utilized for the tuning and validation of sat-

ellite SSTs. However, Kawai and Kawamura (2000) and Kawai et al. (2006b) indicated that the temperature observed with a moored buoy at about 1-m depth would deviate from the actual temperature at the nominal depth when a sharp diurnal thermocline develops just near the surface. Although the exact reason for this still remains unclear, the turbulence and/or heating induced by a buoy hull are suspected. A careful temperature measurement technique that avoids disturbing the near-surface temperature field is required when a sharp diurnal thermocline is formed. One of the solutions is use of a compact profiling float. Ward and Minnett (2001) and Ward et al. (2004b) developed the SkinDeEP autonomous profiler, which can measure near-surface temperature with a fine vertical resolution without disturbance. SkinDeEP is similar to an Argo-type float, but it is smaller and its temperature sensors protrude several tens of centimeters from the top of the body to avoid distorting the stratification. Although this float is excellent in near-surface temperature measurement, only a few prototypes have been built and they are not equipped with telecommunication functionality. At present, many profiling floats are deployed globally under the international cooperation of the Argo Project (e.g., Argo Science Team, 2001; Argo Project Office, 2006), but the normal profiling floats do not measure the surface layer above about 5-m depth in order to prevent degradation of sensor performance caused by contaminants near the sea surface, such as oil (e.g., Kobayashi et al., 2004; Riser and Wijffels, 2005). If the profiling floats are improved to measure near-surface temperature with fine vertical and temporal resolution, this will become a powerful tool for studies on the diurnal SST variability. 8. Summary The importance of the diurnal variability of SST is becoming increasingly clearly recognized. This paper has summarized studies of the diurnal SST variation and its possible impacts on air-sea interaction. We first introduced the latest definitions of the several kinds of “SST” (Section 2), and reviewed the observational facts about the diurnal SST variation (Section 3). Basically, when insolation is strong and wind speed is low, the diurnal rise of SST becomes large. Early studies indicated that the diurnal amplitude of SSTdepth was 0.2–0.6 K in the low and mid latitudes on average, reaching about 1.5 K under clear and calm conditions. Studies conducted during the past two decades have revealed from in situ and satellite observations that the diurnal rise of SSTskin or SSTsubskin can reach 5 K or more in extreme cases. Satellite observation also clarified that the large diurnal rise can occur over wide areas in a specific season. Various kinds of model have been proposed and utilized to simulate diurnal variations in the upper ocean (Section 4). The large diurnal

SST variation can be approximately simulated by the models, although there still remain some challenges in precise modeling. The atmosphere feels SSTskin, not SSTdepth, through surface heat fluxes. Hence the estimations of air-sea heat and gas fluxes are susceptible to the diurnal SST variability and the skin effect. Previous studies of the surface flux estimations were then summarized (Section 5). While the cool skin layer almost always exists through a day, even in windy conditions, the warm layer is formed only in the daytime of a calm and clear day. Therefore, the skin effect may be more important for the flux estimation than the effect of the warm layer. However, for the heat flux, it is indicated that the consideration of the warm layer can increase the net heat flux by 50–60 W m–2 in the daytime, and its temporal mean exceeds 10 W m –2. Furthermore, the significant effect of the skin layer and the warm layer on air-sea gas exchange has already been pointed out, and the surface mixing process on the diurnal scale can also affect marine biology (McCreary et al., 2001). Accurate physical-chemical-biological modeling and material circulation studies will need knowledge of the ocean’s diurnal variability. Recent studies of the relation between the diurnal SST variation and air-sea interaction were then introduced (Section 6). Whether the diurnal SST variation really affects the atmospheric physics significantly is a difficult but interesting question. A few studies have showed that the diurnal variation and/or the skin effect can modify precipitation and surface fluxes variations on an intraseasonal scale, the vertical profile of the air temperature and humidity, and a sea-breeze circulation. Although at present the investigation of this subject is not yet adequate, many researchers expect that the diurnal cycle in SST will have some impact on the atmosphere, and an attempt to simulate the diurnal variation of the upper ocean in a GCM has already begun. The necessity of this attempt at a numerical model has also summarized (Subsection 7.1). Fine vertical/temporal resolution and appropriate parameterization for shortwave extinction in the ocean are required to accurately simulate large diurnal SST rise. Some schemes that enable us to reproduce realistic diurnal SST variations without enormous computational load have been proposed recently (e.g., Schiller and Godfrey, 2005; Zeng and Beljaars, 2005). In conclusion, the issues to be clarified can be summarized as follows: (1) A few studies have pointed out from numerical experiments that the diurnal SST variation and/or the skin effect can significantly affect the atmospheric field over the warm pool (e.g., Li et al., 2001; Clayson and Chen, 2002). However, the processes through which the differences in cloudiness and humidity were caused were not discussed, and still remain unknown.


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(2) The diurnal temperature rise and the temperature drop across the skin layer increase or decrease the air-sea sensible heat flux, and thus will affect the heat content and height of the atmospheric boundary layer. The perturbation of only a few W m–2 of the sensible heat flux, which will be negligible for the ocean, may significantly affect the lower atmosphere. The changes of the atmospheric mixed layer and the surface wind field related with the diurnal SST variation need to be investigated intensively. Furthermore, whether large diurnal SST rise really can be the trigger for the shallow cumulus convection (Slingo et al., 2003), which may control the preconditioning of the MJO active phase, has not been confirmed yet, either. (3) The effect of the diurnal SST rise on a coastal local atmospheric circulation has been indicated by Kawai et al. (2006a). They reported only an idealized calm and clear case. More detailed numerical experiments are necessary for more realistic conditions and/or different areas, especially for the tropics. Consideration of this effect may be significant for practical weather forecasting in coastal regions. (4) While many researchers interested in the diurnal SST variation pay much attention to the tropics and subtropics, the diurnal variation in the high latitudes has rarely been noted. However, as shown in Fig. 5, the diurnal SST rise can become large even in the high latitudes in summer. In situ observations that can resolve the diurnal SST cycle are not sufficiently close to the polar regions. Intensive studies focusing on the high latitudes should be conducted. (5) As mentioned in Subsection 7.1.d, some parameterizations in an atmospheric model would not be appropriate for simulating atmospheric response to the diurnal SST variation. Further improvement of the model parameterizations will be necessary. (6) When a sharp and shallow diurnal thermocline is formed, in situ near-surface temperature measurement is not easy (Subsection 7.2). Careful measurements without instrument-induced turbulence and/or heating are required for studies of diurnal air-sea interaction. Again, the impact of the diurnal SST variability on air-sea interaction has not yet been investigated sufficiently. Even if the diurnal change of SST itself were to have little effect on the atmosphere, it has been confirmed that the formation/decay process of the diurnal thermocline near the surface cannot be neglected in order to reproduce the SST variation on an intraseasonal or longer scale accurately in a model, which is essential to air-sea interaction, especially in the tropics. Further studies using numerical models are necessary, and an observing system that can detect the diurnal cycle in SST all over the world is also indispensable for the corroboration of the model simulation.

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