Spatial and temporal variations of model-derived diurnal amplitude of sea surface temperature in the western Pacific Ocean

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110,, doi: /2004jc002652, 2005 Spatial and temporal variations of model-derived diurnal amplitude of sea surface temperature in the western Pacific Ocean Yoshimi Kawai and Hiroshi Kawamura Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Aoba, Sendai, Japan Received 9 August 2004; revised 24 February 2005; accepted 4 May 2005; published 24 August [1] The diurnal amplitude of sea surface temperature (SST) at 1-m depth is estimated with a simple empirical model in the region of 80 E 160 W, 60 S 60 N covering the western Pacific Ocean using 4 years of satellite-derived wind speed, solar radiation, and latent heat flux data. The advantage of this indirect method is that the diurnal amplitude of SST (DSST) can be obtained even in cloudy areas. DSST is large in the tropics through the year. Summer DSST in the middle and high latitudes of the Northern Hemisphere, especially around Japan, is larger than that of the Southern Hemisphere. However, DSST in the Kuroshio and its extension regions is smaller compared with that in its surrounding ones because of higher wind speed. In the Bay of Bengal, the South China Sea, and the Timor Sea, DSST becomes largest in spring, when the surface wind becomes weak in the transition of the monsoon. The period of about days is clearly seen in the temporal variation of DSST in the tropics. This reflects the Madden-Julian oscillation. Furthermore, there are some differences in DSST between El Niño and La Niña periods. The authors then investigate how clouds affect the detection of DSST. If sampling is limited to clear areas only, the areas of strong solar radiation and weak wind are selectively picked up, and the probability that larger DSST is sampled increases. The mean of the DSST sampled from clear areas only is greater by K compared with the actual mean. Citation: Kawai, Y., and H. Kawamura (2005), Spatial and temporal variations of model-derived diurnal amplitude of sea surface temperature in the western Pacific Ocean, J. Geophys. Res., 110,, doi: /2004jc Introduction [2] The diurnal variation caused by insolation is one of the principal variations in the Earth s fluids. Although the diurnal variation of sea surface temperature (SST) has been often ignored in oceanography and meteorology, nowadays the significance of the diurnal SST variation and the nearsurface warm layer is recognized. For example, Cornillon and Stramma [1985] showed that monthly mean SST was higher by about 0.2 K in the case that diurnal SST variations were included in the data than in the case that they were ignored. Such difference is critical to flux estimation or climate monitoring. Fairall et al. [1996] pointed out that SST with an accuracy of ±0.2 K was needed to estimate the total surface energy budget to an accuracy of 10 W m 2. The heat budget and SST variability are especially important in the warm pool region. It has been also reported that the existence of the diurnal warm layer can significantly affect the estimation of net daily air-sea gas exchange [McNeil and Merlivat, 1996; Ward and Minnett, 2001]. Furthermore, long-term climate monitoring requires an accuracy of about 0.1 K for SST [Smith, 2001]. The diurnal variation of heat flux also affects the model simulation of the oceanic mixed layer. Compared with the case that a Copyright 2005 by the American Geophysical Union /05/2004JC model is driven by a daily mean forcing, the mixed layer becomes deeper and its temperature becomes lower in the case that a model forcing has a diurnal variation. This effect changes the strength of phytoplankton blooming [McCreary et al., 2001] or the spatial distribution of SST and airsea interaction in a model (O. Arakawa and A. Kitoh, unpublished data, 2001). [3] The diurnal SST variation has been studied using satellite or in situ data, and some researchers have proposed empirical models to estimate it [e.g., Price et al., 1987; Webster et al., 1996; Kawai and Kawamura, 2003; Gentemann et al., 2003]. The diurnal amplitude of SST (DSST) becomes larger as the wind becomes weaker and insolation becomes stronger. Stuart-Menteth et al. [2003] first showed the global distribution of DSST and its seasonal variation by subtracting nighttime SST from daytime SST. However, because they used SST derived from satellite infrared data, the investigation of regions where it often becomes cloudy is still insufficient. Furthermore, this daynight SST difference does not always capture the true diurnal amplitude since the satellite sensor does not always observe the daily minimum and maximum SSTs. The day-night difference of polar-orbiting satellite s SST will underestimate the true DSST. Tanahashi et al. [2003] obtained DSST maps from the hourly SST data of Geostationary Meteorological Satellite/Visible and Infrared Spin Scan Radiometer (GMS/ VISSR). They showed that the spatial pattern of the satellite- 1of14

2 Table 1. Regression Coefficients of Equation (1) Determined by Kawai and Kawamura [2003] U>2.5ms 1 U 2.5 m s 1 a b c d derived DSST agreed well with those estimated by empirical models. Their estimation was limited to the short period of 7 months when the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) on board the Advanced Earth Observation Satellite (ADEOS) was available. [4] In this study, we focus on the observation region of GMS (80 E 160 W, 60 S 60 N), which includes the western Pacific Ocean and part of the eastern Indian Ocean. There are some oceanographically or meteorologically interesting areas in the GMS observation region. For example, the warmest SST and the most active atmospheric convection are seen in the warm pool area. The importance of this region to the climate of the globe has been widely recognized [Webster and Lukas, 1992]. Furthermore, the Kuroshio Current is the western boundary current of the subtropical North Pacific gyre and has an important role that transports a large amount of heat to the midlatitudes. In the Kuroshio Current and its extension regions, SST and its variability are high and air-sea heat flux is large. Air-sea interaction in these regions is an important issue [e.g., Qiu, 2002; Nonaka and Xie, 2003]. [5] We here estimate DSST at 1-m depth in the GMS region using a simple empirical model and satellite-derived data, that is, wind speed, solar radiation, and latent heat flux. The advantage of this indirect method is that DSST can be obtained even in cloudy areas. We describe data and analysis procedures used here in section 2. In section 3, characteristic temporal variations of DSST are discussed. We also examine the sampling bias of DSST caused by clouds in section 4. Conclusions are stated in section Data and Analysis Procedures [6] In this study, DSST at 1-m depth, which is the diurnal amplitude of bulk SST, was estimated with the following parametric model proposed by Kawai and Kawamura [2003], DSST ¼ amsþ ð H l þ eþ 2 þ b½lnðuþš þ cmsþ ð H l þ eþ 2 ½lnðUÞŠþ d; ð1þ where MS is the daily mean solar radiation (W m 2 ), U is the daily mean wind speed (m s 1 ), H l is the daily mean latent heat flux (upward is negative, W m 2 ), the constants a, b, c, and d are the regression coefficients given in Table 1, and e is a constant value of 300 W m 2. (Note that there is a misprint in Kawai and Kawamura s [2003] Table 3. The correct value of the coefficient c for U 2.5 m s 1 is not , but ) When the righthand side of equation (1) becomes negative, DSST is set to zero. Hence if SST decreases monotonously through a day, DSST is defined to be zero. Furthermore, the lower limit of U is set to 0.5 ms 1 in this model, and this limit is substituted for U when U is below this limit. The DSST estimated with this model is the SST rise caused by solar heating, and the SST variation due to horizontal or vertical heat advection is not considered in this estimation. Stuart- Menteth et al. [2003] compared day-night SST difference derived from satellite SST data with a model-estimated DSST. They used another empirical model by Kawai and Kawamura [2002], which was the precursor of the model (1) and did not include H l. They showed that there was a strong similarity between the spatial patterns of the observed day-night SST difference and the model-estimated DSST. Kawai and Kawamura [2003] validated the estimated DSST by the model (1) with buoy data distributed widely in the western Pacific and concluded that apart from a marginal sea, where the effect of heat advection often becomes dominant, this model can estimate DSST with a bias of less than 0.1 K and a root mean square (RMS) error of K. Basically, the principal factors determining DSST are solar radiation and wind speed, and DSST can be estimated to a certain extent even without considering latent heat flux directly [Webster et al., 1996; Kawai and Kawamura, 2002; Gentemann et al., 2003], because wind speed itself represents the effect that the heat transfer from the sea surface to the atmosphere increases with wind speed. However, since the simpler model of Kawai and Kawamura [2002] yielded a warm bias of K in the tropics although the bias in the extratropics was less than 0.1 K, Kawai and Kawamura [2003] included latent hear flux into the model in order to reduce the bias in the tropics. Except for the bias in the tropics, the error statistics were almost the same between both the models. [7] For the DSST estimation, we used a solar radiation product derived from GMS-5/VISSR data [Kawai and Kawamura, 2005] and wind speed products of the Special Sensor Microwave Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites, ADEOS/NSCAT, the Tropical Rainfall Measurement Mission satellite s Microwave Imager (TMI), and SeaWinds on board QuikSCAT [Wentz, 1997; Jet Propulsion Laboratory, 1997, 2001]. The information on these satellite wind speed products is listed in Table 2. For wind speed, four or more sensors were available always throughout the periods analyzed in this study (March 1997 to February 1998 and July 1998 to June 2001). The original spatial resolutions of the solar radiation data and the wind speed data were 0.05 and 0.25, respectively. All these data were simply averaged in grid cells by the day. We calculated the daily mean for a grid cell where there were one or more observations in a day, and more than 75% of these grid cells had three or more wind speed observations in a day the majority of the time. Latent heat flux can be calculated from the satellite-derived wind speed, precipitable water, and SST. The SSM/I and TMI precipitable water products of 0.25 resolution and the optimally interpolated SST data set produced by Reynolds et al. [2002] were used for the calculation of latent heat flux [Kawai and Kawamura, 2003]. Although latent heat flux was calculated in grid cells, the effective resolution of the latent heat flux data was rougher than 0.25 since the spatial resolution of this SST was 1. 2of14

3 Table 2. Satellite Sensors for Wind Speed Observations That Were Available in the Analyzed Periods a Satellite Sensor Source Period Analyzed in This Study (Except March to June 1998) Approximate Morning Pass Time DMSP F10 SSM/I Remote Sensing Systems March 1997 to November LST DMSP F11 SSM/I Remote Sensing Systems March 1997 to May LST DMSP F13 SSM/I Remote Sensing Systems March 1997 to June LST DMSP F14 SSM/I Remote Sensing Systems May 1997 to June LST DMSP F15 SSM/I Remote Sensing Systems December 1999 to June LST ADEOS NSCAT NASA Jet Propulsion Laboratory March 1997 to June LST TRMM MI(TMI) Remote Sensing Systems December 1997 to June 2001 non-sun-synchronous QuikSCAT SeaWinds NASA Jet Propulsion Laboratory July 1999 to June LST a ADEOS/NSCAT and QuikSCAT/SeaWinds are scatterometers, and the others are passive radiometers. Reprinted from Kawai and Kawamura s [2005] Table 3 with permission from the Oceanographic Society of Japan. [8] Kawai and Kawamura [2003, 2005] also examined the accuracy of these daily mean input data. According to them, the RMS errors of the daily mean wind speed, solar radiation, and latent heat flux are about 1.0 m s 1, 23 W m 2, and 50 W m 2, respectively. The relations between the model-derived DSST and the inputs are shown in Figure 1. This figure shows the cases when solar radiation is very strong (Figure 1a) or the wind is very weak (Figures 1b and 1c). The errors of the DSST caused by the above RMS errors of U, MS, and H l are also shown in Figure 1. The uncertainty of the DSST caused by the representative error of solar radiation is smaller than those caused by the other input errors. In the cases of Figure 1, the maximum DSST errors caused by the representative error of U, MS, and H l are 0.42 K, 0.18 K, and 0.40 K, respectively. The sensitivity of the DSST on the inputs becomes smaller as the wind becomes stronger and solar radiation becomes lower. In reality the actual error of the DSST is much smaller than the sum of the above maximum error components, and the RMS error of the DSST is K as mentioned above. [9] Note that Kawai and Kawamura s [2003] model estimates the diurnal amplitude of the bulk (buoy-observed) SST at 1-m depth, not at the skin layer. We show here the comparison of the model-estimated DSST with that calculated from the GMS-derived hourly SST data produced by Tanahashi et al. [2003] for June 1997 (Figure 2). The GMS-derived DSST was obtained by subtracting the minimum SST before 0900 LST from the maximum SST after 0900 LST. The GMS DSST was averaged in 5 5 grid cells since the GMS SST was noisy as indicated by Tanahashi et al. The GMS DSST is generally larger than the model one, and the mean of the GMS-model DSST difference is 0.25 K. This will be due to the difference between the sea skin and 1-m depth in the daytime. Although this GMS SST is tuned to bulk SST, its variability reflects characteristics of the skin [cf. Kilpatrick et al., 2001; Stuart-Menteth et al., 2003] and therefore includes the effect of the thermal gradients between the nominal 1-m depth of the buoy temperatures and the surface. On the other hand, the model DSST corresponds to the bulk one. [10] For a cluster analysis and a sampling bias study mentioned later, 3 years of the estimated DSST from July 1998 to June 2001 were used for reasons of our solar radiation data. We missed a few weeks of the GMS solar radiation data before and after this period, and excluded the spring of 1998 from these analyses. (The cluster analysis in this study needed temporally consecutive data.) However, in addition to the above analyzed period, DSST was also estimated from March 1997 to February 1998 when discussing the difference in DSST between the El Niño and La Niña periods. The SST anomaly in the Niño 3.4 region (5 N 5 S, 120 W 170 W) started to grow in the spring of 1997, and it was positive throughout most of the period during March 1997 to February 1998 (Figure 3). The extreme El Niño event ended in May of 1998 [Takayabu et al., 1999], and the La Niña event followed it. The analyzed period that ranges from July 1998 to June 2001 just corresponds to the time when the Niño 3.4 SST anomaly was consecutively negative, i.e., the La Niña and neutral period. We call this period simply LN period hereinafter. [11] In order to see regional characteristics of the temporal variation of DSST, we performed a cluster analysis. Before this analysis the estimated DSST was averaged in grid cells for the reduction of computation. There were some temporal gaps of the satellite data of a few days, and missing values were filled in by temporal linear interpolation. A low-pass filter was applied to the raw DSST time series, and variations whose periods were less than about 30 days were removed. We did not discuss on periods of less than 1 month in this paper. After that, we classified all the grid points into 10 areas, in which grid points have temporal variations similar to each other, by a cluster analysis. The correlation coefficient between mean DSST in an area and DSST at each grid in this area was statistically significant with a significance level of The detailed explanation of the cluster analysis procedure is described in Appendix A. [12] In section 4 we investigated how clouds affect the detection of the diurnal SST warming statistically. SST under clouds cannot be measured with satellite infrared sensors. Since the infrared sensors observe the sea surface only when it is clear, mean day-night SST difference obtained with the infrared sensors, such as shown by Stuart-Menteth et al. [2003] and Tanahashi et al. [2003], may tend to be systematically larger than the actual mean value. Statistical characteristics of the DSSTs sampled from only clear areas were examined in section 4. To judge whether a grid cell was clear or not, we used the GMS-5/ VISSR 0.05 hourly albedo data: The ocean surface albedo was estimated in advance by taking the minimum albedo value for each month at the same time of day [Kawai and Kawamura, 2005]. The threshold value of the discrimina- 3of14

4 Figure 2. Comparison between the model-derived DSST and the one calculated from the GMS SST produced by Tanahashi et al. [2003] in the region of 60 S 60 N, 120 E 160 E for June 1997 (Kelvin). Both data are averaged in 5 grid cells. tion between cloud and clear was set to the sum of the ocean surface albedo and a constant margin (0.03). If more than 95% of the GMS pixels in a 0.25 grid cell during LST were clear, we regarded this grid cell on the day as a clear one. [13] We also utilized the 9-km-resolution daily Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST data set of Version 4.1 and Interim Version 4.1 [Kilpatrick et al., 2001] in section 3. The AVHRR is an infrared sensor on board a series of National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites, which fly over the same place at least twice a day: once in the daytime and once in the nighttime. This data set includes a quality level with each SST grid value. The quality level is determined as a result of various tests for selecting reasonable SST data. A quality level of zero Figure 1. Relations between the model-estimated DSST and (a) the daily mean wind speed (U), (b) the daily mean solar radiation (MS), and (c) the daily mean latent heat flux (H l ). In Figure 1a, MS and H l are fixed to 350 W m 2 and 0Wm 2, respectively. U and are H l are fixed to 1.0 m s 1 and 0 W m 2 in Figure 1b, and U and are MS are fixed to 1.0 m s 1 and 350 W m 2, respectively, in Figure 1c. The intervals between dotted lines show the errors of DSST caused by the representative errors of U, MS, and H l, which are ±1.0 m s 1, ±23 W m 2, and ±50 W m 2, respectively. Figure 3. Monthly SST anomaly in the Niño 3.4 region (5 N 5 S, 120 W 170 W). The values shown were calculated and released by the Climate Prediction Center ( Lines with arrows indicate the periods analyzed in this study. 4of14

5 Figure 4. Seasonal mean of the model-derived DSST from July 1998 to June 2001 (Kelvin). (a) March May, (b) June August, (c) September November, and (d) December February. See color version of this figure at back of this issue. indicates very bad SST data, while a level of seven is the highest quality. According to Figure 15 of Kilpatrick et al. [2001], the residual of the Pathfinder SST against an analytical SST for the quality level of equal to or more than four little depends on the quality level and is much smaller than that for the quality level of less than four. Hence we adopted the SST with a quality level of four or more in this study. 3. Spatial and Temporal Variations of DSST 3.1. Seasonal Mean [14] We first show the spatial DSST distribution averaged in each season for the LN period (Figure 4). DSST is large (about 0.5 K) in the tropics through the year. As indicated by Tanahashi et al. [2003], DSST tends to be larger in some areas near land in the tropics. In particular, seasonal mean DSST exceeds 1.0 K in the Timor Sea in austral spring. DSSTs in the marginal seas around Japan are also especially remarkable in boreal summer. Summer DSST in the middle and high latitudes of the Northern Hemisphere is larger than that of the Southern Hemisphere. This is caused by the difference in the distribution of wind speed, rather than solar radiation, between both the hemispheres (Figure 5). For summer, the surface solar radiation in the middle and high latitudes of the Northern Hemisphere is nearly equal to or smaller than that of the Southern Hemisphere. On the other hand, the wind speed in 20 N 60 N is much lower than that in 20 S 60 S in summer. The characteristics of the DSST spatial distribution mentioned above are similar to those shown by Stuart-Menteth et al. [2003] and Tanahashi et al. [2003]. [15] In boreal spring and summer, DSST in the Kuroshio and its extension regions is smaller compared with that in the surrounding regions. Figure 6 shows the mean summer wind speed, solar radiation, and latent heat flux around the Kuroshio region. This smaller DSST is mainly due to the higher surface wind speed. The spatial pattern of the mean wind speed clearly reflects the current of Kuroshio (Figures 6a and 7), and the DSST map reflects this wind speed distribution. The latent heat flux is also larger in the Kuroshio region (Figure 6c). (Although the position of the current axis was not constant through the period, the current had a tendency to meander in this period as seen in Figure 7.) The mean solar radiation in the Kuroshio region is slightly smaller than in the region south of Kuroshio (Figure 6b), and this will also contribute to the smaller DSST a little. Higher SST makes the atmospheric boundary layer more unstable, and the surface wind speed becomes higher due to the momentum transfer from the upper layer. 5of14

6 DSST was higher by about 0.2 K in the Bay of Bengal and around the equator in the Indian Ocean compared with that in spring of the LN period. The DSST in the El Niño period was also higher than in the LN period in the midlatitudes of Figure 5. Zonal mean of (a) seasonal mean solar radiation (W m 2 ) and (b) seasonal mean wind speed (m s 1 ). Solid, dotted, dashed, and dash-dotted lines represent summer in the Northern Hemisphere (June August), summer in the Southern Hemisphere (December February), winter in the Northern Hemisphere (December February), and winter in the Southern Hemisphere (June August), respectively. This mechanism of strengthening the surface wind over the Kuroshio and its extension regions was reported by Nonaka and Xie. [2003]. Furthermore, air-sea sensible and latent heat fluxes are greater in the strongly unstable atmosphere than in the less unstable atmosphere, and the larger heat loss suppresses the SST rise. Hence if SST becomes higher in an area owing to heat advection when the lower atmospheric condition is horizontally homogeneous, the SST rise in this area becomes smaller than that in its surrounding areas [cf. Katsaros and Soloviev, 2004]. In summary, the higher SST causes higher surface wind speed and larger heat loss in the oceanic surface layer, and these effects suppress the diurnal SST rise. [16] We also show the seasonal mean features of DSST in the preceding extreme El Niño event here (Figure 8). Basically, the spatial pattern of the mean estimated DSST in the El Niño period is almost the same as those in Figure 4. However, there are some local differences between them. For example, in the boreal spring of 1997, when the onset of the El Niño event occurred, the seasonal mean Figure 6. (a) Mean surface wind speed (m s 1 ), (b) mean solar radiation (W m 2 ) and (c) mean latent heat flux (W m 2 ) in boreal summer from 1998 to See color version of this figure at back of this issue. 6of14

7 Figure 7. Mean SST field around Kuroshio in boreal summer from 1998 to 2001 calculated from the AVHRR Pathfinder SST data ( C). SST values with a quality level of equal to or greater than 4 were used. See color version of this figure at back of this issue. the Northern Hemisphere (20 N 30 N, 140 E 160 W) in boreal summer and in the seas around Indonesia in austral summer. These differences were caused by higher solar radiation and lower wind speed in these regions in the El Niño period (Figure 9). Furthermore, the DSST in the El Niño period was higher in the equatorial region east of 170 E from spring to autumn, although the seasonal mean solar radiation in the El Niño period was smaller than that in the LN period. In this region, lower wind speed in the El Niño period made the DSST higher. The DSST distribution tends to reflect the pattern of wind speed rather than that of solar radiation, as shown in Figures 6 and 7. In the austral summer of , when the El Niño was mature, solar radiation became much lower in the central equatorial Pacific and higher in the Maritime Continent and around the South Pacific Convergence Zone (SPCZ) compared with the LN period. This corresponds to the eastward shift of the active convection region from the western equatorial Pacific well known as one of the characteristics of the El Niño event. The wind speed in this season was remarkably higher around 20 S east of 160 E and the diurnal SST variation in this region was much suppressed in spite of higher solar radiation. Note that not all of those mentioned above are the characteristic differences between El Niño and La Niña periods generally, because we have seen only one case. Figure 8. Seasonal mean of the model-derived DSST from March 1997 to February 1998 (Kelvin). (a) March May, (b) June August, (c) September November, and (d) December February. See color version of this figure at back of this issue. 7of14

8 Figure 9. Differences of (a, b) the seasonal mean solar radiation (W m 2 ), (c, d) the seasonal mean wind speed (m s 1 ), and (e, f) the seasonal mean DSST (Kelvin) between the El Niño period and the LN period. Left-hand and right-hand panels show the differences in boreal summer (June August) and boreal winter (December February), respectively. A positive (negative) difference means that the value in the El Niño period is greater (smaller) than that in the LN period. See color version of this figure at back of this issue Cluster Analysis [17] The GMS observation region was then classified into 10 areas by the cluster analysis using the estimated DSST data in the LN period (Figure 10). Figure 11 shows the temporal variation of DSST averaged in each area. The Sea of Okhotsk was excluded from this analysis because it was covered with sea ice in the wintertime. The annual cycle is dominant in the high latitudes and midlatitudes. 8of14

9 Figure 10. Areas classified by cluster analysis. The areas are numbered from 1 to 10. See color version of this figure at back of this issue. The high-latitude area of the Southern Hemisphere (Area 1) has the smallest mean DSST of all the areas. DSST in the midlatitudes of the Southern Hemisphere (Area 2) has the annual cycle with the same phase as in Area 1, and its amplitude is greater than in Area 1 because of lower wind speed and higher solar radiation (Figure 5). In the separated part of Area 1 west of Australia in the Indian Ocean, DSST is suppressed by the strong trade wind. The annual amplitude of DSST in the Northern Hemisphere is much greater than in the Southern Hemisphere, as also seen in Figure 4. In the middle and high latitudes of the Northern Hemisphere, DSST grows faster and greater in Area 6 than in Area 5. Area 6 consists of the Yellow Sea, the Japan Sea, the area around the Kuril Islands, and the zonal area around 30 N. In Area 5, DSST is suppressed by higher wind speed over the Kuroshio and its extension regions (Figure 6) or by both higher wind speed and lower solar radiation over the northern part (Figure 5), which is due to lower-level stratus that is characteristic of the high latitudes in the northwestern Pacific. As the latitude becomes lower, higher-frequency variations appear and the variations become irregular (Areas 3, 4, 7, and 8), while the annual cycle is still dominant. DSST becomes the spatially local minimum in Area 7 from spring to summer (see also Figure 4). This is due to the strong northeasterly trade wind on the edge of the subtropical pressure high (Figure 6a). The southern part of split Area 6 corresponds to the ridge of this pressure high [e.g., Dai and Deser, 1999]. In the equatorial areas, higher-frequency components are more noticeable rather than an annual cycle. The cycles of about 35 days to half a year can be clearly seen in Areas 9 and 10. In Area 10, DSST is Figure 11. Time series of DSST averaged in each classified area (Kelvin). The area numbers are shown in Figure 10. Thin lines above and below the broad lines represent confidence limits of the mean value with a significance level of Dashed line shows a mean value through the whole period. 9of14

10 Figure 12. Time series of mean DSST in Area 10 (thick line, right ordinate) and the MJO index (thin line, left ordinate). The MJO index shown is the Real-time Multivariate MJO series 1 (RMM1) produced by Wheeler and Hendon [2004]. between 0.4 K and 0.8 K through the year, and its mean value through the whole period is largest of all the areas. Its temporal variation is relatively small. [18] Figure 4 shows that DSST becomes largest in boreal spring in the South China Sea (especially in the southern part) and the Bay of Bengal, which are in Area 8 or 9. The Timor Sea in Area 4 also has the largest DSST in austral spring. Around the Bay of Bengal the strong Indian monsoon blows from the southwest in summer and the wind direction turns opposite in winter. Boreal autumn and spring are the transition periods of the Indian monsoon, and the wind speed becomes low [Knox, 1987]. Austral spring is the time just before the onset of the Australian summer monsoon. Over northern Australia the direction of the surface zonal wind turns opposite and its speed becomes low in this season [Holland, 1986]. The seasons when DSST becomes largest in Areas 4, 8, and 9 correspond to the transitions of the monsoons. The cycle of about days can be seen in Areas 3 4 and 7 10, especially clearly in Area 10. The variation of such cycle in atmospheric pressure, zonal wind, and outgoing longwave radiation in the tropical area is well known as Madden-Julian Oscillation (MJO) [Madden and Julian, 1994]. We compared the DSST averaged in Area 10 with an MJO index proposed by Wheeler and Hendon [2004] (Figure 12). They obtained empirical orthogonal functions (EOFs) of the combined fields of near-equatorially averaged 850-hPa zonal wind, 200-hPa zonal wind, and satellite-observed outgoing longwave radiation data. The first-mode EOF describes the familiar situation where the MJO produces enhanced convection at the longitudes of the Maritime Continent. The MJO index shown in Figure 12, which is called the Real-time Multivariate MJO series 1 (RMM1) by Wheeler and Hendon, is the principal component of this first-mode EOF. This index is positive (negative) and large when atmospheric convection is enhanced (suppressed) over the Maritime Continent. Although the DSST in Area 10 and the MJO index are sometimes in phase, for example, in the summer and autumn of 1999, the phases of the DSST and the index tend to be opposite: The trough of the DSST corresponds to the peak of the index. This means that when convection is enhanced over the Maritime Continent, the diurnal SST variation is suppressed. This relationship is reasonable because it is expected that the convection reduces solar radiation and enhances wind speed at the sea surface. While the DSST variation in the tropics does not always reflect MJO directly, this is basically affected by MJO. 4. Cloud Effect on DSST Sampling [19] Day-night SST difference can be observed with the satellite infrared sensors, but only when it is cloudless [Stuart-Menteth et al., 2003]. In this section we investigated how clouds affect the detection of the diurnal SST warming. Figure 13 shows the probability density functions (PDF) of the satellite-derived daily mean solar radiation, wind speed, and model-estimated DSST for the whole GMS observation region from July 1998 to June We produced PDFs in both the cases that DSST values were sampled from all the grids (all-sky case), and sampled from only the clear grids (clear case). The effect of clouds on sampling solar radiation, wind speed, and DSST is discussed below by comparing the PDFs in the all-sky case with those in the clear case. The probability densities of solar radiation, wind speed, and DSST were calculated in each bin of 5.0 W m 2,0.5ms 1 and 0.1 K, respectively, and these probability densities and mean values were obtained by weighting each value with its grid area. [20] For the all-sky case, the probability (integrated probability density) for the range of over 230 W m 2 is and the maximum probability density is at about 270 W m 2 (Figure 13a). On the other hand, the probability density in the clear case is much larger than in the all-sky case in the range of over 230 W m 2. In the clear case the position of the PDF peak shifts to a larger solar radiation value and the probability for this range increases to The distribution of wind speed has the peak at 7 8 m s 1, and 31.4% of all wind speeds are equal to or less than 6.0 m s 1 (Figure 13b). This is similar to that for the whole globe shown by Donlon et al. [2002]. In the clear case the probability density becomes larger for the range of less than about 7 m s 1 compared with that in the all-sky case. These differences between the all-sky and the clear cases mean that certainly the areas of strong solar radiation and weak 10 of 14

11 more than 0.5 K. Table 3 lists the mean DSST and the probability of DSST > 0.5 K. [21] The PDFs of the area 120 E 170 E, 5 S 5 N, which almost corresponds to Area 10, are shown in Figure 14. For the all-sky case, the wind speed of the maximum probability density in this area is lower by about 3ms 1 than that in the whole GMS region, and the mean DSST is much larger. The difference in the solar radiation PDF between the all-sky and clear cases is remarkable. The probability of the solar radiation for the range of over 260 W m 2 increases from about 0.4 in the all-sky case to more than 0.9 in the clear case. As a result, the probability of DSST for the range of over 0.5 K in the clear case increases by more than 0.25 compared with that in the allsky case. The mean DSST in the clear case also increases to 0.80 K. The increase of the probability density at K is especially large. [22] The PDFs in the seas around Japan (120 E 160 E, 35 N 60 N) during May August were also investigated (Figure 15), since DSST becomes especially large in this area from spring to summer. For the all-sky case, the solar radiation PDF has the peak at about 200 W m 2, which is much smaller compared with those for the whole region (Figure 13a) and for the tropical region (Figure 14a). However, in the clear case the large probability density of solar radiation concentrates in the range of W m 2, as well as Figures 13a and 14a. The mean DSST is 0.73 K in the all-sky case and 1.04 K in the clear case, and 84.0% of DSST are greater than 0.5 K in the clear case. The results in this section show that the sampling limited to clear areas causes a DSST bias of K. Note again that the DSST estimated in this study corresponds to the diurnal amplitude of the bulk (buoy-observed) SST at 1-m depth [Kawai and Kawamura, 2002, 2003]. It can be inferred that the sampling bias of DSST at the sea skin is much greater than this value because of thermal stratification formed near the sea surface in the daytime by strong solar radiation. Diurnal vertical temperature difference between the sea surface and few-meters depth also becomes larger as the wind speed decreases and solar radiation increases [Price et al., 1987; Donlon et al., 2002; Kawai and Kawamura, 2002]. Figure 13. Probability density function of (a) daily mean solar radiation (W m 2 ), (b) daily mean wind speed (m s 1 ), and (c) DSST (Kelvin) over the whole GMS observation region from July 1998 to June Solid and dashed lines represent the all-sky case and the clear case, respectively. wind are selectively observed with a satellite infrared sensor. Figure 13c shows that DSSTs sampled from only the clear grids tend to be larger than those from all the grids. Only 20.2% of DSST is greater than 0.5 K and the mean DSST is 0.30 K in the all-sky case, while in the clear case more than 40% of it exceeds 0.5 K and the mean DSST is 5. Conclusions [23] In this study the 1-m-depth DSST was estimated from the satellite-derived meteorological data through the empirical model. The advantage of this method is that we can obtain DSST even in cloudy areas. We investigated the spatial distribution and temporal variations of this DSST in the GMS observation region of 80 E 160 W, 60 S 60 N for March 1997 to February 1998 (El Niño period) and July 1998 to June 2001 (La Niña and neutral period). The seasonal means of DSST showed that DSST was large in the tropics, especially near land, through the year. In the middle and high latitudes the diurnal warming became strong in summer. Summer DSST in the Northern Hemisphere was larger than in the Southern Hemisphere. DSST was remarkably large in the seas around Japan from spring to summer. These characteristics of the DSST spatial distribution were similar to those shown in the previous studies by Stuart-Menteth et al. [2003] and Tanahashi et al. [2003]. 11 of 14

12 Table 3. Mean DSST and Probability of DSST > 0.5 K in the All-Sky Case and the Clear Case Mean DSST Whole Region Probability of DSST > 0.5 K Tropics (120 E 170 E, 5 S 5 N) Mean DSST Probability of DSST > 0.5 K Around Japan in May August (120 E 160 E, 35 N 60 N) Mean DSST Probability of DSST > 0.5 K All-sky Clear The Kuroshio and its extension regions were the local minimum regions of DSST in these seasons because of the higher wind speed compared with that in its adjacent areas. Higher SST makes the atmospheric boundary layer more unstable and causes higher surface wind speed and larger air-sea heat transfer. DSST in the Kuroshio and its extension regions would be suppressed through such mechanism. The spatial distribution of the seasonal mean DSST in the El Niño period was almost the same as that in the LN period. However, the differences in the mean value between both the periods were statistically significant in some areas, and those in the tropics can be interpreted as a result of the change in the atmospheric convection over the western equatorial Pacific well known as one of the characteristics of the El Niño event. [24] We classified the GMS observation region into 10 areas by cluster analysis for the LN period to see the regional characteristics of DSST temporal variation. In the middle and high latitudes, the annual cycle was dominant. The annual amplitude of DSST in the Northern Hemisphere was larger than in the Southern Hemisphere. In the Northern Hemisphere, DSST grows faster and larger in the seas around Japan and the zonal area around 30 N than in the adjacent areas. As the latitude became lower, higherfrequency variations became prominent. The cycles of about 35 days to half a year were dominant in Areas 9 and 10. In Area 10 the mean DSST through the whole period was largest of all the areas and DSST variation was relatively small. DSST becomes largest in boreal spring in the South China Sea and the Bay of Bengal, and also largest in austral spring in the Timor Sea. The seasons when DSST becomes largest in these areas correspond to the transitions of the monsoons. The period of about days was seen in Areas 3 4 and 7 10, especially clearly in Area 10. The variation of such cycle in the tropics has been known as MJO. The temporal variations of DSST reflect MJO and the monsoon cycle in the low latitudes. [25] We then investigated how clouds affect the detection of the diurnal SST warming. It was proved that when the sampling was limited to the clear grids only, the areas of strong solar radiation and weak wind were selectively picked up. As a result, the probability that larger DSST was sampled increased in such case. The mean of the DSST sampled from the clear grids was greater by K than that from all the grids. This estimated DSST corresponds to the buoy-observed value at 1-m depth, and the sampling bias of DSST at the sea skin will be much larger. [26] DSST variability is associated with the monsoon cycles, MJO, and El Niño/La Niña event, as mentioned above. The action between the atmosphere and the ocean on a daily scale may not be one way. The large diurnal SST rise, which is the result of such atmospheric forcing, may Figure 14. Same as Figure 13 except for the area 120 E 170 E, 5 S 5 N. 12 of 14

13 cluster p. The spatial mean of the values on the ith day in cluster p can be written as X ip ¼ X np x ipj j¼1 n p ; ða1þ where n p is the number of grids in cluster p. The sum of square residuals from the mean values in cluster p is defined by S p ¼ Xm i¼1 X np j¼1 2 x ipj X ip ; ða2þ where m is the number of days. The sum of the square residuals of all the clusters is S ¼ Xk p¼1 S p ¼ Xk X m X np p¼1 i¼1 j¼1 2 x ipj X ip ; ða3þ where k is the number of clusters. The function S is an objective function that should be minimized. It is here assumed that the grouping that minimizes S is the most appropriate. [28] When a new cluster t is made by putting cluster p and cluster q together, the relation between S p, S q and S t is the following: S t ¼ S p þ S q þ DS pq DS pq ¼ n pn q X m 2: X ip X iq ða4þ n p þ n q i¼1 The combination of clusters whose DS is smallest among all the combinations is selected when reducing the number of clusters by putting two clusters together. This means that the clusters whose time series of spatial means are similar to each other are appropriate for being put together. The number of clusters is reduced by repeating this procedure, as long as the correlation coefficient between the spatial means in a cluster X ip and values at each grid in the same cluster x ipj is statistically significant with a significance level of Figure 15. Same as Figure 13 except for the area 120 E 160 E, 35 N 60 N in May August. also have some influence on the atmosphere, especially near land in the tropics. The air-sea interaction associated with the monsoon, MJO, and the diurnal SST variation is one of the interesting future studies. Appendix A: Cluster Analysis [27] The procedure of the cluster analysis used in this study is based on the method proposed by Ward [1963]. Here x ipj is a value on the ith day at the jth grid point in [29] Acknowledgments. The SSM/I and TMI products used in this study are produced by Remote Sensing Systems and sponsored by the NASA Earth Science REASoN DISCOVER Project. These data are available at The QSCAT/SeaWinds and ADEOS/NSCAT wind data products are produced by the NASA Scatterometer Projects, and distributed by NASA/Physical Oceanography Distributed Active Archive Center (PO.DAAC). The NOAA/NASA AVHRR Pathfinder SST data set are produced by the Pathfinder Project Team and also distributed by PO.DAAC. The optimally interpolated weekly SST (Reynolds SST) data were provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, USA, from their web site at S. Tanahashi kindly provided us with the GMS SST data. The Niño 3.4 SST anomaly data were calculated and released by the Climate Prediction Center in NOAA/National Weather Service ( data/indices/sstoi.indices). The MJO index produced by M. C. Wheeler and H. H. Hendon was downloaded from the web site at gov.au/bmrc/clfor/cfstaff/matw/maproom/rmm/. We also greatly appreciate F. Sakaida and reviewers comments that contributed to improving this paper. This study is supported by ADEOS-2 projects of the Japan Aerospace Exploration Agency (JAXA), and the Category 7 of MEXT (Ministry of Education, Culture, Sports, Science and Technology, Japan) 13 of 14

14 RR2002 Project for Sustainable Coexistence of Humans, Nature and the Earth, and also by the special coordination fund for promoting science and technology New Generation Sea Surface Temperature of MEXT. References Cornillon, P., and L. Stramma (1985), The distribution of diurnal sea surface warming events in the western Sargasso Sea, J. Geophys. Res., 90, 11,811 11,815. Dai, A., and C. Deser (1999), Diurnal and semidiurnal variations in global surface wind and divergence fields, J. Geophys. Res., 104, 31,109 31,125. Donlon, C. J., P. J. Minnett, C. Gentemann, T. J. Nightingale, I. J. Barton, B. Ward, and M. J. Murray (2002), Toward improved validation of satellite sea surface skin temperature measurements for climate research, J. Clim., 15, Fairall, C. W., E. F. Bradley, D. P. Rogers, J. B. Edson, and G. S. Young (1996), Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment, J. Geophys. Res., 101, Gentemann, C. L., C. J. Donlon, A. Stuart-Menteth, and F. J. Wentz (2003), Diurnal signals in satellite sea surface temperature measurements, Geophys. Res. Lett., 30(3), 1140, doi: /2002gl Holland, G. J. (1986), Interannual variability of the Australian summer monsoon at Darwin: , Mon. Weather Rev., 114, Jet Propulsion Laboratory (1997), Ocean wind products CD-ROM user s manual, JPL Doc. D-14766, Pasadena, Calif. Jet Propulsion Laboratory (2001), SeaWinds on QuikSCAT level 3 daily, gridded ocean wind vectors (JPL SeaWinds project) (Version 1.0), JPL Doc. D-20335, Pasadena, Calif. Katsaros, K. B., and A. V. Soloviev (2004), Vanishing horizontal sea surface temperature gradients at low wind speeds, Boundary Layer Meteorol., 112, Kawai, Y., and H. Kawamura (2002), Evaluation of the diurnal warming of sea surface temperature using satellite-derived marine meteorological data, J. Oceanogr., 58, Kawai, Y., and H. Kawamura (2003), Validation of daily amplitude of sea surface temperature evaluated with a parametric model using satellite data, J. Oceanogr., 59, Kawai, Y., and H. Kawamura (2005), Validation and improvement of satellite-derived surface solar radiation over the northwestern Pacific Ocean, J. Oceanogr., 61, Kilpatrick, K. A., G. P. Podesta, and R. Evans (2001), Overview of the NOAA/AVHRR advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database, J. Geophys. Res., 106, Knox, R. A. (1987), The Indian Ocean: Interaction with the monsoon, in Monsoons, edited by J. S. Fein and P. L. Stephens, pp , Wiley Intersci., Hoboken, N. J. Madden, R. A., and P. R. Julian (1994), Observations of the day tropical oscillation A review, Mon. Weather Rev., 122, McCreary, J. P., K. E. Kohler, R. R. Hood, S. Smith, J. Kindle, A. S. Fischer, and R. A. Weller (2001), Influences of diurnal and intraseasonal forcing on mixed-layer and biological variability in the central Arabian Sea, J. Geophys. Res., 106, McNeil, C. L., and L. Merlivat (1996), The warm oceanic surface layer: Implications for CO2 fluxes and surface gas measurements, Geophys. Res. Lett., 23, Nonaka, M., and S. Xie (2003), Covariations of sea surface temperature and wind over the Kuroshio and its extension: Evidence for ocean-toatmospheric feedback, J. Clim., 16, Price, J. F., R. A. Weller, C. M. Bowers, and M. G. Briscoe (1987), Diurnal response of sea surface temperature observed at the long-term upper ocean study (34 N, 70 W) in the Sargasso Sea, J. Geophys. Res., 92, 14,480 14,490. Qiu, B. (2002), The Kuroshio Extension System: Its large-scale variability and role in the midlatitude ocean-atmosphere interaction, J. Oceanogr., 58, Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang (2002), An improved in situ and satellite SST analysis for climate, J. Clim., 15, Smith, N. (2001), Report of the GODAE high-resolution SST Workshop, 30 Oct. 1 Nov. 2000, GODAE Rep. 7, 66 pp., Int. GODAE Project Off., Bur. of Meteorol., Melbourne, Victoria, Australia. Stuart-Menteth, A. C., I. S. Robinson, and P. G. Challenor (2003), A global study of diurnal warming using satellite-derived sea surface temperature, J. Geophys. Res., 108(C5), 3155, doi: /2002jc Takayabu, Y. N., T. Iguchi, M. Kachi, A. Shibata, and H. Kanzawa (1999), Abrupt termination of the El Niño in response to a Madden- Julian oscillation, Nature, 402, Tanahashi, S., H. Kawamura, T. Takahashi, and H. Yusa (2003), Diurnal variations of sea surface temperature over the wide-ranging ocean using VISSR on board GMS, J. Geophys. Res., 108(C7), 3216, doi: / 2002JC Ward, B., and P. J. Minnett (2001), An autonomous profiler for near surface temperature measurement, in Gas Transfer at Water Surfaces, Geophys. Monogr. Ser., vol. 127, edited by M. A. Donelan et al., pp , AGU, Washington, D. C. Ward, J. H. (1963), Hierarchical grouping to optimize an objective function, J. Am. Stat. Assoc., 58, Webster, P. J., and R. Lukas (1992), TOGA COARE: The Coupled Ocean Atmosphere Response Experiment, Bull. Am. Meteorol. Soc., 73, Webster, P. J., C. A. Clayson, and J. A. Curry (1996), Clouds, radiation, and the diurnal cycle of sea surface temperature in the tropical western Pacific, J. Clim., 9, Wentz, F. J. (1997), A well-calibrated ocean algorithm for SSM/I, J. Geophys. Res., 102, Wheeler, M. C., and H. H. Hendon (2004), An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction, Mon. Weather Rev., 132, Y. Kawai and H. Kawamura, Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Aoba, Sendai , Japan. (kawai@ocean.caos.tohoku.ac.jp) 14 of 14

15 Figure 4. Seasonal mean of the model-derived DSST from July 1998 to June 2001 (Kelvin). (a) March May, (b) June August, (c) September November, and (d) December February. 5of14

16 Figure 6. (a) Mean surface wind speed (m s 1 ), (b) mean solar radiation (W m 2 ) and (c) mean latent heat flux (W m 2 ) in boreal summer from 1998 to Figure 7. Mean SST field around Kuroshio in boreal summer from 1998 to 2001 calculated from the AVHRR Pathfinder SST data ( C). SST values with a quality level of equal to or greater than 4 were used. 6of14and7of14

17 Figure 8. Seasonal mean of the model-derived DSST from March 1997 to February 1998 (Kelvin). (a) March May, (b) June August, (c) September November, and (d) December February. 7of14

18 Figure 9. Differences of (a, b) the seasonal mean solar radiation (W m 2 ), (c, d) the seasonal mean wind speed (m s 1 ), and (e, f) the seasonal mean DSST (Kelvin) between the El Niño period and the LN period. Left-hand and right-hand panels show the differences in boreal summer (June August) and boreal winter (December February), respectively. A positive (negative) difference means that the value in the El Niño period is greater (smaller) than that in the LN period. 8of14

19 Figure 10. Areas classified by cluster analysis. The areas are numbered from 1 to 10. 9of14