Characteristics of the Satellite-Derived Sea Surface Temperature in the Oceans around Japan

Transcription

1 Journal of Oceanography, Vol. 53, pp. 161 to Characteristics of the Satellite-Derived Sea Surface Temperature in the Oceans around Japan YOSHIMI KAWAI and HIROSHI KAWAMURA Center for Atmospheric and Oceanic Studies, Faculty of Science, Tohoku University, Aoba-ku, Sendai , Japan (Received 8 February 1996; in revised form 30 August 1996; accepted 2 September 1996) We compare the sea surface temperatures (SSTs) derived from NOAA/AVHRR data through the multi-channel sea surface temperature (MCSST) algorithm with the in situ SSTs reported from ships and buoys during November 1988 May 1991 in the oceans around Japan. The weekly averages of both the SSTs are computed in 1 -grids. We find from this comparison that the satellite-derived SSTs are lower than the in situ SSTs by more than 0.5 C on the average in the Yellow Sea, the north of the Japan Sea and around the Kuril Islands. On the other hand, the satellite SSTs are higher than the in situ ones by more than 0.5 C in the regions of the Tsushima and Tsugaru warm currents. Furthermore, we find that the most dominant variation of the differences is an annual cycle in the regions north of 40 latitudinal line. In these regions, the satellite-derived SSTs are higher (lower) than the in situ SSTs by more than 1.0 C in summer (winter) on the average. Because there is not such a large amount of water vapor or volcanic aerosols that cause a large error through the MCSST algorithm over the regions, the systematic biases may be related to vertical temperature structures in the ocean surface layer, formed by the strong monsoon. In order to obtain more accurate SSTs from satellite observations in these regions, the characteristics of the vertical temperature structures in the ocean surface layer are needed to be investigated in detail. Keywords: Sea surface temperature, satellite-derived SST, MCSST, systematic bias, seasonal cycle, surface layer, oceans around Japan. 1. Introduction Sea surface facing the lower boundary of the atmosphere influences atmospheric conditions and climate. For example, the sea surface temperature (SST) variation in the tropical Pacific Ocean is one of the indices of the El Niño and southern oscillation phenomena (ENSO) which causes global climate changes. Exchange of heat and water between the atmosphere and the oceans is done through the sea surface. SST is one of its important controllers and is also controlled through the exchanging processes. Therefore, it is very important to understand SST behavior in the global oceans. In order to investigate climatic changes, a global SST data set that is dense, accurate and well-ordered is necessary. Satellite observations of the ocean are expected to satisfy this requirement. The global SST observations using the advanced very high resolution radiometer (AVHRR) sensors on the TIROS-N/NOAA series satellites have been going on for more than 17 years by the National Oceanic and Atmospheric Administration (NOAA). Many studies on SST derivation from satellite observations have been done, and it has been proved that satellite-derived SSTs agree well with in situ SSTs with root mean square (RMS) errors of less than 0.7 C. For example, Strong and McClain (1984) compared the SSTs derived from AVHRR data through the multi-channel sea surface temperature (MCSST) methods (we refer to the SSTs as MCSSTs ) with the in situ SSTs observed with drifting buoys. They showed that the bias (average of MCSST-minus-in situ SST differences) was 0.01 C and the RMS error between them was 0.54 C during November 1981 August 1982 over the Atlantic, Pacific and Indian Oceans. McClain et al. (1985) also showed that the global bias and RMS error of the match-up data in 1985 which consisted of the MCSSTs derived from NOAA-9/AVHRR data and the SSTs observed with drifting buoys were near 0.1 C and 0.5 C, respectively. Barton (1995) discussed selected AVHRR SST algorithms to investigate recent developments in the SST derivation. He used three sets of the atmospheric conditions for typical subtropical, midlatitude summer, and midlatitude winter, and the corresponding infrared measurements by the AVHRR. Although the algorithms have been developed for different AVHRR sensors, different regions, and by theoretical and empirical techniques, there is very good agree- Copyright The Oceanographic Society of Japan. 161

2 ment between the derived SST values for the three sets of data, indicating a robustness of the basic differential absorption algorithm. On the other hand, it has been reported that there are systematic biases between satellite-derived and in situ SSTs over particular areas and conditions. Reynolds et al. (1989) showed day-minus-night MCSST differences for February In the northwestern tropical Pacific Ocean, the nighttime MCSSTs were higher than the daytime ones by more than 0.5 C. This pattern had persisted since the new NOAA-11 satellite became operational in November The comparisons between daytime MCSSTs and driftingbuoy SSTs in this area from November 1988 showed that the former SSTs were estimated lower than the latter ones. Since it is assumed in the MCSST algorithm that the infrared absorption due to water vapor is small, the results of the comparison may suggest that the large errors are caused by a large amount of water vapor in the tropical regions. Bates and Diaz (1991) compared MCSSTs with the in situ SSTs recorded in the comprehensive ocean-atmosphere data set (COADS) for each ocean, and showed that the MCSSTs were lower than the in situ SSTs by 0.19 C to 0.64 C, and the bias averaged over the whole oceans was 0.29 C. It may be the reason why the MCSSTs are lower than the COADS SSTs that ship intake temperatures have a warm bias relative to high-quality (i.e., bucket measurement) surface observations because the majority of the COADS data are reported from ship engine room measurements of the temperatures of water brought in to cool the engine. They have indicated that sparsity of ship data, especially in the southern hemisphere, or the difference physically caused by the measurement of very thin skin temperature (measured by satellite) and upper-ocean bulk temperature (by ship) can be the reasons for the satellite-in situ temperature differences. Sakaida and Kawamura (1992) have shown that when the MCSSTs derived from NOAA-11/AVHRR data are compared with the in situ SSTs observed with moored buoys around Japan, the RMS error between them is about 0.6 C. They have also pointed out that some MCSST values are systematically higher than the corresponding buoy SSTs in the Japan Sea in summer. Hepplewhite (1989) compared the oceanic skin temperatures measured from a ship using an infrared radiometer with the bulk temperature observed through a bucket sampling. As a result, it was found that the bulk-minus-skin temperature differences range from 0.3 C to +1.2 C and the mean difference was +0.3 C. Schluessel et al. (1990) also showed that these differences range between 1.0 C and +1.0 C with mean differences of 0.1 C to 0.2 C depending on wind and surface heat flux conditions. These studies imply that there might be a systematic difference between satellite-derived and in situ SSTs associated with the bulk-skin temperature difference, which is caused by regional specific conditions of the atmosphere and the ocean surface layer. It is also known that the SST observation using the infrared bands from space has a bias toward underestimating SST when there are volcanic aerosols in the atmosphere (e.g., Reynolds et al., 1989; Reynolds, 1993). In order to obtain accurate and dense SST data over the global oceans, it is essential to investigate the causes of these systematic differences, and to correct satellite-derived SST if it has a systematic error. However, there can be a large difference between satellite-derived and in situ SSTs even though the atmospheric correction of a SST deriving algorithm is appropriate because satellite-derived SSTs and in situ SSTs are measured at different depths. In the sections that follow, we compare MCSSTs and in situ SSTs around Japan, and examine differences between them. We then discuss the causes of large-scale spatial biases under no contamination by volcanic aerosols. 2. Data 2.1 The global MCSST data set Since the infrared radiation emitted from the sea surface is attenuated by absorption gases in the atmosphere, it is very difficult to measure SST accurately with only one infrared channel from space. Anding and Kauth (1970) proposed a method to estimate SST from space using the differences of brightness temperatures measured with different bands of wavelength. The theoretical basis of a multiple-window channel algorithm was developed in the 1970 s. The principle and history of improvement of the MCSST algorithm are reviewed in McMillin and Crosby (1984), McClain et al. (1985), and Barton (1995). In the MCSST algorithms, the assumption used is that the amount of absorption gases in the atmosphere is small, yielding a linear relation between SST and satellite-measured brightness temperatures. MCSSTs have been computed operationally from AVHRR infrared data, by NOAA/NESDIS (National Environmental Satellite Data Information Service) since late The AVHRR on board the polar orbiting NOAA satellite has two visible and three infrared channels. Their band widths are: ch. 1, µm; ch. 2, µm; ch. 3, µm; ch. 4, µm; ch. 5, µm. The ch. 4 and ch. 5 are called split-window channels. Since the AVHRR channels used to estimate SST and to detect clouds in daytime are different from those in the nighttime, the coefficients of the MCSST equations are different between daytime and nighttime. In the present study, we use the global MCSST data set, produced from the NOAA/NESDIS MCSST retrievals, by the University of Miami/Rosenstiel School of Marine and Atmospheric Sciences (UM/RSMAS) and provided by the Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory (JPL) (Olson 162 Y. Kawai and H. Kawamura

3 Fig. 1. Number of the daytime NOAA/AVHRR observations available in a 1 -grid during November 1988 May et al., 1988). In the global MCSST data set the cylindrical equi-rectangular grid has dimensions of 2048 (longitude) by 1024 (latitude), and the spatial resolution is about by The MCSSTs are binned at every week. We average the MCSSTs in each 1 -grid in order to compare with in situ data. The number of the daytime MCSST data in each 1 grid during the period November 1988 May 1991 is shown in Fig In situ SST data The in situ SST data provided by the Japan Meteorological Agency (JMA) consist of ship, buoy and aircraft observations. Most of them were reported from ships. A lot of ship tracks are highly concentrated in the oceans around Japan, so as the distribution of the SST data from voluntary observing ships. In the present study, we compare the MCSSTs with the in situ SSTs in the region extending from 20 N to 50 N and from 120 E to 160 E. Since satellitederived SST can be contaminated by volcanic aerosols after the eruption of Mt. Pinatubo in the Philippines which had occurred since June 1991 (Reynolds, 1993), we analyze the data obtained during 10 November 1988 to 5 June 1991 before the eruption (134 weeks). The quality of each SST is checked in the following procedure before analyses: climatological SST in each 1 grid is calculated for every third part of a month from the 10- day mean SST maps published by JMA from 1950 to Then, the root mean square of the differences between the climatological SSTs and the in situ SSTs is computed over each 5 -grid. The in situ SST data whose residual from the climatological SST exceeds three times of the root mean square are eliminated. After this procedure, we average the in situ SST data in each 1 -grid for the weekly periods of the global MCSST data set. The number of the in situ SST data in each 1 -grid during the analyzed period is shown in Fig. 2(a). The number of the data is large in the coastal regions around Japan, in the Japan Sea, and along major ship routes, and small in the Yellow Sea and the ocean southeast of Japan. The larger amounts of data at the grids in the Japan Sea, the East China Sea and the ocean south of Japan are collected by the JMA moored buoys. The standard deviation (root mean square of deviations from the weekly-averaged SSTs) of the in situ SSTs in each 1 -grid is shown in Fig. 2(b). The standard deviation is large in the middle of the Japan Sea and the ocean east of Japan because of large spatial variability. 2.3 Procedure of analysis We compute a mean difference and a scatter for each 1 -grid between the daytime MCSSTs and in situ SSTs. The mean difference (bias) is defined as an average of the MCSST-minus-in situ SST, and the scatter is a standard deviation of the differences (note that the scatter is a bit different from the RMS which is defined as an root mean square of the differences). We try to test the statistical significance of the SST differences (biases). Individual variances of two kind of SST data are needed to know whether or not the biases are significant. However, we have no tool to know the variance of the MCSSTs. Reynolds and Smith (1994) described the new NOAA operational global SST analysis using optimum interpolation in 1 -grids. They showed that the data/guess error (standard deviation) ratios for ship and daytime satellite data are 3.9 and 1.6 in global average, respectively. (The guess error is common for both the data.) Therefore, we assumed that the standard deviation of the daytime MCSSTs Characteristics of MCSST in the Oceans around Japan 163

4 Fig. 2. (a) Number of the quality controlled in-situ SSTs available in a 1 -grid during November 1988 May (b) Standard deviation of the in situ SSTs in a 1 -grid. The contour interval is 0.5 C with heavy contours at 1.0 C, 2.0 C. Regions with values equal to or greater than 2.0 C and less than 2.5 C are shaded, and those with values equal to or greater than 2.5 C are striped. is 1.6/3.9 times as large as that of the in situ SSTs. As a result of the test, most of the mean differences were judged to be significant because the numbers of both the data are large (Figs. 1 and 2(a)) in spite of the large standard deviations of the in situ SSTs (Fig. 2(b)). Furthermore, we examine the significance of the SST differences with an another method (described in Subsection 3.1). In order to investigate the dominant spatial/temporal variability of the differences, we perform the empirical orthogonal function (EOF) analysis for the weekly differences. In order to increase reliability of the EOF analysis, the following preparation processes are conducted: we select the grids in which there are the SST difference data of more than 67 weeks (a half of the analyzed period), and make a complete time series of the difference for each selected grid filling in the weeks without data by linear interpolation. These time series are low-pass filtered through the 7-week running-mean to reduce variations with short periods. (The smoothed data are used only for the EOF analysis. Neither interpolation nor smoothing are done for the calculation of biases and scatters.) Small scale spatial variations are eliminated using the Gaussian filter whose e-folding and cut-off scales in zonal and meridional directions are 1 and 3, respectively. As a result, the temporal variations shorter than about 3 months and the spatial ones less than about 500 km are eliminated. 164 Y. Kawai and H. Kawamura

5 3. Comparison between the MCSSTs and the in situ SSTs (a) 3.1 Analysis The mean differences (biases) between the daytime MCSSTs and the in situ SSTs during the whole analyzed period are shown in Fig. 3. We calculated the 95%-confidence intervals of the biases using the standard deviation of the (b) Fig. 3. Mean difference (bias) between the daytime MCSSTs and the in situ SSTs (MCSSTs minus in situ SSTs) during November 1988 May The contour interval is 0.5 C with heavy contours every 1.0 C starting with 2.0 C. Positive and statistically significant biases are shaded, and negative and statistically significant biases are striped. The asterisks show the locations of the JMA buoy. (c) Fig. 4. Scatter of the differences between the daytime MCSSTs and the in situ SSTs during November 1988 May The contour interval is 0.5 C with heavy contours at 1.0 C, 2.0 C. Values equal to or greater than 2.0 C and less than 2.5 C are shaded, values equal to or greater than 2.5 C are striped. Fig. 5. Spatial pattern of the EOFs of the differences between the daytime MCSSTs and the in situ SSTs. The contour interval is 0.05 with heavy contours at 0.10, 0.0, Regions with values greater than are heavy shaded, and those with values less than 0.05 are striped. Grids not used for the EOF analysis are light shaded. The asterisks show the locations of the JMA buoy. (a) 1st mode, (b) 2nd mode, and (c) 3rd mode. Characteristics of MCSST in the Oceans around Japan 165

6 Fig. 6. Principal component score of the EOFs during the analyzed period. (a) 1st mode, (b) 2nd mode, and (c) 3rd mode. 166 Y. Kawai and H. Kawamura

7 differences (scatters) between both the weekly-averaged SSTs, and the number of the weekly averaged data. If the minimum (maximum) of the confidence interval of the bias is positive (negative), the bias is judged to be significant. The biases are smaller than 0.5 C and significant in the Yellow Sea, the middle of the Japan Sea, and around the Kuril Islands. On the other hand, the biases are larger than +0.5 C and significant in the Tsushima and Tsugaru warm currents regions. The biases are small over most of the Pacific Ocean. The scatters during the analyzed period are shown in Fig. 4. The scatters are larger than 2.0 C in the Yellow Sea, the middle of the Japan Sea, and in the region between 40 N and 45 N in the northwestern Pacific Ocean. We perform the EOF analysis to look for the dominant spatial/temporal variations of the differences between the daytime MCSSTs and the in situ SSTs. The spatial patterns of the 1st, 2nd and 3rd mode EOFs are shown in Fig. 5, and the time series of the mode scores in Fig. 6. The contribution rates of the 1st, 2nd and 3rd modes are 56.1%, 6.9% and 5.1%, respectively. The spatial pattern of the 1st mode EOFs shows a significant variation in the Pacific Ocean south of the Hokkaido Island and the Kuril Islands, the northern Japan Sea, the Sea of Okhotsk (Fig. 5(a)). The regions with the amplitudes of about 1 2 C (as obtained by combination of Fig. 5(a) and Fig. 6(a)) in the 1st mode distribute north of 40 N around Japan. The largest amplitude appears in the Sea of Okhotsk off the north coast of the Hokkaido Island and the Kuril Islands. A dominant annual cycle can be seen in the variation of the 1st mode score (Fig. 6(a)). The 1st mode score becomes Fig. 7. Seasonal variability of the biases. Otherwise same as Fig. 3. (a) Winter (Jan. Mar.), (b) spring (Apr. June), (c) summer (July Sep.), and (d) autumn (Oct. Dec.). Characteristics of MCSST in the Oceans around Japan 167

8 positive during July September in 1989 and during June October in It is known that the year of 1990 was an anomalously hot year in the studied period. The difference of the peak score heights between the years of 1989 and 1990 might be due to the inter-annual variability of the SST or airtemperature field around Japan. These facts mean that the most dominant spatial/temporal variation of the differences in the oceans around Japan is the seasonal one in the regions north of 40 N. The 2nd and 3rd modes have less importance than the 1st mode since the former contribution rates are about one tenth of the latter. Therefore, we do not discuss the 2nd and 3rd modes so much judging that their reliability is not high. However, it may be pointed out that the 2nd mode shows an inter-annual variation with an annual cycle (Fig. 6(b)), and the 3rd mode has a spatial pattern similar to that of the biases shown in Fig. 3 (Fig. 5(c)). In order to examine the 1st mode variability in detail, we average the SST differences for each season. We define winter as January March, spring as April June, summer as July August, and autumn as September December. Spatial variability of the seasonal biases is shown in Fig. 7. In winter, the MCSSTs are lower than the in situ SSTs by more than 1.0 C on the average in the region north of 40 N, and in the Yellow Sea. Oppositely, the former SSTs are higher than the latter ones by more than 1.0 C on the average in the south of the Sea of Okhotsk, and the north of the Japan Sea in summer. The biases in the regions of the Tsushima and Tsugaru warm currents are positive through all seasons. The seasonal spatial distributions of the scatters are shown in Fig. 8. In winter and spring, the scatters are larger than 2.0 C between 35 N and the Kuril Islands, and in the Fig. 8. Seasonal variability of the scatters. Otherwise same as Fig. 4. (a) Winter, (b) spring, (c) summer, and (d) autumn. 168 Y. Kawai and H. Kawamura

9 Yellow Sea. In summer, the regions with the scatters exceeding 2.0 C are located around the Kuril Islands, and the scatters in the Yellow Sea are smaller than 2.0 C. The larger-scatter regions shift to the ocean south of the Kuril Islands in autumn, and appear in the Japan Sea and the East China Sea again. 3.2 Discussion It can be considered that the sources of the differences between satellite-derived and in situ SSTs are (1) ship-borne thermometer accuracy, (2) AVHRR radiometric calibration, (3) atmospheric correction algorithm, (4) sub-pixel cloud contamination, (5) mismatch of satellite and ship measurements due to the followings: vertical structure, horizontal structure and temporal variability (Robinson and Ward, 1989). Strong and McClain (1984) showed that the bias and the root mean square difference are larger when compared MCSSTs with ships-of-opportunity SSTs rather than with drifting-buoy ones. The errors of voluntary-ship observations can not be negligible since the methods and the depths of temperature measurements are not always consistent. Though this can be the reason why the scatters in the present study are larger over the entire studied region than those in the previous researches using buoy SSTs, this can not explain the seasonal variability of the large-scale spatial biases and scatters. In the MCSST algorithm it is assumed that the amount of absorption gases in the atmosphere and the difference between SST and mean atmospheric temperature are small. Therefore, if either of them significantly exceed a mean value, MCSSTs may have a large error against true SSTs. However, the error of the SSTs derived through the splitwindow technique is smaller than 1 C even if the difference between SST and mean atmospheric temperature is about 5 C (May and Holyer, 1993). The amount of water vapor in the atmosphere over the analyzed region, which is in the midlatitudes, is thought to be much smaller than that in the Tropics. Therefore the contribution of the error caused through the atmospheric correction to the large SST differences is small. The regions where the scatters are more than 2.0 C correspond to the polar front in the Japan Sea, the Kuroshio front around 36 N, the Oyashio front around 41 N, and the confluence zone with the fronts associated with the warm eddies detached from the Kuroshio (Fig. 4). These fronts have been well described and studied using AVHRR IR images (e.g., Kawamura et al., 1986). Yoshida (1993) have demonstrated that the SST front in the northwestern Pacific Ocean moves from the region around 40 N to that around the Kuril Islands during spring to summer (Fig. 9). In summer both the scatters and the horizontal SST gradients in the region around the Kuril Islands are largest over the whole analyzed region (see Fig. 8(c)). It can be inferred from the above comparison that the horizontal structure of SST field affects the difference between MCSST and in situ SST. The comparison is made for the 1 -grid- and weekly-averaged SSTs. Thus, the temporal and spatial variations of the SST fronts within 1 -grids and a week may result in the larger scatters in these regions through the temporal or spatial difference of SST sampling between in situ and satellite observations. A correlation coefficient between the scatters and the horizontal SST gradients computed from the daytime MCSSTs for each season shows that both the scatters and the SST gradients have a positive and statistically significant correlation in every season (Table 1). Reynolds and Smith (1994) showed that the guess errors in the global SST analyses using optimum interpolation on 1 -grids reach local maxima in the western boundary regions, especially in the Kuroshio and Gulf Stream regions, where larger SST variability is expected because of the warm eddies and the frontal structures associated with the energetic currents. Positive and negative heat fluxes through the water surface produce vertical temperature gradients in the layer below the surface. The process of the ocean surface layer has been discussed for a long time in terms of the possible error source in satellite SST measurements (e.g., Stewart, 1985). A persistent one-way heat transfer through the ocean surface may result in systematic biases between satellite-derived and in situ SSTs. The coefficients of the MCSST equations are determined through a linear regression method assuming that the relation between the skin and bulk temperatures is random in time and in space. Therefore, if there is the significant vertical temperature gradient which is steadily formed in daytime or nighttime through a season in the surface layer, the relation between the skin and bulk temperatures is not random anymore. Consequently, such a vertical temperature structure may cause the seasonal variation of the MCSST-in situ SST difference. The regions where the 1st mode EOFs are more than are north of the polar front in the Japan Sea and the Oyashio front in the North Pacific (Fig. 5(a)). These regions are strongly influenced by the monsoon winds because of their locations. The cold dry monsoon from the Eurasian Table 1. Correlation coefficients between the scatters and the horizontal MCSST gradients in the 1 -grids for each season and the whole analyzed period. Correlation coefficients Winter (Jan. Mar.) 0.58 Spring (Apr. June) 0.57 Summer (July Sep.) 0.68 Autumn (Oct. Dec.) 0.54 The whole analyzed period 0.64 Characteristics of MCSST in the Oceans around Japan 169

10 Fig. 9. Left: 30 years ( ) average monthly mean SST for February, May, August and November. Right: Southward gradient of the 30 years average monthly mean SST for the months in the left panels (cited from Yoshida, 1993). Continent blows over the regions from autumn to winter. The amount of integrated water vapor in the continental air over the Japan Sea or the Sea of Okhotsk is usually smaller than 0.4 g/cm 2 in winter. On the contrary, there is the moist and warm monsoon blowing from the south-east oceans during summer, and anticyclones cover the regions to bring sunny days. These characteristic patterns of the wind fields may cause a persistent one-way heat transfer through the sea surface. The quantitative investigation on the sea surface heat flux and the wind fields are left for future studies. 170 Y. Kawai and H. Kawamura

11 A large negative bias appears in the Yellow Sea through the studied period (Fig. 3) and has some seasonal features (Fig. 7). The scatters are also large in the Yellow Sea (Figs. 4 and 8). The Yellow Sea is close to the Eurasian Continent and included in the monsoon region. However, the variation pattern of the biases is a bit different from that in the northern Japan Sea and the Okhotsk Sea. One of the characteristics of the Yellow Sea is that a large amount of fresh water is pouring in from the two big rivers, the Yangtze River and the Yellow River. Therefore density stratification is apt to be made in the upper layer in the Yellow Sea. This stratification structure due to the continuously supplied fresh water might play a role in the appearance of the biases in the Yellow Sea. Detailed examination of the fresh water effects is left for future studies. The locations of the JMA moored buoys are shown in Figs. 3 and 5 with asterisks. All the JMA buoys are located southerly out of the regions where the biases are large, and both the biases and the first mode EOFs are nearly zero at those stations. Sakaida and Kawamura (1992) reported that the MCSSTs tuned against global drifting-buoy SSTs agree well with the SSTs observed with the JMA moored buoys in the oceans around Japan. It should be noted that, because of the JMA buoy locations, the seasonal biases in the northern oceans obtained in the present study could not be found in their study. 4. Conclusion We compared the MCSSTs derived from NOAA/ AVHRR data with the in situ SSTs which are reported from ships and buoys in the oceans around Japan. Both the SSTs obtained during November 1988 May 1991 were averaged weekly in each 1 -grid. We found the existence of large biases for the wide oceans averaged for the entire period studied. The biases between the daytime MCSSTs and the in situ SSTs are smaller than 0.5 C and significant in the north of the Japan Sea, the Yellow Sea and around the Kuril Islands. Those are larger than +0.5 C and significant in the Tsushima and Tsugaru warm currents regions. The causes of these biases are other than volcanic aerosols since we selected the period from when the satellite data were not contaminated by volcanic aerosols. Furthermore, we found from the EOF analysis that the most dominant spatial variation of the differences between the daytime MCSSTs and the in situ SSTs is an annual cycle in the regions north of 40 N. In these regions, the winter biases are smaller than 1.0 C, and the summer ones are larger than +1.0 C. The first mode pattern traces well the seasonal distributions of the biases. The biases in the regions of the Tsushima and Tsugaru warm currents are positive through all seasons. The regions where the scatters are larger than 2.0 C correspond to the SST fronts. This fact means that the large scatters in 1 -grids are mainly due to the horizontal SST structure or the large SST temporal variability. MCSSTs are tuned against the SSTs observed with the buoys drifting around in the world oceans, and those agree well with the moored-buoy SSTs in the oceans around Japan (Sakaida and Kawamura, 1992). However, the seasonal biases with the characteristic distribution patterns appear in the oceans near the Eurasian continent and north of 40 N, where there is no moored or drifting buoys. In order to estimate SST more accurately from satellite observations in the oceans around Japan, detailed characteristics of the vertically or horizontally complicated temperature structures in the ocean surface layer needs to be investigated. Acknowledgements We would like to thank the Japan Meteorological Agency for providing us with the in situ SST data set. We would also like to acknowledge PO.DAAC (the Physical Oceanography Distributed Active Archive Center) for providing us with the global MCSST data set. Advice and support from Dr. Futoki Sakaida of Kobe University of Mercantile Marine are very useful and greatly appreciated. We wish to thank all the members of the Physical Oceanography Group, Tohoku University for their valuable discussions. This study is supported by the international cooperative research program on GOOS (Global Ocean Observing System) sponsored by the Ministry of Education, Science and Culture. References Anding, D. and R. Kauth (1970): Estimation of sea surface temperature from space. Remote Sensing Environ., 1, Barton, I. J. (1995): Satellite-derived sea surface temperatures: Current status. J. Geophys. Res., 100, Bates, J. J. and H. F. Diaz (1991): Evaluation of multichannel sea surface temperature product quality for climate monitoring: J. Geophys. Res., 96, Hepplewhite, C. L. (1989): Remote observation of the sea surface and atmosphere. The oceanic skin effect. Int. J. Remote Sensing, 10, Kawamura, H., K. Mizuno and Y. Toba (1986): Formation process of a warm-core ring in the Kuroshio-Oyashio frontal zone December 1981 October Deep-Sea Res., 33, May, D. A. and R. J. Holyer (1993): Sensitivity of satellite multichannel sea surface temperature retrievals to the air-sea temperature difference. J. Geophys. Res., 98, McClain, E. P., W. G. Pichel and C. C. Walton (1985): Comparative performance of AVHRR-based multichannel sea surface temperatures. J. Geophys. Res., 90, McMillin, L. M. and D. S. Crosby (1984): Theory and validation of the multiple window sea surface temperature technique. J. Geophys. Res., 89, Olson, D. B., G. P. Podesta, R. H. Evans and O. B. Brown (1988): Temporal variations in the separation of Brazil and Malvinas currents. Deep-Sea Res., 35, Characteristics of MCSST in the Oceans around Japan 171

12 Reynolds, R. W. (1993): Impact of Mount Pinatubo aerosols on satellite-derived sea surface temperatures. J. Climate, 6, Reynolds, R. W. and T. M. Smith (1994): Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, Reynolds, R. W., C. K. Folland and D. E. Parker (1989): Biases in satellite-derived sea-surface-temperature data. Nature, 341, Robinson, I. S. and N. Ward (1989): Comparison between satellite and ship measurements of sea surface temperature in the north-east Atlantic Ocean. Int. J. Remote Sensing, 10, Sakaida, F. and H. Kawamura (1992): Estimation of sea surface temperatures around Japan using the Advanced Very High Resolution Radiometer (AVHRR)/NOAA-11. J. Oceanogr., 48, Schluessel, P., W. J. Emery, H. Grassl and T. Mammen (1990): On the bulk-skin temperature difference and its impact on satellite remote sensing of sea surface temperature. J. Geophys. Res., 95, Stewart, R. H. (1985): Methods of Satellite Oceanography. University of California Press, Berkeley, 360 pp. Strong, A. E. and E. P. McClain (1984): Improved ocean surface temperatures from space comparisons with drifting buoys. Bull. Am. Meteorol. Soc., 65, Yoshida, T. (1993): Sea surface temperature fronts around the south boundary of the western north Pacific subarctic gyre. Umi to Sora, 69, (in Japanese with English abstract). 172 Y. Kawai and H. Kawamura