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Journal of Oceanography, Vol. 58, pp. 213 to 225, 2002

Keywords:⋅ Remote sensing,⋅ sea surface flux,⋅ radiation,⋅ turbulent flux.

* Corresponding author. E-mail: kubota@mercury.oi.u-tokai.ac.jp

Copyright © The Oceanographic Society of Japan.

to estimate those fluxes and transports globally using in-situ observation data from ships and buoys, since suchdata are extremely sparse in time and space. By contrast,we can obtain extremely homogeneous data with highresolution using analysis and satellite data.

The heat flux between the ocean and the atmosphereconsists of four components, viz., shortwave radiation,longwave radiation, latent heat and sensible heat fluxes.Generally only shortwave radiation transfers heat fromthe atmosphere to the ocean, while the remaining threecomponents transfer heat from the ocean to the atmos-phere. There are broadly three methods to estimate thesurface fluxes at large scales. The first is a traditionalmethod using empirical formulae to estimate the flux fromsurface meteorological observations such as air tempera-ture, humidity and cloudiness. Iwasaka and Hanawa

1. IntroductionThe ocean actively exchanges heat, water and mo-

mentum with the atmosphere through the ocean surface.The exchanged heat, water and momentum are transportedby the general circulation in the ocean and atmosphere toredress the heat imbalance. Since the exchange and trans-porting processes are essential components of the globalclimate, it is quite important to estimate these fluxes be-tween the atmosphere and the ocean and of transports byocean and atmospheric circulation to understand themechanism of the global climate. However, it is difficult

Japanese Ocean Flux Data Sets with Use of RemoteSensing Observations (J-OFURO)

MASAHISA KUBOTA1*, NAOTO IWASAKA2, SHOICHI KIZU3, MASANORI KONDA4

and KUNIO KUTSUWADA1

1School of Marine Science and Technology, Tokai University, Orido, Shimizu, Shizuoka 424-8610, Japan2Tokyo University of Mercantile Marine, Etchujima, Koto-ku, Tokyo 135-8533, Japan3Department of Geophysics, Graduate School of Science, Tohoku University, Sendai 980-8578, Japan4Department of Geophysics, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake, Sakyo-ku, Kyoto 606-8502, Japan

(Received 1 May 2001; in revised form 29 October 2001; accepted 29 October 2001)

We have constructed ocean surface data sets using mainly satellite data and calledthem Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO). The data sets include shortwave radiation, longwave radiation, latent heatflux, sensible heat flux, and momentum flux etc. This article introduces J-OFUROand compares it with other global flux data sets such as European Centre for MediumRange Weather Forecasting (ECMWF) and National Center for Environmental Pre-diction (NCEP) reanalysis data and da Silva et al. (1994). The usual ECMWF data areused for comparison of zonal wind. The comparison is carried out for a meridionalprofile along the dateline for January and July 1993. Although the overall spatialvariation is common for all the products, there is a large difference between them inplaces. J-OFURO shortwave radiation in July shows larger meridional contrast thanother data sets. On the other hand, J-OFURO underestimates longwave radiationflux at low- and mid-latitudes in the Southern Hemisphere. J-OFURO latent heatflux in January overestimates at 10°N–20°N and underestimates at 25°N–40°N. Fi-nally, J-OFURO shows a larger oceanic net heat loss at 10°N–20°N and a smaller lossnorth of 20°N in January. The data of da Silva et al. in July show small net heat lossaround 20°S and large gain around 20°N, while the NCEP reanalysis (NRA) datashow the opposite. The da Silva et al. zonal wind speed overestimates at low-latitudesin January, while ECMWF wind data seem to underestimate the easterlies.

214 M. Kubota et al.

(1990), Esbensen and Kushnir (1981) and Oberhuber(1988) are examples of the estimation of heat fluxes us-ing the empirical formulae and ocean observation datafrom ships and buoys. The heavily biased distribution ofthe observation data is a serious problem when construct-ing a global heat flux data set, even if each observed vari-able is accurate. Moreover, the empirical method is toosimple to precisely evaluate the flux. Kizu (1998) pointedout that the empirical estimation of shortwave radiationsuffers from systematic error in the empirical formulaused. In-situ data will thus be used to validate satellitedata but not to construct a global data set without com-bining them with satellite data, although they are stilluseful to construct a heat flux field for a long period ofabout 50 years in the Northern Hemisphere and more than100 years in certain area of the main shipping routes.

The second method for estimating global surfacefluxes is to use satellite data. Estimating shortwave ra-diation by satellite data has become popular in radiationbudget research. For example, the global radiation budgetwas estimated in the International Satellite Cloud Clima-tology Project (ISCCP) and the Earth Radiation BudgetExperiment (ERBE). The ERBE data have been collectedsince 1984 from three satellites, the Earth RadiationBudget Satellite (ERBS), NOAA-9, and NOAA-10, car-rying scanned and non-scanned ERBE instruments. Thegoal was to measure global albedo, fluxes, and solar in-cidence. NOAA-9 and NOAA-10 provide global cover-age, and ERBS provides coverage between 67.5 degreesnorth and south latitude. On the other hand, ISCCP wasestablished in 1982 as part of the World Climate ResearchProgram (WCRP) to collect radiance data measured byweather satellites and to analyze them to infer global dis-tribution, properties, and diurnal, seasonal and interannualvariations of clouds. The resulting data sets and analysisproducts are being used to study the role of clouds in cli-mate, radiative energy exchanges, and the global watercycle. Moreover, the utility of satellite observation fortempo-spatial monitoring of insolation in the westernPacific has been shown using data from the Japanese sat-ellite, the Geostationary Meteorological Satellite (GMS)(Nunez, 1988; Weymouth and Marshall, 1994; Chou etal., 2001).

The longwave radiation flux at the sea surface hasreceived less attention than the other components. Onereason may be the difficulty in observing the longwaveradiation flux due to the complicated formulae used inthe estimation; furthermore, there are few reliable obser-vation stations for the longwave radiation flux in theocean. One way of estimating the longwave radiation fluxat the sea surface is using satellite remote sensing dataand applying a radiation-transfer model to estimate theflux. In most of the studies of this kind, empirical formu-lae that relate satellite observations to the outputs of so-

phisticated radiation-transfer models are used to obtainlongwave radiation flux from satellite data; for example,Liu et al. (1997) used Special Sensor Microwave/Imager(SSM/I) data, and Gupta (1989) used TIROS OperationalVertical Sounder (TOVS) data. Other investigators, suchas Dr. W. B. Rossow and his group (e.g., Rossow andZhang, 1995) utilized the ISCCP data. When using datafrom either ship or satellite, another problem that there isalmost no reliable sea truth except for some islands andtentative ship measurements. This makes it difficult toevaluate the accuracy of the estimated flux by eachmethod.

Latent and sensible heat fluxes are globally estimatedusing a bulk formula for satellite data, which includesseveral kinds of physical variable, and the estimation ofturbulent heat fluxes is complex. For this estimate, windspeed, specific humidity, and saturated specific humidityare necessary for latent heat, and wind speed, air tem-perature, and sea surface temperature (SST) are neededfor sensible heat. In these necessary variables, specifichumidity and air temperature are not easily obtained fromsatellite data compared with other data. Liu (1986), Schulzet al. (1993), and Schlüssel et al. (1995) proposed an al-gorithm for estimating specific humidity from satellitedata. Some studies proposed a method for estimating thesurface level air temperature by satellite sensors (Kubotaand Shikauchi, 1995; Konda et al., 1996; Prihodko andGroward, 1997; Gautier et al., 1998; Jones et al., 1999).However, satellite-derived specific humidity and air tem-perature are not yet so accurate as to be satisfactory forthe estimation of latent and sensible heat fluxes. In par-ticular, air temperature is one of the most important ele-ments of the climate parameters. Monitoring the globaldistribution of surface-level air temperature is importantnot only for evaluating the strength of thermal couplingbetween ocean and atmosphere but also for monitoringthe long-term global warming and impact of the changeof SST on it. The effort to improve the accuracy of satel-lite-derived air temperature should be encouraged.

The momentum flux through the ocean surface, i.e.wind stress, is one of the essential external forces drivingthe ocean’s motions. Wind measurements by ships andbuoys have been used to calculate surface wind stress forthe last two decades, but their reliability is questionabledue to such measurement errors as uncertain anemom-eter height (e.g., Pierson, 1990). As an alternative, a sat-ellite microwave scatterometer supplies wind speed anddirection data with much better time and space resolu-tion over the world ocean. The first on-board satellitescatterometer, SEASAT, was launched in 1978. SEASATsupplied the wind data only for about three months. Afterabout a decade the European Space Agency (ESA)launched the first European Remote-sensing Satellite(ERS-1) in 1991 and subsequently ERS-2 in 1996. The

Introduction of J-OFURO 215

Active Microwave Instrument (AMI) on ERS-1, 2 hasprovided global wind data about nine years since August1991, allowing us to examine variability on multiple timescales. Another attempt with the satellite called the Ad-vanced Earth Observation Satellite (ADEOS) was under-taken cooperatively by the National Aeronautics andSpace Administration (NASA) in USA and the NationalSpace Development Agency (NASDA) in Japan. Earthobservation by ADEOS unfortunately stopped 11 monthsafter launch due to an accident with the solar panel. Ascompensation for this accident, NASA launched theQuikscat (QSCAT) in June 1999 on board the newscatterometer sensor SeaWinds. NASA scatterometer(NSCAT) on ADEOS and QSCAT/SeaWinds have bettertime and space resolution than the ERS-1, 2. Yet ERS-1,2 provided us with global wind data for a long time, andthe global data set from ERS-1, 2 are used in our data setintroduced below.

The third method is to use Numerical Weather Pre-diction (NWP) data, which are outputs from an atmos-pheric general circulation model (AGCM). However,NWP products are not pure outputs from AGCM becausein-situ data are assimilated into the AGCM. NWP prod-ucts are therefore often called analysis data. NWP prod-ucts provide us global heat flux data as well as satellitedata. Products of the European Centre for Medium RangeWeather Forecasting (ECMWF) and the National Centerfor Environmental Prediction (NCEP) are typical exam-ples of NWP products. It is an advantage that NWP prod-ucts provide us homogeneous grid data, but the charac-teristics of the products are not continuous, because themodel configuration is frequently renewed. Moreover,NWP products suffer from a heavily biased distributionof in-situ observation, since a lot of in-situ data are as-similated into NWP models. This has led to the reanalysisof the NWP products in many recent studies. The NWPproducts are not independent of satellite data in somecases because some satellite data have already been as-similated into the NWP models. For example, ECMWFhas been assimilating surface winds observed by ERSscatterometer since January 1996 and SSM/I since Octo-

ber 1999.Recently we constructed ocean surface flux data sets

using mainly satellite data. The data set, called JapaneseOcean Flux data sets with Use of Remote sensing Obser-vations (J-OFURO), includes radiation flux, turbulent heatflux, momentum flux, sea surface dynamic topography,geostrophic currents, and air temperature. This is theworld’s first global data set of heat fluxes (total and eachcomponent) and momentum flux. The data can be obtainedfrom our website (http://dtsv.scc.u-tokai.ac.jp). Basic in-formation for each data set is shown in Table 1. In thepresent study we introduce each data set of J-OFURO,except for sea surface dynamic topography andgeostrophic currents, which are not directly related tosurface flux. Data and methods are described in Section2. Section 3 shows average fields for each heat flux andtotal heat flux. The results of comparison between J-OFURO and the ECMWF and NCEP/NCAR reanalysisdata are also given in order to clarify the characteristicsof J-OFURO data. A summary and discussion are pro-vided in Section 4.

2. Data and Methods

2.1 Radiation dataKizu (1999) produced a monthly data set of surface-

level downward shortwave radiation fluxes (insolation)from October 1992 to September 1993 with 1° × 1° lati-tude-longitude resolution. This product was extended tomore than twelve years from March 1987 to September1999 by Kizu (2001b), and was included in J-OFURO. Amonthly data set of longwave radiation fluxes from Oc-tober 1992 to September 1993 was produced and will beextended in the near future. The data sets of the shortwaveand longwave radiation cover the region between 60°Nand 60°S, 80°E and 160°W in the eastern Indian Oceanand the western and central Pacific Ocean, because weuse only the GMS data for this estimation. Although thebasic spatial and temporal resolutions of the radiation dataare 0.25° by 0.25° and 1 day, respectively, J-OFURO cur-rently provides monthly radiation data with 1° × 1°.

Table 1. Summary of data sets in J-OFURO.

216 M. Kubota et al.

2.1.1 Shortwave radiationA spectrally-integrated model by Iqbal (1983; his

Model A) was used to formulate fundamental radiativetransfer processes in the atmosphere. Scattering by airmolecules and aerosol, absorption by ozone and watervapor were modeled using simple formulas andparameterization published in the literature. Primary dataused in the estimation are 3-hourly visible and infraredhistogram data from the Visible and Infrared Spin ScanRadiometer (VISSR) on the GMS with 0.25° spatial reso-lution, monthly precipitable water from SSM/I (Wentz,1994), monthly total ozone amount from the Total OzoneMapping Spectrometer (TOMS) on the Nimbus 7 satel-lite, and seasonal climatology of tropospheric aerosolfrom Advanced Very High Resolution Radiometer(AVHRR) on the NOAA satellite (Husar et al., 1997).Long-term degradation of the VISSR visible channel wascorrected by Kizu (2001a). Based on comparison withroutine ground measurements of insolation mostly in Ja-pan and Australia, the statistical error of daily and monthlymean insolation values was estimated to be less than 20% and 10 %, respectively. Mainly because of the simpli-fication of the highly complex relation between cloud andradiation, the estimation error partly depends on the illu-minating and viewing geometry and cloud type. The esti-mation error over the ocean has not been thoroughly in-vestigated due to a severe lack of sea truth. However, simi-lar precision is expected over the ocean as well, since theprimary radiative transfer processes in the atmosphere arecommon over land and ocean. The methodology and itsvalidation results are presented in detail by Kizu (2001b).2.1.2 Longwave radiation

In order to estimate the longwave radiation flux atocean surface from GMS Infrared (IR) data, we first de-tected cloud area in the GMS image as follows: a pseudo-SST was computed from the infrared data in each pixeland compared with the MCSST. A pixel having a pseudo-SST lower than Multi-Channel Sea Surface Temperature(MCSST) by more than 3.0K was assumed to be a cloudpixel. Unrealistic values of the MCSSTs were corrected,and grid points with no data were filled by interpolatingIwasaka and Tanimoto’s climatology (Iwasaka andHanawa, 1990; Tanimoto, 1993). For the cloud pixel, thetemperature in the pixel was assumed to be that at the topof the cloud. The cloud top height was computed fromthe cloud top temperature and the temperature profile inthe pixel inferred from the NCEP reanalysis data. Thecloud bottom height was determined by assuming cloudthickness. Here, we do not deal with multi-layer cloudbecause there are currently no practical ways to detectmulti-layer clouds from satellite observation so far. Cloudthickness in the model was assumed to be 500 m and doesnot vary in space and time.

Downward longwave radiation was then calculated

by applying the black body radiation theory assuming theemissivity of the cloud bottom to be 0.9. Atmosphericabsorption and emission of the longwave radiation fluxbetween the cloud bottom and the ocean surface wereassessed by a narrow band model of radiation and basedon NCEP data. The atmospheric layer was divided intoseven sub-layers between the ocean surface and 300 hPa.The narrow band model proposed by Goody (1964) wasused to compute the emission and absorption of the at-mosphere. The H2O rotation band, the H2O continuum,the CO2 15 µm band and the H2O 6.3 µm band were in-cluded in the radiation model. The parameters in the modelwere obtained from Rogers and Walshaw (1966),Goldman and Kyle (1968) and Roberts et al. (1976). Thespectral range is 0–2200 cm–1, and was divided into 23spectral bands for the radiation computation. Atmosphericradiation below 300 hPa in a cloud-free area was com-puted using the narrow band model and NCEP data. Up-ward longwave radiation was computed following theblack body theory with emissivity at the ocean surface of0.984 given by Konda et al. (1994).

Radiation measurement data at Kwajalein Island lo-cated at 9.25°N, 167.5°E was used as a reference to evalu-ate the error in the estimation of longwave radiation com-puted in this study. The data are recorded every threeminutes at an altitude of 10 m without high surroundingterrain. This observation station is operated by the Cli-mate Monitoring and Diagnostics Laboratory (CMDL) ofthe National Oceanic and Atmospheric Administration(NOAA). The data were obtained through the BaselineSurface Radiation Network (BSRN, http://bsrn.ethz.ch/). The downward longwave radiation tendsto be overestimated under cloudy conditions and to beunderestimated under clear sky. Bias of the estimateddownward radiation is about 10–20 Wm–2 for monthlymean values and its random errors are comparable to themagnitude of the bias. The annual mean value of down-ward radiation is very close to the observation value atKwajalein. However, no quantitative assessment of theestimation error of the flux has been done for the entireregion, because there is almost no reliable in-situ obser-vation in the objective area except for Kwajalein Island.

2.2 Turbulent heat fluxThe data set of turbulent heat flux covers the global

ocean. The turbulent heat flux was computed for the pe-riod from 1991 through 1995. Original spatial and tem-poral resolutions are 1° × 1° and three days, respectively.It takes three days for the SSM/I to cover the whole globe.At present, only monthly mean data (monthly averagesof the three-day mean) are provided on our web site.

A bulk formula provided by Kondo (1975) was usedin the present study to estimate latent heat flux, althoughthe stability condition was not considered in the calcula-

Introduction of J-OFURO 217

tion. Physical variables included in the formula are windspeed, specific humidity, saturated specific humidity, at-mospheric pressure, and air density. Since atmosphericpressure and air density are less effective than the otherthree variables, climatological monthly means in the Com-prehensive Ocean-Atmosphere Data Set (COADS) wereused for these two variables in this calculation. Saturatedspecific humidity was simply derived from SST. Datacoverage of SST observed by an infrared radiometer suchas NOAA/AVHRR is generally incomplete due to clouds.We therefore used gridded SST values provided by NCEP,which are made by blending satellite-based and in-situSST data on a 1° × 1° grid at weekly intervals (Reynoldsand Smith, 1994, 1995).

Wind speed can be observed by three kinds of satel-lite sensor: microwave scatterometer, microwave radiom-eter, and microwave altimeter. We constructed grid datausing wind speed values included in SSM/I geophysicaldata (Wentz, 1994), because specific humidity was alsoestimated from SSM/I data. On the other hand, there areseveral methods to derive specific humidity from satel-lite data (Liu, 1986; Schulz et al., 1993; Schlüssel et al.,1995). Kano and Kubota (2000) compared the resultsderived from these three algorithms with in-situ data andconcluded that Schlüssel et al.’s method provides the bestaccuracy. The algorithm proposed by Schlüssel et al.(1995) was therefore used to compute specific humiditydata using brightness temperature data observed bySSM/I. Kano and Kubota (2000) compared J-OFURO la-tent heat flux data with in-situ data observed by the Ja-pan Meteorological Agency (JMA) and with the TropicalOcean Atmosphere (TAO) array in the Tropical Oceanand Global Atmosphere (TOGA). They showed that theroot-mean-square (RMS) error is about 40 Wm–2 for three-day means and 20 Wm–2 for monthly means.

We also used a bulk formula by Kondo (1975) toestimate sensible heat flux, which requires data of windspeed, SST and air temperature. Several authors have pro-posed some methods for calculating air temperature us-ing satellite data (e.g., Kubota and Shikauchi, 1995;Konda et al., 1996). However, the accuracy of air tem-perature derived from satellite data is inadequate for cal-culating sensible heat flux, as mentioned above. Kubotaand Mitsumori (1997) proposed a method to calculatesensible heat flux by multiplying latent heat flux by theBowen ratio. They compared results by abovementionedmethods with in-situ data in the western North Pacificand proved the effectiveness using the Bowen ratio. Wetherefore adopted this method in this study. Using satel-lite data, we can estimate the latent heat flux but not theBowen ratio, and therefore we used climatological meansof the Bowen ratio derived from ECMWF data. The qual-ity of the Bowen ratio in the polar regions is not verygood, and data lying outside the range of average ± stand-

ard deviation are not used to estimate the Bowen ratio.The variability of the Bowen ratio is not very large, ex-cept in the western boundary current regions and the Po-lar Regions. The RMS errors are about 5 Wm–2 for three-day means in most regions.

2.3 Momentum fluxWe constructed a data set of gridded surface wind/

wind-stress vectors using a data set of wind speed anddirection called Level 2.0, which were obtained from thespaceborne scatterometer of ERS-1, 2. We used a CD-ROM database of the off-line products which has beenconstructed by the Institut Français de Recherche pourl’Exploitation de la Mer (IFREMER). The extraction ofsurface wind vector from measured backscatter valuesproduces 2 to 4 ambiguity in wind direction at each meas-uring point. A procedure for selecting more reliable winddirection values, called ambiguity removal, gives rankedsolutions in wind speed and direction. This process wascarried out for the IFREMER product of wind speed vec-tors greater than 3 m s–1. In calculations of our product,we used the first ranked values in wind speed as well aswind direction. This statistical reliability was describedin detail by Ebuchi et al. (1996) and Graver et al. (1996)who examined the reliability of the IFREMER and theJet Propulsion Laboratory (JPL) products by comparisonwith numerous oceanic buoy measurements.

The ERS/AMI data have an inhomogeneous distri-bution in space depending on the orbital motion of thesatellite, since the swath is only 500 km. In our construc-tion of daily surface wind/wind-stress vectors with highspatial resolution of 1° × 1° grid over the Pacific and In-dian Oceans, 30°E–70°W, 60°S–60°N, we adopted anaveraging method using a weighting function varying withtime and space. This method was originally proposed byLevy and Brown (1986) and was described in detail byKutsuwada (1998). We used radii of influence of 300 kmand 150 km in the zonal and meridional directions, re-spectively, for the NSCAT product (Kutsuwada, 1998) and600 km and 300 km for our ERS-1, 2 products. The ra-dius of influence is related to the time resolution of theproducts and spatial coverage of the original wind dataobtained from the scatterometer. We calculated wind-stress vectors using the conventional bulk formula. Weadopted the drag coefficient depending only on windspeed based on Large and Pond (1981). Wind/wind-stressvectors on each grid point were averaged over a monthand three 10-day periods. Note that the last 10-day prod-uct in each month is not always the average over 10 days,namely, over 10, 11 or 8–9 days.

We compared our products with in-situ measurementsat numerous oceanic buoys in the Pacific. Many of thebuoys are in the TAO array located in the equatorial Pa-cific. Results for NSCAT and ERS-1, 2 products were

218 M. Kubota et al.

documented in Kutsuwada (1998) and Kutsuwada andKazama (2000), respectively. They revealed that the RMSdifferences do not exceed 2.0 m s–1 and 20° for wind speedand wind direction, respectively, corresponding to theaccuracy of normal wind sensors. We can consider thatour products have sufficiently high reliability in the tropi-cal region. On the other hand, the reliability in the areasat mid and high latitudes may not be significantly high,although verification is difficult due to the scarcity of in-situ data in those areas. This problem will hopefully beimproved by future studies.

2.4 Air temperatureThe method used to obtain the near-surface air tem-

perature was based on Konda et al. (1996), who estab-lished the relationship between boundary layer param-eters through the implicit equation of the Bowen ratio. Inthis method we need specific humidity data, which canbe derived from microwave radiometer data. The empiri-cal method to obtain air temperature established bySchlüssel et al. (1995) was used in J-OFURO, rather thanthe method of Liu (1986) used in Konda et al. (1996).Wind speed and SST data were obtained from the SSM/Igeophysical data and the NOAA Pathfinder SST adjustedto the skin temperature, respectively.

The 1991–92 means of the difference of monthlymean air temperature in J-OFURO from the in-situ ob-servation are 1.5 ± 1.4°C, 1.0 ± 3.0°C, 0.5 ± 2.1°C forcomparison with TOGA-TAO arrays, JMA buoys, andNDBC buoys, respectively. We find that the estimated airtemperature tends to be higher than that observed, inde-pendent of the geographical condition. The differencefrom observation is attributable to the estimation errorsof the surface air humidity as described by Konda et al.(1996). The accuracy of the satellite-derived air tempera-ture is very sensitive to that of the elementary physicalparameters such as SST, humidity, and wind speed.

3. Results and IntercomparisonFigure 1 shows average fields from October 1992 to

September 1993 for (a) latent heat flux, (b) sensible heatflux, (c) shortwave radiation, (d) net longwave radiation,and (e) total heat flux. Positive values mean heat transferfrom atmosphere to ocean. We can see a large latent heatloss from the ocean, more than 150 Wm–2 in the subtropi-cal regions, while latent heat loss is small in the equato-rial regions and at high latitudes. Large latent heat losscan also be found over the western boundary current, suchas the Kuroshio, even in the subtropics. Sensible heat fluxis generally quite small and negligible compared withlatent heat flux. However, sensible heat loss over thewestern boundary current and at high latitudes in theNorthern Hemisphere is not negligible. The insolationpattern is nearly zonal and the maximum insolation is

located along the zonal subtropical high centered approxi-mately at 20°N. In the equatorial region, insolationminima are discernable along the Intertropical Conver-gence Zone (ITCZ) and the Southern Pacific ConvergenceZone (SPCZ), leaving an insolation maximum along theequator. Net longwave radiation flux is large in the west-ern North Pacific, especially around Japan and in the cen-tral North Pacific, although it is not so large in other re-gions compared with shortwave radiation and latent heatflux. It should be noted that the asymmetry between theNorthern and Southern Hemispheres is remarkable forshortwave and net longwave radiation fluxes. Finally, thelargest ocean heat gain is found in the equatorial region,while largest heat loss is found over the Kuroshio andKuroshio Extension Regions.

It is not difficult to validate instantaneous values ofsatellite-derived flux data by comparison with in-situ datasuch as buoy data. However, the validation of grid datalike J-OFURO is very difficult, because it is impossibleto obtain a true value for each grid. Therefore,intercomparison between each data set is very importantin order to make clear the differences in characteristicsbetween each data set. In this section, the data of radia-tion, turbulent heat, and momentum fluxes in J-OFUROare basically compared with climatological data (da Silvaet al., 1994) and the NCEP reanalysis (NRA) product andthe ECMWF reanalysis (ERA) product. The advantageof a reanalysis over operational NWP data assimilation isthat it offers several years of fields from an unchangingsystem. We use usual ECMWF product for comparisonof momentum flux in the present study.

The meridional distribution at the dateline of monthlymean insolation of J-OFURO is compared with that ofthe data sets of da Silva et al. (1994), NRA, and ERA forJanuary and July 1993 (Figs. 2a and 2b). The data set ofda Silva et al. is based on Reed’s (1977) empirical for-mula modified with integral constraints on oceanic heattransport. The NRA and ERA data are calculated usingnumerical models without assimilation of radiation data.The most significant difference in insolation data betweenJ-OFURO and the NWP reanalysis products is seen around35°N in northern summer, July 1993, as the J-OFUROdata set shows much larger meridional variation than thereanalysis products. The NRA data may underestimateinsolation in cloudless regions in the subtropical areasouth of 35°N and may overestimate insolation in mostlyovercast regions in the subarctic area north of 35°N. Thedata of da Silva et al. show similar values to J-OFUROin the subtropical area but seem to overestimate insola-tion in the subarctic area. Values in the equatorial regionare different from one data set to another, perhaps reflect-ing large intraseasonal to interannual variability of inso-lation. In contrast, the insolation data south of 15°S in allthe data sets are quite coincident. Differences among the

Introduction of J-OFURO 219

data sets in January are smaller and less systematic thanin July. This holds at high latitudes in the South Pacificwhere none of the data sets is validated using a satisfac-tory reference due to a lack of observations. A more de-tailed comparison among these and other available datasets is ongoing for longer-period data.

A comparison for longwave radiation flux is shownin Figs. 2c and 2d. Differences among the four data setsin January are large at high latitudes in the Southern Hemi-sphere. In particular, J-OFURO data show large valuescompared with other data sets between 30°S and 50°S.On the other hand, J-OFURO gives small values from10°N to 40°N in the Northern Hemisphere. Moreover,NRA gives relatively small values between 30°S and 50°Sand between 45°N and 55°N, while the da Silva et al.data gives relatively large values between 20°S and 5°N.In July, J-OFURO shows a small flux from the ocean southof 10°N and a large flux north of 30°N compared withother data. As a result, J-OFURO presents a small me-ridional trend. The differences between each data set maypartly be attributable to the nature of the estimationscheme of the downward longwave flux. The data of da

Silva et al. are computed with COADS data based on abulk method, and there must be a large estimation errorin the tropics and the Southern Hemisphere where shipobservations are generally sparse. Also, the estimationscheme of the longwave flux used in da Silva et al. is toosimple to obtain a precise estimate and is not suitable fordata-sparse regions. On the other hand, the scheme usedin J-OFURO tends to underestimate the downward fluxunder a clear sky and overestimate under a cloudy sky.The character in the estimation scheme in J-OFURO maycontribute the differences. Another important reason forthe difference may be the capability to reproduce cloudsin the models. The spatial and temporal resolutions ofthe global atmospheric models are too coarse to computecloud formation and change. Parameterization of cloudprocesses is therefore essential to reproduce clouds in themodels and is developed semi-empirically in differentways from model to model. We also suspect that the sur-face longwave radiation flux in the models might be com-puted as a residual of the atmospheric longwave radia-tion budget, because there is no constraint on the down-ward longwave radiation at the ocean surface. The residual

Fig. 1. Maps of heat fluxes in J-OFURO averaged from October 1992 to September 1993. a. Latent heat flux, b. Sensible heatflux, c. Shortwave radiation flux, d. Net longwave radiation flux, e. Net heat flux.

220 M. Kubota et al.

of the longwave radiation budget is estimated from bound-ary conditions at the top of the atmosphere based on sat-ellite observations and the longwave radiation budget ineach layer of the atmosphere determined by temperatureprofiles.

Figures 3a and 3b shows meridional profiles of la-tent heat flux along the dateline in January and July 1993,respectively. In January, the maximum heat loss from theocean at 10°N–20°N and the minimum heat loss around60°S and 50°N in January are common in all the data sets,but the amplitudes are different. J-OFURO shows a largeheat loss between 10°N and 20°N and south of 15°S, anda small heat loss north of 20°N, compared with other data.Josey (2001) assessed the accuracy of surface heat fluxestimates from ERA and NRA by comparison with mooredbuoy measurements in the subduction region of the North-east Atlantic. He pointed out that the NRA and ERA dataoverestimate latent heat loss, especially in winter. If hisresult is applicable to the Pacific Ocean, latent heat loss

between 20°N and 40°N may be overestimated by theNRA, ERA and da Silva et al. data sets, and J-OFUROmay give more accurate values in the subtropics. In theSouthern Hemisphere, some peaks in J-OFURO cannotbe found in NWP products, and J-OFURO presents astructure with a small scale compared with NWP prod-ucts. In July the most remarkable feature is that the fluxby da Silva et al. is extremely small at 30°S–10°S and10°N–30°N, with a difference from other data reachingabout 200 Wm–2 around 10°S. The other data sets showconsiderable coincidence with each other. Although thereis a small systematic difference between 15°S and theequator, the overall difference between the J-OFURO andthe NWP products is extremely small, less than 10Wm–2.

Figures 3c and 3d shows meridional profiles of sen-sible heat flux. All the data sets show that sensible heatloss in January is large in the Northern Hemisphere withsharp peaks around 40°N and 60°N. The difference among

Fig. 2. Meridional profiles of radiation fluxes along 180° in January 1993 and July 1993. a. Shortwave radiation flux (January),b. Shortwave radiation flux (July), c. Longwave radiation flux (January), d. Longwave radiation flux (July).

Introduction of J-OFURO 221

the data sets is relatively large in the Northern Hemi-sphere; the flux in J-OFURO is smaller north of 20°Nthan the other data sets, while the NRA flux is larger thanthe others at 15°N–25°N and 35°N–45°N. The signifi-cant difference around the 40°N peak may be due to alarge difference in zonal wind speed among the data setsaround the peak at 35°N (Fig. 5a). Sensible heat flux inJuly is large in the Southern Hemisphere, but does notshow a sharp peak, unlike January. The NRA flux is largerat 40°S–30°N and smaller north of 40°N than the otherdata sets, and the flux of da Silva et al. is larger at 60°S–40°S and 0°–10°N. The NRA flux may be overestimatedin particular for 35°S–10°S. This cannot be attributed tozonal wind speed, which is not stronger than the otherdata sets (Fig. 5b).

Meridional profiles of the net heat flux are shown inFigs. 4a and 4b. A net heat gain (loss) of ocean occursalmost in the Southern (Northern) Hemisphere in Janu-ary and the Northern (Southern) Hemisphere in July. In

January, the heat loss of J-OFURO is larger at 10°N–20°Nand smaller north of 20°N than the other data sets. Theformer reflects the characteristics of longwave radiationand latent heat fluxes, and the latter reflects the charac-teristics of latent and sensible heat fluxes. In the South-ern Hemisphere, small-scale variations are remarkable andare different among the data sets. For example, the dataof da Silva et al. show heat loss around 10°S, while theother data sets show heat gain there. This may primarilybe due to overestimation of zonal wind speed at 25°S–0°by da Silva et al. (Fig. 5a). In addition, the accuracy ofNWP products in the Southern Hemisphere may be worsethan that in the Northern Hemisphere, owing to fewer in-situ data being assimilated into the NWP models. Thismay be a reason for the various small-scale variations oftotal heat flux in the Southern Hemisphere. In July, thedifference in net heat flux among the four data sets isvery large around 20°S and 20°N. The data of da Silva etal. show a small net heat loss around 20°S and a large net

Fig. 3. Same as Fig. 2, except for turbulent heat fluxes. a. Latent heat flux (January), b. Latent heat flux (July), c. Sensible heatflux (January), d. Sensible heat flux (July).

222 M. Kubota et al.

heat gain around 20°N, while the NRA data show theopposite. The da Silva et al. data tend to underestimatelatent heat loss (Fig. 3b), and the NRA data tend to un-derestimate shortwave radiation flux (Fig. 2b) and over-estimate sensible heat loss (Fig. 3d).

Figures 5a and 5b shows meridional profiles of zonalwind speed. All the data sets show similar maxima, thewesterly maxima around 55°S–45°S, 35°S, and 35°N andthe easterly maxima around 15°S–25°S and 10°N–20°N,although the location and strength of the westerly maximain the Southern Hemisphere are quite different betweenJanuary and July. In January, the data of da Silva et al.are much larger at 25°S–0°and around 10°N than the otherdata sets, and show a remarkable easterly maximum near2°S which is not seen in the others. This discrepancy maybe due to an error involved in the data of da Silva et al.,because they are based on measurement by Volunteer

Observing Ships (VOS) and are relatively sparse in theequatorial region. The data sets of NRA and da Silva etal. conclude that there are much stronger maxima of theeasterly and westerly in the Northern Hemisphere thanthat in J-OFURO. The difference reaches 3 m s–1 around35°N. On the other hand, wind speed in the ECMWF datais weaker than J-OFURO at all latitudes. South of 30°S,the strength of zonal wind and the latitude of thesouthernmost maximum of the westerly are different inall data sets. Figure 5a shows that there is a discrepancyof 20–30% between data sets in January. The discrepancymay be a measure of uncertainty in the zonal wind field;a large discrepancy suggests a large error in the data sets.Differences among the data sets are relatively small inJuly. The discrepancy is mostly within ±2 m s–1 at all lati-tudes. An exception is the much smaller wind speed at25°S–25°N of the ECMWF data. This also holds in other

Fig. 5. Same as Fig. 4, except for zonal wind speed.

Fig. 4. Meridional profiles of net heat flux along 180° in (a) January 1993 and (b) July 1993.

Introduction of J-OFURO 223

months, as indicated by Hackert et al. (2001) also whocompared some types of wind product during the 1997–98 El Niño. Thus, the ECMWF wind data seem to under-estimate the easterlies.

4. Summary and DiscussionWe have constructed new data sets related to ocean

surface flux mainly using satellite data, named JapaneseOcean Flux data sets with Use of Remote sensing Obser-vations (J-OFURO). J-OFURO includes data sets ofshortwave and longwave radiation, turbulent heat fluxes,momentum flux, sea surface dynamic topography,geostrophic currents and surface air temperature. The dataof turbulent heat flux and air temperature are global intheir coverage. The radiation flux data cover 60°S–60°N,80°E–160°W, because they are calculated with the datafrom the Japanese satellite, Geostationary Meteorologi-cal Satellite (GMS). The momentum flux data cover 60°S–60°N, 30°E–70°W.

The average fields derived from J-OFURO data areshown for latent and sensible heat fluxes, shortwave andnet longwave radiation fluxes and total heat flux. A largelatent heat loss from the ocean is found around 20° andover the western boundary currents. Sensible heat flux isnegligible in most regions, except over the western bound-ary current. The insolation pattern is nearly zonal, themaximum insolation being located along the zonal sub-tropical High centered approximately at 20°N. A largeflux of net longwave radiation from the ocean is found inthe western North Pacific, especially around Japan andin the central North Pacific. Finally, the largest ocean heatgain is found in the equatorial region, while the largestheat loss can be found over the Kuroshio and KuroshioExtension Regions. Also, all of the data sets in J-OFUROsucceed very well in detecting annual and interannaulvariability. In particular, interannual variability associ-ated with El Niño and La Niña events in the tropical Pa-cific is detected remarkably well (not shown here).

Nevertheless, a quantitative validation of J-OFUROshould be done. However, the validation of grid data isdifficult, because we cannot obtain a true value for eachgrid since errors in making grid data are considerable forin-situ data due to their low density and the biased distri-bution of data. We have compared J-OFURO with threeexisting data sets of da Silva et al. (1994), NCEPreanalysis data (NRA) and ECMWF reanalysis data(ERA). Intercomparison between these data sets has beendone for meridional profiles along the dateline of heatfluxes (radiation, turbulent heat and total fluxes) and zonalwind speed in January and July 1993. The data of da Silvaet al. are based on in-situ data such as ship and buoy data.Shortwave radiation in January and July and longwaveradiation and latent heat flux in January of J-OFUROshow much greater meridional contrast than the other data

sets. WCRP (2000) pointed out that satellite estimates ofshortwave radiation flux may be more reliable than otherglobal estimates and the NRA and ERA data are smallerin the tropics and larger at higher latitudes than satellite-derived flux, as shown in the present study. The smallmeridional contrast for the NRA and ERA data may bedue to a failure in reproducing cloudiness in the models.As a result, the reanalysis data may underestimate inso-lation in cloudless regions in the subtropical area andoverestimate insolation in overcast regions in the subarcticarea. The difference in longwave radiation flux betweenJ-OFURO and the other data sets is considerable, espe-cially in the Southern Hemisphere in July. J-OFURO mayoverestimate longwave radiation flux south of 15°N inJuly. WCRP (2000), however, pointed out that the zonalmean longwave radiation flux estimated from satellite datais lower than that according to other estimates. J-OFUROshows an underestimation at 10°N–40°N in January and35°N–50°N in July, but this is not so remarkable, exceptat 10°N–30°N in January.

The data set of da Silva et al. very much underesti-mates the latent heat loss from the ocean in the subtrop-ics around 20°S and 20°N in July. This is consistent withWCRP (2000), which showed an underestimate of theCOADS-based product compared with ERA and NRA.The latent heat flux of da Silva et al. seems unreliable inthe subtropics in northern summer. The latent heat fluxin January of J-OFURO overestimates at 10°N–20°N andunderestimates at 25°N–40°N. The NRA data set greatlyoverestimates sensible heat flux in the subtropics of theSouthern Hemisphere in July. WCRP (2000) showed thesame feature of NRA data in terms of zonal mean sensi-ble heat flux. The sensible heat flux of NRA may not bereliable in the southern subtropics in northern summer.

J-OFURO shows a larger net heat loss from the oceanat 10°N–20°N and a smaller loss north of 20°N in Janu-ary than the other data sets. The former is caused by largefluxes of longwave radiation and latent heat, while thelatter is caused by small fluxes of latent and sensible heat.Josey (2001) pointed out that the ERA and NRA data setsoverestimate net heat flux partly due to an overestimateof latent heat flux, in comparison with measurements ofbuoys moored at mid-latitudes in the northeast Atlanticin the subduction program. Our results also suggest anoverestimation by ERA and NRA products compared withJ-OFURO data. Small-scale variations are remarkable inthe Southern Hemisphere compared with the NorthernHemisphere, and are considerably different among all datasets, probably due to inaccuracy caused by sparse in-situobservations. In July, the difference in net heat flux amongthe four data sets is very large around 20°S and 20°N.The data of da Silva et al. show a small net heat lossaround 20°S and a large net heat gain around 20°N, whilethe NRA data show the opposite.

224 M. Kubota et al.

In terms of zonal wind speed, all the data sets showa similar location and strength of westerly and easterlymaxima. Nevertheless, the data of da Silva et al. are verydifferent from the other data sets in the equatorial zone;they show an easterly maximum near 2°S that is not foundin the other products, and greatly underestimate at south-ern low latitudes.

As a result of the intercomparison, it is probable thatthe reliability of COADS-based grid data like da Silva etal. is lower due to few in-situ observations than that ofreanalysis data like the NWP data and satellite-deriveddata like J-OFURO, although one cannot easily concludewhich data set is best. In order to validate various datasets and find the best one, we need much more in-situdata in the ocean, and should make an effort to increasethe number of in-situ observation stations which providehigh quality data by, for example, the Improved Meteor-ology (IMET) buoys. In addition, we need to develop othermethods for data validation, because surface-mooringbuoys cannot cover the whole ocean. One of the methodsfor making more reliable data sets is to assimilate in-situand satellite data into general circulation models of oceanand atmosphere. ECMWF has already begun to assimi-late satellite data observed by ERS and SSM/I into at-mospheric general circulation model. For this purpose, itis crucial to open observed data and calculated resultsand to increase the chance of interaction between dataproviders and users. Intercomparison among open datasets is still an effective way to clarify the characteristicsof each product. In these activities, we should keep inmind that both the good and bad points of a data set de-pend strongly on the purpose of data usage, and cannotbe common for every purpose. Finally, we hope that ourJ-OFURO data set will be useful for scientific research,and will make many people comfortable, like OFURO inJapanese, which means a Japanese-style bath filled withhot water.

AcknowledgementsThis research was partly supported by the Grant-Aid

for Science Research on Priority Areas (No. 08241111)of the Ministry of Education, Science, Sports and Cul-ture, Japan and by Research and Development ApplyingAdvanced Computational Science and Technology, JapanScience and Technology Corporation. We would like tothank Dr. M. Kawabe for helpful comments about thispaper. One of the authors (N. Iwasaka) expresses his sin-cere gratitude to Dr. K. Ohtsuka of Obayashi Corpora-tion Technical Research Institute for his help in develop-ing the computer code for calculating the longwave ra-diation flux. He also addresses his thanks to Mr. A. Shinpoand Dr. H. Nakamura of the Graduate School of Science,University of Tokyo, for providing the NCEP reanalysisdata which they processed for research use from the origi-

nal data. The original NCEP data was provided by theNCAR library. One of the authors (K. Kutsuwada) ex-presses his appreciation to the Institut Français de Re-cherche pour l’Exploitation de la Mer (IFREMER) forkindly supplying the scatterometer data in the calcula-tions of wind and wind-stress vectors. We also thank fortheir great encouragement Drs. H. Masuko and N. Ebuchiand other members in the Japanese Science Team, whichwas organized by the National Space DevelopmentAgency of Japan. Finally we thank H. Tomita for his as-sistance in preparing the figures.

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