Japanese Ocean Flux Data Sets Research Articles

Evaluation of the J-OFURO3 Sea Surface Net Radiation and Inconsistency Correction

A new satellite-based product containing daily sea surface net radiation (Rn) values at a spatial resolution of 0.25° from 1988 to 2013, named the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations, version 3 (J-OFURO3), was recently generated and released. In this letter, the performance of the J-OFURO3 sea-surface Rn product was fully evaluated by using observations from 55 global moored buoy sites. The overall accuracy was satisfactory, with root-mean-square difference (RMSD) of 24.05 and 10.76 Wm−2 at daily and monthly scales, respectively. However, an inconsistency issue was found in the long-term variations in the J-OFURO3 sea-surface Rn values in approximately 2000; this inconsistency may be due to the replacement of the input dataset. To address this issue, a simple but effective inconsistency correction method was developed and conducted in this study. The analysis results demonstrated that the variations in the corrected J-OFURO3 sea-surface Rn data were more reasonable and that its daily validation accuracy was significantly improved by decreasing the bias from 4.67 to 0.27 Wm−2 before the year 2000. Thereby, it is suggested that the inconsistency correction method should be applied before using the J-OFURO3 sea-surface Rn data. However, the data users still should be cautious about another discontinuity issues caused by the quality of the input dataset itself.

Generating a High-Resolution Time-Series Ocean Surface Net Radiation Product by Downscaling J-OFURO3

The ocean surface net radiation ( $R_{n}$ ) characterizing ocean surface radiation budget is a key variable in ocean climate modeling and analysis. In this study, a downscaling scheme was developed to generate a high-resolution (0.05°) time-series (2002–2013) daily ocean surface $R_{n}$ from the third-generation Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO3) at 0.25° based on the Advanced Very-High-Resolution Radiometer (AVHRR) top-of-atmosphere (TOA) observations (AVH021C) and other ancillary information (Clearness Index and cloud mask). This downscaling scheme includes the statistical downscaling models and the residual correction post-processing. A series of angle-dependent downscaling statistical models were established between the daily ocean surface $R_{n}$ in J-OFURO3 and the AVHRR TOA data, and then, the residual correction was conducted to the model estimates $R_{n}$ _AVHRR_est to obtain the final downscaled data set $R_{n}$ _AVHRR. Validation against the measurements from 57 moored buoy sites in six ocean observing networks shows the high accuracy of the downscaled estimates $R_{n}$ _AVHRR_est with a $R^{2}$ of 0.88, RMSE of 23.44 $\text{W}\cdot \text{m}^{-2}$ , and bias of $-0.14\,\,\text{W}\cdot \text{m}^{-2}$ under all-sky condition. The results of the spatio-temporal analysis in $R_{n}$ _AVHRR and intercomparison with Cloud and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) products also indicated that the superior of the $R_{n}$ _AVHRR with more detailed information especially in the hot spot regions, such as central tropical Pacific (warming pool), Atlantic and Equatorial Eastern Indian Ocean (EIO).

Advances in the Estimation of Global Surface Net Heat Flux Based on Satellite Observation: J-OFURO3 V1.1

The reliability of surface net heat flux data obtained from the latest satellite-based estimation [the third-generation Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO3, V1.1)] was investigated. Three metrics were utilized: (1) the global long-term (30 years) mean for 1988–2017, (2) the local accuracy evaluation based on comparison with observations recorded at buoys located at 11 global oceanic points with varying climatological characteristics, and (3) the physical consistency with the freshwater balance related to the global water cycle. The globally averaged value of the surface net heat flux of J-OFURO3 was −22.2 W m−2, which is largely imbalanced to heat the ocean surface. This imbalance was due to the turbulent heat flux being smaller than the net downward surface radiation. On the other hand, compared with the local buoy observations, the average difference was −5.8 W m−2, indicating good agreement. These results indicate a paradox of the global surface net heat flux. In relation to the global water cycle, the balance between surface latent heat flux (ocean evaporation) and precipitation was estimated to be almost 0 when river runoff from the land was taken into consideration. The reliability of the estimation of the latent heat flux was reconciled by two different methods. Systematic ocean-heating biases by surface sensible heat flux (SHF) and long wave radiation were identified. The bias in the SHF was globally persistent and especially large in the mid- and high latitudes. The correction of the bias has an impact on improving the global mean net heat flux by +5.5 W m−2. Furthermore, since J-OFURO3 SHF has low data coverage in high-latitudes areas containing sea ice, its impact on global net heat flux was assessed using the latest atmospheric reanalysis product. When including the sea ice region, the globally averaged value of SHF was approximately 1.4 times larger. In addition to the bias correction mentioned above, when assuming that the global ocean average of J3 SHF is 1.4 times larger, the net heat flux value changes to the improved value (−11.3 W m−2), which is approximately half the original value (−22.2 W m−2).

Comparison of the third-generation Japanese ocean flux data set J-OFURO3 with numerical simulations of Typhoon Dujuan (2015) traveling south of Okinawa

Insufficient in situ observations in high winds make it difficult to verify climatological data sets and the results of tropical cyclone (TC) simulations. Reliable data sets are necessary for developing numerical models that predict TCs more accurately. This study attempted to compare the third-generation Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO3) data, with TC simulations conducted by a 2 km mesh coupled atmosphere-wave-ocean model. This is a case study of Typhoon Dujuan (2015) and the area of approximately 20̊N, 130̊E, south of Okinawa, was selected. The comparison reveals that J-OFURO3 data are reliable for verifying the atmospheric and oceanic components of TC simulations with two different initial sea surface temperature (SST) conditions, although the blank area remains within the inner core area for air temperature, specific humidity, and latent heat flux owing to issues with the construction method. Simulated maximum surface wind speeds (MSWs) are significantly correlated with J-OFURO3 MSWs. The asymmetrical distribution of simulated surface wind speeds within the inner core area can be reproduced well in the J-OFURO3 data set. In terms of the oceanic response to the TC, TC-induced sea surface cooling was reproduced well in the J-OFURO3 data set and is consistent with the simulation results. Unlike simulated SST, simulated surface wind speeds, surface air temperature, and surface specific humidity are still inconsistent with the J-OFURO3 data, even when the J-OFURO3 SST is used as the initial condition. New algorithms, more satellite data used, and model improvement are expected in the future.

Validation of different global data sets for sea surface wind-stress

ABSTRACT Different surface wind-stress products over the global ocean were validated by comparing with in-situ measurements from moored buoys, and by inter-comparing among them. The products are ones constructed from satellite observations by microwave scatterometers and radiometers, and reanalysed ones by data assimilation and numerical models. Comparisons with buoy measurements revealed that the Cross-Calibrated Multi-Platform Version 2 (CCMP v2.0) has the lowest positive bias, the smallest root mean square error (RMSE) and the largest correlation coefficient in this inter-comparison, meaning that the CCMP v2.0 gives the best performance. Inter-comparisons among the different wind-stress products exhibited that the meridional wind-stress data of the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) are not different in the subtropical region from those of other ones, and zonal and meridional wind-stress data of the National Centre for Environmental Prediction/Climate Forecast System Reanalysis (NCEP/CFSR) have relatively low values in the westerly region. In the zonal-spectral analysis, we found that the zonal wind-stress/wind-vector data from the Institut Français de Recherche pour la Recherche et l’Exploitation de la Mer (IFREMER) have short wave-like oscillations. Thus, the surface wind and wind-stress data in IFREMER products have been well affected by spatial resolution of background data used in their gridding procedure. We also investigated the spatial characteristics of wind-stress curl fields for the different data sets, and found that there are differences between the CCMP v2.0 and Japanese Ocean Flux Data sets with Use of Remote Sensing Observations Version 3 (J-OFURO3) which are characterized by spike-like features in the equatorial Pacific where there are the Tropical Atmosphere Ocean and the TRIangle Trans-Ocean buoy Network (TAO/TRITON) buoys. These features were spatially correlated with buoy locations, which suggests that the reliability of the CCMP v2.0 products is governed by buoy locations in this region.

Inverse Modeling of Global and Regional Energy and Water Cycle Fluxes using Earth Observation Data

AbstractThe NASA Energy and Water Cycle Study (NEWS) climatology is a self-consistent coupled annual and seasonal cycle solution for radiative, turbulent, and water fluxes over Earth’s surface using Earth observation data covering 2000–09. Here we seek to improve the NEWS solution, particularly over the ocean basins, by considering spatial covariances in the observation errors (some evidence for which is found by comparing five turbulent flux products over the oceans) and by introducing additional horizontal transports from ocean reanalyses as weak constraints. By explicitly representing large error covariances between surface heat flux components over the major ocean basins we retain the flux contrasts present in the original data and infer additional heat losses over the North Atlantic Ocean, more consistent with a strong Atlantic overturning. This change does not alter the global flux balance but if only the errors in evaporation and precipitation are correlated then those fluxes experience larger adjustments (e.g., the surface latent heat flux increases to 85 ± 2 W m−2). Replacing SeaFlux v1 with J-OFURO v3 (Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations) ocean fluxes also leads to a considerable increase in the global latent heat loss as well as a larger North Atlantic heat loss. Furthermore, including a weak constraint on the horizontal transports of heat and freshwater from high-resolution ocean reanalyses improves the net fluxes over the North Atlantic, Caribbean Sea, and Arctic Ocean, without any impact on the global flux balances. These results suggest that better characterized flux uncertainties can greatly improve the quality of the optimized flux solution.

Impact of using multiple-satellite sensors on the accuracy of daily-mean sea surface wind data

ABSTRACT Most satellites observing sea surface winds have sun-synchronous orbits and provide observation data at the same place two times per a day. A daily mean value estimated from these data suffers from sampling errors because the high-frequency variation including a diurnal change in the wind field cannot be neglected. To overcome this problem, the use of multiple satellites is useful. The purposes of this study were to describe the time variation of the accuracy of daily mean wind data in a third-generation Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO3) and to investigate its causes by comparison with in situ measurement data from moored buoys. The results reveal that the three statistical measures such as bias, Root Mean Square error and cross correlation coefficient have improved over time. A set of scatter diagrams of the number of satellites versus the statistical measures for each year shows strong correlations. The accuracy of daily mean data provided by J-OFURO3 is concluded to depend on the number of satellites. We also focused on specific time intervals for satellite wind observations, particularly the maximum missing time interval (MMTI) within a day. The results showed that the correlations between the three statistical measures and MMTI were quite high. Because the above-mentioned two causes are not independent, we analysed the combined effect of the two causes together. The results show that the accuracy of daily mean data depends more strongly on MMTI than on the number of observations. By applying these results, it is possible to determine the optimal number of sensors and the optimal observation time to achieve maximum target accuracy. This provides very useful information for the design of satellite observation systems for sea surface wind.

An introduction to J-OFURO3, a third-generation Japanese ocean flux data set using remote-sensing observations

Accurate observational estimation of the ocean surface heat, momentum, and freshwater fluxes is crucial for studies of the global climate system. Estimating surface flux using satellite remote sensing techniques is one possible answer to this challenge. In this paper, we introduce J-OFURO3, a third-generation data set developed by the Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO) research project, which represents a significant improvement from older data sets as the result of research and development conducted from several perspectives. J-OFURO3 offers data sets for surface heat, momentum, freshwater fluxes, and related parameters over the global oceans (except regions of sea ice) from 1988 to 2013. The surface flux data, based on a 0.25° grid system, have a higher spatial resolution and are more accurate than the previous efforts. This has been achieved through the adopting of the state-of-the-art algorithms that estimate the near-surface air specific humidity and the improvement of techniques using observations from multi-satellite sensors. Comparisons with in situ observations using a systematic system developed along with the J-OFURO3 data set confirmed these improvements in accuracy, as did comparisons with other data sets. J-OFURO3 data are of good quality, facilitating a clearer understanding of more fine-scale ocean–atmosphere features (such as ocean fronts, mesoscale eddies, and geographic features) and their effects on surface fluxes. The information contained in this long-term (26 year) data set is demonstrably beneficial to understanding climate change and its relationship to oceans and the atmosphere.

Evaluation of air-sea sensible and latent heat fluxes over the Japan Sea obtained from satellite, atmospheric reanalysis, and objective analysis products

In order to evaluate the air-sea sensible and latent heat fluxes (SHF and LHF, respectively) over the Japan Sea obtained from satellite-derived, atmospheric reanalysis, and objective analysis products, we conducted an intercomparison of climatological monthly mean fields and a validation of the daily means with in situ observations. As a result, we found significant differences in the climatological mean fields of SHF in products during the winter. The SHF values from the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations version 2 (J-OFURO2) showed finer-scale spatial structures, large values near the coastal regions, and small values over the center of the Japan Sea. From a comparison with in situ observations, we concluded that J-OFURO2 SHF has better performance compared with the other global products. The differences in LHF in the products are not so large compared with the results for SHF. However, in a comparison between the satellite-derived products and the others, the representations of the frontal features associated with the sub-polar front and the Tsushima warm current were different. A comparison in the near-coastal region showed that application of the creeping-sea-fill technique used in J-OFURO2 provided appropriate data in the region and succeeded in reducing the adverse effects from land contamination encountered when using coarse atmospheric reanalysis data.

Assessment of various global freshwater flux products for the global ice-free oceans

In this study, we compare sea surface freshwater flux products over the global ocean for the period of 1988–2005, taking into consideration the average field, global water budget, and interannual variability and trends. The analysis considers satellite-based products (Japanese Ocean Flux Datasets with Use of Remote Sensing Observations [J-OFURO2], Hamburg Ocean–atmosphere Parameters from Satellite Data 3 [HOAPS3], Remote Sensing Systems [RSS], Global Precipitation Climatology Project [GPCP2], and Climate Prediction Center Merged Analysis of Precipitation [CMAP]); reanalysis (Japanese 25-year reanalysis [JRA25], National Centers for Environmental Prediction [NCEP]/National Center for Atmospheric Research reanalysis [NRA1], NCEP/Department of Energy reanalysis [NRA2], Climate Forecast System Reanalysis [CFSR]); and a hybrid product (Objectively Analyzed Air–Sea fluxes [OAFlux]). Recommendations are made for the developers of future freshwater flux products; these recommendations also aim to guide users to select products most suitable for their applications.For the global average field, evaporation and precipitation products of reanalysis are larger than satellite products in the tropical and subtropical regions. The large evaporation data values obtained by reanalysis are attributed to the low air specific humidity values and the differences between the bulk algorithms for the tropical and subtropical regions. Moreover, for the global water budget and the inter-annual variability and trends, the reanalysis precipitation product is largely dependent on its evaporation product. If we use reanalysis precipitation and satellite evaporation data, we obtain a negative value for the global ocean water budget. This study demonstrates the difficulty of using different evaporation and precipitation products to analyze the global hydrologic cycle.

An assessment of surface heat fluxes from J‐OFURO2 at the KEO and JKEO sites

The daily mean air‐sea heat fluxes over the global oceans have been developed as the version 2 of Japanese Ocean Flux data sets with use of Remote sensing Observations (J‐OFURO2). Net heat flux is available from 1988 to 2006, and the turbulent heat flux is available from 1988 to 2007. To assess the accuracy of the J‐OFURO2 product over the Kuroshio Extension region, air‐sea heat fluxes and related state variables were compared with independent in situ observations from the Kuroshio Extension Observatory (KEO) and JAMSTEC KEO (JKEO) surface moorings. Although seasonal biases were found, these tended to cancel out over the total period, resulting in a total bias and RMS in J‐OFURO2 net heat fluxes of 8.6 and 56.8 W/m2, respectively. Comparisons with other global air‐sea heat flux products from numerical weather prediction, i.e., the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (NRA1), the NCEP/Department of Energy reanalysis (NRA2), and satellite observations, i.e., Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data and merged product, i.e., Objectively Analyzed Air‐Sea Fluxes were also conducted at the KEO and JKEO sites. Comparison results show that the total and seasonal biases are smallest compared with other products, and J‐OFURO2 air‐sea heat fluxes are best data set for air‐sea interaction study over the Kuroshio Extension region.

Numerical Study on the Variability of Mixed Layer Temperature in the North Pacific

Abstract Physical processes important for the interannual variability in mixed layer temperature (MLT) in the North Pacific have been examined by using a three-dimensional bulk mixed layer model. This model was forced by the momentum, heat, and freshwater flux data derived from the NCEP–NCAR reanalysis and geostrophic flow data included in Japanese Ocean Flux datasets with Use of Remote Sensing Observations. The interannual variation in MLT was hindcasted over the course of 11 years from January 1993 to December 2003. The interannual variation in the modeled MLT favorably agreed with that of the available in situ and satellite sea surface temperature (SST) observational data. This agreement depended crucially on whether horizontal heat advection was considered a part of the model dynamics. Although both atmospheric and oceanic processes were required to explain the observed interannual MLT variability, the physical process most important for determining this variability was likely to differ year by year. For example, in the Kuroshio Extension region, it was found that the positive temperature tendency peak in 1997 was attributed to the positive surface thermal forcing, while the temperature tendency in 1998 and 1999 continued to increase in spite of the negative surface thermal forcing. Thus, the abnormal heat loss from the ocean to the atmosphere (hence, the atmospheric circulation change) in 1998–99 was considered to be excited by the ocean dynamics related to the warmer SST. Likewise, it was found that the rapid decrease in SST during 2001–03 was mainly caused by the effect of lateral flux.

Evaluation of a satellite-derived latent heat flux product in the South China Sea: A comparison with moored buoy data and various products

Latent heat flux in the South China Sea (SCS) mainly based on the Tropical Rain Measuring Mission Microwave Imager (TMI) is compared with the Objectively Analyzed air-sea heat Fluxes (OAFlux), the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF2), the Japanese Ocean Flux Data Sets with use of Remote Sensing Observations (J-OFURO), the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction/Department of Energy reanalysis 2 (NCEP2), and International Comprehensive Ocean-Atmosphere Data Set data (ICOADS). These products did not all compare well to in situ data. It is found that TMI latent heat flux and OAFlux approximately reproduce the time variations of buoy data. However, ECMWF and ICOADS were found that had moderate systematic errors with respect to in situ data. All products qualitatively reveal a similar pattern in climatological fields. Although the satellite-derived products are quite similar, TMI latent heat is smaller than those of OAFlux, GSSTF2 and J-OFURO in the northern SCS. The difference between TMI latent heat and ICOADS is large, and the temporal correlation is extremely low in the entire basin except the Luson Strait. This is owned to that the ICOADS hardly reproduces accurate variability in the data-sparse regions. Also the temporal cross-correlation coefficients of between TMI latent heat and ECMWF or NCEP2 are considerably lower in the central SCS, may be affected by the lack of ship observations of ECMWF and NCEP2 there. The present study also compares meridional structures of the zonal averaged latent heat. The TMI latent heat and OAFlux overestimate the other two satellite-derived products during summer, whereas in winter, the TMI latent heat underestimates the other products in the northern regions. Though the examination of the influence of input variables, we note that the underestimation of TMI latent heat flux than other three satellite derived products in the northern region is mainly caused by larger near-surface air humidity there. While, large sea surface saturation humidity contributes to larger ECMWF latent heat flux. On the other hand, lager latent heat flux of NCEP2 in central parts and ICOADS in eastern bands is possibly mainly caused by the difference in winds.

Comparison of Five Satellite-Derived Latent Heat Flux Products to Moored Buoy Data

Abstract Five satellite products of latent heat flux at the sea surface were compared to bulk fluxes calculated with data from 75 moored buoys, on almost 36 successive months from 1998 to 2000. The five products compared are the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Dataset (HOAPS-2), the Japanese Ocean Flux Datasets with Use of Remote Sensing Observations (J-OFURO), the Jones dataset, the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF-2), and the Bourras–Eymard–Liu dataset (BEL). The comparisons were performed under tropical and midlatitude environmental conditions, with three datasets based on 66 Tropical Atmosphere–Ocean array (TAO) buoys in the tropical Pacific, nine National Data Buoy Center (NDBC) buoys off the U.S. coasts, and four Met Office/Météo-France (UK–MF) moorings west of the United Kingdom and France, respectively. The satellite products did not all compare well to surface data. However, for each in situ dataset (TAO, NDBC, or UK–MF) at least one satellite product was found that had a good fit to surface data, that is, an rms deviation of 15–30 W m−2. It was found that HOAPS-2, J-OFURO, GSSTF-2, and BEL satellite products had moderate systematic errors with respect to surface data, from −13 to 26 W m−2, and small biases at midlatitudes (6–8 W m−2). Most of the satellite products were able to render the seasonal cycle of the latent heat flux calculated with surface data. The estimation of near-surface specific humidity was found to be problematic in most products, but it was best estimated in the HOAPS-2 product. GSSTF-2 and J-OFURO strongly overestimated the surface flux variations in time and space compared to surface data and to a flux climatology. With respect to TAO data, Jones fluxes yielded good results in terms of rms deviation (27 W m−2) but also presented a large systematic deviation. Overall, for application of the satellite fluxes to the world oceans, it was found that HOAPS-2 was the most appropriate product, whereas for application to the Tropics, BEL fluxes had the best performance in rms with respect to TAO data (24 W m−2).

An analysis of the accuracy of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J‐OFURO) satellite‐derived latent heat flux using moored buoy data

To analyze the accuracy of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J‐OFURO) latent heat flux (LHF), comparisons with moored buoy data are conducted. In the tropical region, J‐OFURO LHF is characterized by large overestimations when buoy LHF has large values. On the other hand, in the midlatitudes, J‐OFURO LHF tends to be underestimated when buoy LHF has large values. We have concluded that these errors are mainly caused by the accuracy of surface air specific humidity. The large scatter between satellite and buoy products is due to the low accuracy of not only specific humidity data but also wind speed data in the midlatitudes. The J‐OFURO LHF bias errors are compared with the bias errors of selected satellite (GSSTF2 and HOAPS1) and reanalysis (NRA1, NRA2, ERA15 and ERA40) data sets.

INTRODUCTION OF NEW J-OFURO LATENT HEAT FLUX DATA

Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO) includes various kinds of satellite data sets related to ocean surface flux and is provided for research people on the web site (http://dtsv.scc.u-tokai.ac.jp) and anonymous ftp site. Recently we extended the data period for several kinds of data included in J-OFURO. We introduce the new J-OFURO latent heat flux data and some results using new J-OFURO turbulent heat flux data in this paper

Intercomparison of Various Surface Latent Heat Flux Fields

Abstract The Japanese Ocean Flux Data Sets with use of Remote Sensing Observations (J-OFURO) latent heat flux field is compared with the Hamburg Ocean–Atmosphere Parameters and Fluxes from Satellite Data (HOAPS), the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF), ECMWF, NCEP–NCAR reanalysis (NCEP1), and da Silva et al.'s fields. All products qualitatively reveal a similar pattern in the average fields. Although the latent heat fluxes of J-OFURO and GSSTF are quite similar, they are larger than those of HOAPS in the tropical regions. The difference between J-OFURO and the da Silva data is large, and the temporal correlation is extremely low in the Southern Hemisphere. This suggests that the da Silva product hardly reproduces accurate variability in the data-sparse regions. Also the time correlation between J-OFURO and ECMWF or NCEP1 is considerably lower in the Southern Hemisphere than in the Northern Hemisphere. The ECMWF and NCEP1 fields may be affected by the lack of ship observations there. ...

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

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