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A suite of shipboard and satellite observations are analyzed and synthesized to investigate the threedimensional structure of clouds and influences from sea surface temperature fronts over the western North Pacific. Sharp transitions are observed across the Kuroshio Extension (KE) front in the marine atmospheric boundary layer (MABL) and its clouds. The ocean's influence appears to extend beyond the MABL, with higher cloud tops in altitude along the KE front than the surroundings.In winter, intense turbulent heat release from the ocean takes place on the southern flank of the KE front, where the cloud top penetrates above the MABL and reaches the midtroposphere. In this band of high cloud tops, frequent lightning activity is observed. The results of this study suggest a sea level pressure mechanism for which the temperature gradient in the MABL induces strong surface wind convergence on the southern flank of the KE front, deepening the clouds there.In early summer, sea fog frequently occurs on the northern flank of the subtropical KE and subarctic fronts under southerly warm advection that suppresses surface heat flux and stabilizes the surface atmosphere. Sea fog is infrequently observed over the KE front even under southerly conditions, as the warm ocean current weakens atmospheric stratification and promotes vertical mixing. The KE front produces a narrow band of surface wind convergence, helping support a broad band of upward motion at 700 hPa that is associated with the eastward extension of the baiu rainband from Japan in June-July.

Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m −2 and a bias of less than 5 W m −2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500-1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1-3 measurement platforms in each nominal 10 • by 10 • box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development,

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.

[1] Surface heat fluxes from the Kuroshio Extension Observatory (KEO) buoy are compared with surface heat fluxes from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research reanalysis (NRA1) and NCEP/ Department of Energy reanalysis (NRA2). KEO surface measurements include downward solar and longwave radiation, wind speed and direction, relative humidity, rain rate, and air and sea surface temperature. For solar radiation, NRA2 had better agreement with KEO than NRA1. Both reanalyses underestimated shortwave radiation in summer and slightly overestimated it in winter. Turbulent surface heat fluxes are estimated with the KEO surface data using the Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.0 bulk algorithm. Both NRA1 and NRA2 latent heat flux (LHF) are larger than KEO LHF, consistent with previous studies. However, the comparison shows larger errors than previously thought. Indeed, the latent heat flux bias for NRA1 is 41 W m À2 and for NRA2 is 62 W m À2 (indicating that the bias between NRA1 and NRA2 is 21 W m À2 ). For latent heat flux, the large bias is caused primarily by the NRA bulk flux algorithm, while the root mean square (RMS) error is caused primarily by errors in the NRA meteorological variables. The combination of the biases for each heat flux is such that total NRA heat transfer from the ocean to the atmosphere is considerably larger than observed by KEO. These results highlight the importance of maintaining in situ observations for monitoring surface heat fluxes in the Kuroshio/Kuroshio Extension regions.