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, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections.

Global estimates of oceanic evaporation (Evp) from 1958 to 2005 have been recently developed by the Objectively Analyzed Air–Sea Fluxes (OAFlux) project at the Woods Hole Oceanographic Institution (WHOI). The nearly 50-yr time series shows that the decadal change of the global oceanic evaporation (Evp) is marked by a distinct transition from a downward trend to an upward trend around 1977–78. Since the transition, the global oceanic Evp has been up about 11 cm yr−1 (∼10%), from a low at 103 cm yr−1 in 1977 to a peak at 114 cm yr−1 in 2003. The increase in Evp was most dramatic during the 1990s. The uncertainty of the estimates is about ±2.74 cm yr−1. By utilizing the newly developed datasets of Evp and related air–sea variables, the study investigated the cause of the decadal change in oceanic Evp. The decadal differences between the 1990s and the 1970s indicates that the increase of Evp in the 1990s occurred over a global scale and had spatially coherent structures. Larger Evp is most pronounced in two key regions—one is the paths of the global western boundary currents and their extensions, and the other is the tropical Indo-Pacific warm water pools. It is also found that Evp was enlarged primarily during the hemispheric wintertime (defined as the mean of December–February for the northern oceans and June–August for the southern oceans). Despite the dominant upward tendency over the global basins, a slight reduction in Evp appeared in such regions as the subtropical centers of the Evp maxima as well as the eastern equatorial Pacific and Atlantic cold tongues.An empirical orthogonal function (EOF) analysis was performed for the yearly winter-mean time series of Evp and the related air–sea variables [i.e., wind speed (U) and air–sea humidity differences (dq)]. The analysis suggested a dominant role of the wind forcing in the decadal change of both Evp and dq. It is hypothesized that wind impacts Evp in two ways. The first way is direct: the greater wind speed induces more evaporation by carrying water vapor away from the evaporating surface to allow the air–sea humidity gradients to be reestablished at a faster pace. The second way is indirect: the enhanced surface wind strengthens the wind-driven subtropical gyre, which in turn drives a greater heat transport by the western boundary currents, warms up SST along the paths of the currents and extensions, and causes more evaporation by enlarging the air–sea humidity gradients. The EOF analysis performed for the time series of the global annual-mean Evp fields showed that the first three EOF modes account for nearly 50% of the total variance. The mode 1 variability represents the upward trend in Evp after 1978 and is attributable to the increased U, and the mode 2 variability explains much of the downward trend in Evp before 1978 and is correlated to the global dq variability. The EOF mode 3 of Evp captures the interannual variability of Evp on time scales of the El Niño–Southern Oscillation, with the center of action over the eastern equatorial Pacific.

Over the past decade, our understanding of the Indian Ocean has advanced through concerted efforts toward measuring the ocean circulation and air–sea exchanges, detecting changes in water masses, and linking physical processes to ecologically important variables. New circulation pathways and mechanisms have been discovered that control atmospheric and oceanic mean state and variability. This review brings together new understanding of the ocean–atmosphere system in the Indian Ocean since the last comprehensive review, describing the Indian Ocean circulation patterns, air–sea interactions, and climate variability. Coordinated international focus on the Indian Ocean has motivated the application of new technologies to deliver higher-resolution observations and models of Indian Ocean processes. As a result we are discovering the importance of small-scale processes in setting the large-scale gradients and circulation, interactions between physical and biogeochemical processes, interactions between boundary currents and the interior, and interactions between the surface and the deep ocean. A newly discovered regional climate mode in the southeast Indian Ocean, the Ningaloo Niño, has instigated more regional air–sea coupling and marine heatwave research in the global oceans. In the last decade, we have seen rapid warming of the Indian Ocean overlaid with extremes in the form of marine heatwaves. These events have motivated studies that have delivered new insight into the variability in ocean heat content and exchanges in the Indian Ocean and have highlighted the critical role of the Indian Ocean as a clearing house for anthropogenic heat. This synthesis paper reviews the advances in these areas in the last decade.

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km2. In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.