Accurate long-term measurements of ocean temperatures are required to understand key physical processes at the ocean-atmosphere interface and any changes that may occur to these processes over time. Most long-term temperature records are comprised of measurements made in situ from instruments mounted on ships, moorings and drifting buoys. Such records are therefore impacted by upper ocean temperature gradients arising from absorption of diurnally varying solar insolation, heat exchange with the atmosphere and subsurface mixing, as the in situ measurements are made at different depths. Measurements of ocean surface temperatures can also be made using remote sensing methods, which exploit the spectral distribution of emitted radiation according to Planck’s radiation law. For example, infrared (IR) radiometers can provide an estimate of the sea surface skin temperature (SSTskin), which at a depth of several micrometres, is as close to the atmosphere-ocean interface as is currently practical to measure.
The first Copernicus Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR-A) was launched on 16th February 2016, and now provides nearly three years of SSTskin data to add to the existing long-term record from the Along-Track Scanning Radiometer (ATSR) series. SLSTR-A is a multi-spectral dual-view radiometer with two on-board blackbodies and cooled detectors ensuring accurate radiometric measurements. SLSTR-A was joined in orbit by its twin, SLSTR-B, on 25th April 2018. In this presentation we summarise SSTskin products from SLSTR-A and SLSTR-B and show how the SSTskin measurements are validated using in situ measurements from various depths, including Fiducial Reference Measurements (FRM) from IR radiometers mounted on ships of opportunity. The combination of in situ measurement and FRM provides high confidence in the quality of all datasets, as well as models of the skin-layer and diurnal variability employed to adjust the temperature measurement from various depths. We conclude with recommendations for continuing and preserving long-term SSTskin records.

Infrared satellite retrievals of sea surface temperature (SST) have become essential for many applications in meteorology, climatology, and oceanography. Infrared satellite radiometers passively measure the infrared energy emitted by the Earth's surface including a component of reflected atmospheric emission, which is then modified by the intervening atmosphere. Tropospheric aerosols increase infrared signal attenuation and affect the accuracy of infrared satellite SST retrievals. The goal of this study is to understand better the characteristics of aerosol effects on satellite retrieved infrared SSTskin, and to derive empirical formulae for improved accuracies in aerosol-contaminated regions.
To improve the accuracy of satellite-derived skin SST in the eastern tropical Atlantic Ocean, where there are frequent outflows of Saharan dust aerosols, we compare satellite-derived skin SST with collocated skin SST derived from the hyperspectral measurements of the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) deployed on ships during several research cruises of the Aerosols and Ocean Science Expeditions (AEROSE) and quality-controlled, collocated subsurface drifter temperatures. Satellite-derived skin SST contaminated by the presence of clouds were identified and removed from the analysis. In the region off W. Africa in the Saharan outflow area, skin SST retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the Aqua satellite have cool biases compared to shipboard radiometric measurements; these biases are > 1 K at aerosol optical depths > 0.5. In this study, a new method to derive night-time Dust-introduced SST Difference Index (DSDI) algorithms based on simulated brightness temperatures at infrared wavelengths of 3.9, 8.7, 10.8 and 12.0 μm, is derived using simulated top-of-atmosphere brightness temperatures radiative transfer model - RTTOV. Algorithms for correcting MODIS retrievals were derived by regression of the difference between the MODIS SSTskin and the in-situ measurements against the DSDI. To avoid reducing the accuracies of MODIS SSTskin retrievals in the absence of aerosols, the additional DSDI-based correction is only applied when the DSDI > 0.8. The mean errors and standard deviations of the differences between the MODIS and in situ SSTs are reduced by 0.25K and 0.19K after the DSDI correction.

Anthropogenic emissions of carbon dioxide (CO₂) levelled out in 2016, but have since begun to increase again. This continued increase in anthropogenic emissions means that it is now critical to monitor ocean carbon uptake. This long-term uptake of CO₂ by the oceans is reducing the ocean pH, a process commonly known as ocean acidification. The uptake is also altering the ocean chemistry and ecology, impacting marine ecosystems on which we rely. Recent work has begun to investigate the use of satellite Earth observation, exploiting empirical methods and salinity and sea surface temperature data, to monitor surface-ocean carbonate chemistry. These techniques complement in situ approaches by enabling the first synoptic-scale observation-based assessments of the global oceans and are particularly well suited to monitoring large episodic events. The nine year time series (May 2010-present) of observations of ocean salinity from space now provides the potential for time series analyses.

We will first present results from a completed European Space Agency (ESA) project, Pathfinders Ocean Acidification, which demonstrates that observations from space can be used to estimate surface total alkalinity and total dissolved inorganic carbon with performance comparable to that of in situ driven empirical approaches. Example satellite derived estimates of upwelling due to surface winds, including those from hurricanes and cyclones, will also be presented. We will then present early results from the ESA Satellite Oceanographic Datasets for Acidification (OceanSODA) project (figure 1) demonstrating how this upwelling (of low pH waters) and compound events and their subsequent impact on the carbonate system can also be inferred from satellite observations. Our intended exploitation of these advancements for supporting coral reef conservation, the designation of marine protected areas and investigating the health of wild fisheries will be presented. These routes for exploitation are being co-developed with the U.S. National Oceanic and Atmospheric Administration (NOAA) and the World Wide Fund for nature (WWF).


Figure 1: Study regions (global and six geographic regions, and two further alternative regions) within OceanSODA will include areas of river plumes (large freshwater discharge) in the Amazon and Orinoco (A), Congo (B), Mississippi (C), St Lawrence estuary and outflow (D), and vulnerable eastern boundary upwelling systems, in the Benguela (E) and Canary (Mauritanian, F) upwelling systems. Possible alternative upwelling systems (to replace E or F) are the Californian (G) or the Humboldt (H). The Arctic region (I) will be studied by a separately funded PhD which will exploit OceanSODA outputs and datasets.

Dimethylsulfide (DMS) is a volatile compound produced by microbial food webs in the surface ocean. DMS emission can promote the formation and growth of new aerosol particles in remote marine atmospheres with low background aerosol levels. In addition to scattering incident sunlight, these aerosols can seed cloud droplet formation, altering cloud shortwave albedo (shortwave forcing), heat retention (longwave forcing), and precipitation. In the Arctic, where climate is extremely sensitive to changes in the radiative budget, a better knowledge of DMS emission is needed to appraise its role in climate change. However, current estimates of marine DMS emission based on in situ data (e.g., gridded DMS climatologies) lack sufficient spatiotemporal resolution, whereas prognostic or diagnostic DMS models have generally poor predictive skill. To fill this gap we developed a new remote sensing algorithm, DMS-SAT, that estimates sea-surface DMS concentration and emission using bio-optical (chlorophyll a, Kd490, particulate inorganic carbon) and geophysical (sea surface temperature, salinity, sea ice concentration, wind speed) variables. We implemented the algorithm using ocean color data (SeaWiFS and MODIS-Aqua) covering the period 1998-2016. This dataset is, to our knowledge, the first-ever large-scale time series of DMS emission based on Earth Observation data. Here we analyze this time series to understand the drivers of DMS emission in the context of dramatic sea ice melt and concurrent changes in the functioning of the Arctic Ocean ecosystem. We show that Arctic summer DMS emission has increased at a mean rate of 33% per decade north of 70°N. This trend is consistent with independent atmospheric measurements showing an increasing trend of methanesulfonic acid (MSA), a DMS oxidation product, at high Arctic aerosol sampling stations. The reduction in sea ice extent explains 68% of interannual DMS emission changes between 1998 and 2016. By extrapolating regional relationships between ice extent and DMS emission, we estimate that DMS emission could increase by two- to threefold in an ice-free Arctic summer compared to present emission. The remaining (32%) interannual variability in DMS emission between 1998-2016 is explained by the interplay between phytoplankton productivity, ice retreat patterns and atmospheric forcing. Despite the robust relationship between ice cover and DMS emission, the emission time series shows a non-monotonic pattern, with a peak in 2012 and a decrease thereafter. This unexpected result stems from changes in phytoplankton biomass in ice-free waters, particularly over productive Siberian Arctic shelves. Failing to take this variability and nonlinearity into account could hamper our understanding of plankton-aerosol-cloud interactions in the Arctic, and their interplay with anthropogenic sulfur emissions. Exploitation of satellite Earth Observation data enables us to move beyond the classical climatological view. To conclude, we explore the synergies between the new DMS-SAT algorithm, in situ and satellite-based aerosol observations, as well as chemical transport and Earth System models.

While there are a variety of sources for TC outer-core wind data, the only inner-core wind data that are routinely available at present are collected by airplanes and limited to Atlantic and East pacific hurricane seasons. C-Band high resolution radar (SAR) is the only space-borne instrument able to probe at very high resolution O(1 km) and over all ocean basins the sea surface under extreme conditions. When co-analyzed with SFMR ocean surface wind measurements, the radar backscatter signals from both Sentinel-1 and Rardarsat-2 SAR acquired over major hurricanes, reveal high sensitivity in the cross-polarized channel for wind speeds up to 75 m/s. The combination of the two co- and cross-polarized channels can then be used to derive high resolution ocean surface wind speed.
On September, 7th, 2017, Sentinel-1 measurements intercepted the Irma TC during its category-5 stage. SAR-derived wind speeds are compared to SFMR wind speed measurements yielding remarkable low bias and rms, of about 1.5 m/s and 5.14 m/s, respectively. The retrieved wind structure parameters (34-, 50- and 64-knts wind radii) for the outer core are also found to be in very close agreement to the Best-Track or existing multiple satellite platform analysis. SAR high resolution measurements then uniquely enable the instantaneous two dimensional description of the inner core, to provide independent measurements of the maximum wind speed, the radius of maximum wind speed for each azimuth, and the inner core wind radii. As obtained, within the inner core region, i.e. wind radii between 50 and 110 knots, the wind profile displays a circular shape. An increase of about 64 m/s in less than 10 km is estimated, corresponding to an instantaneous absolute vorticity of 210f. Using a parametric Holland model and the environmental surface pressure (1011 hPa) given by the Best-Track, SAR-derived wind speeds correspond to a central surface pressure of 918 hPa (921 hPa from the Best-Track) in the Irma Eye.
Statistical comparison of the TC parameters derived from SAR and provided by the Best-Track analysis are presented. In particular, the rain impact on C-Band SAR is discussed. Other parameters tightly related to the high resolution of SAR systems such as the asumetry of the TC eyes are also documented. Finaly, the radial wind speeds derived from SAR are also compared to parametric TC models used to predict the ocean surface wind speed with respect to the distance from the TC center.

Satellite observations of climate variables in the ocean are crucial to understand rates and magnitude of the global water cycle, ocean circulation, and climate change. Cyanobacteria are photosynthetic microorganisms blooming on the ocean’s surface that are visible from space and can change the physical features of the sea surface. Utilizing a remote-controlled catamaran, we provide high-resolution in situ observations of large cyanobacterial blooms floating in a biofilm-like sea-surface microlayer. We identified the cyanobacterium Trichodesmium sp. in extremely high abundance in microlayer samples from the bloom areas compared to the bulk water. In the presence of the blooms, large anomalies of temperature and salinity of 0.95°C and -0.49 practical salinity unit were found in the microlayer, but a substantially cooler (-0.22°C) and saltier microlayer (0.19 practical salinity unit) was found in the absence of surface blooms. Positive temperature anomaly (i.e. warmer sea surface) and negative salinity anomaly (i.e. fresher sea surface) were concomitant with the catamaran passing through the bloom. The largest anomalies of temperature (0.95 °C) and salinity (-0.49 PSU) occurred with the largest enrichments of fluoresent dissolved organic matter (FDOM) at the surface by a factor of 20 compared to the bulk water. Overall, our observations describes biologically controlled warming and a new phenomenon of an apparent freshening of the sea surface – in the literature assumed to occur only by precipitation. Furthermore, the observed tendency of warmer temperature across the upper 80 μm microlayer compared to the upper 20 μm (by ship-based IR radiometers) indicates the need of real ground-truth in situ data from a layer equivalent to the penetration depth of signals from stallite-based IR and microwave radiometers. Overall, our work highlights new challenges in the development of algorithms and validation of remotely-sensed temperature and salinity from space.