Satellite altimetry has revolutionised our understanding of ocean dynamics thanks to frequent sampling and global coverage. Nevertheless, coastal data have been flagged as unreliable due to land and calm water interference in the altimeter and radiometer footprint and uncertainty in the modelling of high-frequency tidal and atmospheric forcing.\ud \ud Our study addresses the first issue, i.e. altimeter footprint contamination, via retracking, presenting ALES, the Adaptive Leading Edge Subwaveform retracker. ALES is potentially applicable to all the pulse-limited altimetry missions and its aim is to retrack both open ocean and coastal data with the same accuracy using just one algorithm.\ud \ud ALES selects part of each returned echo and models it with a classic “open ocean” Brown functional form, by means of least square estimation whose convergence is found through the Nelder–Mead nonlinear optimisation technique. By avoiding echoes from bright targets along the trailing edge, it is capable of retrieving more coastal waveforms than the standard processing. By adapting the width of the estimation window according to the significant wave height, it aims at maintaining the accuracy of the standard processing in both the open ocean and the coastal strip.\ud \ud This innovative retracker is validated against tide gauges in the Adriatic Sea and in the Greater Agulhas System for three different missions: Envisat, Jason-1 and Jason-2. Considerations of noise and biases provide a further verification of the strategy. The results show that ALES is able to provide more reliable 20-Hz data for all three missions in areas where even 1-Hz averages are flagged as unreliable in standard products. Application of the ALES retracker led to roughly a half of the analysed tracks showing a marked improvement in correlation with the tide gauge records, with the rms difference being reduced by a factor of 1.5 for Jason-1 and Jason-2 and over 4 for Envisat in the Adriatic Sea (at the closest point to the tide gauge)

Ice-edge blooms are significant features of Arctic primary production, yet have received relatively little attention. Here we combine satellite ocean colour and sea-ice data in a pan-Arctic study. Ice-edge blooms occur in all seasonally ice-covered areas and from spring to late summer, being observed in 77–89% of locations for which adequate data exist, and usually peaking within 20 days of ice retreat. They sometimes form long belts along the ice-edge (greater than 100 km), although smaller structures were also found. The bloom peak is on average more than 1 mg m⁻³, with major blooms more than 10 mg m⁻³, and is usually located close to the ice-edge, though not always. Some propagate behind the receding ice-edge over hundreds of kilometres and over several months, while others remain stationary. The strong connection between ice retreat and productivity suggests that the ongoing changes in Arctic sea-ice may have a significant impact on higher trophic levels and local fish stocks.

Sea level is a very sensitive index of climate change since it integrates the impacts of ocean warming and ice mass loss from glaciers and the ice sheets. Sea level has been listed as an essential climate variable (ECV) by the Global Climate Observing System (GCOS). During the past 25 years, the sea level ECV has been measured from space by different altimetry missions that have provided global and regional observations of sea level variations. As part of the Climate Change Initiative (CCI) program of the European Space Agency (ESA) (established in 2010), the Sea Level project (SL_cci) aimed to provide an accurate and homogeneous long-term satellite-based sea level record. At the end of the first phase of the project (2010–2013), an initial version (v1.1) of the sea level ECV was made available to users (Ablain et al., 2015). During the second phase of the project (2014–2017), improved altimeter standards were selected to produce new sea level products (called SL_cci v2.0) based on nine altimeter missions for the period 1993–2015 (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612; Legeais and the ESA SL_cci team, 2016c). Corresponding orbit solutions, geophysical corrections and altimeter standards used in this v2.0 dataset are described in detail in Quartly et al. (2017). The present paper focuses on the description of the SL_cci v2.0 ECV and associated uncertainty and discusses how it has been validated. Various approaches have been used for the quality assessment such as internal validation, comparisons with sea level records from other groups and with in situ measurements, sea level budget closure analyses and comparisons with model outputs. Compared with the previous version of the sea level ECV, we show that use of improved geophysical corrections, careful bias reduction between missions and inclusion of new altimeter missions lead to improved sea level products with reduced uncertainties on different spatial and temporal scales. However, there is still room for improvement since the uncertainties remain larger than the GCOS requirements (GCOS, 2011). Perspectives on subsequent evolution are also discussed.

Sea state information is needed for many applications, ranging from safety at sea and on the coast, for which real time data are essential, to planning and design needs for infrastructure that require long time series. The definition of the wave climate and its possible evolution requires high resolution data, and knowledge on possible drift in the observing system. Sea state is also an important climate variable that enters in air-sea fluxes parameterizations. Finally, sea state patterns can reveal the intensity of storms and associated climate patterns at large scales, and the intensity of currents at small scales. A synthesis of user requirements leads to requests for spatial resolution at kilometer scales, and estimations of trends of a few centimeters per decade. Such requirements cannot be met by observations alone in the foreseeable future, and numerical wave models can be combined with in situ and remote sensing data to achieve the required resolution. As today's models are far from perfect, observations are critical in providing forcing data, namely winds, currents and ice, and validation data, in particular for frequency and direction information, and extreme wave heights. In situ and satellite observations are particularly critical for the correction and calibration of significant wave heights to ensure the stability of model time series. A number of developments are underway for extending the capabilities of satellites and in situ observing systems. These include the generalization of directional measurements, an easier exchange of moored buoy data, the measurement of waves on drifting buoys, the evolution of satellite altimeter technology, and the measurement of directional wave spectra from satellite radar instruments. For each of these observing systems, the stability of the data is a very important issue. The combination of the different data sources, including numerical models, can help better fulfill the needs of users.