Abstract. 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)(2011)(2012)(2013), an initial version (v1.1) of the sea level ECV was made available to users . 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 improvementPublished by Copernicus Publications. 282J.-F. Legeais et al.: An improved and homogeneous altimeter sea level record from the ESA since the uncertainties remain larger than the GCOS requirements (GCOS, 2011). Perspectives on subsequent evolution are also discussed.

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Flux products quantifying exchanges between ocean and atmosphere are needed for forcing models, understanding ocean dynamics, investigating the ocean's role in climate, and assessing coupled models. Research experiments are essential to improve flux parameterizations, and longer research deployments are required to sample rare events. Urgently needed technological improvements include longer battery life, more robust sensors and improvement of sensors for humidity, precipitation and direct gas and particle fluxes. A range of different flux products are needed, incorporating data from ships, satellites and models in different combinations and using different methods. All products must be characterized with uncertainty estimates. Dataset validation requires high quality observations from ocean flux reference sites and from ships. The continued development of flux products from satellites provides much-needed sampling. Continual intercomparisons among products and with high quality observations will lead to improved flux datasets, while improvements to the flux data management system would facilitate these intercomparisons.

Attempts to automatically estimate surface current velocities from satellite‐derived thermal or visible imagery face the limitations of data occlusion due to cloud cover, the complex evolution of features and the degradation of their surface signature. The Geostationary Ocean Color Imager (GOCI) provides a chance to reappraise such techniques due to its multiyear record of hourly high‐resolution visible spectrum data. Here we present the results of applying a Maximum Cross Correlation (MCC) technique to GOCI data. Using a combination of simulated and real data we derive suitable processing parameters and examine the robustness of different satellite products, those being water‐leaving radiance and chlorophyll concentration. These estimates of surface currents are evaluated using High Frequency (HF) radar systems located in the Tsushima (Korea) Strait. We show the performance of the MCC approach varies depending on the amount of missing data and the presence of strong optical contrasts. Using simulated data it was found that patchy cloud cover occupying 25% of the image pair reduces the number of vectors by 20% compared to using perfect images. Root mean square errors between the MCC and HF radar velocities are of the order of 20 cm s−1. Performance varies depending on the wavelength of the data with the blue‐green products out‐performing the red and near infra‐red products. Application of MCC to GOCI chlorophyll data results in similar performance to radiances in the blue‐green bands. The technique has been demonstrated using specific examples of an eddy feature and tidal induced features in the region.

A regional cross-calibration between the first Delay-Doppler altimetry dataset from Cryosat-2 and a retracked Envisat dataset is here presented, in order to test the benefits of the Delay-Doppler processing and to expand the Envisat time series in the coastal ocean. The Indonesian Seas are chosen for the calibration, since the availability of altimetry data in this region is particularly beneficial due to the lack of in-situ measurements and its importance for global ocean circulation. The Envisat data in the region are retracked with the Adaptive Leading Edge Subwaveform (ALES) Retracker, which has been previously validated and applied successfully to coastal sea level research.The study demonstrates that CryoSat-2 is able to decrease the 1-Hz noise of sea level estimations by 0.3 cm within 50 km of the coast, when compared to the ALES-reprocessed Envisat dataset. It also shows that Envisat can be confidently used for detailed oceanographic research after the orbit change of October 2010. Cross-calibration at the crossover points indicates that in the region of study a sea state bias correction equal to 5% of the significant wave height is an acceptable approximation for Delay-Doppler altimetry.The analysis of the joint sea level time series reveals the geographic extent of the semiannual signal caused by Kelvin waves during the monsoon transitions, the larger amplitudes of the annual signal due to the Java Coastal Current and the impact of the strong La Niña event of 2010 on rising sea level trends.