Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the past two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, intercalibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and a sea level budget closure approach, as well as comparisons with ocean reanalyses and climate model outputs.

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.

Coastal zones are highly dynamical systems affected by a variety of natural and anthropogenic forcing factors that include sea level rise, extreme events, local oceanic and atmospheric processes, ground subsidence, etc. However, so far, they remain poorly monitored on a global scale. To better understand changes affecting world coastal zones and to provide crucial information to decision-makers involved in adaptation to and mitigation of environmental risks, coastal observations of various types need to be collected and analyzed. In this white paper, we first discuss the main forcing agents acting on coastal regions (e.g., sea level, winds, waves and currents, river runoff, sediment supply and transport, vertical land motions, land use) and the induced coastal response (e.g., shoreline position, estuaries morphology, land topography at the land–sea interface and coastal bathymetry). We identify a number of space-based observational needs that have to be addressed in the near future to understand coastal zone evolution. Among these, improved monitoring of coastal sea level by satellite altimetry techniques is recognized as high priority. Classical altimeter data in the coastal zone are adversely affected by land contamination with degraded range and geophysical corrections. However, recent progress in coastal altimetry data processing and multi-sensor data synergy, offers new perspective to measure sea level change very close to the coast. This issue is discussed in much detail in this paper, including the development of a global coastal sea-level and sea state climate record with mission consistent coastal processing and products dedicated to coastal regimes. Finally, we present a new promising technology based on the use of Signals of Opportunity (SoOp), i.e., communication satellite transmissions that are reutilized as illumination sources in a bistatic radar configuration, for measuring coastal sea level. Since SoOp technology requires only receiver technology to be placed in orbit, small satellite platforms could be used, enabling a constellation to achieve high spatio-temporal resolutions of sea level in coastal zones.

Originally designed for applications over the ocean, satellite altimetry has been proven to be a useful tool for hydrologic studies. Altimeter products, mainly conceived for oceanographic studies, often fail to provide atmospheric corrections suitable for inland water studies. The focus of this paper is the analysis of the main issues related with the atmospheric corrections that need to be applied to the altimeter range to get precise water level heights. Using the corrections provided on the Radar Altimeter Database System, the main errors present in the dry and wet tropospheric corrections and in the ionospheric correction of the various satellites are reported. It has been shown that the model-based tropospheric corrections are not modeled properly and in a consistent way in the various altimetric products. While over the ocean, the dry tropospheric correction (DTC) is one of the most precise range corrections, in some of the present altimeter products, it is the correction with the largest errors over continental water regions, causing large biases of several decimeters, and along-track interpolation errors up to several centimeters, both with small temporal variations. The wet tropospheric correction (WTC) from the on-board microwave radiometers is hampered by the contamination on the radiometer measurements of the surrounding lands, making it usable only in the central parts of large lakes. In addition, the WTC from atmospheric models may also have large errors when it is provided at sea level instead of surface height. These errors cannot be corrected by the user, since no accurate expression exists for the height variation of the WTC. Alternative and accurate corrections can be computed from in situ data, e.g., DTC from surface pressure at barometric stations and WTC from Global Navigation Satellite System permanent stations. The latter approach is particularly favorable for small lakes and reservoirs, where GNSS-derived WTC at a single location can be representative of the whole lake. For non-timely critical studies, for consistency and stability, model-derived tropospheric corrections from European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis ERA Interim, properly computed at surface height, are recommended. The instrument-based dual-frequency ionospheric correction may have errors related with the land contamination in the Ku and C/S bands, making it more suitable to use a model-based correction. The most suitable model-based ionospheric correction is the Jet Propulsion Laboratory (JPL) global ionosphere map (GIM) model, available after 1998, properly scaled to the altimeter height. Most altimeter products provide the GIM correction unreduced for the total electron content extending above the altitude of these satellites, thus overestimating the ionospheric correction by about 8%. Prior to 1998, the NIC09 (NOAA Ionosphere Climatology 2009) climatology provides the best accuracy.