The world's coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of-0.04 m, which is 50% lower than the MB of the previous GTSR dataset. By the end of the century (2071-2100), it is projected that the 1 in 10-year water levels will have increased 0.34 m on average for RCP4.5, while some locations may experience increases of up to 0.5 m. The change in return levels is largely driven by SLR, although at some locations changes in storms surges and interaction with tides amplify the impact of SLR with changes up to 0.2 m. By presenting an application of the CoDEC dataset to the city of Copenhagen, we demonstrate how climate impact indicators derived from simulation can contribute to an understanding of climate impact on a local scale. Moreover, the CoDEC output locations are designed to be used as boundary conditions for regional models, and we envisage that they will be used for dynamic downscaling.

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 Frontiers in Marine Science | www.frontiersin.org 1 July 2019 | Volume 6 | Article 348Benveniste et al.Requirements for a Coastal Zone Observing System 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 multisensor 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.

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This chapter describes observed changes in sea level and wind waves in the Baltic Sea basin over the past 200 years and the main climate drivers of this change. The datasets available for studying these are described in detail. Recent climate change and land uplift are causing changes in sea level. Relative sea level is falling by 8.2 mm year −1 in the Gulf of Bothnia and slightly rising in parts of the southern Baltic Sea. Absolute sea level (ASL) is rising by 1.3-1.8 mm year −1 , which is within the range of recent global estimates. The 30-year trends of

2D sea level trend and variability fields of the Baltic Sea were reconstructed based on statistical modeling of monthly tide gauge observations, and model reanalysis as a reference. The reconstruction included both absolute and relative sea level (RSL) in 11 km resolution over the period 1900-2014. The reconstructed monthly sea level had an average correlation of 96% and root mean square error of 3.8 cm with 56 tide gauges independent of the statistical model. The statistical reconstruction of sea level was based on multiple linear regression and took land deformation information into account. An assessment of the quality of an open ocean altimetry product (ESA Sea Level CCI ECV, hereafter "the CCI") in this regional sea was performed by validating the variability against the reconstruction as an independent source of sea level information. The validation allowed us to determine how close to the coast the CCI can be considered reliable. The CCI matched reconstructed sea level variability with correlation above 90% and root-mean-square (RMS) difference below 6 cm in the southern and open part of the Baltic Proper. However, areas with seasonal sea ice and areas of high natural variability need special treatment. The reconstructed RSL change, which is important for coastal communities, was found to be dominated by isostatic land movements. This pattern was confirmed by independent observations and the values were provided along the entire coastline of the Baltic Sea. The area averaged absolute sea level change for the Baltic Sea was 1.3 ± 0.3 mm/yr for the 20th century, which was slightly below the global mean for the same period. Considering the relative shortness of the satellite era, natural variability made trend estimation sensitive to the selected data period, but the linear trends derived from the reconstruction (3.4 ± 0.7 mm/yr for 1993-2014) fitted with those of the CCI (4.0 ± 1.4 mm/yr for 1993-2015) and with global mean estimates within the limits of uncertainty.