* A Specification Review, the objective of which is to validate the specification of all the evolutions foreseen based on upstream R&D activities and to assess the readiness for the design phase.

* A Design Review, the objective of which is to validate the design in the operational system of all the evolutions scheduled for the upcoming year against the specifications and to assess the readiness for the development phase.

* An Acceptance Review, at the end of the development phase of each evolution, with pre-operational trials. The objective of this review is to assess the successful completion of the development and verification activities and to demonstrate readiness for integration.

<figure><img src="image_50.png" /><figcaption>FIGURE 6.2: Research-to-operations: the implementation of product evolutions is coordinated by MOi through a formal development and a series of reviews.</figcaption></figure>

I currently chair the Specification Reviews of the Copernicus Marine Service, which occur 2 years before the entry into service of the corresponding Copernicus Marine new or upgraded products (Fig. 6.2). I also contribute to the Design Review for the coordination and validation of the proposed scientific evolutions.

### 6.4.4 Dissemination activities

Since 2017, a Copernicus Marine Service session has been organized each year at the European Geophysical Union (EGU) General Assembly and I've convened this session. The session included contributions from the R&D Service Evolution programme, but also from user uptake activities, external initiatives relevant for the Service, development of upstream data, etc.

Mercator Ocean International also organized a special issue on Copernicus Marine Service scientific advances during its first phase of implementation (2015-2018), for which I was guess editor.

The special issue was published in Ocean Science and gathered 24 scientific articles covering various topics related to the different MFCs and TACs and to improve scientific ocean knowledge.

Finally, I've also contributed to various communications regarding the evolution of the Copernicus Marine Service, especially towards coastal zones (e.g. in the Copernicus Observer, a journal of the European Commission).

### 6.4.5 Concluding remarks

Over the first implementation phase of the Copernicus Marine Service, the R&D Service Evolution programme led to major and continuous improvements of the Service, with a large uptake. This includes the addition of new products, a better scientific understanding of ocean dynamics,
the upgrade or preparation of the next generation of systems operated in the Copernicus Marine Service, and the development of enhanced or new service lines. The R&D Service Evolution programme covered the various thematics addressed by the Service. By managing this programme, I've widened and rounded my knowledge of ocean sciences, for their blue (ocean circulations, waves), green (biogeochemistry and marine ecosystems) and white components (sea-ice), from ocean reanalyses to forecasts and observations.

The mid- and long-term Service Evolution activities have contributed to and will continue to pave the way for future key evolutions for enhanced or new service lines envisioned by MOi for the Service, budget pending. These include a step-change monitoring of the marine biology going up to high-trophic levels (exploited and protected marine species), a step-change monitoring of the Arctic Ocean, 20th century ocean reanalyses, ensemble forecasts, higher-resolution products and improved CO2 air-sea fluxes monitoring, but also a coastal ocean core monitoring and climate projections over the 21st century dedicated to the marine environment (including coastal zones and marine ecosystems). The latter two are discussed in section 6.5.2.

## 6.5 Copernicus Marine, Sea Level Changes and Coastal Zones

### 6.5.1 Earth observations for monitoring marine coastal hazards and their drivers

Earth observations provide invaluable information for monitoring coastal zones. Together with numerical models, they provide added-value products with a full spatio-temporal monitoring of the ocean and forecasts of the ocean state, such as these developed and distributed by the Copernicus Marine Service. This capacity is a clear asset for monitoring and forecasting marine coastal hazards and their drivers. As anthropogenic activities and climate change can exacerbate marine coastal hazards of natural origins (e.g. coastal flooding), sustained coastal zone monitoring programs, forecasting and early warning systems will be increasingly needed to reduce risks for the coastal zone environment and population and to guide coastal zone management (see also section 7.2).

In Melet et al., 2020b, we provided a review on the essential role of Earth observations for monitoring marine coastal hazards and their drivers. This review includes (but is not limited to, see Fig. 6.3) the monitoring of marine coastal flood hazards, the monitoring of coastal zone characteristics such as coastal bathymetry and topography, as well as vertical land motions, all of which are important to assess relative sea level changes at the coast, and of meteorological and oceanic variables that can drive marine coastal floods (e.g. sea level, winds, waves). Current gaps were also identified and recommendations made on the use of EO for a reliable monitoring and more accurate forecasts of marine coastal hazards and their drivers. Requirements for future EO missions for the monitoring of marine hazards in dynamic coastal zones include ensuring the continuity of present capabilities to build longer geophysical records, getting higher revisit frequencies and spatial resolution to cover more systematically synoptic and fine scales, getting multi-band or multi-frame observations to capture rapide evolutions and information on propagative phenomena (e.g. wind-waves), advanced capabilities to monitor surface currents, coastal winds and other parameters. Future Sentinel missions such as Sentinel-10 (CHIME), Sentinel-8 (LSTM) and Sentinel-11 (CIMR), as well as Sentinel-NG in the 2030's, will provide an improved monitoring of coastal zones.
<figure><img src="image_51.png" /></figure>

FIGURE 6.3: Schematic representation (non-exhaustive) of the coastal zone, hazards (in normal font), and metocean variables (in bold font) that are relevant for coastal marine hazards and their monitoring (HFR: high-frequency radar; HAB: harmful algae bloom). From Melet et al., 2020b.

**6.5.2 The Copernicus Coastal Roadmap and envisioned evolutions for sea level and coastal zones**

During the Copernicus 2014-2021 phase, workshops and consultation meetings were organized to analyze priorities for the evolution of the Copernicus Services (especially CMEMS and CLMS) to better address coastal user needs. Based on these, and at the request of the European Commission, we elaborated a roadmap for the evolution of CMEMS and CLMS to better serve coastal users **(Mercator Ocean International and European Environmental Agency, 2018)**. Furthermore, we engaged discussions with C3S and CEMS for coordinated developments addressing the coastal zone. In the coming years, a strengthening of interfaces, cooperation and co-production between the services is foreseen and will allow to maximize the added-value of the Copernicus Program.

When preparing Copernicus for the 2021-2028 period (2^nd^ delegation agreement), we refined the Copernicus Coastal Roadmap to better define proposed evolutions of the Copernicus Marine Service during this period. Of particular importance for the evolution of Copernicus wrt sea level rise and coastal zones are the envisioned evolutions of the Copernicus Marine Service dealing with a (i) core, pan-European monitoring of the coastal environment and (ii) regional climate projections of the marine environment.

**A core, pan-European monitoring of the coastal environment**

This evolution corresponds to a major improvement of the Copernicus Marine Service with two main service lines: (i) coastal zone core monitoring through satellite observations and (ii) improved capacities to link coastal systems with Copernicus Marine Service upstream systems and co-production between Copernicus Marine Service and more local coastal systems, such as these operated by Member States.

To prepare this long-term evolution of the Service, we've recently set up coordinating cross-cutting activities on interfaces of Copernicus Marine with the coastal ocean. Such activities include the specification of operational interfaces between Copernicus Marine modeling systems (MFCs) and downstream coastal systems, an enhanced explicit representation of relevant processes for
coastal zones (e.g. wetting and drying, tides and surges, coupling effects specific to shallow regions), an improved land-estuary-ocean interface, improved / upgraded processing algorithms to provide satellite derived information on the ocean state and surface winds in coastal zones and the assimilation of observations in coastal zones and shelf seas.

One of the evolutions identified in the Copernicus Coastal Roadmap (Mercator Ocean International and European Environmental Agency, 2018) and refined in the Copernicus Marine 2021-2027 activity plan wrt coastal zone core monitoring concerns satellite-derived dynamic coastal bathymetry. Such an evolution would complement existing services and data portals, such as EMODnet-Bathymetry, by further exploiting Copernicus Sentinel satellite data (e.g. Sentinel-2, Sentinel-1).

The bathymetry is indeed a key environmental parameter in coastal zones for a wide variety of coastal applications, e.g. for coastal engineering and protection, marine food, energy, submarine cables, dredging, coastal engineering, navigation, sovereignty, science research and preservation. An improved characterization of the bathymetry in coastal zones, including in dynamic areas where it changes due to sediment transport, has been unanimously required by users (e.g., European Commission, 2019) and would also be beneficial for the implementation of various marine directives (e.g., EU Marine Strategy Framework Directive; Flood Directive; etc.). An improved representation of the bathymetry would also benefit coastal ocean modeling, including for water level changes at the coast (section 5.3.2).

To prepare a potential Copernicus Marine new service line on a pan-European, dynamic satellite-derived coastal bathymetry product at intermediate horizontal resolution, I've co-supervised Guillaume Cesbron (CDD) at MOi with the objective to conduct a broad market research to understand the current landscape of coastal bathymetry products and future requirements. To do so, three approaches have been used: a literature review, a market survey among Copernicus Marine and other users, and individual meetings/interviews with users. Results were summarized in Cesbron et al., 2021.

### Regional climate projections of the marine environment

This new service line could be achieved through a dynamical downscaling of global climate projections, in collaboration with C3S and by leveraging on regional modeling systems operated in the Copernicus Marine Service MFCs to overcome limitations of global climate models. The proposed regional climate projections will translate the C3S climate projections into generic/core information on the future evolution of the ocean and marine ecosystems at scales that fit decision making for e.g. developing maritime spatial planning, improving natural risk preventions and also solutions for resilience to climate change.

To prepare this evolution on MOi's side, I'm supervising a PhD student, Alisée Chaigneau, on regional projections of mean sea level and extreme water level changes along the European Atlantic coasts. This is detailed in the next Chapter (section 7.3) and corresponds to a research perspectives on my side.

### 6.5.3 The Copernicus Coastal Thematic Hub

A comprehensive monitoring of coastal zones puts Copernicus Services in front of a set of new challenges. These challenges also reside in the fact that the coast is per definition also the border of the thematic domains of the Land, Marine and other Copernicus Services. The strong mutual interaction and influence between the ocean and the land require from the outset of any activity a cross service approach.
The strengthening of interfaces between Copernicus Services could be exemplified by the de-
velopment (pending on the EC authorization) of Copernicus Thematic Hubs (CTH) during 2021-
2027. The main objective of CTH is (i) to group under one single entry point at the level of the
Copernicus program the ensemble of data, products and information generated by several Coper-
nicus services or components for specific thematic or geographical area, (ii) to provide an easier
user journey with a simplified access to key information on coastal zones under a single-entry
point, and with a central service desk to provide guidance and support to users. These CTH will
allow Copernicus to address a range of policy aspects that are difficult to address through a single
Copernicus service.

A high-priority CTH is the one dealing with coastal zones. The coastal CTH would gather and maintain the products and information generated on coastal zones by several Copernicus Services, also including access to Sentinel data with different levels of processing (from L2 to L4) and to other relevant information. MOi has been identified for the development and implementation of the Copernicus Coastal Thematic Hub. If this is confirmed by the EC, I would participate to the management of this activity.

The way Copernicus Services all together can provide information on a specific thematic is exemplified in the next section for sea level rise adaptation in coastal zones.

6.5.4 Copernicus Services for sea level rise adaptation

A strong cooperation between Copernicus Services is essential to address the broad and diverse range of requirements to inform on SLR and support the related adaptation in coastal zones. Through its services, Copernicus is already delivering a set of products that can be relevant for SLR-adaptation. I coordinated a cross-service overview of the current status and envisioned future products and services provided by CMEMS, C3S, CEMS and CLMS that are relevant for sea level rise adaptation (Melet and Le Traon, 2021, Fig. 6.4). Such a review could notably be useful for developing research projects on this topic.
<figure><img src="image_52.png" /></figure>

FIGURE 6.4: Illustrative Copernicus services products and activities relevant to sea-level rise adaptation (in gray boxes). Activities listed in plain text correspond to existing products or activities. Activities listed in italics correspond to potential new services in Copernicus 2 (2021-2027). Symbols indicate the different corresponding Copernicus Services: CMEMS in blue, C3S in red, CEMS in orange, CLMS in green. In the figure center, the components of SLR related risks, e.g., hazard, exposure and vulnerability, are listed. Adaptation aims at reducing SLR risks. SL stands for sea level. Adapted from Melet et al., 2021b.
# 7 Perspectives: Sea Level Change at the Coast and Climate Services

*I never think of the future - it comes soon enough.*

Albert Einstein

*Chaque demi-degré compte, chaque année compte, chaque choix compte.*

*Each half-degree matters, each year matters, each choice matters.*

Valérie Masson Delmotte, comment on the IPCC Special Report on Global Warming of 1.5°C (2019)

> *Quand tu veux construire un bateau, ne commence pas par rassembler du bois, couper des planches et distribuer du travail; mais réveille au sein des Hommes le désir de la mer grande et belle.*

> *When you want to build a boat, don't start by gathering wood, cutting planks and distributing work; but awaken in the heart of Men the desire of the great and beautiful sea.*

Antoine de Saint-Exupéry

> *We must boost the power of prediction for everyone and build their capacity to act. On this World Meteorological Day, let us recognize the value of early warnings and early action as critical tools to reduce disaster risk and support climate adaptation.*

António Guterres, Secretary-General of the United Nations (March 2022)

## 7.1 Introduction

Past and future sea level change information is getting available closer to the coast (chapter 5), but are still suffering from various limitations (e.g. resolution, missing physics in models, scarcity of observations) and are not necessarily directly supporting stakeholder needs. In parallel, core European services have been put in place (chapter 6). Yet, authoritative, consistent and decision-oriented climate services to support policies and decision-making wrt sea level rise are still in their early development worldwide. Risks due to sea level rise, the importance of mitigation and adaptation to SLR and the need for useful and authoritative SL changes information to guide
adaptation are highlighted in the following paragraphs. To contribute to bridge the gap between sea level science and actionable information for society, I've started to develop research activities on sea-level changes at regional to coastal scales that are actionable through climate services, and will develop this research stream over the coming years. In this chapter, I thus describe on-going and future activities in which I'm engaged on that thematic as well as perspectives. These activities aim at (i) improving knowledge of past, current and projected coastal sea level changes along European coasts, with a new focus for me on extreme events, and (ii) contributing to build climate services dealing with sea level change at the coast. Along that path, I'm developing interactions with stakeholders and with communities working on the other components of climate risks (e.g. exposure, vulnerability, and risk itself for coastal floods).

### 7.1.1 Risks induced by sea level rise

Risks associated with SLR are due to a combination of sea-level hazards, exposure, and vulnerability. Coastal zones and communities are expected to be increasingly threatened by sea-level changes acting at various timescales, ranging from episodic extreme events to interannual-to-centennial changes and rise linked to climate modes of variability and to climate change (Hallegatte et al., 2013, Oppenheimer et al., 2019, Le Cozannet et al., 2022, Glavovic et al., 2022, Bednar-Friedl et al., 2022). In fact, as reported in the sixth IPCC Assessment Report, the impacts of floods (both marine and riverine/pluvial) on people, the built environment and the economy is one of the four key climate change induced risks identified for Europe. Sea level rise induced risks include coastal flooding (mild and chronic at high-tides or intense and episodic during storms), permanent submersion of coastal zones, coastal erosion, salt intrusion in surface and ground water (with adverse impacts on drinkable water, agriculture), drainage difficulties, and coastal ecosystems degradation or loss (e.g. for coastal wetlands, mangrove, coral reefs which contribute to coastal zones protection, to biodiversity and carbon storage).

In terms of exposure and vulnerability, coastal zones are increasingly more densely populated than the hinterland (Small and Nicholls, 2003), exhibit higher rates of population growth and urbanisation, and are concentrating economic assets and critical infrastructures, a trend accompanied by land conversion. The exposure of people and assets to SLR hazards is therefore widespread and increasing. The low-elevation coastal zone, defined as the contiguous and hydrologically connected zone of land along the coast with an elevation above sea level of less than 10 m, covers only 2% of the world's land area but close to 10% of the world population lives there (McGranahan et al., 2007; Neumann et al., 2015). In Europe alone, more than 50 million people live in the low-elevation ($\le$ 10m) coastal zone and 30 million in the 100-year event flood coastal plain (Neumann et al., 2015, Fig. 7.1). The growth of coastal population is projected to continue over the coming years, in response to the overall population growth and coastward migration associated with the global trend of urbanisation. The world population is expected to reach 8.5 billion in 2030 and 9.7 billion in 2050 (projections of United Nations 2015) and most of the world's megacities are located in the coastal zone, many in large deltas (McGranahan et al., 2007). Coastal migration is also driven by the combinations of specific economic, geographic and historical conditions, concentration of densely settled agricultural areas in well-watered, fertile deltas and coastal plains (Hugo, 2011; McGranahan et al., 2007).

Due to the large economic value of coastal zones, economic losses due to coastal flood risks are huge (Abadie et al., 2020; Hallegatte et al., 2013). Presently, the expected annual damage for Europe alone is of €1.25 billion euros, but could increase by 2-3 orders of magnitudes if coastal adaptation is only maintained to its current level (Vousdoukas et al., 2018a).

Marine coastal ecosystems are among the most productive on Earth and provide a range of social and economic benefits to humans. Reefs, mangroves and sand dunes play a regulating role and protect the shoreline, for instance by largely attenuating the energy of wind-generated waves.
Coral reefs are among the most biodiverse ecosystems on Earth, and together with river estuaries, have a high biomass productivity. However, the ability of these ecosystems to cope with climate change notably depends on the rate of SLR (Perry et al., 2018).

The increased hazards posed by SLR in response to climate change has been identified as the main driver of the future rise in coastal flood losses for Europe, with the relative importance of exposure (coastward migration, urbanization and rising asset values) diminishing over time (Vousdoukas et al., 2018a).

Therefore, SLR creates risks for people, ecosystems, land uses, the built environment, and human activities (Le Cozannet et al., 2022).

<figure><img src="image_53.png" /></figure>

FIGURE 7.1: Mean sea level rise (SLR, in m) and present exposure of coastal population in Europe for global warming levels of +1.5°C and +3°C. SLR data consider the long term period (2081-2100) and SSP1-2.6 for (a) and SSP3-7.0 for (b). Adapted from Fig 13.4 of Chapter 13 of AR6 WG2 (Bednar-Friedl et al., 2022).

### 7.1.2 Adaptation and mitigation

Countries and state organizations have issued various policies to monitor, mitigate and adapt to SLR risks. Among the different existing policies, the Integrated Coastal Zone Management, EU Flood Risk Directive, EU Water Framework Directive, the EU Green Deal and the new EU Strategy on Adaptation to Climate Change are of particular importance (e.g. Bisaro et al., 2020). Mitigation and adaptation measures can reduce SLR related risks (e.g. Bednar-Friedl et al., 2022). Mitigation of climate change is crucial for limiting climate change and related hazards, such as sea level rise. Sea level is committed to rise for centuries to millennia due to the strong inertia of the (deep) ocean, with the global mean ocean thermal expansion relating to the time-integral of green-house gazes radiative forcing (section 3.3), and due to the long response time-scales of the icesheets (e.g. Golledge et al., 2015). Over the next 2000 years, sea level is committed to rise by 2 to 6 meters if peak warming is limited to 2°C, or by 19 to 22 meters if peak warming is limited to 5°C (Clark et al., 2016; Van Breedam et al., 2020). As seen by the large differences in multi-millennial committed sea level rise under different warming scenarios, mitigation of climate change can act to
reduce the magnitude of sea level rise. Importantly, mitigation can reduce the rate of sea level rise (e.g. from around 12 mm/yr under SSP5-8.5 to around 5 mm/yr under SSP1-2.6, at the end of the century Fox-Kemper et al., 2021). This is buying us time to adapt to sea level rise, considering the long timescales needed for validating and implementing adaptation solutions, and provides us with a wider range of adaptation solutions and pathways (e.g. Glavovic et al., 2022).

Different adaptation measures can be implemented to reduce SLR induced risks. The main ones are adaptation through protection (e.g. dykes, groynes), accommodation (e.g. flood-resistant buildings, lifted housing), benefits from ecosystems protection (e.g. dunes, mangroves, reefs...) and retreat inland (planned relocation) (Oppenheimer et al., 2019). In Europe, a cost-benefit analysis of adaptation to SLR through additional protection with dykes showed that at least 83% of flood damages could be avoided by elevating dykes along a quarter to a third of Europe's coastline (Vousdoukas et al., 2020).

### 7.1.3 Sea level information to guide adaptation and climate services

But to guide adaptation, useful and authoritative information is a prerequisite. Different kinds of SLR information, including uncertainties, are needed for different kinds of contexts to support coastal adaptation decision making (Haasnoot et al., 2020; Hinkel et al., 2019; Le Cozannet et al., 2022). Countries are developing their plans for SLR adaptation (Fig. 7.2). At national level, stakeholders are requiring more regionalized, local sea level changes information than provided by global climate models and the IPCC Assessment Reports so far (illustrated in Fig. 7.1). Different European countries are thus producing regionalized sea level assessments and reports (e.g. Simpson et al., 2015, UK Climate Projections: UKCP18, Lowe et al., 2018; Palmer et al., 2018). Stakeholders also need more information on how contributors to coastal sea level changes such as wind-waves, tides and storm surges will be impacted by climate change (section 7.4).

To support the Paris Agreement, enhancing adaptation to climate change and narrowing the gap between climate science and policy, the need for improved sharing of information and of "strengthening scientific knowledge on climate, including research, systematic observation of the climate system and early warning systems, in a manner that informs climate services and supports decision-making" was highlighted (UNFCCC, 2015). The corresponding need to translate the massive amount of climate data and information into authoritative information that can be effectively used to support climate related adaptation and policies is what motivated the development of climate services.

As defined by Global Framework for Climate Services (GFCS, Hewitt et al., 2012) of the World Meteorological Organization (WMO), "Climate services provide climate information to help individuals and organizations make climate smart decisions". According to the European Commission's Roadmap for Climate Services (2015) definition, climate services cover "the transformation of climate-related data - together with other relevant information - into customized products such as projections, forecasts, information, trends, economic analysis, assessments (including technology assessment), counseling on best practices development and evaluation of solutions and any other services in relation to climate that may be used for the society at large". Despite their name, climate services are thus not restricted to information on climate timescales (e.g. longer than roughly 30 years). Rather, they can provide information on past, present and future weather and climate change, including weather and ocean forecasts, prediction (seasonal, decadal) and projection (centennial scale).

Climate services therefore provide and translate climate variability and change information from research into added-value, qualified datasets for policy and economic decision contexts, as well as for the public. On top of this added-value products layer, additional layers can be developed to analyze and process the data into information useable by the scientific community, managers and policy makers, the downstream sector and to provide services associated to the information
<figure><img src="image_54.png" /></figure>

FIGURE 7.2: Sea level rise (SLR) vulnerability and national planning in Europe. (a) Map of countries in Europe summarizing the amount of SLR each country is planning for, at different time horizons (blue bars) and present population (2020) at risk of a 100-year coastal flood event (orange bars) (Haasnoot et al., 2021). The amounts of SLR and time horizons reflect national guidance or planning; local or project-based levels may differ (McEvoy et al., 2021). (b) Projected population at risk to a 1 in 10-year coastal flood event under RCP2.6-SSP1 and RCP8.5-SSP5 assuming present protection and population, as well as population change according to respectively, SSP1 and SSP5, based on Merkens et al., 2016; and (c) projected population at risk to 1 in 100-year coastal flood event under RCP2.6-SSP1 and RCP8.5-SSP5, assuming present protection and population, as well as population change according to respectively, SSP1 and SSP5, based on Merkens et al., 2016 (Haasnoot et al., 2021). Figure 13.5 of Chapter 13 of the Working Group II of the IPCC 6th Assessment Report (Bednar-Friedl et al., 2022).

delivery (as done in Copernicus Services, chap. 6). This includes the development of (i) relevant sectoral indicators, assessment and reporting activities, and (ii) service activities, such as user desk and support, web platforms for interactive visualization of data and risks under different scenarios, and interaction with users to provide products that better address their needs.

Existing climate services have focused on different thematic areas that vary by region and country, with wide applications areas (e.g. agriculture, health, disaster management, insurance). In Europe, they provide essential climate projections relevant for mean and extreme temperatures and precipitation and their impacts, but much less information is made available to support coastal adaptation (Le Cozannet et al., 2017). Existing climate services relevant for SLR induced coastal risks include (and is not restricted to) the NOAA Sea-Level Rise Viewer, the Aqueduct
Global Flood Analyzer and the Climate Central Coastal Risk Screening Tool. The six services of the European Copernicus Program (Chapter 6) are the main providers of European climate services (Benveniste et al., 2020) and deliver authoritative climate information relevant for sea level related risk, as discussed in Chapter 6.

In the following sections, I describe on-going and future activities I’m developing in the framework of different projects and initiatives and that aim at contributing to build climate services dealing with sea level change at the coast, both for early warning systems based on short-term forecasts, and for long-term sea level rise and changes in water level extremes, at improving our understanding of coastal sea level changes, and at identifying science knowledge gaps.

## 7.2 Toward a pan-European early warning system for marine coastal floods

This section describes activities performed in the framework of the H2020 ECFAS project led by C. Armaroli (IUSS) (Grant Agreement No 101004211). In this project, I lead the work package on forecasts of total water level and the identification of thresholds triggering coastal flood mapping. This work includes large contributions by Maialen Irazoqui at MOi, whom I’ve been supervising since January 2021.

### 7.2.1 ECFAS in brief

Early Warning Systems (EWSs) are integrated systems allowing a real time monitoring of potential natural hazards, issuing warnings when a natural hazard is forecasted, and informing stakeholders (e.g. civil protection agencies, regional and local authorities, environmental agencies) as part of an integrated risk assessment cycle to mitigate risks. EWS can play a critical role in the natural risk cycle, supporting the preparedness and response phases, including the deployment of emergency measures for rapid response after a disaster, as well as longer term damage assessment after the occurrence of an event. EWS were found to be an efficient adaptation measure by providing more than a tenfold return on investment (Adaptation, 2019).

<figure><img src="image_55.png" /><figcaption>FIGURE 7.3: Overview of the H2020 ECFAS project. The warning component of ECFAS is framed with an orange box while the work-package activities I’m leading are framed with a yellow box.</figcaption></figure>
The on-going ECFAS H2020 project aims at contributing to the evolution of the Copernicus Emergency Management Service (CEMS) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Such a system would complement the existing early-warning system of the CEMS for river/pluvial floods, CEMS-EFAS (European Flood Awareness System), by adding a pan-European marine coastal flood awareness system and by tackling coastal resilience to climate risk (marine storminess and exposure).

ECFAS operates at different levels to provide an integrated risk cycle monitoring and management service (Fig. 7.3). Two ECFAS components are developed:

* The ECFAS-Warning component aiming at building an awareness system for coastal areas,

* The ECFAS-Rapid Mapping (RM) and Risk and Recovery Mapping (RRM) aiming at providing added-value products to improve the Rapid Mapping (preparedness phase) and Risk and Recovery Mapping (response phase) services of CEMS, adding coast-targeted products in the aftermath of a marine flood event (e.g. shoreline displacement, maps of flooding and damages).

With my activities in ECFAS, I've started to broaden my expertise to short-term forecasts of total water levels at the coast, their drivers, non-linear interactions between them, as well as to extreme water level events and extreme value analyses.

### 7.2.2 Forecasts of water level at the coast: quality assessment

The warning component of ECFAS is based on forecasts of total water levels (TWL, e.g. mean sea level plus tides, plus storm surge, plus wave setup) at the coast built from the Copernicus Marine Service operational systems. When exceeding pre-defined thresholds, forecasts of TWL can trigger the rapid mapping and risk and recovery mapping components of ECFAS (Fig. 7.3). Thresholds are identified based on an extreme value analysis performed on a hindcast of total water level at the coast, selecting a given return period. A calibration and validation of the marine forcing part of the ECFAS-Warning component is thus needed for both forecasts and hindcasts of TWL at the coast. I've coordinated these activities, which are reported in Melet et al., 2021a. I provide below a short overview of the assessment of the quality of the Copernicus Marine Service best analyses and forecasts of TWL at the coast.

A thorough assessment of the quality of best analyses and forecasts of TWL produced by the Copernicus Marine Service regional monitoring and forecasting systems (Chapter 6) was performed through comparisons of water levels, tides and non-tidal residuals to tide gauge data over 2018-2020 (period of availability of past analyses and forecasts). A special emphasis was put on extreme events, given the critical role they play in activating the awareness system. In order to overcome the limitations imposed by the short validation period to the evaluation of extremes, a methodology to detect storm-driven extreme sea level events from tide-gauge records has been developed with a combinaison of thresholds for TWL and non-tidal residuals.

Overall, best analyses of water levels show satisfactory comparisons to tide gauge data. However, modeled peak water levels tend to be underestimated (Fig. 7.4). This could be due to an inaccurate atmospheric forcing forecasts, to missing physics in the models and/or to the resolution of the models. By separating the non-tidal and tidal contributions to the extremes, the potential sources of the prediction misfits were identified. Surge errors contribute by almost 80% to the peak water level error during events for more than 50% of the tide-gauges. An illustration of the representation of water levels and surges (non-tidal residuals) in the models, in comparison to tide gauge records, is provided in Fig. 7.5 for a selection of storms leading to extreme water
<figure><img src="image_56.png" /></figure>

FIGURE 7.4: Average peak magnitude errors in absolute (a, c, in m) and normalized (b, d, in %) values for modeled WL (a, b) and surge (c, d) that were detected in tide gauge records.

<figure><img src="image_57.png" /></figure>

FIGURE 7.5: Water-level (WL) and surge in meters during a selection of extreme water level events (corresponding to storms a-Eleanor, b- Emma, c-Vaia, d-Detlef, e-Gloria, f-Alfrida). Blue: observed values. Dark red: Modeled values (best analysis). WL and surge percentile-thresholds (percentile 99 for surges and 99.9 for water levels) for model and observations are shown with the corresponding colors, in horizontal dashed lines. Vertical blue lines denote the observed peak time for the plotted component.

levels along EU's coasts during 2018-2020. The skill of the models to flag extreme water levels that were actually observed in tide gauge records was also assessed. It was found that models are able
to independently flag about 75% of the tide-gauge observed extreme events.

<figure><img src="image_58.png" /></figure>

FIGURE 7.6: Absolute (a, in cm) and normalized relative (b, in % of observed peak magnitude) errors of modeled peak water-level magnitude for forecasting lead time from 1 to 5 days (FC01 to FC05) using tide gauges as a reference (the amplitude of the error is given by the size of the concentric circles). Warm colors denote and increase in skills, cold colors denote a decrease in skills. It can be seen that forecasting skills of peak WL are substantially degraded at forecast lead time longer than 4 days (purple and magenta colors).

The evolution of forecast skills with forecast lead times (from 0-day for the best analysis to a 5-day forecast lead-time) were also assessed through comparisons of past forecasts to tide gauge records. Overall, the impact of the forecast lead time on TWL extremes remains limited (-5% and -10% for water-level and surge peaks, respectively). However, the skills of WL forecasts are severely impacted at some locations, especially so from forecast lead times longer than 4 days (Fig. 7.6). The skills of WL forecasts are thus not strongly affected up to forecasts lead-times of 3 days, which corresponds to the minimum acceptable forecast lead-time for TWL for users based on the user survey analysis performed in the project.

The results of this assessment will be submitted for publication. They also inform on desired evolutions of the Copernicus Marine Service regional monitoring and forecasting systems (Chapter 6) for an improved monitoring and forecasting of total water levels along the EU's coastline.

### 7.2.3 Wave-ocean coupling effects on water level at the coast

I've coordinated activities performed by M. Irazoqui (MOi) and G. Mouttapa (ENSTA-Paris in-
ternship), both of whom I supervised at MOi, on the comparison of ocean regional simulations
over the northeastern Atlantic with different levels of wave to ocean feedbacks. These simula-
tions covering the year 2018 were performed by Meteo-France based on the Iberia-Biscay-Ireland
regional seas system of the Copernicus Marine Service. The different wave to ocean feedbacks
considered were the Stokes drift, the wave-modified momentum flux, and the wave energy flux
to the ocean (inducing mixing).

It was found that accounting for wave coupling effects has a substantial impact on the represen-
tation of water levels along the coasts of the North Sea especially, in line with available literature
(e.g. Staneva et al., 2017). Regarding extreme events, WL peaks are of larger amplitude when wave
coupling effects are considered, and especially so in the North Sea and German Bight and around
the UK (Fig. 7.7). Accounting for wave coupling effects in the ocean model can thus reduce the
underestimation of water level peaks during extreme events.

These results need to be consolidated but contribute to the identification of optimal model configurations for forecasts of extreme events along EU's coasts.

<figure><img src="image_59.png" /></figure>

FIGURE 7.7: Normalized difference (in %) of surge peak magnitudes during 2018 between a simulation with wave coupling effects (Stokes drift, wave modified momentum flux and wave energy flux) and a simulation without wave coupling effects. Red colors indicate larger peak amplitudes when wave coupling effects are accounted for.

### 7.2.4 Transfer to core services and perspectives

The proof-of-concept developed in ECFAS is informed by user needs and by the regulatory framework. Interactions with users were developed through a user survey, webinars, user trainings, etc. This activity of interfacing directly with user needs for coastal floods is emerging for me, with the participation to the aforementioned dedicated activities, and will be further developed in other related initiatives described below (sections 7.3.2, 7.4). Policies, legislation and regulations on the assessment and reduction of EU coastal flood risk are quickly evolving, with e.g. the 2021 EU Strategy on adaptation to climate change and the 2021 EU climate law. This strengthens the need
for early warning systems dedicated to coastal flood risk.

In ECFAS, I’m also chairing the core service exploitation team (CSET) of the project, which is composed by representatives of the Copernicus services (CMEMS, CEMS, CLMS, C3S) and the European Space Agency (ESA). This team advises on the exploitation and scientific progress of ECFAS products and services and provides recommendations regarding the developed tools, methods and algorithms. The overall objective is to foster the exploitation of ECFAS products among core services, and to maximize the potential for uptake in the Copernicus Emergency Management Service after the project completion.

The technical and operational feasibility of integration of the ECFAS proof-of-concept into CEMS as well as potential evolutions improving the system will be identified in the ECFAS roadmap for integration. For instance, evolutions are needed in terms of inclusion of processes (e.g. coupling effects, as highlighted in section 7.2.3, river discharges and compound events, wave runup for overtopping, etc), of a more seamless ocean-land modeling, accounting for coastal defenses, probabilistic forecasting, a more robust interface between marine forcings and coastal flood modeling etc.

If ECFAS or a similar system is operated in the following years by CEMS by leveraging on Copernicus Marine Service hindcasts / forecasts for the marine hazard component of the system, interfaces will need to be strengthened between the two services. The long-term evolution envisioned by the Copernicus Marine Service with a co-production between core regional systems and coastal operational forecasting systems (section 6.5.2, Ponte et al., 2019), such as these operated by Member States, could also strengthen the coherence between systems operated at national and pan-European levels.

## 7.3 Projected European coastal water level changes

Future climate change information is increasingly needed, especially for mitigation and adaptation purposes to protect lives and assets from adverse effects of climate change. Current climate change ocean projections, as provided by C3S, are global and rely on the information derived from global climate models (e.g. CMIP6 models). However their typical ocean and atmospheric resolution is coarse and finer-resolution dynamical processes impacting the sea level at the coast (Woodworth et al., 2019) are not represented (Meyssignac et al., 2017b). Finer resolution have the potential to reduce biases in regional dynamic sea levels thanks to better resolved ocean circulations (Hermans et al., 2020; Jin et al., 2021; Li et al., 2022; Sannino et al., 2022; Westen et al., 2020; Zhang et al., 2017). In addition, sea level variations at the coast are induced by global to regional sea level variations due to glaciers and ice sheets mass loss and to ocean thermal expansion, and by coastal variations due to atmospheric surges, tides and wave set-up and swash (Chapter 5). So far, the contributions of these coastal processes to total water-level changes have not been included in climate-based SLR projections. Furthermore, projected changes in extreme water levels are traditionally inferred by assuming a stationarity in the tide, wave and surge distributions (so called static method) or by neglecting non-linear interactions between the SLR and the contributors to extreme total water levels (Fox-Kemper et al., 2021).

Dynamical downscaling methods can be used to refine GCM projections at regional scales by increasing the spatial resolution of the model, by tuning the model specifically for the region of interest and by explicitly including more processes. Coastal sea level representation in ocean model have been shown to be improved with a higher resolution (e.g. Balmaseda et al., 2015; Li et al., 2022). For sea level changes, relevant processes to be added notably include tides and atmospheric surface pressure forcing. Regional dynamical downscaling methods rely on the implementation
of a high-resolution regional model driven by a coarser resolution, larger domain model (e.g a cli-
mate model) outputs. I've gained expertise on dynamical downscaling during my PhD (e.g. **Melet
et al., 2010a**) and following it, by contributing to the supervision of a PhD student (B. Djath) with
a further refinement of the regional model (**Djath et al., 2014a**).

Dynamical downscaling for regional sea level projections is developing and has been per-
formed for different regions (e.g. Adloff et al., 2018; Hermans et al., 2020; Jin et al., 2021; Liu et al.,
2016; Sannino et al., 2022; Zhang et al., 2017). So far, only Gomis et al., 2016 provided regional
ocean projections covering part of the northeastern Atlantic (around Iberia). Yet, the development
of sound regional sea level projection is still in its infancy, and would gain from more international
coordination. It also corresponds to a desired long-term evolution of the Copernicus Marine Ser-
vice (sections 6.5 and 7.5).

I've initiated the development of regional 21st century projections of mean sea level rise and to-
tal water level changes (including wave setup) for extremes over the northeastern Atlantic and cor-
responding western European coasts at MOi. This activity leverages on ocean and wave regional
models developed and operated in the framework of the Copernicus Marine Service (Chapter 6).

**7.3.1 First regional projections of sea level changes: methodological considerations**

This section describes work being performed during the PhD thesis of Alisée Chaigneau, that I co-supervise with A. Voldoire (CNRM) and G. Reffray (MOi), with contributions from S. Law Chune (MOi) for the wave projections.

In this PhD, high-resolution projections of sea level rise and total water level changes at the coast have been developed for the northeastern Atlantic Iberia-Biscay-Ireland (IBI) region (Fig. 7.8) based on a dynamical downscaling of the CMIP6 CNRM-CERFACS high-resolution climate model (CNRM-CM6-1-HR, Voldoire et al., 2019). To that end, both ocean and wind-wave regional simulations have been performed over the 1970-2100 period using two climate change scenarios: a low-emission, high-mitigation scenario (SSP1-2.6, with 1 chance over 2 to limit global warming to +1.8°C at the end of the century) and a high-emission, low-mitigation scenario (SSP5-8.5, with 1 chance over 2 to limit global warming to +4.4°C at the end of the century). This work has focused on methodological aspects, to prepare the regional configuration for the production of long-term simulations dedicated to sea level changes, to test the sensitivity of the regional simulations of sea level to different methodological choices (e.g. to coupling effects, bias corrections), to analyse methods for extreme value analyses etc. The identification of biases in the global climate model forcing the regional models benefited from the preliminary work performed during the Master 2 internship of Alisée Chaigneau that I co-supervised, on the low-resolution counterpart of the climate model (section 4.2.2). This work will pave the way for the future development of more comprehensive ensemble projections of regional and coastal sea level changes in the northeastern Atlantic (section 7.3.2).

The development and analysis of the high-resolution regional ocean and wave projections will
provide insights on the following methodological and scientific questions:

* What is the specific response of the northeast Atlantic and western Europe coastal ocean to climate change? What uncertainties arise from local-scale unresolved physics in coarser resolution climate models (e.g. the mesoscale activity, slope current at the continental slope, shelf wind-induced currents, currents induced by density changes at the estuaries)?

* What is the impact on projected sea level changes of correcting the mean ocean and atmo-spheric state through corrections of the forcings provided by the climate model?
<figure><img src="image_60.png" /><figcaption>FIGURE 7.8: Bathymetry (m) and schematic description of main oceanographic features in the IBI domain. From Chaigneau et al., 2022.</figcaption></figure>

* How to account for components of global mean sea level rise in the regional simulations?

* What are the relative contributions of SLR, tides, atmospheric surges and waves to total water-level changes at various timescales, from extreme events to interannual to centennial changes?

* How important are the interactions between contributors to total water-level changes, as non-linear feedbacks between SLR and coastal contributors have been highlighted for shallow coastal areas?

* To what extent projected changes in extreme total water levels are sensitive to the non-stationarity of waves and surges, changes in tides, and to their non-linear interactions?

**Regional projections of the ocean and sea level changes**

*This section discusses the contribution of Chaigneau et al., 2022.*

The regional ocean model, developed at MOi in the framework of the Copernicus Marine Service, is based on the NEMO ocean code, is at a 1/12° horizontal resolution (~ 5-7 km) and explicitly includes tides and surface atmospheric pressure forcing (needed for the representation of storm surges). A special attention was paid to the correction of bias and climate drift in the climate model forcings (see also section 4.2.2) to limit their propagation in the regional model, and on how to correct for and impose sea level rise at the ocean boundaries. A first study was performed to investigate questions related to sea level changes in the IBI region in terms of processes and methodological choices. The methodology is described in Chaigneau et al., 2022. When compared to reanalyses and observations over the historical period, the regional model shows better large-scale performances than its parent global climate model, mostly thanks to bias-corrections applied to the forcings. As extreme sea levels are also satisfactorily represented in the regionally
downscaled historical simulation thanks to the explicit representation of relevant processes missing in global climate models, the model will be used to analyse changes in extreme water levels at the coast (section 7.3.1).

<figure><img src="image_61.png" /><figcaption>FIGURE 7.9: Projected changes (2081-2100 vs. 1986-2005) in dynamic sea level (Gregory et al., 2019) under the SSP5-8.5 scenario in (a) CNRM-CM6-1 (1° ocean resolution), (b) CNRM-CM6-1-HR (1/4° ocean resolution), (c) IBI-CCS_raw (1/12° ocean resolution, with raw climate model forcings) and (d) IBI-CCS_corr (1/12° ocean resolution, with bias-corrected climate model forcings). Note that the dynamic sea level mean over the IBI zone is 0 in the regional model. The shelf break (defined by the 200 m isobath) is indicated in yellow. Adapted from Chaigneau et al., 2022.</figcaption></figure>

This first study focused on methodological aspects. To disentangle the respective effects of dynamical downscaling and of applying bias corrections both on the current climate and on the projected changes, twin experiments were carried out. The impact of the regional downscaling per se (e.g. increase in resolution and representation of additional processes) was assessed by comparing regional simulations without bias correction (IBI-CCS_raw) with its parent climate model simulation counterpart. The direct impact of dynamical downscaling was found to be rather limited in the region of study (Fig. 7.9b,c) due to the use of a high-resolution climate model in the first place(1/4° ocean component) to force the regional ones. Differences are larger when compared to the low-resolution climate model counterpart, which has a more typical 1° ocean component (Fig. 7.9a,c). Over the shelf, the Rossby radius of deformation is much smaller than in the deep ocean and a resolution of at least 1/50° would be required for the model to be eddy-resolving. Nonetheless, substantial differences in projected dynamic sea level changes were found along the Norwegian coasts due to a projected strong weakening of the Norwegian Coastal Current in the global climate model but not in the regional one (Fig. 7.9b,c and 7.10a,b).

The impact of bias corrections of the climate model forcings was assessed by comparing IBI-CCS_raw simulations with their twin simulations IBI-CCS_corr in which seasonal mean bias corrections were applied to the GCM outputs (2-D and 3-D variables) prior to their use in the regional model. Large differences in dynamic and steric sea level changes were found in the deep ocean due to bias corrections (Fig. 7.10c,d). Bias corrections also have an impact on projected changes in extreme sea levels, as shown in Fig. 7.11 for the 99th percentile of non-tidal residuals.

This methodological study allowed to validate the regional configuration as a suitable tool to investigate questions related to climate change over the IBI region in terms of processes and to provide a methodological framework for dynamically downscaled projections of sea level changes over this region. It lays the ground for forthcoming analysis (e.g. changes in extreme total water levels, section 7.3.1) and for future ensemble projections of sea level changes (section 7.3.2).
<figure><img src="image_62.png" /></figure>

FIGURE 7.10: Differences (2081-2100 vs. 1986-2005 under the SSP5-8.5 scenarios) between CNRM-CM6-1-HR and IBI-CCS_raw for (a) dynamic sea level changes and (b) steric sea level changes, and between IBI-CCS_corr and IBI-CCS_raw for (c) dynamic sea level changes and (d) steric sea level changes. A Student t test has been performed with a confidence interval of 95%, and the significant differences between the two simulations have been marked with white hatching. Adapted from Chaigneau et al., 2022.

## Regional projections of the wave climate and sea-level / wind-waves coupling effects

Extreme water levels have traditionally been studied through the analysis of tide gauge records (e.g. **Oppenheimer et al., 2019**, Haigh et al., 2010; Menendez and Woodworth, 2010; Wahl et al., 2017) and tide-surge hydrodynamical models (e.g. Kirezci et al., 2020; Muis et al., 2020). Tide gauges are typically located in sheltered areas and depending on their location relative to wave dissipation, their records do not or only partially capture wave setup (e.g. **Woodworth et al., 2019**). Thus, wave contributions to extreme sea levels have often been omitted in previous studies or a stationarity hypothesis made for projections (Fox-Kemper et al., 2021; Kirezci et al., 2020). The latter case corresponds to the static (or mean sea level offset) approach: a projected mean regional relative sea level rise is added to historical distributions of tides, surges and waves, supposing a stationary wave and surge climate (e.g. Kirezci et al., 2020). Yet, projected changes in storm surges (e.g. Muis et al., 2020), wave setup (**Melet et al., 2020a**, Vousdoukas et al., 2018b) and tides (Haigh et al., 2020) have been deemed important to consider for future changes in extreme total water levels (ETWL, incl. mean sea level, tides, storm surges and wave setup) (**Oppenheimer et al., 2019**, Fox-Kemper et al., 2021).
<figure><img src="image_63.png" /><figcaption>FIGURE 7.11: (a) Projected changes of the non-tidal residual 99th percentile in meters for the SSP5-8.5 scenario between the 2081-2100 and 1986-2005 periods in IBI-CCS_corr. (b) Differences with projected changes in IBI-CCS_raw. From Chaigneau et al., 2022.</figcaption></figure>

In addition, non-linear interactions between drivers of ETWL are not well captured to date (Fox-Kemper et al., 2021), although they can be important (e.g. Arns et al., 2020; Bonaduce et al., 2020; Idier et al., 2019). In particular, projections of wind-waves typically do not account for sea level changes, be it from tides, storm surges, or long-term sea level rise. Yet, the water levels over which the waves propagate control the shallow water wave dynamics and the refraction by bathymetric gradients, as well as bottom friction induced wave breaking. Wave statistics have been shown to be sensitive to sea level rise (e.g. Arns et al., 2017; Chini et al., 2010; Wandres et al., 2017) and to tide-surges during extreme events (e.g. Alari, 2013; Lewis et al., 2019). Projections of wave characteristics during extreme events could thus differ substantially when interactions between water level and waves are accounted for.

In this on-going study, we aim at investigating the sensitivity of projected (i) wind-wave climate characteristics, (ii) wave setup changes (for coastal flooding) and (iii) wave energy flux changes (as a proxy for coastal erosion) to the non-linear interactions between wind-waves and water level changes along the Atlantic European coastline (IBI region, Fig 7.8).

To do so, global and regional wave projections over the northeastern Atlantic have been performed driven by the same global CNRM-CM6-1-HR climate model as for ocean regional projections presented before (section 7.3.1, Fig. 7.12). Twin simulations are performed: IBI-CCS-WAV simulations with a time-invariant water-depth (traditional approach) and IBI-CCS-WAV_ssh simulations in which hourly sea level changes from the IBI-CCS ocean simulations (section 7.3.1) are accounted for (Fig. 7.12). In practice, sea levels are added to the topographic depth at each forcing time step of the wave model (MF-WAM). To save time, wave propagation parameters for shallow water such as group velocities and wave numbers are calculated during the pre-processing phase of the model run according to a fixed list of depths and frequencies (look up tables). Regional simulations have been validated with the few available observations and with a regional wave reanalysis and projected regional changes agree with the literature in both sets of simulations (decrease in mean significant wave height and period in the North Atlantic, decrease in extreme
<figure><img src="image_64.png" /><figcaption>FIGURE 7.12: Sketch of the downscaling strategy for waves to assess the sensitivity to sea level - wave interactions.</figcaption></figure>

significant wave height over the northeastern Atlantic, e.g. Morim et al., 2019).

Preliminary results indicate that in shallow water areas, the significant wave height and wave setup scaling time-series from IBI-CCS-WAV_ssh oscillate in phase with the tides, especially at locations with a large tidal range as illustrated in Fig. 7.13 at the Mont Saint Michel. Projected changes in both the time-mean and extremes wave setup are also sensitive to sea level changes, as illustrated in Fig. 7.14.

<figure><img src="image_65.png" /><figcaption>FIGURE 7.13: Time series of incoming wave conditions at Mont Saint Michel during an extreme significant wave height event. The curves represent IBI-CCS-WAV (yellow curve) and IBI-CCS-WAV_ssh (red curve) and 4 variables are displayed: (a) significant wave height (Hs, in m), (b) peak period (Tp, in s), (c), scaling for wave setup ($\sqrt{HsL_p}$, in m), (d) wave energy flux and (e) differences in the wave setup scaling between the two simulations. Sea level variations are shown as thin gray lines, with the right y-axis on each panel.</figcaption></figure>
(e.g. Mentaschi et al., 2016).

Future activities regarding ETWL will notably address the following questions:

* The impact of methodological approaches: How different are projected changes in ETWL when inferred from a dynamic approach (considering time-evolving components of TWL as provided by dynamic models) vs a static / SLR offset approach?

* The impact of processes: How ETWL change (e.g. amplification factors of return periods, allowances) when wave setup is accounted for (Lambert et al., 2020) along the western EU coasts? When sea level-waves interactions are accounted for in wave setup estimates?

### 7.3.2 Toward an ensemble of regional sea level projections

This section describes work to be performed during an upcoming PhD thesis in the framework of the H2020 CoCliCo project led by G. Le Cozannet (BRGM) (Grant Agreement No 01003598). In this project, I co-lead the work package on mean and extreme sea level projections, led by I. Losada (IH Cantabria).

Climate change adaptation practitioners need authoritative, useable and understandable information for decision making on coastal risk management and adaptation, such as the identification of coastal territories at risk from inundation, coastal land use planning or maintaining coastal infrastructure services. To meet such needs, the Coastal Climate Core Services (CoCliCo) H2020 project develops an integrated core service dedicated to coastal adaptation to sea-level rise with an open-source web platform informing users on present-day and future coastal risk. Through the platform, users will be able to visualize, download and analyze multiple decision-oriented coastal risk scenarios.

An overview of the project is provided in Fig. 7.15. In this project, I’m co-leading the work package on mean and extreme sea level projections, which overall objective is to develop improved medium- and high-resolution regional mean sea-level hindcast and projections. As part of this project, a report on the state of the art has been produced (Elshinnawy et al., 2022) to review available past climate datasets and future projections under different greenhouse gas emission scenarios (SSPs), to evaluate their suitability as coastal flood and erosion drivers along the EU coast and to identify research gaps and potential improvements to be addressed in the project.

<figure><img src="image_66.png" /><figcaption>FIGURE 7.15: Overview of the H2020 CoCliCo project.</figcaption></figure>

The work performed during A. Chaigneau's PhD is paving the way for the development of more comprehensive projections of regional and coastal sea level changes in the northeastern Atlantic. The methodology can now be applied to produce ensemble-based regional sea level projections over the IBI domain, allowing to characterize uncertainties and likely ranges of SL changes
and thereby providing useful information on projected sea level changes along European Atlantic coasts. This will be done as part of a PhD to start in mid-2022 and that I will supervise. During this PhD, we also aim at better understanding future changes in sea level changes from the regional scale (northeast Atlantic) to the coasts of western Europe, and from long-term trends to extreme events.

In addition to the production of this ensemble of high-resolution projected sea level changes, additional simulations and analyses could be performed to provide insights on the following questions:

* How different is coastal sea level rise and variability compared to the open ocean sea level rise at various time scales? Further refinement of the model in terms of resolution and physics could be operated over key coastal regions through embedded zooms, using the AGRIF software (as done during my PhD and the follow-up one by B. Djath, e.g. Djath et al., 2014a; Djath et al., 2014b; Melet et al., 2010a).

* What is the timing of various milestones associated to sea level rise (e.g. when will SLR reach +50cm or +1m or the SLR accounted for in different countries in their adaptation strategies compared to the historical baseline (Fig. 7.2))?

* Can we detect and attribute to anthropogenic forcing changes in extreme total water levels?

Methodological developments could also be considered, such as:

* Impacts of the resolution of the forcings: how different are regionally downscaled sea level changes projections when using typical CMIP6 resolution atmospheric and / or oceanic forcings instead of these from HighResMIP? This could also include the comparison of projected dynamic sea level changes in HighResMIP and scenarioMIP.

* Impacts of refined model physics on projected sea level changes and extremes, such as the implementation of wetting and drying to better represent components of sea level changes in shallow waters, including their non-linear interactions.

* Atmospheric forcings: what are the impacts of implementing an atmospheric boundary layer (ABL) model to better represent air-sea interactions instead of directly forcing the ocean model with atmospheric forcings from climate models?

* How to interface regional ocean projections with storm-surge systems at higher resolution to refine the representation of water levels at the coast?

Finally, I'll work on the exploitation of the CoCliCo project. The objective is to ensure the sustainability and legacy of the CoCliCo web-platform beyond the project timeframe and to maximise the effective use of the results. The potential for the integration of CoCliCo's legacy in the Copernicus Marine Service post-2027 or in the Copernicus Coastal Thematic Hub (section 6.5) will be analyzed and a roadmap for integration will be issued.

## 7.4 A European assessment report on sea level rise for stakeholders

Recently, the Joint Programming Initiative (JPI) for Climate and for Oceans have launched the **Knowledge Hub on Sea Level Rise**. This knowledge hub focuses on regional to local sea level changes in Europe and aims at supporting the development and implementation of related policies at local, national and European level. In terms of organization, four task groups have been created for (i) the co-design of the knowledge hub with stakeholders, (ii) policy and social sciences, (iii) physical sciences, and (iv) outreach. Since late 2021, I've co-chaired the task group on
physical sciences with R. van de Wal (Utrecht University).

The long-term ambition of the Knowledge Hub on Sea Level Rise is to provide periodic assessments of knowledge on sea level rise drivers, impacts and policy options for each of the major European regional seas. These assessment reports aim at complementing existing global and European assessments by providing additional geographical and contextual detail, tailored to regional, national and European policy development and implementation. A first assessment report is to be provided at the end of the first three-year implementation phase of the knowledge hub, by the end of 2023. A scoping process, to which I contribute, is undergoing through regional scoping workshops where research and policy professionals with different disciplinary backgrounds and expertise join to discuss what information and knowledge needs to be assessed to bridge the science-policy making for well-informed decisions regarding protective and adaptive measures. This scoping process is already highlighting the needs of stakeholders for more regionalized information on future sea level changes and for more information on how contributors to coastal sea level changes such as wind-waves, tides and storm surges will be impacted by climate change. The scoping process will ultimately inform the agenda of a larger-scale, pan-European expert conference and the outline of the first assessment report, to which I will contribute in the coming years.

## 7.5 Towards coordinated regional ocean climate predictions and projections

Regionally downscaled climate change projections have emerged over the past decades, for the atmosphere essentially. While regionally downscaled projections dedicated to the ocean are developing, as illustrated in this chapter for sea level changes, they are not yet coordinated as their atmospheric counterpart in Coordinated Regional Climate Downscaling Experiment (CORDEX, Gutowski Jr. et al., 2016), endorsed by the World Climate Research Programme (WCRP). The vision of CORDEX is to advance and coordinate the science and application of regional climate downscaling through global partnerships. CORDEX also evaluates the added-value of regional downscaling, which has been debated (e.g. Hall, 2014). However, CORDEX is mostly focused on the atmosphere, even though a Flagship Pilot Study on coupled ocean-atmosphere systems and on the importance of resolving small scale ocean scales over the Mediterranean Sea has been endorsed. Regional climate models are moving towards Earth system models, with the atmosphere being coupled to the ocean, sea-ice, vegetation etc, but often for the benefits of an improved atmospheric state, and at high computational costs (Solman et al., 2021).

<figure><img src="image_67.png" /></figure>

FIGURE 7.16: Illustration of the possibility to develop an Ocean-CORDEX like initiative building on Copernicus Services, from modeling to ocean monitoring indicators and reporting activities.
Yet, relevant information on past and future ocean climate changes is increasingly needed at regional to local scales to support decision-making for adaptation (e.g. for maritime spatial planning, improved natural risk preventions and also solutions for resilience to climate change), to support a sustainable use of the ocean, for climate reporting assessment and to better understand the regional marine environment changes depending on climate change scenarios. This is especially the case for the coastal ocean, given its importance for the green and blue economies and the high population density and critical assets in coastal zones (Melet et al., 2020b).

By leveraging on the capacities of the Copernicus Climate Change Service (C3S, providing global climate projections), of the Copernicus Marine Service (CMEMS, regional modeling systems for the ocean physics and biogeochemistry with a refined representation of the ocean processes, chapter 6) and on their model quality evaluation assessment framework (Sotillo et al., 2021) and on their climate change monitoring and reporting activities (e.g. Von Schuckmann et al., 2021b, Von Schuckmann et al., 2021a), an Ocean-CORDEX like initiative could be developed over the European Seas (Fig. 7.16, section 7.3). Regional climate projections based on CMEMS systems would translate the bias-corrected C3S global climate projections into generic/core information on the future evolution of the ocean and marine ecosystems at scales that fit decision making, through a coordinated framework. Due to computational constrains, a prioritization of potential evolutions is needed when developing new versions of models. Such regional projections would be based on models calibrated and developed with a clear focus on an improved representation of the ocean (hence potentially favoring an improvement in the ocean model resolution, physics, compared to e.g. a full coupling with a costly atmospheric model). In addition, as regional models are usually forced at their boundaries by global, coarser resolution coupled climate models, drifts, biases and uncertainties in those boundary conditions can propagate in the regional domain. To mitigate this issue, specific processings (e.g. bias corrections) could be needed. A coordinated framework for scientific and technical aspects would be useful for efficient dynamical downscaling techniques, including the correction of biases, experiment protocols for inter-comparison exercises.

As stakeholders are interested in decadal time horizons for decision making (e.g. 2030, 2050) in addition to longer time horizons (e.g. 2100, 2300 for sea level changes), decadal predictions dedicated to the marine environment could also be beneficial. As for centennial-scale projections, changes in the ocean climate over the next decades will be due to anthropogenic forcing (emissions of radiatively active gases and aerosols), internal variability of the climate system (e.g. climate modes, section 5.3.2) and natural variability (e.g. natural changes in solar radiation, volcanic forcing, section 3.2.1). But for decadal predictions, the initialization of the prediction is also important (especially for the ocean), decadal predictions being a mixed initialization and forcing problem. Initialized decadal predictions show improved skills compared to climate projections over the same timescales. The ocean, in particular, with its inertia, exhibit longer-lived variations. As such, coordinated and operational decadal predictions dedicated to the marine environment (blue, green, white) could be developed in core services by leveraging on operational forecasting and initialized systems operated by the Copernicus Marine Service. Such a long-term evolution would be developed, if at all, in coordination with the Copernicus Climate Change service for getting access to decadal predictions of needed boundary forcing fields. Dedicated studies would be needed to assess the predictive capability for different variables on annual to decadal timescales, and the skills of decadal predictions for different societal application. To that end, decadal forecasts (or retrospective decadal predictions) could be performed and analysed.

Thinking wider, the development of these new capacities for decadal predictions and multi-decadal to centennial projections dedicated to the marine environment could also contribute to the development of the Digital Twin of the Ocean, which is under development at Mercator Ocean
International. "What-if" scenarios could be tested in this virtual environment to investigate for in-
stance the impact of the implementation of policies on the evolution of the marine environment.

Ocean Monitoring Indicators (e.g. Von Schuckmann et al., 2021a) could be developed for decadal predicted and multi-decadal to centennial projected changes in the marine environment for Copernicus Marine Service areas of benefits. This would support the development of regional climate services dedicated to the ocean (such as CoCliCo for sea level changes and related impacts) and regional ocean climate change assessment, which has emerged in the latest IPCC Assessment Report on impacts, adaptation and vulnerability (e.g. Bednar-Friedl et al., 2022, for Europe), but is still lacking in the Physical Science Basis report (Fox-Kemper et al., 2021). I'd like to play a role in the potential development of coordinated regional ocean climate predictions and projections, in relation with the Copernicus Marine Service evolution (Mercator Ocean International and European Environmental Agency, 2018, Service evolution strategy- R&D roadmap, Chapter 6).
<figure><img src="image_68.png" /></figure>
A List of 5 selected publications

* Melet A., B. Meyssignac, R. Almar, G. Le Cozannet, 2018. Underestimated wave contributions to coastal sea level rise. Nature Climate Change, 8, 234-239, 10.1038/s41558-018-0088-y

* Chaigneau A., G. Reffray, A. Voldoire, A. Melet, 2022. IBI-CCS: a regional high-resolution model to simulate sea level in western Europe. Geoscientific Model Development, 15(5), 2035-2062, 10.5194/gmd-15-2035-2022

* Melet A., C. Buontempo, M. Mattiuzzi, P. Salomon, P. Bahurel, G. Breyannis, S. Burgess, L. Crosnier, P-Y. Le Traon, L. Mentaschi, J. Nicolas, L. Solari, F. Vamborg, E. Voukouvalas, 2021. European Copernicus Services to inform on sea-level rise adaptation: current status and perspectives. Frontiers in Marine Science, 10.3389/fmars.2021.703425

* Melet A., R. Almar, M. Hemer, G. Le Cozannet, B. Meyssignac, P. Ruggiero. Contribution of wave setup to projected coastal sea level changes, 2020. Journal of Geophysical Research, 125(8), e2020JC016078, 10.1029/2020JC016078

* Melet A., B. Meyssignac, 2015. Explaining the spread in global mean steric sea level rise in CMIP5 climate models. Journal of Climate, 28 (24), 9918-9940, 10.1175/JCLI-D-15-0200.1
<figure><img src="image_69.png" /></figure>
# B List of publications

List of publications as of May 5, 2022. Only publications in peer-review international journals are listed.

Publications related to sea level changes are highlighted in blue.

Publications with students I've supervised are indicated as follows: the *name* of the student is underscored and followed by an asterix.

## B.1 Submitted or in preparation

* **A. Chaigneau***, S. Law Chune, **A. Melet**, A. Voldoire, G. Reffray, L. Aouf. Impact of waves-sea level interactions for the projections of mean and extreme wave conditions in western Europe. To be submitted to Ocean Science.

* **M. Irazoqui***, **A. Melet**. Evaluation of coastal water-level representation skill in the CMEMS forecasting models: An application for coastal flooding. To be submitted to Frontiers in Marine Science.

* **J. Boucharel**, **M. David***, R. Almar, **A. Melet**. The constrained influence of climate modes on coastal sea level components interannual variability. Submitted to Climate Dynamics.

## B.2 Publications in peer-review international journals

53 **A. Chaigneau***, G. Reffray, A. Voldoire, **A. Melet**, 2022. IBI-CCS: a regional high-resolution model to simulate sea level in western Europe. Geoscientific Model Development, 15(5), 2035-2062, doi: 10.5194/gmd-15-2035-2022

52 **G. Cesbron***, **A. Melet**, R. Almar, A. Liermann, D. Tullot, L. Crosnier, 2021. Satellite-Derived Coastal Bathymetry - Review, User Needs and Future Services. Frontiers in Marine Science, 8:740830, doi: 10.3389/fmars.2021.740830

51 International Altimetry Team, 2021. Altimetry for the future: Building on 25 years of progress. Advances in Space Research, 68 (2), 319-363, doi: 10.1016/j.asr.2021.01.022

50 R. Almar, R. Ranasinghe, E.W.J. Bergsma, H. Diaz, **A. Melet**, F. Papa, M. Vousdoukas, P. Athanasiou, O. Dada, L. Pedro Almeida, E. Kestenare, 2021. A global analysis of extreme coastal water levels with implications for potential coastal overtopping. Nature Communications 12, 3775, doi: 10.1038/s41467-021-24008-9.

49 **A. Melet**, C. Buontempo, M. Mattiuzzi, P. Salomon, P. Bahurel, G. Breyannis, S. Burgess, L. Crosnier, P-Y. Le Traon, L. Mentaschi, J. Nicolas, L. Solari, F. Vamborg, E. Voukouvalas, 2021. European Copernicus Services to inform on sea-level rise adaptation: current status and perspectives. Frontiers in Marine Science, doi: 10.3389/fmars.2021.703425

48 J.M. Lellouche, E. Grenier, R. Bourdallé-Badie, G. Garric, **A. Melet**, M. Drévillon, C. Bricaud, M. Hamon, O. Le Galloudec, C. Regnier, T. Candela, C-E. Testut, F. Gasparin, G. Ruggiero,
M. Benkiran, Y. Drillet, P.-Y. Le Traon, 2021. The Copernicus global 1/12° oceanic and sea-ice GLORYS12 reanalysis. Frontiers in Marine Science, doi: 10.3389/feart.2021.698876

47 K. Richter, B. Meyssignac, A. Slangen, **A. Melet**, J. Church, X. Fettweis, B. Marzeion, C. Agosta, S. Ligtenberg, G. Spada, M. Palmar, C. Roberts, N. Champollion, 2020. Detecting a forced signal in satellite-era sea-level change. Environmental Research Letters, 15, 094079, doi: 10.1088/1748-9326/ab986e.

46 **A. Melet**, R. Almar, M. Hemer, G. Le Cozannet, B. Meyssignac, P. Ruggiero. Contribution of wave setup to projected coastal sea level changes, 2020. Journal of Geophysical Research, 125(8), e2020JC016078, doi: 10.1029/2020JC016078

45 **A. Melet**, P. Teatini, G. Le Cozannet, A. Conversi, C. Jamet, J. Benveniste, R. Almar, 2020. Earth observations for Monitoring Marine Coastal Hazards and Their Drivers. Surveys in Geophysics, doi: 10.1007/s10712-020-09594-5

44 J. Benveniste, M. Mandea, **A. Melet**, P. Ferrier, 2020. Earth observations for coastal hazards monitoring and international services: A European perspective. Surveys in Geophysics, doi: 10.1007/s10712-020-09612-6

43 J.-F. Legeais, W. Llovel, **A. Melet**, B. Meyssignac, 2020. Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of global mean sea level. In CMEMS Ocean State Report **B. Djath4**, editors: K. von Schuckmann, P.-Y. Le Traon. Journal of Operational Oceanography.

42 M. Oppenheimer, B. Glavovic, J. Hinkel, R. van de Wal, A. K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R. M. Deconto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, Z. Sebesvari, R. Biesbroek, M. K. Buchanan, G. Le Cozannet, C. Domingues, S. Dangendorf, P. Döll, V.K.E. Duvat, T. Edwards, A. Ekaykin, D. Forbes, J. Ford, M. D. Fortes, T. Frederikse, J.-P. Gattuso, R. Kopp, E. Lambert, J. Lawrence, A. Mackintosh, **A. Melet**, E. McLeod, M. Merrifield, S. Narayan, R.J. Nicholls, F. Renaud, J. Simm, A.J. Smit, C. Sutherland, N. Minh Tu, J. Woodruff, P.P. Wong, S. Xian, 2019. Sea level rise and implications for low lying islands, coasts and communities. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC).

41 G. Dodet, **A. Melet**, F. Ardhuin, X. Bertin, D. Idier, R. Almar, 2019: The contribution of wind generated waves to coastal sea level changes. Surveys in Geophysics, 40, 1562-1601, doi: 10.1007/s10712-019-09557-5

40 R. Ponte, M. Carson, M. Cirano, C. Domingues, S. Jevrejeva, M. Marcos, Mitchum, R. van de Wal, P. Woodworth, M. Ablain, F. Ardhuin, V. Ballu, M. Becker, J. Benveniste, F. Birol, E. Bradshaw, A. Cazenave, P. de Mey, F. Durand, T. Ezer, L.L. Fu, I. Fukumori, K. Gordon, M. Gravelle, S. Griffies, W. Han, A. Hibbert, C. Hughes, D. Idier, V. Kourafalou, C. Little, A. Matthews, **A. Melet**, M. Merrifield, B. Meyssignac, S. Minobe, T. Penduff, N. Picot, C. Piecuch, R. Ray, L. Rickards, A. Santamaria, D. Stammer, K. Thompson, P. Thompson, S. Vignudelli, J. Williams, S. Williams, G. Woppelmann, L. Zanna, X. Zhang, 2019. Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level. Frontiers in Marine Science, Oceanobs'19 Community White Paper, doi: 10.3389/fmars.2019.00437

39 J. Benveniste, A. Cazenave, S. Vignudelli, L. Fenoglio-Marc, R. Shah, R. Almar, O.B. Andersen, F. Birol, P. Bonnefond, J. Bouffard, F. Mir Calafat, E. Cardellach, P. Cipollini, G. Le Cozannet, C. Dufau, M.J. Fernandes, F. Frappart, J. Garrison, C. Gommenginger, G. Han, J.L. Hoyer, V. Kourafalou, E. Leuliette, Z. Li, H. Loisel, K. Madsen, M. Marcos, **A. Melet**, B. Meyssignac,
A. Pascal, M. Passaro, S. Ribo, R. Scharroo, Y.T. Song, S. Speich, J. Wilkin, P. Woodworth, G. Wöppelmann, 2019. Requirements for a Coastal Hazards Observing System. Frontiers in Marine Science, Oceanobs'19 Community White Paper, doi: 10.3389/fmars.2019.00348

38 PY. Le Traon, A. Reppucci, E. Alvarez Fanjul, L. Aouf, A. Behrens, M. Belmonte, A. Bentamy, L. Bertino, EV. Brando, B.M. Kreiner, M. Benkiran, T. Carval, S.A. Ciliberti, H. Claustre, E. Clementi, G. Coppini, G. Cossarini, M. De Alfonso Alonso-Muñoyerro, A. Delamarche, G. Dibarboure, F. Dinessen, M. Drevillon, Y. Drillet, Y. Faugere, V. Fernández, A. Fleming, I. Garcia-Hermosa, M. Sotillo, G. Garric, F. Gasparin, C. Giordan, M. Gehlen, ML. Gregoire, S. Guinehut, M. Hamon, C. Harris, F. Hernandez, B. J. Hinkler, J. Hoyer, J. Karvonen, S. Kay, R. King, T. Lavergne, B. Lemieux-Dudon, L. Lima, C. Mao, M.J. Martin, S. Masina, **A. Melet**, B. Buongiorno Nardelli, G. Nolan, A. Pascual, J. Pistoia, A. Palazov, J-F. Piolle, M.I. Pujol, A.C. Pequignet, E. Peneva, B. Pérez Gómez, L. Petit de la Villeon, N. Pinardi, A. Pisano, S. Pouliquen, R. Reid, E. Remy, R. Santoleri, J. Siddorn, J. She, J. Staneva, A. Stoffelen, M. Tonani, L. Vandenbulcke, K. von Schuckmann, G. Volpe, C. Wettre, A. Zacharioudaki, 2019: From observation to information and users: the Copernicus Marine Service perspective. Frontiers in Marine Science, 6, 234, Oceanobs'19 Community White Paper, doi: 10.3389/fmars.2019.00234

37 P. L. Woodworth, **A. Melet**, M. Marcos, R. D. Ray, G. Wöppelmann, Y. N. Sasaki, M. Cirano, A. Hibbert, J. M. Huthnance, S. Montserrat, M. A. Merrifield, 2019: Forcing factors affecting sea level changes at the coast. Surveys in Geophysics, 40, 1351-1397, doi: 10.1007/s10712-019-09531-1

36 C. Vic, A. C. Naveira Garabato, J. A. M. Green, A. F. Waterhouse, Z. Zhao, **A. Melet**, C. de Lavergne, M. C. Buijsman, G. R. Stephenson, 2019: Deep-ocean mixing driven by small-scale internal tides. Nature Communication, 10, 2099, doi: 10.1038/s41467-019-10149-5

35 **A. Melet**, B. Meyssignac, R. Almar, G. Le Cozannet, 2019: Reply to 'Waves do not contribute to global sea level rise'. Nature Climate Change, 9, 3, doi: 10.1038/s41558-018-0378-4

34 **A. Melet**, B. Meyssignac, R. Almar, G. Le Cozannet, 2018: Underestimated wave contributions to coastal sea level rise. Nature Climate Change, 8, 234-239, doi: 10.1038/s41558-018-0088-y

33 K. von Schuckmann et al., 2018: The Copernicus Marine Environment Monitoring Service Ocean State Report - Issue 2. Journal of Operational Oceanography, 11, doi: 10.1080/1755876X.2018.1489208

32 **V. Kumar***, **A. Melet**, B. Meyssignac, A. Ganachaud, W.S. Kessler, A. Singh, J. Aucan, 2018: Reconstruction of local sea-levels at south west Pacific islands - a multiple linear regression approach (1988-2014). Journal of Geophysical Research, 123, 1502-1518. doi: 10.1002/2017JC013053

31 B. Meyssignac, A.B.A. Slangen, **A. Melet**, J. Church, X. Fettweis, B. Marzeion, C. Agosta, S. Ligtenberg, G. Spada, K. Richter, M. Palmer, C. Roberts, N. Champollion, 2017: Evaluating Model Simulations of Twentieth-Century Sea-Level Rise. Part II: Regional Sea-Level Changes. Journal of Climate, 30, 8565-8593, doi: 10.1175/JCLI-D-17-0112.1

30 A.B.A. Slangen, B. Meyssignac, C. Agosta, N. Champollion, J. A. Church, X. Fettweis, S.R.M. Ligtenberg, B. Marzeion, **A. Melet**, M.D. Palmer, K. Richter, C.D. Roberts, G. Spada, 2017: Evaluating model simulations of 20th century sea-level rise. Part 1: Global mean sea-level change. Journal of Climate, 30, 8539-8563, doi: 10.1175/JCLI-D-17-0110.1

29 M. Alberty, J. Sprintall, J. MacKinnon, A. Ganachaud, S. Cravatte, G. Eldin, C. Germineaud, **A. Melet**, 2017: Spatial Patterns of Mixing in the Solomon Sea. Journal of Geophysical Research, 122, doi:10.1002/2016JC012666
28 J. MacKinnon, M. Alford, J. K. Ansong, B. K. Arbic, A. Barna, B. P. Briegleb, F. O. Bryan, M. C. Buijsman, E. P. Chassignet, G. Danabasoglu, S. Diggs, P. Gent, S. M. Griffies, R. W. Hallberg, S. R. Jayne, M. Jochum, J. M. Klymak, E. Kunze, W. G. Large, S. Legg, B. Mater, **A. Melet**, L. M. Merchant, R. Musgrave, J. D. Nash, N. J. Norton, A. Pickering, R. Pinkel, K. Polzin, H. L. Simmons, L. C. St. Laurent, O. M. Sun, D. S. Trossman, A. F. Waterhouse, C. B. Whalen, Z. Zhao, 2017: Climate Process Team on Internal-Wave Driven Ocean Mixing. Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-16-0030.1

27 P. Timko, B. K. Arbic, J. A. Goff, J. K. Ansong, W. Smith, **A. Melet**, A. J. Wallcraft, 2017: Impact of synthetic abyssal hill roughness on resolved motions in numerical global ocean tide models. Ocean Modelling, 112, 1-16, doi:10.1016/j.ocemod.2017.02.005

26 A. Rubio, J. Mader, L. Corgnati, C. Mantovani, A. Griffa, A. Novellino, C. Quentin, L. Wyatt, J. Schulz-Stellenfleth, J. Horstmann, P. Lorente, E. Zambianchi, M. Hartnett, C. Santos, V. Zervakis, P. Gorringe, **A. Melet**, I. Puillat, 2017: HF Radar Activity in European Coastal Seas: Next Steps Towards a Pan-European HF Radar Network. Frontiers in Marine Science, 4, 8, doi: 10.3389/fmars.2017.00008

25 F. Birol, N. Fuller, F. Lyard, M. Cancet, F. Niño, C. Delebecque, S. Fleury, F. Toublanc, **A. Melet**, M. Saraceno, F. Léger, 2017: Coastal applications from nadir altimetry: example of the X-TRACK regional products. Advances in Space Research, 59, 936-953, doi: 10.1016/j.asr.2016.11.005

24 P. Cipollini, F. M. Calafat, S. Jevrejeva, **A. Melet**, P. Prandi, 2017: Monitoring sea level in the coastal zone with satellite altimetry and tide gauges. Surveys in Geophysics, 38, 33-57, doi: 10.1007/s10712-016-9392-0

23 K. von Schuckmann et al., 2017: The Copernicus Marine Environment Monitoring Service Ocean State Report – Issue 1. Journal of Operational Oceanography, 9(2), s235-s320, doi: 10.1080/1755876X.2016.1273446. Awarded the 2017 IMarEST Denny Medal - Journal of Operational Oceanography, Team award.

22 G. Abessolo, R. Almar, E. Kestenare, A. Bahini, G.-H. Houngue, J. Jouanno, Y. Du Penhoat, B. Castelle, **A. Melet**, B. Messignac, E. Anthony, R. Laibi, G. Alory, R. Ranasinghe, 2016: Potential of video cameras in assessing event and seasonal coastline behaviour: a case study at Grand Popo, Benin (Gulf of Guinea). Journal of Coastal Research, Special Issue, No. 75, 442-446, doi: 10.2112/SI75-089

21 **A. Melet**, R. Almar, B. Meyssignac, 2016: What dominates sea level at the coast: a case study for the Gulf of Guinea. Ocean Dynamics, 66 (5), 623-636, doi: 10.1007/s10236-016-0942-2

20 **A. Melet**, S. Legg, R. Hallberg, 2016: Climatic impacts of parameterized local and remote tidal mixing. Journal of Climate, 29 (10), 3473-3500, doi: 10.1175/JCLI-D-15-0153.1

19 **A. Melet**, B. Meyssignac, 2015: Explaining the spread in global mean steric sea level rise in CMIP5 climate models. Journal of Climate, 28 (24), 9918-9940, doi: 10.1175/JCLI-D-15-0200.1

18 **A. Lefauve***, C. Muller, **A. Melet**, 2015: A three-dimension map of tidal dissipation over abyssal hills. Journal of Geophysical Research - Oceans, 120 (7), 4760-4777, doi: 10.1002/2014JC010598

17 **A. Melet**, R. Hallberg, A. Adcroft, M. Nikurashin, S. Legg, 2015: Energy flux into internal lee waves: sensitivity to future climate changes. Journal of Climate, 28 (6), 2365-2384, doi: 10.1175/JCLI-D-14-00432.1

16 J.-M. Brankart, G. Candille, F. Garnier, C. Calone, **A. Melet**, P. A. Bouttier, P. Brasseur, J. Verron, 2014: A generic approach to explicit simulation of uncertainty in the NEMO ocean model. Geoscientific Model Development, 8, 615-643, doi: 10.5194/gmdd-8-615-2015
15 A. Ganachaud, S. Cravatte, **A. Melet**, A. Schiller, N. Holbrook, B. Sloyan, M. Widlansky, M. Bowen, J. Verron, P. Wiles, K. Ridgway, P. Sutton, J. Sprintall, C. Steinberg, G. Brassington, W. Cai, R. Davis, F. Gasparin, L. Gourdeau, T. Hasagawa, W. Kessler, C. Maes, K. Richards, U. Send, 2014: Ocean circulation of the Southwest Pacific: new insights from the Southwest Pacific Ocean and Climate Experiment (SPICE). *Journal of Geophysical Research - Oceans*, 119, 7660-7686, doi: 10.1002/2013JC009678

14 **B. Djath***, J. Verron, **A. Melet**, L. Gourdeau, B. Barnier, J.-M. Molines, 2014: Multiscale dynamical analysis of a high-resolution numerical modem simulation of the Solomon Sea circulations. *Journal of Geophysical Research - Oceans*, 119 (9), 6286–6304, doi: 10.1002/2013JC009695

13 L. Gaultier, **B. Djath**, J. Verron, J.-M. Brankart, P. Brasseur, **A. Melet**, 2014: Assessing the feasibility of the inversion of submesoscales in a high-resolution Solomon Sea model. *Journal of Geophysical Research - Oceans*, 119 (7), 4520–4541, doi: 10.1002/2013JC009660

12 **A. Melet**, R. Hallberg, S. Legg, M. Nikurashin, 2014: Sensitivity of the ocean state to Lee wave driven mixing. *Journal of Physical Oceanography*, 44 (3), 900-921, doi: 10.1175/JPO-D-13-072.1

11 L. Gourdeau, J. Verron, **A. Melet**, F. Marin, W. S. Kessler, 2014: Exploring the mesoscale activity in the Solomon Sea: a complementary approach with a numerical model and altimetric data. *Journal of Geophysical Research - Oceans*, 119 (4), 2290-2311, doi: 10.1002/2013JC009614.

10 **B. Djath***, **A. Melet**, J. Verron, J.M. Molines, B. Barnier, L. Gourdeau, L. Debreu, 2014: A 1/36° model of the Solomon Sea embedded into a global ocean model: on the setting up of an interactive open boundary condition nested model system. *Journal of Operational Oceanography*, 7 (1), 34-46. ISSN 1755-876X. Awarded the 2014 IMarEST Denny Medal for Best Paper in the Journal of Operational Oceanography.

9 **A. Melet**, M. Nikurashin, C. Muller, S. Falahat, J. Nycander, P. Timko, B. Arbic, J. Goff, 2013: Abyssal hill roughness impact on internal tide generation: Linear theory. *Journal of Geophysical Research - Oceans*, 118 (11), 6303-6318, doi: 10.1002/2013JC009212

8 **A. Melet**, R. Hallberg, S. Legg, K. Polzin, 2013: Sensitivity of the ocean state to the vertical distribution of internal-tide driven mixing. *Journal of Physical Oceanography*, 43, 602-615, doi: 10.1175/JPO-D-12-055.1

7 **A. Melet**, L. Gourdeau, J. Verron, **B. Djath***, 2013: Solomon Sea circulations and water mass modifications: response at ENSO timescales. *Ocean Dynamics*, 63 (1), 1-19, doi: 10.1007/s10236-012-0582-0

6 M. Doron, P. Brasseur, J.M. Brankart, S. N. Losa, **A. Melet**, 2013: Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical-biogeochemical model. *Journal of Marine Systems*, 117 (18), 81-95. doi: 10.1016/j.jmarsys.2013.02.007

5 **A. Melet**, J. Verron, J.M. Brankart, 2012: Potential outcomes of glider data assimilation in the Solomon Sea: Control of the water mass properties and parameter estimation. *Journal of Marine Systems*, 94, 232-246, doi: 10.1016/j.jmarsys.2011.12.003

4 **A. Melet**, J. Verron, L. Gourdeau, A. Koch-Larrouy, 2011: Equatorward pathways of Solomon Sea water masses and their modifications. *Journal of Physical Oceanography*, 41 (4), 810-826, doi: 10.1175/2010JPO4559.1
3 M. Grenier, S. Cravatte, B. Blanke, C. Menkes, A. Koch-Larrouy, F. Durand, **A. Melet**, C. Jeandel, 2011. From the western boundary currents to the Pacific Equatorial Undercurrent: Modeled pathways and water mass evolutions. Journal of Geophysical Research – Oceans, 116, C12044, doi: 10.1029/2011JC007477

2 **A. Melet**, W. S. Kessler, L. Gourdeau, J. Verron, J.M. Molines, 2010: Thermocline circulation in the Solomon Sea: A modeling study. Journal of Physical Oceanography, 40 (6), 1302-1319, doi: 10.1175/2009JPO4264.1

1 **A. Melet**, L. Gourdeau, J. Verron, 2010: Variability of the Solomon Sea from altimetry sea level data. Ocean Dynamics, 60 (4), 883-900, doi: 10.1007/s10236-010-0302-6.
# C Supervision

## C.1 PhD students

**2022-2024** PhD director of M. Irazoqui (Mercator Ocean). University of Toulouse.
Co-supervisor: G. Reffray (MOi).
Title: Characterizing sea level rise along the northeastern Atlantic coasts during the 21st century.

**2019-2022** PhD director of A. Chaigneau (Mercator Ocean, CNRM). University of Toulouse.
Co-director: A. Voldoire (CNRM, Toulouse).
Supervisor: G. Reffray (Mercator Ocean).
Title: High-resolution regional projections of sea level changes along the northeastern Atlantic coasts during the 21st century.
PhD defended on Dec 8, 2022.

**2014-2019** PhD co-supervisor of V. Kumar (Univ. of the Pacific/LEGOS). University of Toulouse.
PhD directors: A. Ganachaud (LEGOS, Toulouse), E. Holland (Univ. Pacifique, Fidji).
Supervisor: B. Meyssignac (LEGOS, Toulouse).
Title: Statistical downscaling of island sea levels in the southwest Pacific: a multiple linear regression approach.
PhD defended on Nov 8, 2019.

**2010-2013** PhD co-supervision of B. Djath (IGE/MEOM). University of Grenoble.
PhD directors: J. Verron (IGE, Grenoble), B. Barnier (IGE, Grenoble), L. Gourdeau (LEGOS, Toulouse).
Title: Ocean dynamics in the Solomon Sea: high-resolution numerical modelling.
PhD defended on January 23, 2014.

## C.2 Postdocs, research engineers

**2021-** Supervisor of K. Guihou (Mercator Ocean), postdoc, research engineer.
Topic: Global climate projections of ocean physics and biogeochemistry for studying climate change impacts on tunas in the Pacific Ocean during the 21st century.

**2021-2022** Supervisor of M. Irazoqui (Mercator Ocean), research engineer.
Topic: Forecasts of sea level changes along the EU coastline (project H2020 ECFAS).

**2022, 3 months** Supervisor of visiting postdoc M. Le Gal (Univ. Ferrara, Italy).
Topic: Ocean-wave interactions and impacts on extreme sea levels along the Atlantic coasts of EU (project H2020 ECFAS).

**2017, 6 months** Supervisor of S. Padilla Podo (CNES), research engineer.
Co-supervisor: B. Meyssignac (LEGOS).
Topic: Processing and analysis of satellite altimetry data and of numerical simulations to study changes in sea level variations from the deep ocean to coastal zones.
2020-2021, 1 yr Supervisor of G. Cesbron (CNES/LEGOS/Mercator Ocean), research engineer.
Co-supervisor: L. Crosnier (Mercator Ocean), R. Almar (LEGOS).
Topic: Analysis of technical characteristics of satellite derived bathymetry products and market analysis.

### C.3 Internships (M2, L3, gap year)

2021, 5 months Supervisor of gap year internship (cesure) of G. Mouttapa (ENSTA-Paris engineering school).
Topic: Ocean-wave interactions and impacts on extreme sea levels along the Atlantic coasts of EU (project H2020 ECFAS).

2021, 5 months Supervisor of M2 internship of M. David (ENSTA-Bretagne engineering school, IUEM).
Co-supervisors: R. Almar, J. Boucharel, E. Kestenare (LEGOS).
Topic: Influence of climate modes on the different components of coastal sea level changes.

2019, 5 months Supervisor of M2 internship of A. Chaigneau (SeaTech engineering school).
Co-supervision: A. Voldoire (CNRM), G. Reffray (Mercator Ocean).
Topic: Ocean and atmosphere surface conditions in the northeastern Atlantic Ocean in the CMIP6 CNRM-CM6-1 climate model: comparison to observations, reanalyses and projected changes.

2014, 5 months Supervisor of M2 internship of M. Nonti (Master SOAC, University of Abomey-Calavi, Benin and Univ. of Toulouse).
Co-supervision: F. Birol (LEGOS, Toulouse).
Topic: Sea level variations from the open ocean to the coastal zone: analysis of satellite altimetry data over western Africa.

2014, 2 months Supervisor of L3 internship of B. Chide (Supaero engineering school).
Topic: Sea level variations from the open ocean to the coastal zone: analysis of satellite altimetry data over the southwestern Pacific.
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