Copernicus Marine Service Ocean State Report, Issue 3

Karina von Schuckmann, Pierre-Yves Le Traon, Neville Smith, Samuel Djavidnia, Jean-Pierre Gattuso, Marilaure Grégoire, Glenn Nolan, Signe Aaboe, Eva Aguiar, Enrique Álvarez Fanjul, et al.

► To cite this version:

Karina von Schuckmann, Pierre-Yves Le Traon, Neville Smith, Samuel Djavidnia, Jean-Pierre Gattuso, et al. Copernicus Marine Service Ocean State Report, Issue 3. Journal of Operational Oceanography, 12, pp.S1-S123, 2019, 10.1080/1755876X.2019.1633075. [hal-02331356](https://hal.science/hal-02331356)

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Copernicus Marine Service Ocean State Report,
Issue 3

Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith (Chair) ((Review Editor)), Ananda Pascual ((Review Editor)), Samuel Djavidnia ((Review Editor)), Jean-Pierre Gattuso ((Review Editor)), Marilaure Grégoire ((Review Editor)), Glenn Nolan ((Review Editor)), Signe Aaboe, Eva Aguiar, Enrique Álvarez Fanjul, Aida Alvera-Azcárate, Lotfi Aouf, Rosa Barciela, Arno Behrens, Maria Belmonte Rivas, Sana Ben Ismail, Abderrahim Bentamy, Mireno Borgini, Vittorio E. Brando, Nathaniel Bensoussan, Anouk Blauw, Philippe Bryère, Bruno Buongiorno Nardelli, Ainhoa Caballero, Veli Çağlar Yumruktepe, Emma Cebrian, Jacopo Chiggiato, Emanuela Clementi, Lorenzo Corgnati, Marta de Alfonso, Álvaro de Pascual Collar, Julie Deshayes, Emanuele Di Lorenzo, Jean-Marie Dominici, Cécile Dupouy, Marie Drévillon, Vincent Echevin, Marieke Eleveld, Lisette Enserink, Marcos García Sotillo, Philippe Garnesson, Joaquim Garrabou, Gilles Garric, Florent Gasparin, Gerhard Gayer, Francis Gohin, Alessandro Grandi, Annalisa Griffa, Jérôme Gourrion, Stefan Hendricks, Céline Heuzé, Elisabeth Holland, Doroteaciro Iovino, Mélanie Juza, Diego Kurt Kersting, Silvija Kipson, Zafer Kizilkaya, Gerasimos Korres, Mariliis Kōuts, Priidik Lagemaa, Thomas Lavergne, Heloise Lavigne, Jean-Baptiste Ledoux, Jean-François Legeais, Patrick Lehodey, Cristina Linares, Ye Liu, Julien Mader, Ilja Maljutenko, Antoine Mangin, Ivan Manso-Narvarte, Carlo Mantovani, Stiig Markager, Evan Mason, Alexandre Mignot, Milena Menna, Maeva Monier, Baptiste Mourre, Malte Müller, Jacob Woge Nielsen, Giulio Notarstefano, Oscar Ocaña, Ananda Pascual, Bernardo Patti, Mark R. Payne, Marion Peirache, Silvia Pardo, Begoña Pérez Gómez, Andrea Pisano, Coralie Perruche, K. Andrew Peterson, Marie-Isabelle Pujol, Urmas Raudsepp, Michalis Ravdas, Roshin P. Raj, Richard Renshaw, Emma Reyes, Robert Ricker, Anna Rubio, Michela Sammartino, Rosalia Santoleri, Shubha Sathyendranath, Katrin Schroeder, Jun She, Stefania Sparnocchia, Joanna Staneva, Ad Stoffelen, Tanguy Szekely, Gavin H. Tilstone, Jonathan Tinker, Joaquín Tintoré, Benoît Tranchant, Rivo Uiboupin, Dimitry Van der Zande, Karina von Schuckmann, Richard Wood, Jacob Woge Nielsen, Mikel Zabala, Anna Zacharioudaki, Frédéric Zuberer & Hao Zuo

To cite this article: Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith (Chair) ((Review Editor)), Ananda Pascual ((Review Editor)), Samuel Djavidnia ((Review Editor)), Jean-Pierre Gattuso ((Review Editor)), Marilaure Grégoire ((Review Editor)), Glenn Nolan ((Review Editor)), Signe Aaboe, Eva Aguiar, Enrique Álvarez Fanjul, Aida Alvera-Azcárate, Lotfi Aouf, Rosa Barciela, Arno Behrens, Maria Belmonte Rivas, Sana Ben Ismail, Abderrahim Bentamy, Mireno Borgini, Vittorio E. Brando, Nathaniel Bensoussan, Anouk Blauw, Philippe Bryère, Bruno Buongiorno Nardelli, Ainhoa Caballero, Veli Çağlar Yumruktepe, Emma Cebrian, Jacopo Chiggiato, Emanuela Clementi, Lorenzo Corgnati, Marta de Alfonso, Álvaro de Pascual Collar,
Julie Deshayes, Emanuele Di Lorenzo, Jean-Marie Dominici, Cécile Dupouy, Marie Drévillon, Vincent Echevin, Marieke Eleveld, Lisette Enserink, Marcos García Sotillo, Philippe Garnesson, Joaquim Garrabou, Gilles Garric, Florent Gasparin, Gerhard Gayer, Francis Gohin, Alessandro Grandi, Annalisa Griffa, Jérôme Gourrion, Stefan Hendricks, Céline Heuzé, Elisabeth Holland, Doroteaciro Iovino, Mélanie Juza, Diego Kurt Kersting, Silvija Kipson, Zafer Kizilkaya, Gerasimos Korres, Mariliis Kōuts, Priidik Lagemaa, Thomas Lavergne, Heloise Lavigne, Jean-Baptiste Ledoux, Jean-François Legeais, Patrick Lehodey, Cristina Linares, Ye Liu, Julien Mader, Ilja Maljutenko, Antoine Mangin, Ivan Manso-Narvarte, Carlo Mantovani, Stiig Markager, Evan Mason, Alexandre Mignot, Milena Menna, Maeva Monier, Baptiste Mourre, Malte Müller, Jacob Woge Nielsen, Giulio Notarstefano, Oscar Ocaña, Ananda Pascual, Bernardo Patti, Mark R. Payne, Marion Peirache, Silvia Pardo, Begoña Pérez Gómez, Andrea Pisano, Coralie Perruche, K. Andrew Peterson, Marie-Isabelle Pujol, Urmas Raudsepp, Michalis Ravdas, Roshin P. Raj, Richard Renshaw, Emma Reyes, Robert Ricker, Anna Rubio, Michela Sammartino, Rosalia Santoleri, Shubha Sathyendranath, Katrin Schroeder, Jun She, Stefania Sparnocchia, Joanna Staneva, Ad Stoffelen, Tanguy Szekely, Gavin H. Tilstone, Jonathan Tinker, Joaquín Tintoré, Benoît Tranchant, Rivo Uiboupin, Dimitry Van der Zande, Karina von Schuckmann, Richard Wood, Jacob Woge Nielsen, Mikel Zabala, Anna Zacharioudaki, Frédéric Zuberer & Hao Zuo (2019) Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075

To link to this article: https://doi.org/10.1080/1755876X.2019.1633075

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COPERNICUS MARINE SERVICE

OCEAN
STATE
REPORT

Issue 3, 2019

Journal of Operational Oceanography

Volume 12, Supplement 1

Copernicus
Europe's eyes on Earth

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Copernicus
Marine Service

Implemented by

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MERCATOR
OCEAN
INTERNATIONAL

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Taylor & Francis
Taylor & Francis Group

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# Journal of Operational Oceanography

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Erik Buch – *Danish Meteorological Institute*, Denmark

Changshen Chen – University of Massachusetts-Dartmouth, USA

Kevin Ewans – Shell Technology Centre Bangalore, India

Gus Jeans – *Oceananalysis Ltd*, UK

Johnny Johannessen – Nansen Environmental and Remote Sensing Center, Norway

Bev Mackenzie – Institute of Marine Engineering Science and Technology, UK

Johannes Karstensen – Holmholtz Centre for Ocean Research Kiel GEOMAR, Germany

Nadia Pinardi – University of Bologna, Italy

Roger Proctor – University of Tasmania, Australia

Michel Rixen – World Climate Research Programme, Australia

Roland Rogers – *Emeritus Fellow*, UK

Andreas Schiller – CSIRO, Australia

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COPERNICUS MARINE SERVICE OCEAN
STATE REPORT, ISSUE 3

Editors

Karina von Schuckmann

Pierre-Yves Le Traon

Review Editors

Neville Smith (Chair)

Ananda Pascual

Samuel Djavidnia

Jean-Pierre Gattuso

Marilaure Grégoire

Glenn Nolan

To cite the entire report

How to cite the entire report: von Schuckmann, K., P.-Y. Le Traon, N. Smith, A. Pascual, S. Djavidnia, J.-P. Gattuso, M. Grégoire, G. Nolan (2019) Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s1-s123; DOI: 10.1080/1755876X.2019.1633075

To cite a specific section in the report (example)

Raudsepp, U., I. Maljutenko, M. Kõuts (2019). Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26-s30; DOI: 10.1080/1755876X.2019.1633075
AUTHOR AFFILIATIONS (ALPHABETICAL BY NAME)

Signe Aaboe, MET, Tromso, Norway

Eva Aguiar, SOCIB, Illes Balears, Spain

Enrique Álvarez Fanjul, Puertos del Estado, Madrid, Spain

Aida Alvera-Azcárate, Université de Liège, Liège, Belgium

Lotfi Aouf, Météo-France, Toulouse, France

Rosa Barciela, Met Office, Exeter, United Kingdom

Arno Behrens, Helmholtz-Zentrum Geesthacht (HZG), Geesthacht, Germany

Maria Belmonte Rivas, KNMI, GA De Bilt, Netherlands

Sana Ben Ismail, INSTM, Tunis, Tunisia

Abderrahim Bentamy, IFREMER, Plouzané, France

Mireno Borgini, Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), Pozzuolo di Lerici (SP), Italy

Vittorio E. Brando, Institute of Marine Sciences (ISMAR) – National Research Council (CNR), Rome, Italy

Nathaniel Bensoussan, Institute of Marine Sciences-CSIC, Barcelona, Spain

Anouk Blauw, Deltares, Delft, Netherlands

Philippe Bryère, Argans, Brest, France

Bruno Buongiorno Nardelli, Institute of Marine Sciences – CNR, Napoli, Italy

Ainhoa Caballero, AZTI, Pasaia – GIPUZKOA (Spain)

Veli Çağlar Yumruktepe, NERSC, Bergen, Norway

Emma Cebrian, Universitat de Girona, Girona, Spain

Jacopo Chiggiato, Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), Venezia, Italy

Emanuela Clementi, CMCC, Bologna, Italy

Lorenzo Corgnati, Institute of Marine Sciences – National Research Council ISMAR-CNR, La Spezia, Italy

Marta de Alfonso, Puertos del Estado, Madrid, Spain

Álvaro de Pascual Collar, Puertos del Estado, Madrid, Spain

Julie Deshayes, LOCEAN/CNRS, Paris, France

Emanuele Di Lorenzo, Georgia Institute of Technology, États-Unis, USA

Jean-Marie Dominici, Parc Naturel Régional de Corse, Galeria, France

Cécile Dupouy, MIO, IRD, Marseille, France

Marie Drévillon, Mercator Ocean International, Ramonvill St-Agne, France

Vincent Echevin, LOCEAN/IRD, PARIS, France

Marieke Eleveld, Deltares, Delft, Netherlands

Lisette Enserink, Rijkswaterstaat, Delft, Netherlands

Marcos García Sotillo, Puertos del Estado, Madrid, Spain

Philippe Garnesson, ACRI-ST, Sophia-Antipolis, France

Joaquim Garrabou, ICM/CSIC, Barcelona, Spain

Gilles Garric, Mercator Ocean, Ramonville Saint-Agne, France

Florent Gasparin, Mercator Ocean International, Ramonville Saint-Agne, France

Gerhard Gayer, Helmholtz-Zentrum Geesthacht (HZG), Geesthacht, Germany

Francis Gohin, IFREMER, Plouzané, France

Alessandro Grandi, CMCC, Bologna, Italy

Annalisa Griffa, CNR-ISMAR, La Spezia, Italy

Jérôme Gourrion, Oceanscope, Incheon, South Korea

Stefan Hendricks, Alfred-Wegener-Institut, Bremerhaven, Germany

Céline Heuzé, University of Gothenburg, Gothenburg, Sweden

Elisabeth Holland, University of South Pacific, Fiji, Australia

Doroteaciro Iovino, CMCC, Bologna, Italy

Mélanie Juza, SOCIB, Illes Balears, Spain

Diego Kurt Kersting, Freie Universität, Berlin, Germany

Silvija Kipson, PMF, Zagreb, Croatia

Zafer Kizilkaya, Mediterranean Conservation Society, Bornova/İzmir, Turkey

Gerasimos Korres, HCMR, Attica, Greece

Mariliis Kōuts, Marine Systems Institute at Tallinn University of Technology, Tallinn, Estonia

Priidik Lagemaa, Marine Systems Institute at Tallinn University of Technology, Tallinn, Estonia

Thomas Lavergne, MET, Tromso, Norway

Heloise Lavigne, RBINS, Brussels, Belgium

Jean-Baptiste Ledoux, ICM/CSIC, Barcelona, Spain

Jean-François Legeais, Collecte Localisation Satellite (CLS), Ramonville-Saint-Agne, France

Patrick Lehodey, Collecte Localisation Satellite (CLS), Ramonville-Saint-Agne, France

Cristina Linares, University of Barcelona, Barcelona, Spain

Ye Liu, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
**Julien Mader**, AZTI, Pasaia – GIPUZKOA (Spain)

**Ilja Maljutenko**, Marine Systems Institute at Tallinn University of Technology, Tallinn, Estonia

**Antoine Mangin**, ACRI-ST, Sophia-Antipolis, France

**Ivan Manso-Narvarte**, AZTI, Pasaia – GIPUZKOA (Spain)

**Carlo Mantovani**, CNR-ISMAR, La Spezia, Italy

**Stiig Markager**, Aarhus University, Roskilde, Denmark

**Evan Mason**, IMEDEA (CSIC-UIB), Illes Balears, Spain

**Alexandre Mignot**, Mercator Ocean International, Ramonville Saint-Agne, France

**Milena Menna**, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), Sgonico (TS), Italy

**Maeva Monier**, CELAD/Mercator Ocean International, Ramonville St-Agne, France

**Baptiste Mourre**, SOCIB, Illes Balears, Spain

**Malte Müller**, MET, Tromso, Norway

**Jacob Woge Nielsen**, Danish Meteorological Institute, Copenhagen, Denmark

**Giulio Notarstefano**, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), Sgonico (TS), Italy

**Oscar Ocaña**, Fundación Museo del Mar de Ceuta, Ceuta, Spain

**Ananda Pascual**, IMEDEA (CSIC-UIB), Illes Balears, Spain

**Bernardo Patti**, CNR-IAS, Campobello di Mazara, Italy

**Mark R. Payne**, Technical University of Denmark (DTU-Aqua), Denmark

**Marion Peirache**, Parc National de Port-Cros, Hyeres cedex, France

**Silvia Pardo**, PML, UK

**Begoña Pérez Gómez**, Puertos del Estado, Madrid, Spain

**Andrea Pisano**, CNR-ISMAR, Roma, Italy

**Coralie Perruche**, Mercator Ocean International, Ramonville Saint-Agne, France

**K. Andrew Peterson**, ECCC, Québec, Canada

**Marie-Isabelle Pujol**, Collecte Localisation Satellites (CLS), Ramonville St-Agne, France

**Urmas Raudsepp**, Marine Systems Institute at Tallinn University of Technology, Tallinn, Estonia

**Michalis Ravdas**, HCMR, Attica, Greece

**Roshin P. Raj**, Nansen Environmental and Remote Sensing Center, Bergen, Norway

**Richard Renshaw**, Met Office, Exeter, United Kingdom

**Emma Reyes**, SOCIB, Palma, Spain

**Robert Ricker**, Alfred-Wegener-Institut, Bremerhaven, Germany

**Anna Rubio**, AZTI, Pasaia – GIPUZKOA (Spain)

**Michela Sammartino**, CNR-ISMAR, Rome, Italy

**Rosalia Santoleri**, CNR-ISMAR, Rome, Italy

**Shubha Sathyendranath**, PML, United Kingdom

**Katrin Schroeder**, CNR-ISMAR, Venezia, Italy

**Jun She**, Danish Meteorological Institute (DMI), Copenhagen, Denmark

**Stefania Sparnocchia**, CNR-ISMAR, Trieste, Italy

**Joanna Staneva**, Helmholtz-Zentrum Geesthacht (HZG), Geesthacht, Germany

**Ad Stoffelen**, KNMI, GA De Bilt, Netherlands

**Tanguy Szekely**, Oceanscope, Incheon, South Korea

**Gavin H. Tilstone**, Plymouth Marine Laboratory, Plymouth, UK

**Jonathan Tinker**, Met Office, Exeter, United Kingdom

**Joaquín Tintoré**, SOCIB/IMEDEA (CSIC-UIB), Balears, Spain

**Benoit Tranchant**, Collecte Localisation Satellites (CLS), Ramonville St-Agne, France

**Rivo Uiboupin**, TalTech, Tallinn, Estonia

**Dimitry Van der Zande**, RBINS, Brussels, Belgium

**Karina von Schuckmann**, Mercator Ocean International, Ramonville St-Agne, France

**Richard Wood**, Met Office, Exeter, United Kingdom

**Jacob Woge Nielsen**, Danish Meteorological Institute, Copenhagen, Denmark

**Mikel Zabala**, UB/IRBIO, Barcelona, Spain

**Anna Zacharioudaki**, HCMR, Attica, Greece

**Frédéric Zuberer**, OSU PYTHEAS, Cedex, France

**Hao Zuo**, ECMWF, United Kingdom
CONTENTS

<table><tr><td>Chapter 1: Introduction</td><td>s1</td></tr><tr><td>1.1 Trends over the past decades</td><td>s3</td></tr><tr><td>1.2 Anomalies and extreme events</td><td>s6</td></tr><tr><td>Chapter 2: State, variability and change in the marine environment: new monitoring indicators</td><td>s8</td></tr><tr><td>2.1 Sea surface winds and Ekman pumping</td><td>s8</td></tr><tr><td>2.2 The seasonal intensification of the slope Iberian Poleward Current</td><td>s13</td></tr><tr><td>2.3 Mediterranean deep and intermediate water mass properties</td><td>s18</td></tr><tr><td>2.4 Phytoplankton blooms in the Baltic Sea</td><td>s21</td></tr><tr><td>2.5 Cod reproductive volume potential in the Baltic Sea</td><td>s26</td></tr><tr><td>2.6 The North Pacific Gyre Oscillation</td><td>s29</td></tr><tr><td>2.7 Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations</td><td>s31</td></tr><tr><td>Chapter 3: Case studies</td><td>s43</td></tr><tr><td>3.1 The use of Copernicus Marine Service products to describe the State of the Tropical Western Pacific Ocean around the Islands: a case study</td><td>s43</td></tr><tr><td>3.2 Review of the use of ocean data in European fishery management and monitoring applications</td><td>s48</td></tr><tr><td>3.3 Synergy between CMEMS products and newly available data from SENTINEL</td><td>s52</td></tr><tr><td>3.4 Joint Monitoring Programme of the EUtrophication of the NOrthSea with SATellite data user case</td><td>s56</td></tr><tr><td>3.5 Regional mean time series for the Northwest European Shelf Seas</td><td>s61</td></tr><tr><td>3.6 Using CMEMS and the Mediterranean Marine Protected Areas sentinel network to track ocean warming effects in coastal areas</td><td>s65</td></tr><tr><td>3.7 Combined analysis of Cryosat-2/SMOS sea ice thickness data with model reanalysis fields over the Baltic Sea</td><td>s73</td></tr><tr><td>3.8 Chlorophyll-a evolution during the last 21 years and its relation with mussel growth and optimal repartition for aquaculture and fishery</td><td>s79</td></tr></table>
<table>
  <tr>
    <td>Chapter 4: Specific events 2017</td>
    <td>s91</td>
  </tr>
  <tr>
    <td>4.1 The Weddell Sea Polynya</td>
    <td>s91</td>
  </tr>
  <tr>
    <td>Céline Heuzé, Gilles Garric and Thomas Lavergne.</td>
    <td></td>
  </tr>
  <tr>
    <td>4.2 Temperature and salinity anomalies in the North Atlantic subpolar gyre</td>
    <td>s93</td>
  </tr>
  <tr>
    <td>Jérôme Gourrion, Julie Deshayes, Mélanie Juza and Tanguy Szekely</td>
    <td></td>
  </tr>
  <tr>
    <td>4.3 Anticyclonic Eddy Anomaly: impact on the boundary current and circulation in the western Mediterranean Sea</td>
    <td>s98</td>
  </tr>
  <tr>
    <td>Eva Aguiar, Mélanie Juza, Baptiste Mourre, Ananda Pascual, Evan Mason, Aida Alvera-Azcárate and Joaquín Tintoré.</td>
    <td></td>
  </tr>
  <tr>
    <td>4.4 Insights on 2017 Marine Heat Waves in the Mediterranean Sea</td>
    <td>s101</td>
  </tr>
  <tr>
    <td>Nathaniel Bensoussan, Jacopo Chiggiato, Bruno Buongiorno Nardelli, Andrea Pisano and Joaquim Garrabou</td>
    <td></td>
  </tr>
  <tr>
    <td>4.5 Reversal of the Northern Ionian circulation in 2017</td>
    <td>s108</td>
  </tr>
  <tr>
    <td>Giulio Notarstefano, Milena Menna and Jean-François Legeais</td>
    <td></td>
  </tr>
  <tr>
    <td>4.6 'Silent' storm surge extremes in the western Baltic Sea on 4 January 2017</td>
    <td>s111</td>
  </tr>
  <tr>
    <td>Jun She and Jacob Woge Nielsen.</td>
    <td></td>
  </tr>
  <tr>
    <td>4.7 The 2017 coastal El Niño</td>
    <td>s117</td>
  </tr>
  <tr>
    <td>Florent Gasparin, Vincent Echevin, Alexandre Mignot, Coralie Perruche and Marie Drévillon.</td>
    <td></td>
  </tr>
</table>
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

OPEN ACCESS

<figure><img src="image_19.png" /></figure>

Check for updates

# Copernicus Marine Service Ocean State Report, Issue 3

## Chapter 1: Introduction

The fundamental role of the ocean for life and well-being on Earth is more and more recognised at the highest political level. In 2015, the United Nations (UN) Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development were adopted by world leaders. The SDG 14 ‘Conserve and sustainably use the oceans, seas and marine resources for sustainable development’ is dedicated to the oceans. The mention of the ocean in the Paris Agreement signed in 2016 marked a decisive milestone. In 2018, the UN Decade of Ocean Science for Sustainable Development (2021–2030) has been proclaimed (https://en.unesco.org/ocean-decade): The Intergovernmental Oceanographic Commission of UNESCO will gather ocean stakeholders worldwide behind a common framework to foster evidence-based policy-making. In fall 2019, the IPCC special report on ocean and cryosphere will be published, and will provide an opportunity to increase awareness and action before COP25¹ (already claimed as ‘Blue COP’).

This unprecedented ocean agenda is a timely one: multiple anthropogenic stressors such as climate change, over-exploitation and pollution are becoming a major threat on the marine environment and its services for human benefits and biodiversity at large. The annual Ocean State Report of the European Union’s Copernicus Marine Service contributes to this unrivalled mobilisation of the global scientific community, and is one of the priority tasks given by the EU Delegation Agreement for CMEMS implementation (CMEMS 2014). Ocean observing, monitoring and forecasting are key to unravel the ocean’s responses to pressures, understand and predict the evolution of the oceans and develop management actions for sustainable development, including for mitigation and adaptation to climate change.

Since its launch in 2016 with the publication of the first issue (von Schuckmann et al. 2016a), a fundamental baseline of regular science-based ocean reporting has been established. It covers the blue ocean (i.e. physical processes driven by changes in temperature, salinity and currents), the green ocean (i.e. biogeochemical processes such as fluctuations of ocean chlorophyll identifying changes at the base of the marine food chain,

eutrophication processes, the uptake of carbon by the ocean, and ocean deoxygenation), and the white ocean (i.e. the rapid evolution of ice-covered polar regions). The evaluations in the Copernicus Marine Ocean State Report span local scales (e.g. extreme variability of sea level, sea surface temperature and significant wave height at the coast), the European regional seas (e.g. monitoring of key essential variables and interpretation of variations and trends), large scale (e.g. analysis of the unusual cold and fresh conditions or the Meridional Overturning Circulation in the North Atlantic), to global scale (e.g. delivering an Earth system view on the uptake of heat and carbon by the ocean). The assessments cover climate-relevant time scales (e.g. ocean deoxygenation since the 1960s), multi-decadal time scales (e.g. global mean sea level rise over the past three decades), and the evaluation of specific events taking place in the marine environment close to real time (e.g. extreme sea ice conditions in the Arctic ocean in 2016, or the polynya event in the Weddell Sea during 2017).

The scientific assessments developed in the Copernicus Marine Ocean State Report are based on a wide range of reprocessed in-situ and satellite observation data products and ocean reanalysis model products in seven ocean regions (Figure 1.1). A large fraction of these products are distributed via the Copernicus Marine Service web portal (http://marine.copernicus.eu/). In addition, products from the Copernicus Climate Change Service are used, in particular for climate-related studies such as sea level rise (i.e. a Global Climate Indicator as identified by WMO/GCOS, https://gcos.wmo.int/en/global-climate-indicators). Additional data products have been included in the Ocean State Report analyses aiming to strengthen the scope of the report, e.g. to take an Earth system perspective (i.e. the role of the ocean heat uptake in the Earth energy budget), to further investigate exchanges and processes with other components of the Earth system (e.g. air-sea exchanges), or to complement the analyses with biological data for impact studies on the marine ecosystem. All products used in each section are listed in a specific product table, which includes data source information and
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

**Figure 1.1.** Schematic overview on the Copernicus Marine Ocean State Report and Ocean Monitoring Indicator activities, which are both linked to each other, and cover seven principal regions. The interplay of scientific knowledge and expertise, as well as the data products (in situ, satellite, model) from Copernicus services and other sources is the key ingredient for the Copernicus Marine scientific evaluation and reporting. See text for more details. Regions include: 1 – Global Ocean; 2 – Arctic Ocean from 62°N to the North Pole; 3 – Baltic Sea, including Kattegat at 57.5°N from 10.5°E to 12.0°E; 4 – European North West Shelf Sea, which includes part of the North East Atlantic Ocean from 48°N to 62°N and from 20°W to 13°E. The border with the Baltic Sea is situated in the Kattegat Strait at 57.5°N from 10.5°E to 12.0°E; 5 – Iberia-Biscay-Ireland Regional Seas, which includes part of the North East Atlantic Ocean from 26°N to 48°N and 20°W to the coast. The border with the Mediterranean Sea is situated in the Gibraltar Strait at 5.61°W; 6 – Mediterranean Sea until the Gibraltar Strait at 5.61°W and the Dardanelles Strait; 7 – Black Sea until the Bosporus Strait.

documentations (product manuals, quality assessment documents, and scientific publications).

The major objective of the Copernicus Marine Ocean State Report activity is to provide scientifically assessed added-value information, for a wide range of topical domains in the marine environment and at different space and time scales. Expert collaboration is indispensable to achieve this goal. Since the launch of this activity, more than 150 European scientific experts from more than 25 European institutions have joined forces to develop the Copernicus Marine Ocean State Report and the number of new collaborators is steadily increasing with the evolution of the reporting activity. This activity is a breeding ground for new and innovative science activities across multidisciplinary expertise (e.g. joint analysis on physical and biogeochemical topics), space scales (e.g. between different European basins), and time scales (e.g. interplay of climate change and natural variability).

The Ocean State Report content is designated to a specific audience, including scientists, expert stakeholders and European and International environmental agencies and organisations (e.g. EEA, WMO, IPCC, ...). In order to enlarge the audience, two supplement tools have been established by the Copernicus Marine Service. The first tool is the preparation of a summary for each issue of the Ocean State Report in collaboration with communication and graphical experts, highlighting and synthetising key outcomes of the scientific

publication. This summary is aiming to reach out to policy and decision-makers, as well as to increase general public awareness about the status of, and changes in, the marine environment. These documents are freely available at the Copernicus Marine web portal (http://marine.copernicus.eu/science-learning/ocean-state-report/).

The second tool includes the dissemination of numerical values, quality and background documentation and figures of key variables used to track the vital health signs of the ocean and changes in line with climate change and natural variability. All elements (data, documentation, figures) are regularly updated, and build the Copernicus Marine Ocean Monitoring Indicator framework (http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators/). For example, close-to-real-time knowledge of how much heat is stored in the ocean, the pH of the ocean, how fast sea level is rising and sea ice is melting, is essential to understanding the current state and changes in the ocean and climate. This information is critical for assessing and confronting ocean and climate changes associated with global warming and they can be used by scientists, decision-makers, environmental agencies, economy, the general public, and in measuring our responses to environmental directives. The Ocean Monitoring Indicators were developed through a long process of scientific analysis and validation, with the consensus
of scientific experts after review as part of the Ocean State Report. The online publication of an Ocean Monitoring Indicator generally requires that the scientific rationale, validation and interpretation went through the Ocean State Report peer review.

A general rule is that a scientific topic already addressed in one of the issues of the Ocean State Report should not be repeated in upcoming new issues. However, in order to maintain a comprehensive review on the variations and changes in the blue, green and white ocean as part of the Ocean State Report, the Ocean Monitoring Indicator framework is indispensable. From the third issue of the Ocean State Report onwards, two synthesis figures based on the Ocean Monitoring Indicator information, as well as on scientific evaluations in the corresponding issue will be included in Chapter 1 (i.e. the introduction). One figure will summarise the long-term changes reported over the past decades (Figure 1.2), and the second figure is dedicated to deliver an overview of the anomalies close to real time for the European regional seas and the global ocean (Figure 1.3).

What else is new in the third issue of the Copernicus Marine Ocean State Report? Beside the new strategy for Chapter 1 as described above, a large number of new topics have been gathered in this new issue. In Chapter 2 – which addresses the state, variability and change in the marine environment – topics such as sea wind, coastal and regional current systems, phytoplankton blooms, hydrographic pressure on cod stocks and extreme variability have been analysed. Chapter 3 discusses selected case studies that analyse specific aspects of the ocean change that are of scientific and more general interest. For example, this issue proposes a marine atlas for the Pacific Ocean Island states, which responds directly to Fiji’s requests at the 2017 United Nation Oceans for SDG 14, life below water and the 2017 COP23 for SDG13, climate action. A review on the use of ocean data in European fishery management is developed in this issue as well. Other specific studies include for example a joint analysis between Copernicus Marine and Marine Protected Areas (e.g. t-mednet.org) to analyse the impact of thermal stress on marine biodiversity, and reported environmental changes and their impact on aquaculture. Chapter 4 reports on specific events during the year 2017, including for example the Weddell Sea polynya, marine heat waves and the 2017 coastal El Niño.

## 1.1. Trends over the past decades

Continuous reporting of trends contribute to the understanding of observed changes in the marine environment around the world, and improves knowledge of the likely responses to climate change affecting social,

environmental and economic systems, i.e. the three pillars of sustainable development. As also reported in the second issue of the Ocean State Report, the results of the third issue show ocean surface and subsurface warming, rising total and thermosteric sea level and a decrease in global sea ice extent over the past 25 years (Figure 1.2). Ocean deoxygenation is shown to take place over the past decades. Decreasing and increasing regional trends since the year 2007 are reported for chlorophyll-a. These changes can be observed not only at global scale, but also in the seven European regional seas. The following changes are highlighted:

* The ocean surface continues to warm, and sea surface temperature trends for the European regional seas range from 0.03 to 0.07°C year⁻¹ at 0.002–0.005°C year⁻¹ uncertainty ranges. Sea surface temperature trends have also been evaluated as part of the Copernicus Marine Atlas for Pacific Islands (Section 3.1), as sea surface temperature is a much-needed variable for assessing coral reef health and bleaching, as well as for tropical cyclone forecasting. While the ocean surface is warming in the Western and Central Pacific Islands areas, we note a strong variability over various time scales, such as for example the El Niño Southern Oscillation.

* The subsurface ocean continues to warm (von Schuckmann et al. 2016b, 2018). Global ocean heat content of the upper 700 m increases currently at a rate of 0.9 ± 0.1 Wm⁻² as obtained over the period 1993–2017 (Figure 1.2). Over the shorter time window 2005–2017 during which Argo provides the best available coverage of the global ocean observing system (e.g. Riser et al. 2016), this rate of change is smaller (0.6 ± 0.1 Wm⁻²), and needs correction for short-term climate variability (Cazenave et al. 2014). Increasing the integration depth to 2000 m yields a rate of change of ocean heat content of 1.2 ± 0.1 Wm⁻² due to the excess heat sequestered into the deeper layers of the oceans (e.g. Meehl et al. 2011; Abraham et al. 2013). By adding a contribution of 0.1 Wm⁻² for the deep ocean below 2000 m depth (Purkey and Johnson 2010; Desbruyères et al. 2016), we obtain an estimate of the Earth energy imbalance of 0.5–0.7 ± 0.1 Wm⁻² based on the CMEMS reporting (1993–2017, 0–700 m: 0.7 Wm⁻²; 2005–2017, 0–700 m: 0.5 Wm⁻²; 0–2000 m: 0.7 Wm⁻², taking into account that the heat content is related to the ocean surface only i.e. multiplied by 0.7). At regional scales, subsurface ocean warming in the upper 700 m depth increases at rates close to the global value in the Mediterranean Sea and the Central Pacific Islands area, and doubles for the Western Pacific Islands area (Figure 1.2).
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

Copernicus
Marine Service

# TREND VALUES

## THERMOSTERIC SEA LEVEL

<figure><img src="image_24.png" /></figure>

Units: mm year⁻¹

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

Trend from 1993 to 2017

<figure><img src="image_27.png" /></figure>

Trend from 2005 to 2017

<figure><img src="image_28.png" /></figure>

## SEA SURFACE TEMPERATURE

<figure><img src="image_29.png" /></figure>

Units: °C year⁻¹ - Trend from 1993 to 2017

<table><thead><tr><th>Mediterranean Sea</th><th>Baltic Sea</th><th>Black Sea</th></tr></thead><tbody><tr><td>+0.04 ± 0.002</td><td>+0.03 ± 0.003</td><td>+0.07 ± 0.005</td></tr><tr><td>Western Pacific Islands</td><td>Central Pacific Islands</td><td>Pacific Islands total area</td></tr><tr><td>+0.02 ± 0.01</td><td>+0.01 ± 0.02</td><td>+0.02 ± 0.01</td></tr></tbody></table>

<figure><img src="image_30.png" /></figure>

## SEA LEVEL

<figure><img src="image_31.png" /></figure>

Units: mm year⁻¹
Trend from 1993 to 2017

<table><thead><tr><th>Mediterranean Sea</th><th>Black Sea</th><th>Baltic Sea</th><th>North West Shelf</th></tr></thead><tbody><tr><td>+2.4 ± 2.2</td><td>+1.9 ± 2.2</td><td>+4.3 ± 2.2</td><td>+2.7 ± 2.0</td></tr><tr><td>Iberian Biscay Irish</td><td>Western Pacific Islands</td><td>Central Pacific Islands</td><td>Pacific Islands total area</td></tr><tr><td>+3.4 ± 2.0</td><td>+4.8 ± 2.5</td><td>+2.8 ± 2.5</td><td>+3.5 ± 2.5</td></tr></tbody></table>

<figure><img src="image_32.png" /></figure>

GLOBAL
+3.2
±0.4

## CHLOROPHYLL-α

<figure><img src="image_33.png" /></figure>

Units: % year⁻¹ - Trend from 1997 to 2017

<table><thead><tr><th>Western Pacific Islands</th><th>Central Pacific Islands</th><th>Pacific Islands total area</th><th>Mediterranean Sea</th></tr></thead><tbody><tr><td>-0.4 ± 0.02</td><td>-0.7 ± 0.001</td><td>-0.4 ± 0.001</td><td>+0.5 ± 0.002</td></tr><tr><th>Black Sea</th><th>Baltic Sea</th><th>North Atlantic Ocean</th><th>Arctic Ocean</th></tr><tr><td>-0.7 ± 0.01</td><td>+2.5 ± 0.01</td><td>+1.1 ± 0.01</td><td>+1.72 ± 0.01</td></tr></tbody></table>

<figure><img src="image_34.png" /></figure>

GLOBAL

+0.63
±0.01

## OXYGEN INVENTORY

<figure><img src="image_35.png" /></figure>

Units: mol m⁻² year⁻¹
Trend from 1955 to 2017

<table><tr><th>Black Sea</th><th>Black Sea</th></tr><tr><td>-0.15 ± 0.02</td><td></td></tr></table>

<figure><img src="image_36.png" /></figure>

## OCEAN HEAT CONTENT

<figure><img src="image_37.png" /></figure>

Units: Watts m⁻²

<table><thead><tr><th>Mediterranean Sea</th><th>Western Pacific Islands</th><th>Central Pacific Islands</th><th>Pacific Islands total area</th></tr></thead><tbody><tr><td>+0.8 ± 0.1</td><td>+1.9 ± 1.5</td><td>+0.8 ± 0.7</td><td>+1.2 ± 0.7</td></tr></tbody></table>

<figure><img src="image_38.png" /></figure>

## SEA ICE EXTENT

<figure><img src="image_39.png" /></figure>

Units: km² decade⁻¹ (% decade⁻¹)
Trend from 1993-2017

<table><tr><th>Northern Hemisphere</th><th>+0.77*10<sup>6</sup> ± 0.06*10<sup>6</sup><br>(-5.89 ± 0.47)</th></tr></table>

<figure><img src="image_40.png" /></figure>

<table><tr><th>Southern Hemisphere</th><th>+0.07*10<sup>6</sup> ± 0.12*10<sup>6</sup><br>(+0.57 ± 0.94)</th></tr></table>

<figure><img src="image_41.png" /></figure>

**Figure 1.2.** Overview on trend values reported in the third issue of the CMEMS Ocean State Report, and in the CMEMS Ocean Monitoring Indicator framework (http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators/). Upward arrow indicates increasing trends, downward arrow decreasing trends. Time intervals for trend evaluation are indicated for each parameter, respectively.
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /><figcaption>**Figure 1.3.** Anomalies and extreme events during the year 2017 as reported in the third issue of the CMEMS Ocean State Report and from the Copernicus Marine Ocean Monitoring Indicator framework (see text for more details) for the global ocean (upper panel), and the European regional seas (lower panel). A legend for all icons is included. Red coloured icons signify higher-than-average anomalies and extremes, and blue coloured icon show lower-than-average values.</figcaption></figure>
<figure><img src="image_46.png" /></figure>

* Global sea level continues to rise at a rate of 3.2 ± 0.4 mm year⁻¹ (Figure 1.2). As the ocean warms, its volume expands (thermosteric effect), which is a major cause of global mean sea level rise. The upper ocean (0–700 m) thermosteric sea level has been rising since 1993 at a rate of 1.4 ± 0.1 mm year⁻¹. Sea level rises also in all European regional seas at rates that even exceed the global mean rate (e.g. the Baltic Sea). Sea level has been also included in the Copernicus Marine Atlas for the Pacific Islands areas as it is a key ocean variable to better inform climate adaptation and coastal planning. Sea level in these areas is rising as well and a strong rate of 4.8 ± 2.5 mm year⁻¹ can be noticed in the western part (Chapter 3).

* Since 1993, there has been a sea ice extent loss of nearly 770,000 km² decade⁻¹ in the northern hemisphere, and a sea ice extent gain of 80,000 km² decade⁻¹ in the southern hemisphere (Figure 1.2).

* Chlorophyll-a, the main photosynthetic pigment contained in all phytoplankton, has shown increasing and decreasing trends over the past 19 years (1998–2017). At global scale, chlorophyll-a has been increasing by 0.6 ± 0.01% year⁻¹ (Figure 1.2). Increasing trends are also reported in the Mediterranean Sea, Baltic Sea, North Atlantic and Arctic Ocean. Decreasing trends occur in the Black Sea, as well as in the areas of the Pacific Islands. However, given that the chlorophyll-a time series from remote sensing used here is only 19 years long characterised by large signals of decadal variability, the time series are too short to disentangle the effect of interannual variability and longer-term climate change.

* There is an ongoing deoxygenation trend reported in the Black Sea, with a decrease at a rate of −0.16 ± 0.02 mol m⁻² year⁻¹ (Figure 1.2).

## 1.2. Anomalies and extreme events

Anomalies and extreme events observed in 2017 are summarised in Figure 1.3. The results have been drawn from the third issue of the CMEMS OSR, as well as from the CMEMS OMIs. Chapters 2, 3 and 4 contain reported anomalous changes in the marine environment during the year 2017. Some specific events have been highlighted and described in Chapter 4. The anomalous changes are summarised in Figure 1.3, which include:

* The northern parts of the European regional seas (e.g. the Baltic Sea, the North West Shelf Sea) have been characterised by lower-than-average sea ice extent and chlorophyll-a values respectively during 2017. Moreover, extreme cold temperature conditions

have been reported in these areas, together with higher-than-average ocean temperature and sea level in the Baltic Sea.

* The southern parts of the European regional seas have undergone significant changes during the year 2017: the Mediterranean Sea has been impacted by strong heat wave events during boreal summer in the eastern and western basins (Section 4.4), several events of extreme variability in the western basin, and higher-than-average ocean surface and subsurface water temperature. Chlorophyll-a values have been larger than previous years. Air-to-sea carbon fluxes show higher-than-average values in this area. Higher-than-average significant wave height, surface water temperature and salinity have been reported in the Black Sea.

* The Arctic Ocean has experienced lower-than-average sea ice extent and ocean freshwater content during the year 2017. In addition, extreme sea surface temperature anomalies of values up to +6°C have been reported, for example in the East Siberian Sea and the Beaufort Sea (Section 2.6).

* In the Antarctic region, a large hole in the winter sea ice cover (polynya) appeared in the Weddell Sea and stayed open for almost three months (Section 4.1). This was the first occurrence of such an event since 1976. Additionally, the Antarctic ocean area has showed lower-than-average air-to-sea carbon flux. Chlorophyll-a concentration was below its average value.

* At global scale, surface and subsurface temperature and sea level are particularly high in several areas such as the southwestern Indian Ocean, eastern tropical Pacific, subtropical and southwestern Atlantic and southeast of Australia. Anomalous cold and fresh conditions together with low sea level have prevailed in the subpolar North Atlantic, the western tropical and north Pacific, and in the northeastern Indian Ocean. Higher-than-average chlorophyll-a is reported in the southeastern Pacific and in the Indian Ocean south of Madagascar, while chlorophyll-a was anomalously low in the northwestern Indian Ocean, and in the eastern tropical Atlantic and Pacific. The tropical Pacific was in neutral El Niño Southern Oscillation conditions in 2017. However, coastal El Niño conditions have been reported along the coast of Peru and Ecuador at the beginning of the year.

### Note

1. 25th meeting of the Conference of the Parties to the United Nations Framework Convention on Climate Change.
<figure><img src="image_47.png" /></figure>

## References

Abraham JP, Baringer M, Bindoff NL, Boyer T, Cheng LJ, Church JA, Conroy JL, Domingues CM, Fasullo JT, Gilson J, et al. 2013. Monitoring systems of global ocean heat content and the implications for climate change, a review. Rev Geophys. 51:450–483. doi:10.1002/rog.20022.

Cazenave A, Dieng H-B, Meyssignac B, von Schuckmann K, Decharme B, Berthier E. 2014. The rate of sea level rise. Nat Geosci. 4(5). doi:10.1038/NCLIMATE2159.

CMEMS. 2014. Technical annex to the delegation agreement with Mercator Ocean for the implementation of the Copernicus Marine Environment Monitoring Service (CMEMS). http://www.copernicus.eu/sites/default/files/library/CMEM_TechnicalAnnex_PUBLIC.docx.pdf.

Desbruyères DG, Purkey SG, McDonagh EL, Johnson GC, King BA. 2016. Deep and abyssal ocean warming from 35 years of repeat hydrography. Geophys Res Lett. 43:10356–10365. doi:10.1002/2016GL070413.

Meehl GA, Arblaster JM, Fasullo JT, Hu A, Trenberth KE. 2011. Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nat Clim Chang. 1:360–364.

Purkey SG, Johnson GC. 2010. Warming of global abyssal and deep southern ocean waters between the 1990s and 2000s: contributions to global heat and sea level rise budgets. J Clim. 23:6336–6351. doi:10.1175/2010JCLI3682.1.

Riser SC, Freeland HJ, Roemmich D, Wijffels S, Troisi A, Belbéoch M, Gilbert D, Xu J, Pouliquen S, Thresher A, et al. 2016. Fifteen years of ocean observations with the global Argo array. Nature Clim Change. 6(2):145–153. doi:10.1038/nclimate2872.

von Schuckmann K, Le Traon P, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik L-A, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, et al. 2016a. The Copernicus marine service ocean state report issue 1. J Oper Oceanogr. 9. doi:10.1080/1755876X.2016.1273446.

von Schuckmann K, Cazenave A, Chambers D, Hansen J, Josey S, Kosaka Y, Loeb N, Mathieu P-P, Meyssignac B, Palmer M, et al. 2016b. An imperative to monitor Earth’s energy imbalance. Nat Clim Chang. 6:138–144. doi:10.1038/nclimate2876.

von Schuckmann K, Storto A, Simoncelli S, Raj RP, Samuelsen A, de Pascual Collar A, Garcia Sotillo M, Szerkely T, Mayer M, Peterson KA, et al. 2018. Ocean heat content, in CMEMS ocean state report 2. J Oper Oceanogr. 11(sup1):S1–S142. doi:10.1080/1755876X.2018.1489208.
# Chapter 2: State, variability and change in the marine environment: new monitoring indicators

## 2.1. Sea surface winds and Ekman pumping

**Authors:** Maria Belmonte Rivas, Ad Stoffelen, Abderrahim Bentamy

**Statement of main outcome:** Sea surface wind stress and Ekman transport changes are assessed that imply changes in both large-scale and smaller-scale ocean forcing. During 2017 a transition from gradually warming El Niño Southern Oscillation conditions to non-El Niño-Southern-Oscillation conditions in the tropical Pacific is observed. The North Atlantic is influenced by sustained high North Atlantic Oscillation conditions, with higher-than-average Azores High and south-easterly wind anomalies in the Labrador Sea. We note sustained high westerlies in the Southern Ocean, and increased south-easterly trades in the South Indian Ocean, with enhanced wind convergence off Sumatra. Trends and anomalies in Ekman pumping (ocean circulation forcing) are consistent with the respective trends and anomalies observed in the wind. Inter-annual variability is dominated by changes in mean large-scale conditions, rather than changes in transient smaller-scale wind activity, without any substantial trends in globally averaged annual mean or transient winds. Regional trends and anomalies in wind variability are generally of opposite sign to those in the mean wind, as they feed on the mean flow and counterbalance it. These wind stress changes imply changes in both large-scale and smaller-scale ocean forcing.

### Products used

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>2.1.1</td><td>Product:<br/>WIND_GLO_WIND_L3_REP_<br/>OBSERVATIONS_012_005/<br/>Dataset (reprocessed ASCAT-A 25 km<br/>Ascending): KNMI-GLO-WIND_L3-<br/>REP-OBS_METOP-<br/>A_ASCAT_25TI ASC<br/>Remote sensing</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-WIND-PUM-012-002-005.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-WIND-QUID-012-002-003-005.pdf</td></tr></tbody></table>

Winds blowing over the ocean induce sheared flows and waves that generate ocean turbulence. This turbulence transfers the momentum imparted by the winds down into the ocean and transports heat and constituents. In addition, ocean winds evaporate water and thus affect heat and mass fluxes and ocean salinity. Changing winds and associated air-sea interaction, currents and waves strongly affect the ocean state, both on global

and local scales. Monitoring changes in ocean winds and stress on all spatial scales is therefore crucial to understand how changes in the ocean state (waves, currents, sea surface temperature, sea surface salinity, chlorophyll) are related to its atmospheric forcing. More in particular, Earth rotation causes the wind to generate the so-called Ekman ocean transport, which vertical component (pumping/suction) forces the Antarctic Circumpolar Current and ocean gyres through the wind stress curl.

In climate modelling and ocean applications ocean forcing is provided by atmospheric general circulation models. Although these models often use scatterometer observations as input, unresolved or poorly determined processes, such as wind dynamics or structure related to moist convection, the stable surface layer and atmospheric drag, cause both mean and variable errors in ocean wind forcing. Given the importance of model winds for this report on the ocean state, an assessment of the errors in atmospheric general circulation models forcing is provided with respect to the scatterometer observations.

Global monitoring of ocean mean winds, stress, its local variability and the associated Ekman pumping is introduced in this section. We provide a summary view of the current conditions and recent evolution in global sea surface winds from observational scatterometer data, highlighting major large-scale events in the context of climatology and trends, and exposing potential connections with the other ocean variables (such as waves, currents, sea surface temperature, sea surface salinity, chlorophyll) and global climate indicators (such as El Niño Southern Oscillation, North Atlantic Oscillation, Pacific Decadal Oscillation, Indian Ocean Dipole index, monsoons or tropical convection).

An approach to describing mean and variability of sea surface winds consists of resolving the field of motion into a time-mean component (called mean or steady wind, $\langle u \rangle$) and a time-variable component (called transient eddy wind, $u'$) superposed upon it (Lorenz 1955) as:

$$u(t) = \langle u \rangle + u_s(t) + u'(t)$$

$$v(t) = \langle v \rangle + v_s(t) + v'(t)$$

where $u_s(t)$ is a seasonally dependent quantity representing the departure of the monthly mean from the annual mean wind, satisfying $\langle u_s(t) \rangle = 0$. The total wind kinetic energy is partitioned in mean, seasonal and transient eddy components as:

$$TKE = \frac{1}{2} \langle u^2 + v^2 \rangle = \text{MKE} + \text{SKE} + \text{TEKE}$$
<figure><img src="image_48.png" /></figure>

$$MKE = \frac{1}{2}((u)^2 + (v)^2)$$

$$SKE = \frac{1}{2}((u_s^2) + (v_s^2))$$

$$TEKE = \frac{1}{2}((u'^2) + (v'^2))$$

In this section, the mean wind climatology is determined from the annual mean zonal and meridional wind components as

$$u_{\text{mean}} = \langle u \rangle$$

$$v_{\text{mean}} = \langle v \rangle$$

And the transient eddy wind climatology is determined as the square root of the annual mean variance of the zonal and meridional wind components around monthly means as

$$u_{\text{eddy}} = \sqrt{\langle u'^2 \rangle}$$

$$v_{\text{eddy}} = \sqrt{\langle v'^2 \rangle}$$

Accordingly, we show how the total wind kinetic energy is partitioned in mean and transient eddy components, since both affect ocean circulation and its gyres, the former through large-scale Ekman transport and upwelling/downwelling, the latter through vertical mixing via ocean eddies, surface and internal wave motions, etc. (Large et al. 1994). Aside from mean and transient wind statistics, this section introduces Ekman pumping as ocean-monitoring parameter. Ekman pumping is defined as curl(τ/ρ₀f) (Ekman 1905), and it relates to the curl of the wind stress vector, τ (de Kloe et al. 2017), scaled by a reference ocean density, ρ₀, and the Coriolis parameter f. As a rule of thumb, cyclonic winds will induce a net divergence in Ekman transport and upwelling, or Ekman suction, while anticyclonic winds will induce a net convergence and downwelling, or Ekman pumping.

In this chapter, the reference climatology is calculated over the period 2007–2014. The 2017 anomalies in mean wind, transient eddy wind and Ekman pumping are interpreted in the context of the 2007–2014 climatology and the 2007–2017 trend.

### 2.1.1. Global mean wind

The climatology, 2007–2017 trends and 2017 anomaly in the global mean sea surface winds are illustrated in Figure 2.1.1, with major features listed in Table 2.1.1. In the tropical Pacific, the 2007–2017 trend is dominated by a general shift from cold (La Niña) to warm (El Niño) Southern Oscillation conditions, with a gradual

deceleration of easterlies in the central and western Tropical Pacific (Tag A1 in Figure 2.1.1(b)), along with enhanced easterlies and mean wind convergence in the eastern Pacific (Tag A2 in Figure 2.1.1(b)). 2017 is a year with a short or aborted warm El Niño Southern Oscillation event and a return to more neutral conditions, showing weak westerly anomalies in the western Tropical Pacific and a more easterly flow with reduced wind convergence in the eastern Tropical Pacific (Figure 2.1.1(c)), connected to cooling of sea surface temperatures in the central and eastern Tropical Pacific (cf. http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators/). In the eastern Tropical Pacific, northwesterly wind anomalies are also associated with the onset of a 'Coastal El Niño' in 2017 (Tag A3 in Figure 2.1.1, cf. Section 4.7 of this report).

Connected to the gradually warmer El Niño Southern Oscillation conditions (reversed Walker Circulation) during the climatology period, the 2007–2017 mean wind trend is dominated by cyclonic anomalies over the North and South Pacific subtropical gyres (Tag B in Figure 2.1.1(b)). In line with the trend, the 2017 mean wind anomaly registers cyclonic anomalies over the Pacific subtropical gyres, together with a reduction of the Aleutian Sea Low in the North Pacific (Tag C), and an intensification of the South Pacific High (Tag D). Cyclonic anomalies in the North Pacific are associated with a weakening of the central and eastern branches of the North Pacific Gyre (cf. Section 2.7) and related to the Pacific Decadal Oscillation index, which projects onto a warm phase in 2017, connected with warm SST anomalies along the US Coast and cold SST anomalies across the northern Pacific (cf. Section 1.1.1).

The 2007–2017 mean wind trend in the Southern Ocean is dominated by a shift towards higher Southern Annular Mode indices (Thompson and Solomon 2002), corresponding to stronger-than-average westerlies over the mid-high latitudes (50S–70S, see Tag F in Figure 2.1.1(b)) and weaker westerlies in the mid-latitudes (30S–50S). The higher Southern Annular Mode indices have been associated with large but statistically insignificant meridional wind anomalies (northerly near the Antarctic Peninsula, Tag E1, and southerly in the Ross Sea, Tag E2) during the 1992–2010 period (Holland and Kwok 2012) suggesting that a deepening of the autumn Amundsen Sea Low is connected with the evolution of West-Antarctic climate and sea ice in the Pacific sector (Raphael et al. 2016). The variability and change of the Amundsen Sea Low remains complex (Turner et al. 2013), and it fails to provide a consistent deepening trend over the 2007–2014 period analysed here.

The 2007–2014 climatology over the Atlantic Ocean is dominated by a shift to higher North Atlantic Oscillation
<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /><figcaption>**Figure 2.1.1.** Global map of annual mean wind (product reference 2.1.1, ASCAT observations): 2007–2014 climatology (top), 2007–2017 decadal trend (middle, arrows at 95% significance level) and anomaly in 2017 relative to the climatology (bottom). Tagged features are listed in Table 2.1.1.</figcaption></figure>

conditions, which are associated to increasing westerlies along the 55N–60N band (Tag G in Figure 2.1.1(b)). In line with the trend, the 2017 mean wind anomaly

shows enhanced westerlies in the North Atlantic, accompanied by an enhancement of the Azores High and deepening of the Icelandic Low. In contrast to the general trend of increasing southerly meridional anomalies observed in the Greenland and Norwegian Seas, the 2017 anomaly shows enhanced subpolar north-easterlies (Tag H in Figure 2.1.1(c)). We also observe southeasterly wind anomalies in the Labrador Sea (Tag I in Figure 2.1.1(c)), weakening the local mean flow and connected to the weakening of the North Atlantic cold sea surface temperature anomaly as it is pushed further into the subpolar gyre (cf. Section 4.2). The 2007–2017 mean wind trend and anomalies are dominated by anticyclonic anomalies over the North and South Atlantic subtropical gyres (Tag J in Figure 2.1.1(b)), with an intensification of the South Atlantic High near the Brazil-Malvinas Confluence, connected to unusually high sea surface temperatures in that region (http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators) and associated with the Azores High near the Gulf Stream.

Finally, no significant trends are observed over the South Indian Ocean during the 2007–2017 period, except for a small cyclonic trend over the Mascarenes High off Madagascar (Tag K1 in Figure 2.1.1(b)). The 2017 mean wind anomaly is opposite to the trend, with a large anticyclonic anomaly over the South Indian gyre (Tag K2 in Figure 2.1.1(c)), enhancing the prevailing south-easterly trades and connected with unusually strong wind convergence off Sumatra. The enhanced south-easterly trades in the southern Indian Ocean are connected to colder than average sea surface temperatures on the eastern side, and warmer sea surface temperatures to the west (http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators), also associated with the Indian Ocean Dipole Index, which projects into a positive phase in 2017.

## 2.1.2. Global transient eddy winds

The climatology, trends and 2017 anomaly in the global transient eddy winds are illustrated in Figure 2.1.2 below. Attending to their lower amplitude, we note that regional trends and anomalies in transient eddy winds are generally smaller than in the mean winds, indicating that most of the inter-annual variability in wind power is dominated by changes in persistent large-scale conditions, rather than changes in transient wind activity. We also note that trends and anomalies in transient wind are generally of opposite sign to trends and anomalies in the mean wind, indicating that changes in the mean wind are counterbalanced by changes in wind transience, which is reasonable when one considers that transient disturbances feed on the mean flow.
<figure><img src="image_51.png" /><figcaption>**Table 2.1.1.** Major features in global sea surface wind trends and anomalies, with associated ocean features as indicated in Figure 2.1.1.</figcaption></figure>

<table><thead><tr><th>TAG</th><th>Wind feature</th><th>Ocean feature</th></tr></thead><tbody><tr><td>A1</td><td>El Niño Southern Oscillation trend: deceleration of easterlies in the central/western Tropical Pacific</td><td>Sustained extra-equatorial upwelling anomalies at latitudes from 3 to 7 deg.</td></tr><tr><td>A2</td><td>El Niño Southern Oscillation trend: enhanced easterlies and mean wind convergence in the eastern Pacific</td><td>Sustained extra-equatorial downwelling anomalies at latitudes from 3 to 7 deg.</td></tr><tr><td>A3</td><td>Coastal El Niño Southern Oscillation in 2017</td><td>Coastal circulation and production</td></tr><tr><td>B</td><td>Cyclonic anomalies over the North and South Pacific subtropical gyres</td><td>Upwelling trend and anomaly</td></tr><tr><td>C</td><td>Reduction of the Aleutian Sea Low</td><td>Downwelling anomaly Bering Strait</td></tr><tr><td>D</td><td>Intensification of the South Pacific High</td><td>Downwelling trend</td></tr><tr><td>E1</td><td>Deep Amundsen Sea Low anomalies: northerly near the Antarctic Peninsula</td><td>Ocean circulation and sea ice</td></tr><tr><td>E2</td><td>Deep Amundsen Sea Low anomalies: southerly in the Ross Sea</td><td>Ocean circulation and sea ice</td></tr><tr><td>F</td><td>Higher Southern Annular Mode</td><td>Northerly Ekman flow trend with associated down(up)welling to S(N)</td></tr><tr><td>G</td><td>Higher North Atlantic Oscillation conditions</td><td>Gulf Stream Dynamics</td></tr><tr><td>H</td><td>Increased subpolar north-easterlies in Greenland and Norwegian Seas</td><td>Enhanced ocean circulation/transport</td></tr><tr><td>I</td><td>Southeasterly wind anomalies in the Labrador Sea</td><td>Downwelling anomaly & trend off Newfoundland</td></tr><tr><td>J</td><td>Intensification of the Azores High and South Atlantic High</td><td>Atlantic Meridional Overturning</td></tr><tr><td>K1</td><td>Cyclonic trend over the Mascarenes High off Madagascar</td><td>Upwelling trend in Indian Ocean Gyre</td></tr><tr><td>K2</td><td>Anticyclonic anomaly in eastern tropical Indian Ocean</td><td>Positive phase of the Indian Ocean Dipole index</td></tr></tbody></table>

In connection with the trends in annual mean wind, we observe increasing wind variability over the central and western Tropical Pacific during the 2007–2017 period (Tag A1 in Figure 2.1.1 and Table 2.1.1), spatially correlated with the slowdown of trades and connected to the shift towards warmer El Niño Southern Oscillation conditions. Also note the localised regions of increased wind variability around Newfoundland and the Kamchatka Peninsula, both in the trends and the 2017 anomaly, and decreased wind variability over the Gulf of Alaska. We moreover observe a generally lower wind variability over the Southern Ocean (Tag F in Figure 2.1.1 and Table 2.1.1), in connection with a higher Southern Annular Mode – except in the Pacific sector. We also note a trend of increased wind variability over the subtropical Pacific gyres (Tag B in Figure 2.1.1 and Table 2.1.1) in connection with cyclonic anomalies in the mean winds, and lower wind variability in the eastern tropical Indian Ocean (Tag K2 in Figure 2.1.1 and Table 2.1.1) in 2017 connection with a strong anticyclonic anomaly in the mean wind.

Time series of globally averaged annual mean and eddy kinetic energy contributions for the 2007–2017 period split into zonal and meridional components for ASCAT observations and the ERA-Interim spatio-temporally interpolated collocations (product reference 2.1.1) have been computed (but not shown). It is interesting to compare the representation of the globally averaged wind kinetic energy provided by reanalyses to that of observations, since the accuracy of the former is limited by inhomogeneities introduced by changes in the observing system, as well as systematic errors inherited from the underlying model physics. Despite the regional trends noted earlier, there are no significant trends in globally averaged annual mean wind or wind variabilities over the 2007–2017 period. The partition of kinetic

energy into mean and transient eddy components is however pictured somewhat differently in the ERA-Interim reanalysis, with a 6% higher mean wind energy and an 8% lower transient eddy wind energy than in the ASCAT measurements. The partition of mean wind energy into zonal and meridional components is also different in ERA-Interim, with stronger mean zonal winds and weaker meridional winds than in the ASCAT measurements, along with weaker zonal and meridional wind variabilities (not shown here, see Belmonte Rivas and Stoffelen 2019).

### 2.1.3. Ekman pumping

The climatology, trends and 2017 anomaly in Ekman upwelling are illustrated in Figure 2.1.3. We recall that trends and anomalies in Ekman suction/pumping, as derived from the curl of the wind stress vector, follow from the cyclonic/anticyclonic trends and anomalies in the annual mean wind. As an indication of large-scale vertical motions in the global oceans, ocean upwelling influences biological productivity (upwelling waters are usually rich in nutrients) and brings about changes in sea surface temperature that may affect weather and climate.

Due to their biological impact, a lot of research has focused on eastern boundary upwelling systems and their modes of variability, namely, the California and Peru-Humboldt systems, which are particularly impacted by El Niño Southern Oscillation; the Benguela system, which is affected by El-Niño-Southern-Oscillation-like variability and the Southern Annular Mode; or the Canary/Iberian system, which is impacted by the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation (Bakun et al. 2015). Note that current scattemometer products lack data values closer than 25 km off the coast, as depicted in all figures. Some other
<figure><img src="image_52.png" /></figure>

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

**Figure 2.1.2.** Global map of observed annual transient eddy wind (product reference 2.1.1, ASCAT observations): 2007–2014 climatology (top), 2007–2017 decadal trend (middle, arrows at 95% significance level) and anomaly in 2017 (bottom) with horizontal/vertical arrows showing the relative weight of the zonal/meridional components.

<figure><img src="image_54.png" /></figure>

**Figure 2.1.3.** Global map of observed annual mean Ekman Upwelling (product reference 2.1.1, ASCAT observations): 2007–2014 climatology (top), 2007–2017 trend (middle, colours enhanced at 95% significance level) and anomaly in 2017 (bottom). Estimates between 5N and 5S are shaded as in (Risien and Chelton 2008) because the Coriolis force is small there.
researchers have focused on describing inter-annual variability in open-ocean upwelling.

In the tropical Pacific, we observe a persistent easterly flow that should create a thin tongue of upwelling cold water around the equator due to divergent Ekman transport approximately between 3S and 3N (within the shaded region in Figure 2.1.3). The trend of reduced easterlies in the central and western Tropical Pacific (associated to the general shift to warmer El Niño Southern Oscillation conditions during the 2007–2017 period) should result in downwelling anomalies (i.e. less upwelling) along the equatorial strip (Bograd and Lynn 2001), also within the shaded region in Figure 2.1.3. What we can see are the compensating downwelling zones North and South of the Equator (i.e. the extra-equatorial bands between 3 and 7 deg in both hemispheres) sustaining strong upwelling anomalies in the central and western Tropical Pacific (Tag A1 in Figure 2.1.1 and Table 2.1.1). These upwelling trends are indirectly related to reduced upwelling along the equatorial strip during the 2007–2017 period. In 2017, we still observe weak upwelling anomalies in the western extra-equatorial tropical Pacific, and somewhat more remarkable downwelling anomalies in the eastern extra-equatorial Pacific connected to enhanced easterlies there (Tag A2 in Figure 2.1.1 and Table 2.1.1).

Over the North and South Pacific Subtropical Gyres, we observe small upwelling trends and anomalies associated to persistent cyclonic anomalies in the mean wind (Tag B in Figure 2.1.1 and Table 2.1.1), along with a downwelling anomaly in the Bering Sea connected to the reduction of the Aleutian Sea Low (Tag C in Figure 2.1.1 and Table 2.1.1), and a downwelling anomaly (Tag D in Figure 2.1.1 and Table 2.1.1) connected with the intensification of the South Pacific High.

The strengthening of westerly winds over the Southern Ocean creates a band of enhanced northward Ekman flow with increased subpolar upwelling (trends and anomalies) to the south and enhanced downwelling to the north (Tag F in Figure 2.1.1 and Table 2.1.1) in connection with a stronger Southern Annular Mode (Hall and Visbeck 2002). In the Atlantic Ocean, we observe downwelling trends and anomalies off Newfoundland associated to a high pressure anomaly there (Tag I in Figure 2.1.1 and Table 2.1.1) (Spall and Pickart 2003), and upwelling trends and anomalies further east across the North Atlantic and around Iceland (Pelegri et al. 2006). Finally, we observe increased upwelling trends and anomalies in the eastern tropical Indian Ocean, which previous research has connected to Indian Ocean Dipole and El Niño Southern Oscillation conditions (Chen et al. 2016), along with a strong downwelling anomaly over the southern Indian Ocean connected

to a large anticyclonic anomaly in the mean wind observed in 2017.

### 2.1.4. Concluding remarks

Wind, stress and Ekman variations are important in so far as they may be driving changes in other components (SST, SSS, sediment, biology) or initiate (local or remote) coupled processes that may push the climate system towards new states of equilibrium. Coupled processes, coastal processes, etc., have a host of relevant applications in economy and society. While these processes are best studied locally, we show that local changes, affecting ocean users, are strongly associated to changes in global atmospheric circulation. We moreover depict that the input used by most oceanographers suffers from large-scale forcing issues, in addition to the well-documented regional issues in processes and resolution. The associations between ocean indicators in other domains (sections) and the tagged features in this section's figures, appear a good way to connect the global atmospheric circulation changes to regional and local ocean circulation features and indicators. We also imply that for many of our users, the large-scale mean and transient errors in NWP forcing will be relevant in user applications, as it will provide errors in the time-dependent ocean response. It will be useful to further document and monitor these errors and the temporal changes in them. Finally, we note that efforts are ongoing to improve the coastal processing of scatterometer data, hence enhancing the usefulness of our products for ocean applications.

## 2.2. The seasonal intensification of the slope Iberian Poleward Current

**Authors:** Anna Rubio, Ivan Manso-Narvarte, Ainhoa Caballero, Lorenzo Corgnati, Carlo Mantovani, Emma Reyes, Annalisa Griffa and Julien Mader

**Statement of main outcomes:** The combination of surface HF radar data and data from ADCP moorings is showcased as a promising approach for the monitoring of the slope currents in the southeastern Bay of Biscay and the Iberian Poleward Current seasonal intensification. Persistent and intense eastward currents are observed during winter periods and affect the measured water column down to 150 m depth. During summer time, stronger vertical shear is observed, both during eastward and westward current events. Strong fluctuations occur in parallel to intense north-northwestern and southern wind changes in both seasons. This variability is expected to have a significant impact on the residence time and the water exchanges between the coast and the open ocean in the area.
<figure><img src="image_55.png" /></figure>

Products used:

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>2.2.1</td><td>INSITU_IBI_NRT_013_033</td><td>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf<br>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013.pdf</td></tr><tr><td>2.2.2</td><td>Insitu total surface currents from HF radar</td><td>NRT data to be included in CMEMS v5 portfolio, REP data to be included in CMEMS v7 portfolio.<br>REVIEW PAPER: https://doi.org/10.3389/fmars.2017.00008<br>EUROPEAN HF STANDARD FOR HF RADAR DATA AND METADATA: http://www.marineinsitu.eu/wp-content/uploads/2018/02/EGU2018-13317_presentation.pdf</td></tr></tbody></table>

The Iberian Poleward Current drives the ocean slope circulation in the southeastern Bay of Biscay from the surface down to 300 m depth. The intensification and variability of this current at seasonal and shorter scales can have a significant impact on the marine environment in this coastal area, since it can modulate the residence time of nutrient-rich waters, pollutants, planktonic organisms as well as the water exchange between the coast and the open ocean. The monitoring and forecast of the surface and subsurface variability of currents in coastal areas is key for their accurate management where a myriad of socio-economic activities coexist (e.g. commercial and recreational fisheries and navigation, tourism, industry, ports and harbours, etc.). While coastal numerical models can provide three-dimensional fields of currents and enable the computation of Lagrangian quantities with application to biological, geochemical and environmental issues, the joint analysis of multi-platform observations, with complementary spatial coverage, is an interesting approach for a better understanding of the three-dimensional coastal circulation and the validation of the models. In this contribution we combine HF radar-derived hourly surface current maps with ADCP data and wind measurements from three moorings to showcase their use to continuously monitor and analyse the IPC seasonal variability along the period 2009–2017 and its intensification under certain prevailing wind conditions.

The Iberian Poleward Current is one of the main along-slope flows that promote trans-boundary interactions along the Atlantic margin of Europe. In the southeastern Bay of Biscay (see Figure 2.2.1), the Iberian Poleward Current is a persistent feature and affects the 0–300 m depth layer in the area. Together with the presence of slope eddies that grow from current instabilities

<figure><img src="image_56.png" /><figcaption>**Figure 2.2.1.** Study area corresponding to the southeastern Bay of Biscay (northeastern Iberian Peninsula) and schematic view of the winter shelf-slope current and mesoscale regime (grey arrows, note that although only anticyclonic arrows are represented, eddies of anticyclonic and cyclonic polarity are observed in different locations along the slope). The nodes for the computation of HF radar total currents (product ref. 2.2.2) are shown by the grey dots. The stars provide the location of the HF radar antennas in Matxitxako and Higer (Donostia) Capes. The black dots provide the location of the slope moorings (product ref. 2.2.1) used in this study and the black lines the surface cross-transects used to plot HF radar along-slope currents in Figure 2.2.2. Bathymetry is given by the contours (in metre).</figcaption></figure>

and the wind-induced circulation, this has a significant impact on the surface transport patterns, the residence times and the water exchange between the coast and the open ocean (Rubio et al. 2018).

The Iberian Poleward Current flows over the slope, advecting warm surface waters eastwards (northwards) along the Spanish (French) coast (Le Cann and Serpette 2009; Herbert et al. 2011; Charria et al. 2013) during winter. The flow is reversed and three times weaker (Solabarrieta et al. 2014) during summer. In addition to this markedly seasonal density-driven circulation, variability at shorter scales is observed linked to wind-induced currents (e.g. Solabarrieta et al. 2015), mesoscale eddies (e.g. Rubio et al. 2018), tides and inertial motions (e.g. Rubio et al. 2013). The generation of slope eddies occurs mainly in winter, when the intensified Iberian Poleward Current interacts with the abrupt bathymetry (Pingree and Le Cann 1992; Teles-Machado et al. 2016). During autumn and winter, southwestern winds dominate and generate northward and eastward drift over the shelf. The rest of the year the winds are much weaker and less persistent, which makes wind-driven currents more variable (Lazure 1997; González et al. 2004; Solabarrieta et al. 2015). The observation and monitoring of the surface and
subsurface current, together with its variability is thus
crucial to obtain accurate estimation of the coastal
transport in this area and to evaluate its potential
impacts on the coastal ecosystem.

Several previous works have focused on the study of different aspects of the Iberian Poleward Current variability in the area by using remote sensing data (e.g. Herbert et al. 2011), moorings (Rubio et al. 2011), drifters (e.g. Charria et al. 2013) and HF radar data (e.g. Solabarrieta et al. 2014) independently. The main contribution of this study is the use of multi-platform observing systems with complementary spatial coverage for observing and monitoring the surface and subsurface currents and their variability, to accurately estimate the coastal transport in the southeastern Bay of Biscay. Particularly, in this case, we combine data from emerging technologies as the HF radar-derived hourly surface current maps (product ref. 2.2.2) with ADCP water column velocity profiles from two slope moorings and wind velocity from a third mooring (product ref. 2.2.1) to monitor the Iberian Poleward Current seasonal variability along the period 2009–2017 and its intensification under certain prevailing wind conditions.

HF radar is a land-based remote sensing technology which can infer ocean surface currents over wide areas (distances from the coast over 150 km) with high spatial (300 m–5 km) and temporal (≤1 h) resolution. Nowadays, HF radars are the unique technology that can offer such a continuous observation of surface coastal current patterns over wide areas at high-spatial resolution and thus the possibility to monitor the associated transports. The combination of HF radar data with water-column data is especially interesting since it can broaden the application of this technology to biological, geochemical and environmental issues, since plankton or pollutants can be located deeper in the water column and not only follow surface dynamics. The inclusion of near-real-time and reprocessed HF radar data into the European Copernicus Marine Environment Monitoring Service is foreseen in 2019 and 2021, respectively. It will enable a sustained delivery of HF radar data and related products, including the expansion of the approach presented here to other coastal areas.

The HF radar system located in the southeastern Bay of Biscay (www.euskoos.eus) emits at a central frequency of 4.5 MHz and a 40-kHz bandwidth and provides surface hourly current maps in an area up to 150 km from the coast (Figure 2.2.1). HF radar-derived surface currents are quality controlled using advanced procedures based on velocity and variance thresholds, noise to signal ratios and radial and total coverage (Rubio et al. 2011, 2018; Solabarrieta et al. 2014, 2015, 2016), and reprocessed (for obtaining gap-filled surface currents)

using the Open-boundary Modal Analysis (Kaplan and Lekien 2007). The historical HF radar data used here cover the whole period 2009–2017. Wind data and current profiles in the water column are obtained from the CMEMS reference product 2.2.1. Winds are obtained from the Bilbao Vizcaya mooring (location shown in Figure 2.2.1) and we use current data from two buoys moored over the upper part of the slope with a downward looking ADCP (150 kHz, 8 m vertical bins, up to 200 m): Matxitxako mooring historical data spanning the period 2007–2013 and Donostia mooring the period 2007–2017 (see locations in Figure 2.2.1). While the slope buoys’ data time coverage is irregular, and no data are available for the Matxitxako buoy after August 2013, the HF radar data coverage is quite good from the end of 2010.

To explore the variability of the current we follow a two-step approach. First, the data series are analysed jointly to identify the main variability patterns in relation to the seasonal cycle and winds, exploiting the spatial complementarity of the measurements for characterising the variability in the horizontal and vertical extension of the current. Second, we compute spatial maps and vertical profiles of temporal cross-correlation between the velocity measurements at the moorings location and the rest of the nodes within the HF radar and vertical levels to obtain an estimation of the area and levels where the point-wise current measurements from the moorings and the surface currents from the HF radar can be considered as representative of the whole slope area. The areas and levels containing high cross-correlation values between measurements provide thus the 3D volume that can be well monitored by the combination of the two observing systems.

The along-slope current is marked by strong seasonal variability (Figure 2.2.2). Persistent and intense eastward currents are observed during winter periods, and affect the measured water column down to 150 m depth. The cross-shore extension of the winter eastward current shows significant variability, being some of the winter events constricted to the shelf-slope area of the Matxitxako mooring (W2), while others show higher extension being observed up to 43.9°N (W3, W4 or W5). Remarkable differences between the two mooring locations are also observed (see also Rubio et al. 2011): the signature of the slope current is much stronger at Matxitxako, and generally weaker and less stable at Donostia mooring. Moreover, observed intensifications of the eastward winter current are also weaker at the Donostia location and more constrained to the slope (latitudes between 43.5 and 43.75°N). The strongest eastward currents are observed in periods of intense north-northwestern winds, particularly during winter time. During some of the winter periods the (e.g. W1 in Donostia or W4, see Figure 2.2.2) strong
<figure><img src="image_57.png" /></figure>

fluctuations together with altering east/west intense cur-
rents occur in parallel to intense north-northwestern and
southern wind changes. In December 2014, the wind-
induced intensification of the slope current and its
subsequent relaxation triggered the generation of an
intense anticyclonic eddy described in Rubio et al.
(2018) (see also Figure 2.2.2(a): intense countercurrent
after W4 event). The signature of the eddy at Donostia
latitudinal transect is less intense and less persistent but
can be observed to be vertically coherent down to
150 m depth.

During summer time, stronger vertical shear is
observed, both during eastward (S1, S2) and westward

(S3 and S4) current events. In 2014, 2015 and 2016
intense summer westward currents (S3, S4) are
observed in both locations. The westward event
during July and August 2016 is especially remarkable
because of its intensity and persistence, although it is
limited to the surface (of a few tens of meters).

The cross-correlation maps for the sub-inertial
along-slope component (Figure 2.2.3(a–d)) show high
correlation between Matxitxako and Donostia locations
and the nearby areas over the self and slope, from 1°30′
to 3°W, and from the coast to 43°50′. In winter, cross-
correlation values over 0.6 are observed in the area
between 43°50′N and the Spanish coast. This is due

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

**Figure 2.2.2.** Hovmöller diagrams of along-slope surface currents derived from the HF radar (product ref. 2.2.2) along the surface cross-transects shown in Figure 2.2.1, at the longitude of the (a) Matxitxako and (c) Donostia moorings. Hovmöller diagrams of along-slope current profiles up to 150 m depth from downward looking ADCP data in (b) Matxitxako (only for the period 2009–2013) and (d) Donostia moorings (product ref. 2.2.1). Low-pass filtered wind vectors (product ref. 2.2.1)) from Bilbao Vizcaya mooring are shown in (e). A selection of winter (W*) and summer (S*) current events discussed more in detail in the text are identified at the top of (a) panel.
to the persistent and spatially homogeneous surface sig-
nature of the winter slope current flowing eastwards
along the Spanish slope. In the case of Matxitxako
mooring (which samples very well the core of the
slope current) an area of high cross-correlation values
(over 0.5) is also observed along the French slope
(Figure 2.2.3(a)). We relate this to the surface signature
of the slope current in this area where, due to the
change of orientation of the bathymetry, it flows to
the north-northwest (so the variability of the east-
west component of the flow over the French slope is
well correlated with east-west flow changes at Matxit-
xako mooring location). As mentioned before, the sea-
sonal variability is weaker at Donostia location, being
less evident the influence of the winter current regime.
Vertically, the cross-correlation is also seasonally
modulated (Figure 2.2.3(e,f)), and shows significant
spatial differences. The highest cross-correlations at
150 m are observed in winter in Matxitxako, which is
again in coherence with a stronger slope current influ-
ence at this location. The strongest stratification in
summer leads to a higher vertical shear, and makes
the correlation drop up to 0.5 at 60 and 50 m in

Matxitxako and Donostia, respectively. From this
level to 150 m the decay in the correlation is again
much higher in Donostia, which indicates that other
than the absence of a vertically coherent slope regime
is favouring a stronger vertical shear in this area of
complex bathymetry.

Recent work has shown the impact of the slope circ-
culation and its variability on the residence time and
the water exchange between the coast and the open
ocean in the southeastern Bay of Biscay (Rubio et al.
2018; Declerck et al. 2019). The monitoring and fore-
cast of the surface ocean variability is key for the accu-
rate assessment of the distribution and transport of
organic and inorganic matter, in this area where differ-
ent human activities coexist (commercial and rec-
reational fisheries and navigation, tourism, industry,
etc.). From our results, the combination of surface
HF radar data (product ref. 2.2.2) and data from
ADCP moorings (product ref. 2.2.1) arises as a promis-
ing approach for the monitoring and characterisation of
the Iberian Poleward Current seasonal variability and its
intensification under certain prevailing wind directions.
Moreover, our analysis on the spatial and temporal

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

**Figure 2.2.3.** Spatial maps of cross-correlations between the low-pass filtered time series of the HF radar along-slope velocity component (product ref. 2.2.2) at (a,b) Matxitxako and (c,d) Donostia locations and the rest of the nodes within the HF radar footprint area for total currents (the maps for winter/summer are shown in (a) and (c)/(b) and (d) subplots, respectively). ADCP data cross-correlations along the vertical range of ADCP data (product ref. 2.2.1) for (e) Matxitxako and (f) Donostia and the along-slope velocity component for summer (stratified) and winter (well-mixed) periods. A 10th order digital Butterworth filter was applied to obtain sub-inertial current previous to the computation of cross-correlation between time series. The significance level is over 0.99 for all the cross-correlations values plot in the figure.
<figure><img src="image_60.png" /></figure>

coherence in the seasonal and mesoscale variability observed by independent observing systems is a first step in the blending of both data sets, for an improved real-time monitoring of three-dimensional coastal transports in the area. Besides, the use of these data sets for the improvement of the existing numerical models, through validation and data assimilation, would potentially enable enhanced forecast skills. Operational gap-filled HF radar coastal surface currents could also be used to monitor the transport properties of the surface flow, based on the Lagrangian approach, aiming to identify the different scenarios that favour the local retention and dispersal of shelf waters.

## 2.3. Mediterranean deep and intermediate water mass properties

**Authors:** Katrin Schroeder, Jacopo Chiggiato, Sana Ben Ismail, Mireno Borghini, Bernardo Patti, Stefania Sparnocchia

**Statement of outcome:** The Mediterranean Sea is a mid-latitude marginal sea which has been recognised to be a climatic hotspot. During the past decade, its water masses have experienced strong and fast increases in temperature and salinity, evidencing the tendency of this area to respond very rapidly to global warming and to changes in the regional freshwater budget. Based on *in situ* data here it is shown where and how fast these changes occur, with a particular focus on the Western Mediterranean Deep Water, the Levantine Intermediate Water and the upper Eastern Mediterranean Deep Water. Such trends are at least one order of magnitude higher than the global mid-latitude average trends. Indicator-type curves, routinely updated, for subsurface temperature and salinity evolution represent an outcome that is envisaged to be important for climate science, environmental agencies, concerned citizens as well as regional policy-makers.

### Products used:

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>2.3.1</td><td>INSITU_MED_NRT_ OBSERVATIONS_013_035<br>In situ data<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/history/mooring/MO_TS_MO_6101022.nc</td><td>QUID:<br>http://cmems-resources.cls.fr/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf<br>PUM:<br>http://cmems-resources.cls.fr/documents/PUM/CMEMS-INS-PUM-013.pdf</td></tr></tbody></table>

(Continued)

Continued.

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>2.3.2</td><td>INSITU_MED_NRT_ OBSERVATIONS_013_035<br>In situ data<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/history/mooring/MO_TS_MO_SardiniaChannel.nc</td><td>QUID:<br>http://cmems-resources.cls.fr/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf<br>PUM:<br>http://cmems-resources.cls.fr/documents/PUM/CMEMS-INS-PUM-013.pdf</td></tr><tr><td>2.3.3</td><td>INSITU_MED_NRT_ OBSERVATIONS_013_035<br>In situ data<br>Monthly files from<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/monthly/mooring/MO_201301_TS_MO_SardiniaChannel.nc<br>to<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/monthly/mooring/MO_201711_TS_MO_SardiniaChannel.nc</td><td>QUID:<br>http://cmems-resources.cls.fr/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf<br>PUM:<br>http://cmems-resources.cls.fr/documents/PUM/CMEMS-INS-PUM-013.pdf</td></tr><tr><td>2.3.4</td><td>INSITU_MED_NRT_ OBSERVATIONS_013_035<br>In situ data<br>Yearly files as<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/history/vessel/MO_PR_C_TenAlgerianBasin_yyyy.nc<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/history/vessel/MO_PR_C_TenSardiniaChannel_yyyy.nc<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/history/vessel/MO_PR_C_TenSicilyChannel_yyyy.nc</td><td>QUID: http://cmems-resources.cls.fr/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf<br>PUM:<br>http://cmems-resources.cls.fr/documents/PUM/CMEMS-INS-PUM-013.pdf</td></tr><tr><td>2.3.5</td><td>INSITU_MED_NRT_ OBSERVATIONS_013_035<br>In situ data<br>Monthly files as<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/monthly/vessel/MO_yyyymm_PR_C_TenAlgerianBasin.nc<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/monthly/vessel/MO_yyyymm_PR_C_TenSardiniaChannel.nc<br>ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_ OBSERVATIONS_013_035/monthly/vessel/MO_yyyymm_PR_C_TenSicilyChannel.nc</td><td>QUID:<br>http://cmems-resources.cls.fr/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf<br>PUM:<br>http://cmems-resources.cls.fr/documents/PUM/CMEMS-INS-PUM-013.pdf</td></tr></tbody></table>

Strong increases in evaporation and reduction in precipitation and river runoff have greatly modified the freshwater budget of the Mediterranean Sea and a further increase of net evaporation is projected to occur during the twenty-first century in this region, as a result of anthropogenic climate change (Giorgi 2006).

Long-term changes in temperature, but also in salinity, are observed in all water masses, from the inflowing Atlantic Water, to the intermediate water
<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /><figcaption>**Figure 2.3.1.** Temporal and spatial evolution of the processes associated to the Western Mediterranean Transition: white/light-grey curves denoted by years indicate the upper interface of the new Western Deep Water. Updated from Schroeder et al. (2016).</figcaption></figure>

and the deep waters (e.g. Vargas-Yáñez et al. 2017). In the western basin, the rather homogeneous layer of Western Mediterranean Deep Water has always been considered a stable medium in which precise quantification of trends in heat and salt contents is possible. Indeed, it has been observed that deep temperature and salinity have increased almost steadily for about half a century, due to different and concurrent factors (Bethoux and Gentili 1999; Rixen et al. 2005; Josey et al. 2011). However *in situ* data gathered between 2005 and 2017, during dozens oceanographic cruises (CTD casts) and by means of submerged moorings in key areas, revealed a decade of enhanced thermohaline variability in the deep and intermediate western Mediterranean water masses (Schroeder et al. 2016): the gradual increasing trend in the western deep water was interrupted by an abrupt shift towards higher temperature and salinity (with yearly increases during 2005–2017 that are more than 2.5 times faster than during 1961–2004). The onset of this shift (Figure 2.3.1), called the Western Mediterranean Transition, has been a major dense water formation event in winter 2004/2005 in the north-western

<figure><img src="image_63.png" /><figcaption>**Figure 2.3.2.** (upper panels) ΘS diagrams from 800 m depth to the bottom, in a repeat station (2800 m, 37.98°N, 4.65°E, product ref. 2.3.4–2.3.5) of the southern western Mediterranean (pressure is colour coded) in 2010 (left, light grey points refer to preceding years, i.e. 2005–2009, dark grey points refer to 2004, the pre-existing situation), 2013 (middle, light grey 2005–2010, dark grey 2004) and 2015 (right, light grey 2005–2014, dark grey 2004); (lower panels) ΘS diagrams at the sill in the Sardinia Channel (1915 m, 38.33°N, 9.33°E), (left) from bottom mooring data (product ref. 2.3.2–2.3.3) and (right) from repeated CTD profiles (years 2003–2017 are colour-coded, product ref. 2.3.4–2.3.5). Updated from Schroeder et al. (2016).</figcaption></figure>
<figure><img src="image_64.png" /></figure>

Mediterranean. Following winters also have contribu-
ted to the formation of anomalously warm and salty
dense water.

Since then this anomaly started to spread from its
formation region into the interior of the sea. The for-
mation of large amounts of anomalously warm and
salty deep water continued during the following win-
ters and the new deep water started to fill up the
entire basin (Figure 2.3.1). The increases of tempera-
ture and salinity in the deep western basin keep evol-
ving stepwise, with the deep temperature-salinity
diagrams becoming more and more complex (Figure
2.3.2, upper panels). From these diagrams it is poss-
ible to note how the Western Mediterranean

Transition has changed the basic structure and the
properties of the deep layers in the western basin.
Over time, these waters eventually spread westward
to flow out through the Strait of Gibraltar towards
the Atlantic Ocean (Figure 2.3.1), as has been detected
by a monitoring site at the sill where the character-
istics of the outflowing waters in years 2015–2016
are consistently saltier and warmer than in 2005–
2006 (Naranjo et al. 2017).

The new deep water spread also eastward: the Sardi-
nia Channel allows exchanges of the upper part of the
deep waters to occur between the western basin interior
and the Tyrrhenian Sea. At the sill a CTD cast has been
performed about once a year and a deep sea mooring

<figure><img src="image_65.png" /></figure>

**Figure 2.3.3.** Daily (grey) and monthly (black) time series (1993–2017) of (A) temperature and (B) salinity at 400 m in the Sicily Channel (mooring, product ref. 2.3.1), updated from Schroeder et al. (2017); (C) Hovmöller diagram of the *in situ* salinity in the central part of the Sicily Channel (36.5°N, 13.2°E, north-west of Malta) from repeated CTD casts, 1978–2017 (product ref. 2.3.4–2.3.5). Updated from Gačić et al. (2013).
<figure><img src="image_66.png" /></figure>

(belonging to the CIESM Hydrochanges network, see Schroeder et al. 2013) monitors the overflowing dense waters in detail since 2003. While until 2005 only the 'classical' old deep water was found at the sill, the new denser ones started to cross it since then (Figure 2.3.2 lower panels), being uplifted by even denser new deep waters that were produced in the following winters (Figure 2.3.1). By 2014 the whole layer below 500 m, i.e. the halocline/thermocline and the deep water, has densified to values of 29.11–29.12 kg m⁻³, becoming denser than the 'classical' resident water found at <3000 m in the Tyrrhenian Sea (Schroeder et al. 2016).

Given that all processes of dense water formation involve Atlantic and Intermediate Water to some extent, the latter being the main contributor to the heat and salt contents of the newly formed waters, all Mediterranean water masses are intimately related to each other, so that significant modifications to one will also affect the others sooner or later. Indeed, recent studies have also focused on the long-term strong warming and salinification of intermediate water: since the mid-1990s its temperature and salinity have increased by 0.28°C/decade and 0.08/decade in the Sicily Channel (respectively Figure 2.3.3(A-B)), a key site where the intermediate water flowing from east to west may be intercepted. Such trends are at least one order of magnitude higher than the average values reported for the global ocean intermediate layer at midlatitudes (Schroeder et al. 2017). These outcomes are also of great benefit for intercomparisons with numerical model results (as those in von Schuckmann et al. 2018, their Figure 3.4.3).

Warmer and drier regional climatic conditions over the eastern basin are favouring the formation of increasingly warmer and saltier intermediate water: indeed, the Levantine region in particular is undergoing a dramatic drought since the late 1990s (Cook et al. 2016), the driest period in the past 500 years. As a result, also the upper part of the Eastern Mediterranean deep waters is experiencing a warming (not shown) and a salinification (Figure 2.3.3(C)), as is evident from repeated CTD casts in the central Sicily Channel (between the islands of Pantelleria and Malta) that reach depths below 1700 m, where the upper part of the eastern deep water is found. The thermohaline evolution at this location has already been discussed in Gasparini et al. (2005), Ben Ismail et al. (2014) and Gačić et al. (2013). At any moment, the salinity profile shows a maximum at around 300 m depth, associated with the Levantine Intermediate Water. The temporal evolution reveals two maxima before the very recent period (until

2011), around 1992 and 2008, while since 2011 the layer is warming and becoming saltier at a much higher rate than before, each year reaching higher peak values within the water mass core. Since 2010–2011 this pattern is involving the whole water column down to 1700 m, where the deep water is now as warm and salty as it was the intermediate water during the peak in 2008.

## 2.4. Phytoplankton blooms in the Baltic Sea

**Authors:** Urmas Raudsepp, Jun She, Vittorio E. Brando, Rosalia Santoleri, Michela Sammartino, Mariliis Kõuts, Rivo Uiboupin, Ilja Maljutenko

**Statement of outcome:** The Baltic Sea phytoplankton bloom characteristics are evaluated based on spring and summer bloom statistics, summer chlorophyll-a and attenuation coefficient (Kd). Over the past 20 years, the mean start day of the spring bloom changed from day 120 in 1998 to around days 80–60 from the year 2003 onward. The intensity and spatial coverage of summer blooms decreased since 2009, and they tend to be more intensified in the subsurface layer. Low attenuation coefficient and chlorophyll-a anomaly, a late start date and early end date of the spring bloom, as well as low spatiotemporal coverage of the bloom show that the spring bloom in 2017 was exceptionally weak. Summer phytoplankton bloom was strong in the Gulf of Bothnia and dominated by subsurface bloom in 2016, but shifted to the northern Baltic Proper in 2017.

**Products used:**

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>2.4.1</td><td>OCEANCOLOUR_BAL_OPTICS_<br>L3_REP_observations_009_097<br>Remote sensing</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-OC-PUM-009-ALL.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-080-097.pdf</td></tr><tr><td>2.4.2</td><td>OCEANCOLOUR_BAL_CHL_<br>L3_REP_observations_009_080<br>Remote sensing</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-OC-PUM-009-ALL.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-080-097.pdf</td></tr></tbody></table>

Recently comprehensive classifications of eutrophication status of the Baltic Sea were made using the third version of the HELCOM Eutrophication Assessment Tool (HEAT 3.0), applying indicators with commonly agreed targets of good environmental status (HELCOM
<figure><img src="image_67.png" /></figure>

2015). The indicators were grouped under three 'cri-
teria': (1) nutrient levels (i.e. winter dissolved inorganic
phosphate and nitrogen in the upper 10 m of the water
column), (2) direct effects (summer chlorophyll-a in the
upper 10 m and secchi depth) and (3) indirect effects
(annual oxygen debt below the halocline and benthic
invertebrates). Spring bloom has been less considered
for the assessment of the Baltic Sea eutrophication.
Here we propose that spring and summer bloom stat-
istics, summer chlorophyll-a and attenuation coefficient
(Kd) from the products 2.4.1 and 2.4.2 of the product
table could be successfully used as one of the relevant
indicators in evaluating the Baltic Sea eutrophication
status. Annual oxygen content and nutrient conditions
in the Baltic Sea will be subject of the forthcoming
studies to complete the set of eutrophication status indi-
cators of the Baltic Sea.

CMEMS ocean colour product references 2.4.1 and 2.4.2 provide a good spatiotemporal coverage of the surface bio-optical features in the Baltic Sea, although their uncertainties are still high (r² = 0.4, for chlorophyll-a, see Pitarch et al. 2016). The phytoplankton abundance and succession in the Baltic Sea are characterised by dinoflagellate- and diatom-dominated spring bloom and cyanobacterial summer bloom (Kahru and Nõmmann 1990; Kahru et al. 2018). The spring bloom (spring is from days 31 to 160) is detected using the Siegel (2002) approach (i.e. it is a spring bloom if chlorophyll-a > median + 5%) for each pixel in the basin, after which the statistics for the bloom onset are calculated as in Groetsch et al. (2016) and presented as distribution of the start, peak and end days over the whole basin per each year. The spring and summer bloom spatiotemporal coverage (day km²) is aggregated from daily subsurface and surface bloom following Hansson and Håkansson (2007). Summer blooms are detected by applying the thresholds defined by Hansson et al. (2010) on remote sensing reflectance spectra (Rrs) at the wavelength of 550 nm and Rrs at 676 nm for the subsurface and surface blooms, respectively. Although 20 years of satellite data are available in the Baltic Sea, it should be noted that the remote sensing indexes computed by SMHI for HELCOM, recorded from 2010 onwards, should not be directly compared with the 1997–2009 values, as an improved detection method is now used (i.e. Hansson et al. 2010). The time series for Rrs, chlorophyll-a and attenuation coefficient (Kd490) used in this study are fully homogeneous as they are based on a merged product from different satellites that corrects for inter-sensor differences (Mélin et al. 2017; Sathyendranath et al. 2017). Attenuation coefficient values are proxies for Secchi depth, accounting for water transparency.

During 1998–2001, only SeaWiFS (O’Reilly et al.
1998) was available; while from the year 2002 onwards
there are two or three sensors available (Brewin et al.
2015).

We observed the start, peak and end day of the spring bloom during the period of 1998–2017 from product reference 2.4.2 (Figure 2.4.1). In most years, the spring bloom starts first in the western and southern Baltic Sea, then in the central Baltic, and the latest in the Bothnian Sea and Bothnian Bay (Fennel 1999; Wan et al. 2013). This gives significant spatial variability to the starting date of the spring bloom in the Baltic Sea. However, there are also years when the spring bloom starts early, e.g. in 1985 and 1986 as shown by Kahru and Nõmmann (1990). The mean start day of the spring bloom shifted earlier during the observation period (from day 120 in 1998 to day 50 in 2015). According to the start day and spatial variability, most of the spring bloom can be divided into three types:

* late onset spring bloom (later than day 100) that shows small spatial variability in the Baltic Sea, evident in 1998–2002;

* medium onset spring bloom (around day 80) that has large spatial variability, mostly found in 2003–2011;

* early onset spring bloom (around day 60) with rather small spatial variability, mostly found in 2012–2016 (Figure 2.4.1(a)).

The year 2017 deviates from this trend of declining start day by a late onset spring bloom (days 80–120). It also differs from the previously mentioned three types of spring blooms due to its high spatial variability that can be inferred by the interquartile range (Figure 2.4.1 (a)). There is a good consistency between the start day and spatiotemporal coverage of spring bloom (Figure 2.4.2(a)) – in general, later start day results in smaller spatiotemporal coverage. The seven late onset spring bloom years, 1998–2002, 2006 and 2017 (Figure 2.4.1 (a)) also represent the lowest spatiotemporal coverage (Figure 2.4.2(a)). Although the top two years with the largest spatiotemporal coverage (2008 and 2011) are marked with earlier start day (day 55 and day 65), earlier start day does not always result in large spatiotemporal coverage, as found in the years 2012–2016. These years also have quite small spatial variability of the start day in the Baltic Sea. The end day of spring bloom has shifted slightly later, from days 145–148 to day 150 from 2002 onwards. The peak day of spring bloom has fluctuated with no clear trend.

Since 2002 the spring bloom spatiotemporal cov-
erage has shown general tendency to increase until
<figure><img src="image_68.png" /></figure>

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /><figcaption>**Figure 2.4.1.** Time series of the spring bloom statistics (Siegel 2002; Groetsch et al. 2016) for start day (a), peak day (b), end day (c) for the period of 1998–2017 of the Baltic Sea from product reference 2.4.2. The bar refers to first quartile, median (black line) and third quartile, while the whiskers are the 5th and 95th percentiles respectively for start, peak and end days over the basin.</figcaption></figure>
<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /><figcaption>**Figure 2.4.2.** Time series of spring (a) and summer bloom (b) spatiotemporal coverage (day km²) (1998–2017) using method by Hansson and Håkansson (2007). Results are based on CMEMS product reference 2.4.2 and 2.4.1 respectively.</figcaption></figure>

2008–2011, followed by a decrease afterwards (Figure 2.4.2(a)). Spring blooms have been reported to weaken in intensity but lengthen during the period of 2000–2014 (Groetsch et al. 2016). Summer bloom coverage, however, increased from 1998 until 2005, and then decreased practically until present with a local minimum in 2012 (Figure 2.4.2(b)). Thus, high spatiotemporal coverage of the spring bloom is detected in 2007, 2008, 2009 and 2011, but peak years of the summer bloom spatiotemporal coverage are 2002, 2003, 2005 and 2008. There are several sub-periods of co-changes of the spatiotemporal coverage of the spring and summer blooms. For instance, the spring and summer blooms have a positive correlation in the period of 2013–2017. There is a slight positive trend in the spring bloom spatiotemporal coverage for the period of 2002–2011, whereas summer bloom shows a decreasing trend during the partially overlapping period of 2005–2012. Over the longer period of 1979–2017, the summer (cyanobacteria) blooms are reported to

be oscillating without a clear, significant trend (Kahru et al. 2018).

Since 2009, the summer bloom has been on a relatively low level, featured by more subsurface than surface bloom. The previous years (1998–2008) were dominated by the opposite feature (Figure 2.4.2(b)). The subsurface bloom declined continuously from 2014 to 2017, whereas the surface bloom increased in 2017, compared to the previous year.

One of the indicators of the eutrophication status of the Baltic Sea is secchi depth (HELCOM 2015) for which we use light attenuation coefficient (Kd) as proxy in this study. Summer mean attenuation coefficient (Kd) values in the Baltic Sea from the period of 1998–2017 derived from product reference 2.4.1. In open water light attenuation is mainly caused by phytoplankton species, while coloured dissolved organic matter (CDOM) and resuspended particulate matter are the main optically active substances in the coastal zone. Summer mean attenuation coefficient values derived from satellite remote sensing reflectance
<figure><img src="image_74.png" /><figcaption>**Figure 2.4.3.** Time series of the attenuation coefficient (Kd490) averaged over the Baltic Sea in summer of 1998–2017 (product reference 2.4.1).</figcaption></figure>

(Figure 2.4.3) stand for high values in the coastal zones, as reflected on the chlorophyll-a anomaly also (Figure 2.4.5).

In general, low attenuation coefficient values are observed in 1998–2001, followed by an increasing trend until 2010 and a decline until present. Last two years, i.e. 2016 and 2017, indicate an increase in summer mean attenuation coefficient values again. The attenuation coefficient anomaly in the summer of 2016 is positive over the entire sea area except for the eastern part of the Gulf of Finland (Figure 2.4.4(b)). Especially high values are observed in the Bay of Bothnia. In summer 2017, the area with positive attenuation coefficient anomaly has been reduced to the northern and eastern Baltic Proper and southern Gulf of Riga (Figure 2.4.4(d)). The attenuation coefficient anomaly in the springs of 2016 and 2017 shows strong inter-annual changes (Figure 2.4.4(a,c)). Excluding the coastal areas close to the major river inlets, where coloured dissolved organic

<figure><img src="image_75.png" /><figcaption>b) Kd490 anomaly summer 2016</figcaption></figure>

<figure><img src="image_76.png" /><figcaption>c) Kd490 anomaly spring 2017</figcaption></figure>

<figure><img src="image_77.png" /><figcaption>d) Kd490 anomaly summer 2017</figcaption></figure>

<figure><img src="image_78.png" /><figcaption>**Figure 2.4.4.** Attenuation coefficient (Kd) anomaly fields in spring 2016 (a) and 2017 (c) relative to the 1998–2014 spring mean field, and in summer 2016 (b) and 2017 (d) relative to the 1998–2014 summer mean field in the Baltic Sea. Data from product reference 2.4.1.</figcaption></figure>
<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /><figcaption>**Figure 2.4.5.** Chlorophyll-a anomaly fields in the Baltic Sea in spring 2017 relative to the 1998–2014 spring mean field (a) and in summer 2017 relative to the 1998–2014 summer mean field (b). Data from product reference 2.4.2.</figcaption></figure>

matter could be a major contributor to the high attenuation coefficient values, elevated attenuation coefficient values are seen in the entire Baltic Proper and the Bothnian Sea. In spring 2017, attenuation coefficient anomaly is negative all over the Baltic Sea (Figure 2.4.4(c)). A very high positive attenuation coefficient anomaly in the southeastern Gulf of Riga and eastern Gulf of Finland could include contamination of attenuation coefficient values from ice coverage. The differences in the attenuation coefficient anomaly in the springs of 2016 and 2017 are well reflected in the spatiotemporal coverage of the spring bloom, with the 2017 spring bloom being less than half of the spring bloom in 2016 (Figure 2.4.2(a)). The attenuation coefficient anomaly in summer 2016 is higher than in 2017 (Figure 2.4.4(b,d)), which is reflected also in the summer mean attenuation coefficient values (Figure 2.4.3) and in the spatiotemporal coverage of subsurface bloom, but not in the surface bloom (Figure 2.4.2(b)). Measured phytoplankton wet weight during spring bloom has been substantially higher in 2016 than in 2017, but comparable in the summers of 2016 and 2017 (Wasmund et al. 2017, 2018). In general, by comparing time series of summer mean attenuation coefficient values and summer bloom spatiotemporal coverage (Figures 2.4.2(b) and 2.4.3), we conclude no obvious match of these two parameters.

Chlorophyll-a spring anomaly in 2017 showed high values in the Gulf of Riga and the eastern part of the Gulf of Finland. High values with a smaller extent were visible in the northern coastal area of the Bothnian Bay and coastal areas of the Gulf of Gdansk and the Curonian Lagoon. To a larger extent, lower values are evident in the western part of the Gulf of Finland. The spatial

distributions of measured chlorophyll-a are versatile in the spring of 2017 (Wasmund et al. 2018). In March, chlorophyll-a concentrations show higher values near the Danish Straits, with decreasing gradient towards the Gotland Basin. In May, the spatial gradient of chlorophyll-a was reversed – chlorophyll-a concentrations were significantly higher in the eastern and southern Gotland basin and lower near the Danish Straits.

Chlorophyll-a summer anomaly in 2017 has highest values in the eastern part of the Gulf of Finland and the Gulf of Riga, which extend further in space, compared to same areas in 2016 (Raudsepp et al. 2018). Extensive high chlorophyll-a values in the northern Baltic Proper in 2017 are not visible in 2016. At the same time, high chlorophyll-a values, which are seen across the Bothnian Bay in 2016, are only limited to the eastern coast in 2017. Both years share similarly low summer chlorophyll-a anomaly values in the Gulf of Gdansk and on the coasts of Lithuania and Kaliningrad, except for the outflow area from Curonian lagoon. In summer 2017, spatial differences in measured chlorophyll-a concentrations were relatively uniform in the southern Baltic, but much higher in the Eastern Gotland basin (Wasmund et al. 2018).

## 2.5. Cod reproductive volume potential in the Baltic Sea

**Authors:** Urmas Raudsepp, Ilja Maljutenko, Mariliis Kõuts

**Statement of outcome:** Cod (*Gadus morhua*) is a characteristic fish species in the Baltic Sea with major economic importance. The Baltic cod stocks have
<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /><figcaption>**Figure 2.5.1.** Map of the Southern Baltic Sea showing locations of the basins. Sixty metre isodepth is marked with grey line. Blue contour show geographical extent of cod reproductive volume (CRV) for 2016-12-31 based on CMEMS product reference 2.5.1 and 2.5.2.</figcaption></figure>

gone through a steep decline in the late 1980s (ICES 2016). In this section we focus on the impact of the Major Baltic Inflows on the eastern cod population. Hydrographic conditions affect cod directly through the survival of eggs. Our results indicate that the water volume with suitable conditions for cod reproduction has stayed mostly on the same level during the period of 1993–2017. We verify the importance of the Major Baltic Inflows shaping a suitable environment for cod reproduction and the Bornholm basin as the most important spawning ground. Cod reproductive volume is on a relatively stable level in the Bornholm basin, while highly dependent on inflows in the downstream basins. Available data about the Major Baltic Inflows can be used as an indicator of important biological processes.

**Products used:**

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>2.5.1</td><td>BALTICSEA_REANALYSIS PHY_003_011<br>Reanalysis</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-BAL-PUM-003-011.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-BAL-QUID-003-011.pdf</td></tr><tr><td>2.5.2</td><td>BALTICSEA_REANALYSIS PHY_003_012<br>Reanalysis</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-BAL-PUM-003-012.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-BAL-QUID-003-012.pdf</td></tr></tbody></table>

Atlantic cod (*Gadus morhua*) is a major fish species of the Baltic Sea with high economic importance. The Baltic Sea cod consists of two genetically different populations – eastern and western Baltic cod. We focus on the eastern cod as it contributes to the landing numbers the most and has several spawning grounds in the deep areas of the Baltic Sea, east of Bornholm island. The eastern Baltic cod stock has been at the absolute low since the 1980s (ICES 2005; FishSource). There are two sets of governing factors – anthropogenic and natural. Anthropogenic effects mostly manifest through high fishing pressure. Natural conditions vary in the Baltic Sea in relation to water exchange with the North Sea – the Major Baltic Inflows in particular, which create the specific environmental conditions for cod to reproduce. We focus on the Major Baltic Inflows, the most important controlling factor of cod recruitment in the Baltic Sea (MacKenzie et al. 1996; Köster et al. 2005). The dramatic decline of cod stocks overlaps with the decrease in the frequency and intensity of the North Sea advections in the late 1980s (Heikinheimo 2008). Recruitment started to decline in the mid-1980s despite the spawning stock and the number of eggs produced by old females still being high (Cardinale and Arrhenius 2000). There was no significant change in fishing pressure during that period (ICES 2005). This implies that other factors besides fishing might have been important (Heikinheimo 2008). Hydrographic factors affect cod recruitment directly through the survival of eggs, and indirectly via species composition and availability of the prey organisms which are essential for cod larvae survival (Heikinheimo 2008). As a result, cod reproduction in the eastern areas of the sea, including the Gdansk Deep, almost completely ceased and continued only in the Bornholm Basin (Nielsen 1998). Karasiova (2011) found a positive correlation between the interannual fluctuations of egg production and water volumes with conditions favourable for cod reproduction in the four main spawning areas in the Baltic Sea.

The Bornholm Basin is the most important spawning ground for the eastern Baltic cod stock, followed by the Gotland and Gdansk basins (MacKenzie et al. 1996; Hinrichsen et al. 2017) (Figure 2.5.1). In order to describe the relationship between cod reproduction and the Major Baltic Inflows we use ‘cod reproductive volume’ (CRV), which is based on previous research and takes into account the three most important influencing factors of cod reproductive success: salinity over 11 g/kg (product reference 2.5.1), oxygen content > 2 ml/l (product reference 2.5.2) and water temperature over 1.5°C (product reference 2.5.1) (MacKenzie et al. 1996; Heikinheimo 2008; Plikshs
<figure><img src="image_83.png" /></figure>

et al. 2015). Our aim is to derive the reproductive potential of the eastern Baltic cod from dynamics, characteristics and volume of the inflowing water and compare these results with the published numbers. This approach enables to use available data about Major Baltic Inflows as an indicator for important biological processes, which could be of use in evaluating the status and development of Baltic cod stocks in the future. The boundary of the Baltic Sea has been taken from the western boundary of the Bornholm Basin according to the ICES fishery subdivisions.

Our results show that the cod reproductive volume has been more or less stable in the Bornholm Basin during the period of 1993–2017 (Figure 2.5.2(a)). There were periods of elevated cod reproductive volume in the Baltic Sea in 2003–2004, 2006–first half of the 2007, 2014–first half of the 2015 and 2016, which contribute to a small positive trend of 8.8 km³/year over the study period. Saline water volume of the Baltic Sea, however, shows considerable increase since the 1993 Major Baltic Inflow (Figure 2.5.2(a)) and due to the following inflows in 1997, 2003 and 2014. Cod biomass values were estimated to be slightly higher around 2010, which might reflect the period with sufficient conditions after 2003 (Karasiova 2011). This is in an agreement with the

highest values of cod stock size indicator (Figure 2.5.2(a)). The ICES stock size indicator is a combined biomass index, in kg/h, of cod ≥30 cm from the Baltic International Trawl Survey (BITS) in quarters 1 and 4 in the Baltic Sea subdivisions of 25–28 (ICES 2018). Also, egg counts responded to Major Baltic Inflows with higher values in 1994, 1997 and 2003 (Karasiova 2011). The cod reproductive volume is in minimum in 2000 and in 2011 with the values comparable to low cod reproductive volume values at the end of the stagnation period in the Baltic. The saline water volume has never dropped to a value comparable to the value at the end of the stagnation period. The lowest saline water volume values can be seen in the Baltic Sea in 2002 and 2013.

There is a strong positive correlation between cod reproductive volume and saline water volume in the Bornholm Basin ($R^2$ = 0.82) and the two volumes vary less compared to the rest of the Baltic Sea (Figure 2.5.2(a)). In the Bornholm Basin, average saline water volume and cod reproductive volume are 315 km³ (SD = 59 km³) and 220 km³ (SD = 80 km³), respectively. In the rest of the Baltic Sea, the saline water volume is associated with Major Baltic Inflows – the larger the inflow, the bigger the volume of salty water that goes to other basins. The impact of the inflow events was bigger following the inflows of

<figure><img src="image_84.png" /></figure>

**Figure 2.5.2.** (a) Time series of saline water volumes (SWV, S > 11 g/kg) black line and cod reproductive volumes (CRV) in the Baltic Proper (thick lines), excluding Arkona Basin; and in Bornholm Basin (thin lines); and cod stock size indicator. Dots in the red line represent the geometric mean of the biomass of ≥30 cod specimens in the previous year from the Baltic International Trawl Survey in subdivisions 25–28. (b) Time series of cod reproductive volume (CRV, S > 11 g/kg, O₂ > 2 ml/l) extending outside the Bornholm Basin. Time series are calculated from CMEMS product reference 2.5.1 and 2.5.2.
<figure><img src="image_85.png" /></figure>

2003 and 2014. Similarly to salinity, increase in cod reproductive volume reflects the impact of the Major Baltic Inflows to the basins downstream of the Bornholm Basin (Figure 2.5.2(a,b)). Suitable conditions for cod reproduction persist for a shorter time period (Figure 2.5.2(b)) than that of the saline water volume (Figure 2.5.2(a)) which means that oxygen content declines faster than salinity in the bottom layers of the Baltic Sea (Raudsepp et al. 2018).

The effect of the Major Baltic Inflows on cod reproduction has been studied earlier and results indicate that the effect can be small to important based on the pre-existing hydrological conditions, which determine how big the potential spawning area will be. The inflow of 1993–1995 had a small effect because the conditions before did not predispose the formation of large spawning areas, while the effect of the Major Baltic Inflow in 2003 was bigger (Karaseva and Zezera 2016). We used data from ICES to compare our values with the stock size indicator. The data fits our model results with increased cod reproductive volume having a positive effect on cod reproduction success, which reflects in an increase of stock size indicator ca. 4–5 years after the Major Baltic Inflow. The inflow in 2003 resulted in an increased stock size between 2007 and 2012 (Figure 2.5.2(a)). Eastern Baltic cod reaches maturity around age 2–3, depending on the population density and environmental conditions. Lack of oxygen and low salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin 1999; Karasiova et al. 2008). Since there was a Major Baltic Inflow again in 2014, which increased the cod reproductive volume up to 900 km³ in the Baltic Sea (Figure 2.5.2(a)), we expect an increase of cod stock size starting from 2018–2019.

## 2.6. The North Pacific Gyre Oscillation

**Authors:** Benoît Tranchant, Isabelle Pujol, Emanuele Di Lorenzo, Jean-François Legeais

**Statement of main outcome:** The North Pacific Gyre Oscillation is defined as the second dominant mode of variability of SSH anomaly and SST anomaly in the North East Pacific. This mode emerges as the leading mode of decadal variability for surface salinity and upper ocean nutrients, and is in a negative phase since 2014. This condition reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients. Here, this indicator is inferred from a long time series of

SSH anomalies over the North East Pacific (25°–62°N, 180°–250°E) and will be regularly updated.

**Product used:**

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>2.6.1</td><td>SSH anomalies<br>Numerical Ocean Model</td><td>Di Lorenzo et al. (2008)</td></tr><tr><td>2.6.2</td><td>SSS anomalies<br>Observations</td><td>Scripps Pier measurements collected by the Birch Aquarium at Scripps staff and volunteers. Data provided by the Shore Stations Program sponsored at Scripps Institution of Oceanography by California State Parks, Division of Boating and Waterways. Contact: shorestation@ucsd.edu.</td></tr><tr><td>2.6.3</td><td>NCEP wind stress 1950–2004<br>Numerical Atmospheric Model</td><td>The NCEP/NCAR 40-Year Reanalysis Project (Kalnay et al. 1996)</td></tr><tr><td>2.6.4</td><td>Altimeter data: previous version of SEALEVEL_GLOPHY_L4_REP OBSERVATIONS_008_047 (see below)<br>Remote sensing</td><td>Product User Manual (CMEMS-SL-PUM-008-032-051)<br>http://marine.copernicus.eu/documents/PUM/CMEMS-SL-PUM-008-032-051.pdf<br>Quality Information Document (CMEMS-SL-QUID-008-032-051)<br>http://marine.copernicus.eu/documents/QUID/CMEMS-SL-QUID-008-032-051.pdf</td></tr><tr><td>2.6.5</td><td>SEALEVEL_GLOPHY_L4_REP OBSERVATIONS_008_047<br>Remote sensing</td><td>Product User Manual (CMEMS-SL-PUM-008-032-051)<br>http://marine.copernicus.eu/documents/PUM/CMEMS-SL-PUM-008-032-051.pdf<br>Quality Information Document (CMEMS-SL-QUID-008-032-051)<br>http://marine.copernicus.eu/documents/QUID/CMEMS-SL-QUID-008-032-051.pdf</td></tr><tr><td>2.6.6</td><td>SEALEVEL_GLOPHY_L4_REP OBSERVATIONS_008_046<br>Remote sensing</td><td>Product User Manual (CMEMS-SL-PUM-008-032-051)<br>http://marine.copernicus.eu/documents/PUM/CMEMS-SL-PUM-008-032-051.pdf<br>Quality Information Document (CMEMS-SL-QUID-008-032-051)<br>http://marine.copernicus.eu/documents/QUID/CMEMS-SL-QUID-008-032-051.pdf</td></tr></tbody></table>

The Decadal North Pacific Gyre Oscillation is a climate pattern defined by Di Lorenzo et al. (2008). The North Pacific Gyre Oscillation term is used because its
<figure><img src="image_86.png" /></figure>

<figure><img src="image_87.png" /></figure>

**Figure 2.6.1.** (b) Time series of La Jolla Scripps Pier SS Sa (reference product 2.6.2) (blue) compared to the NPGO index (correlation R = 0.51, 99% significance), defined as the second PC of model SSHa (black). The SS Sa records are detrended and a 12 months running mean is applied. (d) Regression maps of model NPGO indices with the model SSHa. The white (black) contours indicate regions of positive (negative) wind stress curl (Ekman upwelling) as inferred from a regression of the model PCs with the NCEP wind stress curl (reference product 2.6.3). The % variance accounted for by each regression map is indicated. (f) Regression maps of the model NPGO index with satellite altimeter data gathered between 1993 and 2004 (reference product 2.6.4). The colour range is modified to [3.5 3.5] in Figure 2(f) to properly display the map (black contours are satellite/drifter-derived mean dynamic height [Niiler et al. 2003]). Credit from Di Lorenzo et al. (2008).

fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyre circulations. Thus, it has been defined as the 2nd dominant mode of sea surface height variability in the Northeast Pacific (25°–62°N, 180°–250°E, see Figure 2.6.1(d,f)). The North Pacific Gyre Oscillation index has been defined as the second Principal Component of model Sea Surface Height anomaly calculated over the period 1950–2004 (reference product 2.6.1), see Figure 2.6.1(b). Di Lorenzo et al. (2008) suggest that the North Pacific Gyre Oscillation is the oceanic expression of the atmospheric North Pacific Oscillation (Walker and Bliss 1932). This seems reinforced by the recent work of Yi et al. (2018) that show consistent pattern feature between the atmospheric North Pacific Oscillation and the oceanic North Pacific Gyre Oscillation.

This index measures change in the North Pacific gyres circulation and explains key physical-biological ocean variables including temperature, salinity, sea level, nutrients, chlorophyll-a. One example is the coherent variation of model and observed/modelled SSS (Sea Surface Salinity) in the Northeast Pacific (Di Lorenzo et al. 2008), see Figure 2.6.1(b). Like the Pacific decadal oscillation, the North Pacific Gyre Oscillation is a basin-scale feature and captures prominent low-frequency changes in the Pacific physical and biological fields. This is why the North Pacific Gyre Oscillation index provides a strong indicator of fluctuations in the mechanisms driving planktonic ecosystem dynamics.

A positive North Pacific Gyre Oscillation phase is a dipole pattern with negative SSH anomaly north of 40°N and the opposite south of 40°N. The North Pacific Gyre Oscillation index is presently in a negative phase, associated with a positive SSH anomaly north of 40°N and negative south of 40°N. This reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients.

The EOF's amplitude pattern (pers. Comm. Di Lorenzo) has been inferred from the principal component analysis. This regression map of the model North Pacific Gyre Oscillation index is used/projected with normalised satellite altimeter delayed-time sea level anomalies to calculate and update the NPGO index. An example is given on Figure 2.6.2 where the NPGO index has been projected on the last version of the reprocessed altimeter sea level record (CMEMS reference product 2.6.5).
<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /><figcaption>**Figure 2.6.2.** NPGO index monthly averages. From 1993 to 2003, the NPGO index is calculated from ref. 2.6.4, see Di Lorenzo et al. (2008). From 2004 onward, the NPGO index has been projected on normalised satellite altimeter Delayed-Time (DT) (CMEMS reference product 2.6.5) and Near-Real-Time (NRT) (CMEMS reference product 2.6.6).</figcaption></figure>

## 2.7. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations

**Authors:** Enrique Álvarez Fanjul, Álvaro de Pascual Collar, Begoña Pérez Gómez, Marta De Alfonso, Marcos García Sotillo, Joanna Staneva, Emanuela Clementi, Alessandro Grandi, Anna Zacharioudaki, Gerasimos Korres, Michalis Ravdas, Richard Renshaw, Jonathan Tinker, Urmas Raudsepp, Priidik Lagemaa, Ilja Maljutenko, Gerhard Geyer, Malte Müller, Veli Çağlar Yumruktepe

**Statement of main outcome:** This section explores the temporal and spatial variability of the extremes of sea level, waves and sea surface temperature over European seas and the Arctic region. The analysis combines information from both in-situ observations and model data available through CMEMS, demonstrating the potential of the service and the benefits of merging observational and modelled products. The main outcomes of this joint analysis of anomalies in 2017 relative to long-term average conditions include: (i) sea level extremes variability was near average in all coastal areas of Europe and the Arctic except for a few stations in the UK, west of Normandy and the northwestern Gulf of Bothnia in the Baltic, (ii) wave extremes were larger than usual in most of the Iberia-Biscay-Ireland region (anomalies of significant wave height about +0.5 m) and in the western Mediterranean Sea (+0.75 m) and lower than usual in the eastern Mediterranean and near the Irish coast (−0.5 m). In the Black Sea,

the usual spatial pattern is inverted, being the largest extremes on the eastern and central areas (up to 0.7 m). (iii) Sea surface temperature extremes anomalies were strongly positive in most of the Mediterranean Sea, typically by +1°C, and in the Northern Arctic regions reaching as high as +6°C. Negative anomalies were found in the Baltic (up to −3°C) and the North Sea (~−1°C).

**Products used:**

<table><thead><tr><td>Ref. No.</td><td>Product name and type</td><td>Documentation</td></tr></thead><tbody><tr><td>2.7.1</td><td>INSITU_IBI_TS_REP<br/>OBSERVATIONS_013_040<br/>Observations reprocessed</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-040.pdf</td></tr><tr><td>2.7.2</td><td>INSITU_NWS_TS_REP<br/>OBSERVATIONS_013_043<br/>Observations reprocessed</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-043.pdf</td></tr><tr><td>2.7.3</td><td>INSITU_MED_TS_REP<br/>OBSERVATIONS_013_041<br/>Observations reprocessed</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-041.pdf</td></tr><tr><td>2.7.4</td><td>INSITU_BAL_TS_REP<br/>OBSERVATIONS_013_038<br/>Observations reprocessed</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-038.pdf</td></tr></tbody></table>

(Continued)
<figure><img src="image_90.png" /></figure>

<figure><img src="image_91.png" /><figcaption>**Figure 2.7.1.** Map of mean 99th percentile significant wave height computed from reanalysis model data and *in situ* measurements (inset circles) for the Iberian-Biscay-Ireland region (a) (product ref. 2.7.22 and 2.7.1), Mediterranean Sea (c) (product ref. 2.7.16 and 2.7.3) and Black sea (e) (product ref. 2.7.14). Map of 99th percentile anomaly in 2017 for the Iberian-Biscay-Ireland region (b) (product ref. 2.7.11 and 2.7.5), Mediterranean Sea (b) (product ref. 2.7.24 and 2.7.7) and Black sea (f) (product ref. 2.7.13).</figcaption></figure>

each year of data, from 1993 to 2016 for sea level, and
from 2000 to 2016 for waves and sea surface tempera-
ture. Once the datasets based on models and obser-
vations are obtained, results from buoys and tide
gauges are superimposed on the percentile maps gener-
ated from the model multi-year products (Figures 2.7.1-
2.7.3 – left panels). In a second step, this process is
repeated for the data of year 2017. Finally, 2017 and
mean data are subtracted, producing, for each variable,
a 2017 anomaly map (Figures 2.7.1–2.7.3 – right panels).
For in-situ data, a minimum number of years (15 for tide

gauges and 10 for buoys) with a percentage of valid
measurements larger than 70% is required for a station
to be included in the study. To avoid problems related
to changes in reference level, all sea level data was
detrended by subtracting each year the annual
mean. This does not affect the main objective of the
study, to analyse how anomalous 2017 was in terms of
extremes.

For several regions the lack of data at CMEMS made
impossible the analysis of some variables, as shown in the
following sections.
<figure><img src="image_92.png" /></figure>

## 2.7.1. Extremes variability in the Iberian-Biscay-Ireland area.

### Waves

Figure 2.7.1(a) shows that wave extremes vary with latitude (Pontes 1998), being larger in northern seas (99th percentile larger than 8 m). The buoys in the area show results consistent with the model. Some exceptions are present, corresponding to some stations that are moored in shallow water (i.e. Gijón coastal buoy to the north of Spain), were local effects are not resolved by the model.

The year 2017 shows positive anomalies in most of the domain (Figure 2.7.1(b)), with values up to 1 m. In contrast, the anomalies are negative west of Ireland (–1 m) and around 34°N. The map of 2017 anomalies, presenting bands of positive and negative values depending on latitude, is consistent with the well-known yearly North–South displacement of the storm tracks. These

latitudinal displacements alter the wind patterns over the region and, therefore, the extreme waves.

In general, there is good agreement between the 2017 data from model (product ref. 2.7.11) and from observations (product ref. 2.7.5), but with some exceptions. Off the North coast of Spain, some buoys show negative anomalies in a region where the model shows positive values. This is most likely linked to extreme values not captured by the buoys due to malfunction during severe individual storm events.

### Sea Surface Temperature

Sea surface temperature mean 99th percentile in the Iberian-Biscay-Ireland area (Figure 2.7.2(a)) is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). Also visible is the upwelling signal off the African and western Iberian Peninsula coasts, present because the maximum extreme values

<figure><img src="image_93.png" /></figure>

**Figure 2.7.2.** Map of mean 99th percentile sea surface temperature computed from reanalysis model data and *in situ* measurements (inset circles) for the Iberian-Biscay-Ireland region (a) (product ref. 2.7.10 and 2.7.1), North-West Shelf region (b) (product ref. 2.7.18 and 2.7.2), Mediterranean Sea (e) (product ref. 2.7.15 and 2.7.3), Baltic Sea (g) (product ref. 2.7.20 and 2.7.4) and Arctic Ocean (h) (product ref. 2.7.26). Map of 99th percentile anomaly in 2017 for the Iberian-Biscay-Ireland region (c) (product ref. 2.7.12 and 2.7.5), North-West Shelf region (d) (product ref. 2.7.17 and 2.7.6), Mediterranean Sea (f) (product ref. 2.7.23 and 2.7.7), Baltic Sea (g) (product ref. 2.7.19 and 2.7.8) and Arctic Ocean (h) (product ref. 2.7.25).
occur in summer, coinciding with upwelling events (Ambar and Días 2008).

The level of consistency with in-situ measurements (product ref. 2.7.1) is excellent. This agreement is due to the accuracy of the modelled product and because sea surface temperature extremes occur in the summer, when malfunctions on the buoys are rare due to mild weather conditions, and therefore high values are usually properly captured.

Results for 2017 (Figure 2.7.2(c)) show negative anomalies in most of the Atlantic domain, with values around -1°C, except in a band centred at 42°N and in the south, around the Canary Islands, where positive values can be found (~1°C). The African coast shows a positive anomaly of the extremes, reflecting a weaker than usual upwelling during the extreme temperature events at the region. Positive anomalies can also be seen in the English Channel. Buoy and model results for 2017 extremes are in good agreement.

## Sea Level

Spatial differences in the mean 99th percentile of sea level reflect mainly the large differences in tidal amplitude in the region (Figure 2.7.3(a)): highest sea levels are found in the waters surrounding the British Isles and in the English Channel, where the astronomical tide is amplified (Pugh 2004). The mean 99th percentile of sea level is in considerable agreement for both model and in situ data at most of the stations.

Figure 2.7.3(b) shows positive anomalies (<0.1 m) for year 2017 below 40°N in the Atlantic, and negative values northwards. These anomalies agree well for both model and tide gauge data along the Spanish coast (very small and even negative anomalies in year 2017) and in the Mediterranean part of the Iberian-Biscay-Ireland domain, where sea level variability is lower (tide amplitude <0.5 m). Largest positive and negative anomalies for year 2017 (-0.3 to 0.5 m) are present around the British Isles and the English Channel.

In the Western coast of the British Isles the mean percentiles from the model are in generally good agreement with tide gauge data. This is clear in higher resolution plots (not shown). However, the anomalies for year 2017 reveal differences between tide gauge data (very small or zero values) and model data (larger positive and negative values). Differences in this overlapping region between this model and the one used for the North-West Shelf analysis (product ref. 2.7.17 and 2.7.18) are also evident. This is due to different astronomical tide propagation in the two different configurations of the Iberian-Biscay-Ireland model for the period 1993–2016 (product ref. 2.7.10) and for year 2017 (product ref. 2.7.12): both models have different spatial resolutions (1/12° vs. 1/36° respectively) and this is important for modelling the astronomical

tide in coastal regions with complex bathymetry andography (Shum et al. 1997; Turner et al. 2013).

## 2.7.2. Extremes variability in North-West Shelf region

### Sea Surface Temperature

The mean results (Figure 2.7.2(b)) show the latitudinal dependence of 99th percentile sea surface temperature, with values ranging from 22°C at 40°N to 9°C near Iceland. The shallower waters of the North Sea present warmer extreme values than those at equivalent latitudes in the Atlantic.

2017 (Figure 2.7.2(d)) show positive anomalies in north and south of the domain. There is an extensive area of positive anomaly in the north-east region, extending to the coast of Iceland. This warm anomaly extends onto the North-West Shelf around north-eastern Scotland and is possibly advected with the Fair Isle current into the North Sea. This is also visible around north-eastern Scotland, from Aberdeen to north of the Orkneys, and near the Hebrides. There is a positive anomaly extending from the English Channel into the Southern Bight of the southern North Sea, which is coincident in both the North-West Shelf and Iberian-Biscay-Ireland model domains. There is a narrow band of positive anomaly along the coast of the northern part of the Bay of Biscay, near Nantes, although the exact extent varies between the North-West Shelf and Iberian-Biscay-Ireland domains. Anomaly values are negative in the rest of the domain, especially near the Baltic Sea and south of the Scandinavian Peninsula.

In general, model and in-situ results are very consistent.

### Sea Level

The annual mean of the 99th percentile sea level map (product ref. 2.7.18) for the North-West Shelf reflects the spatial distribution of tidal ranges (Figure 2.7.3(c)), consistent with the $M_2$ amplitude co-tidal chart (O'Dea et al. 2012, 2017). Mean 99th percentiles model reanalysis data (product ref. 2.7.18) are consistent (Figure 2.7.3(c)) with the tide gauge data (product ref. 2.7.2).

The 2017 99th percentile value is very similar to that of the reanalysis period over most of the domain, in both pattern and amplitude (not shown) and this is reflected by the anomaly map (Figure 2.7.3(d)) which has values ranging from -10 cm to +40 cm. The largest differences occur in the North Sea, with 2017 being up to 30 cm higher in 2017, than in the mean. This is particularly significant when looking at the percentage differences (not shown) as the mean tidal ranges are relatively low here (see Figure 2.7.3(c)) – 2017 was >60% higher than in
<figure><img src="image_94.png" /></figure>

**Figure 2.7.3.** Map of mean 99th percentile sea level computed from reanalysis model data and *in situ* measurements (inset circles) for the Iberian-Biscay-Ireland region (a) (product ref. 2.7.10 and 2.7.1), North-West Shelf region (c) (product ref. 2.7.18 and 2.7.2) and Baltic Sea (e) (product ref. 2.7.20 and 2.7.4). Map of 99th percentile anomaly in 2017 for the Iberian-Biscay-Ireland region (b) (product ref. 2.7.12, 2.7.5 and 2.7.9), North-West Shelf region (d) (product ref. 2.7.17, 2.7.6 and 2.7.9) and Baltic Sea (f) (product ref. 2.7.19 and 2.7.8).
the reanalysis period. This appears to be a large difference in the western English Channel, with the 2017 extreme sea levels being less than in the reanalysis period, by ~10 cm, but given the large tidal range here, is ~10% lower.

Interestingly, in the Skagerrak and Kattegat there is a disagreement between the model and *in situ* anomalies (up to 40 cm difference), with the tide gauges showing a negative anomaly, while the model shows a positive bias. This is likely a result of the Baltic boundary conditions used in the North-West Shelf reanalysis. There is a relatively good agreement between the model and tide gauges anomalies around the UK.

North-West Shelf and Iberian-Biscay-Ireland model generally a good spatial agreement. Both show the negative anomaly in the eastern English Channel, and across the centre of the Irish Sea, with adjacent positive anomaly in the western English Channel and Celtic Sea, and Irish Sea. To the west of the UK there is a large difference in the sea level anomaly, with the Iberian-Biscay-Ireland showing a negative anomaly in the central and northern North Sea. This difference could be related, as previously discussed, to the different Iberian-Biscay-Ireland tidal solutions in reanalysis and operational products.

### 2.7.3. Extremes variability in the Mediterranean region

#### Waves

Figure 2.7.1(c) shows that the highest values of the mean annual 99th percentile are located where high winds and long fetch are simultaneously present. Specifically, such values extend from the Gulf of Lion to southwestern Scandinia through the Balearic Sea. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that are accelerated by orography (Menendez et al. 2014) and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves whilst in the Aegean and Levantine Seas, high waves are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014).

During the year 2017 (Figure 2.7.1(d)), the positive anomalies in the west (up to 1.25 m) and central-west Mediterranean and the negative anomalies in the east (up to -0.75 m) are in turn indicative of stronger than usual winds in the former case and weaker than usual in the latter. Model data and in-situ data show consistent results.

#### Sea Surface Temperature

The mean annual 99th percentile (Figure 2.7.2(e)) in the period 1993–2016 presents lower values in the Alborán

Sea, linked to the inflow of cold Atlantic waters through the Gibraltar Strait, in the Gulf of Lion, caused by the strong Mistral wind, and in the North Aegean Sea, which also can be explained by the action of local winds; while highest sea surface temperature values are mainly located in the Levantine Sea, which is the warmest area of the Mediterranean Sea, and in the Gulf of Gabes.

Results for the year 2017 (Figure 2.7.2(f)) are characterised by very large positive anomalies in most of the domain, with maximum value of around 1.8°C. Negative anomalies are located in limited areas (Gulf of Lion and Eastern Aegean Sea) with values down to -1.5°C.

### 2.7.4. Extremes variability in the Baltic region

#### Waves

The distribution of extreme significant wave heights during 2017 from product reference 2.7.21 corresponds to the fetch limited wind waves generated by prevailing westerly and south-westerly winds (Isemer et al. 2008). The highest significant wave heights up to 4.5 m were in the southeastern Baltic Proper remaining over 4 m until the entrance of the Gulf of Finland. In the Gulf of Finland wave growth is limited by the narrowness of the gulf (Tuomi et al. 2011) and therefore percentiles remain in range of 2–3 m. In the northern Baltic extreme significant wave heights reach up to 4 and 3.5 m in Bothnian Sea and Bothnian Bay respectively.

It is worth to mention, nevertheless, that results from the wave field for the year 2017 (not shown) are in good agreement with the data from the two buoys available at the region.

There is no multi-year CMEMS wave product available for the Baltic at this moment. Therefore the 2017 anomaly values can't be computed. However, the differences were evaluated using published results of extreme wave heights from long-term wave field simulations (Björkqvist et al. 2017). The wave conditions in the Bothnian Bay were more severe in 2017 exceeding long-term percentiles by approximately 0.50 m. In the rest of the Baltic the 2017 significant wave height extremes remained within the range of ±0.25 m to the reported long-term percentiles by Björkqvist et al. (2017). In the calculation of wave statistics for 99th percentiles Björkqvist et al. (2017) excluded wave data from the calculations during ice covered period, while in CMEMS zero significant wave height values were used.

#### Sea Surface Temperature

Mean sea surface temperature extremes (product reference 2.7.20) show latitudinal gradient with temperatures in range of 20–22°C in the southern and 17–19°C in the northern part (Figure 2.7.2(g)). Lower sea surface
<figure><img src="image_95.png" /></figure>

temperature values along the western coast of the Baltic Sea represent the most frequent upwelling areas (Myrberg and Andrejev 2003; Lehmann et al. 2008, 2012) in response to the prevailing winds from the southwest (Isemer et al. 2008). In the Gulf of Finland, low temperature is obvious along both the northern and the southern coasts. These low value regions are due to coastal upwelling during different periods. The upwelling area along the northern coast is more extensive than along the southern coast, but the sea surface temperature values are slightly higher. The western part of the southern coast of the Gulf of Finland is described with lower sea surface temperature and wider offshore extent of the upwelling zone. The upwelling in the area is caused by strong wind events from the northeast. This peculiarity arises from shallower thermocline position and steeper slope on the southern coast of the Gulf of Finland (Kikas and Lips 2016). The eastern part of the southern Gulf of Finland is less affected by the upwellings, as there the sea surface temperature does not distinguish from the open sea area. Only long-lasting winds from the east or sequence of the storm from the east can cause intensive upwelling and drop of the sea surface temperature there (Suursaar and Aps 2007; Väli et al. 2011).

2017 presents a very clear negative anomaly over the whole domain (Figure 2.7.2(i)), both at the measuring stations (product ref. 2.7.8) and in the model data (product ref. 2.7.19). Sea surface temperature extremes show differences of -2 to -3°C from the mean. There are clear signals of upwelling events along the northern and eastern coasts of the Baltic Proper as well as on the northern coast of the Gulf of Finland. This corresponds to typical upwelling pattern with prevailing south-westerly winds. Especially distinctive is the upwelling zone extending along the northern coast of the Gulf of Finland, which is clearly visible on maps of sea surface temperature extremes for 2017 (not shown). There was no major upwellings on the southern coast of the Gulf of Finland, therefore the anomaly shows higher temperatures there compared to offshore/background differences.

## Sea Level

Mean sea level 99th percentiles range from approximately 0.4-0.8 m (Figure 2.7.3(e)) in the Baltic (product reference 2.7.20). The extremes in the Baltic Sea are mainly caused by wind setup amplified by the seiches and remotely generated long waves (Hünicke et al. 2015). The extremes are underestimated by about 0.1 m in the model. The discrepancy increases towards the southern Baltic Sea, being largest in the Danish Sounds. This could be attributed to the possible mismatch between model and measurements in the transition zone between the North Sea and the Baltic Sea.

The sea level extremes are slightly lower in the open part of the sea compared to the coastal areas, affected by the wind setup.

The anomaly of the sea level extremes in 2017 (Figure 2.7.3(f)) is in the range of ±0.15 m and -0.15-0.05 m in the model (product reference 2.7.19) and in the measurements (product ref. 2.7.8), respectively, which are not significant differences from the long-term mean extremes. Within this small range of variation, the model and measurements tend to agree in the Gulf of Bothnia and in the Gulf of Finland. The most notable differences are for the southern part of the Baltic Proper, where model results/measurements show higher/lower sea level extremes than the long-term mean. At the local scale, the model shows high sea level around the Archipelago Sea, but the measurements have lower extremes there. Obviously, local positive sea level anomalies there are a model artefact, as measurements show continuous increase in sea level anomaly from the Gulf of Finland through the Archipelago Sea to the Bothnian Sea.

## 2.7.5. Extremes variability in the Black Sea region

### Waves

Since there are no available wave observations in this region matching the requirements of this study, the analysis for this domain is based on model data only. Figure 2.7.1(e) shows a well-known pattern demonstrating that the highest values of the mean annual 99th percentile are in the areas where high winds and long fetch are simultaneously present (from multi-year wave product ref. 2.7.14). The mean extreme values in the region tend to be largest in the western Black Sea, with values as high as 3.5 m. Values in the eastern part of the basin are around 2.5 m (Behrens et al. 2019).

In 2017 (product reference 2.7.13), the highest values of the 99th percentile are in the central part of the basin (between 34 and 36 deg. E, not shown). In consequence, the anomaly values for 2017 show a strong longitudinal dependency, with negative anomalies in the western part of the Black Sea (up to 0.5 m) and positive ones in the central and eastern Black Sea (up to 0.7 m, Figure 2.7.1(f)). Results from wave fields for 2017 are in good agreement with satellite data (Jason-3 and Sentinel-3a) as well as with the Varna wave-buoy, showing values of mean SWH up to 1.8 m in the centre of the Black Sea. (Quality Information Document of 2.7.13 product).

## 2.7.6. Extremes variability in the Arctic region

### Sea Surface Temperature

The distribution of the mean annual 99th percentile of sea surface temperature (Figure 2.7.2(h)) reflects the
dominant currents of the region. The warmest waters with the subarctic (10–20°C) occur within the region of the northern European continental shelf and the North Atlantic Current, as well as within its extension through the Nordic Seas (Wekerle et al. 2017) and into the Barents Sea (Skagseth 2008). Relatively warmer (5–10° C) waters also extend northward to the west of Svalbard (Seidov et al. 2015) carried by the western branch of the Norwegian Atlantic Current. The Bering Strait connects the Arctic to the Pacific and exhibits relatively warm surface waters (>5°C) which extend into the Chukchi Sea (Seidov et al. 2015). Cold Polar Surface Water (Rudels 2015) which exists throughout the Arctic basin extends southwards through the Canadian Arctic Archipelago and along the east coast of Greenland, due to presence of the Greenland boundary current.

Anomalies of SST within the Arctic region and its extended domain during 2017 show a complex regional pattern of positive and negative anomalies. Negative anomalies are found throughout the northern Atlantic and much of the Canadian archipelago and the Laptev Sea. Positive anomalies of up to 6°C are found in the East Siberian Sea, the Beaufort Sea and the coastal regions of the Kara Sea. Positive anomalies of up to 3° C are seen throughout the Greenland Sea and in the northern Barents Sea. Positive anomalies of up to 4°C occur in the Chukchi Sea and in Hudson Bay.

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## Section 2.6. The North Pacific Gyre Oscillation

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<figure><img src="image_97.png" /></figure>

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## Section 2.7. Sea level, sea surface temperature and SWH extreme percentiles from model results and in situ observations in European Seas

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<figure><img src="image_98.png" /></figure>

## Chapter 3: Case studies

### 3.1. The use of Copernicus Marine Service products to describe the State of the Tropical Western Pacific Ocean around the Islands: a case study

**Authors:** Elisabeth Holland, Karina von Schuckmann, Maeva Monier, Jean-François Legeais, Silvia Prado, Shubha Sathyendranath, Cecile Dupouy

**Statement of main outcome:** The Western Tropical Pacific Ocean remains one of the last frontiers for scientific research. This area of the ocean is exposed to strong variations at inter-annual to decadal scales forced by climate modes such as the El Niño Southern Oscillation, as well as long-term changes driven by global warming. These Pacific Island States lie in the main route of devasting storms and this area of the ocean lacks ocean observations. We have created a Copernicus Marine Atlas for the Pacific Ocean States, that delivers ocean data to address the needs of decision-makers and to meet climate directives. It responds directly to Fiji's requests at the 2017 United Nations Oceans Conference for the Sustainable Development Goal (SDG)14 (for life below water) and in the 2017 COP23 conference for SDG13 (on climate action). The Copernicus Marine Atlas for the Pacific Islands States shows sustained and drastic ocean warming, sea level rise, and a decrease in the base of the marine food chain (phytoplankton) in this area. The Pacific Big Ocean States are vulnerable to the changing marine environment and face unprecedented threats to the three pillars of sustainable development: economy, environment, and society.

**Products used:**

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>3.1.1</td><td>1.5.1 DUACS (Data Unification and Altimeter Combination System) delayed-time altimeter daily sea level products, Altimetry</td><td>http://climate.copernicus.eu/climate-data-store</td></tr><tr><td>3.1.2</td><td>GLOBAL_REANALYSIS PHY_001_026<br>Reanalysis</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-026.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-026.pdf</td></tr><tr><td>3.1.3</td><td>INSITU_GLO_TS_OA_REP OBSERVATIONS_013_002_b<br>In situ<br>for the year 2017:<br>INSITU_GLO_NRT OBSERVATIONS_013_030<br>In situ</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-002-a.pdf;<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-002a.pdf)<br>for the year 2016:</td></tr></tbody></table>

(Continued)

Continued.

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>3.1.4</td><td>GLOBAL_REP PHY_001_021<br>In situ, remote sensing</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-030-036.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf<br>PUM:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-021.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-021.pdf</td></tr><tr><td>3.1.5</td><td>OCEANCOLOUR_GLO_CHL_L3_REP OBSERVATIONS_009_065<br>Remote sensing</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMSOC-PUM-009-ALL.pdf<br>QUID:<br>http://cmemsresources.cls.fr/documents/QUID/CMEMS-OC-QUID-009-064-065-093.pdf</td></tr></tbody></table>

Fiji served as President of the UN General Assembly in 2017, linking climate (SDG13) and ocean (SDG14) as the foundation of blue economies for island and coastal states around the world. The resulting United Nations Oceans outcome statement stressed

the importance of enhancing understanding of the health and role of our ocean and the stressors on its ecosystems, including through assessments on the state of the ocean, based on science and on traditional knowledge systems. We also stress the need to further increase marine scientific research to inform and support decision-making, and to promote knowledge hubs and networks to enhance the sharing of scientific data, best practices and 'know-how'. (UN 2017)

The Southern Pacific Ocean remains one of the last frontiers for scientific research. Few *in situ* monitoring systems exist to document the state of the Pacific Ocean. Indeed, accessing available fisheries data is compromised because of the competitive nature of the fishing industry (Transform Agorau, pers. comm.). The effective and growing Argo float network, with 3907 floats in February 2019 (http://www.argo.ucsd.edu/About_Argo.html), has truly revolutionised large-scale physical oceanography (Riser et al. 2016). The continuing limited capability of climate system models to adequately simulate ocean-climate coupling and dynamics, including the El Niño Southern Oscillation, underscores the importance of
<figure><img src="image_99.png" /></figure>

integrating the available data sets (Bellenger et al. 2014).

The Pacific Islands Meteorological Services Directors have repeatedly emphasised the need to include ocean forecasting and services in the suite of the WMO Global Framework for Climate Services (SPREP 2012), and the third Pacific Meteorological Council Meeting (SPREP 2017). As president of COP23, Prime Minister Frank Bainimarama has emphasised the importance of the climate and ocean connection and the need to protect ocean health to protect the planet: ‘We are all in the same canoe’ (https://cop23.com.fj/fijian-prime-minister-cop23-president-remarks-assuming-presidency-cop23/). The Copernicus Marine Service Atlas for Pacific Ocean States compiled by the author team responds directly to Fiji’s requests at the 2017 United Nation Oceans for SDG 14, life below water and the 2017 COP23 for SDG13, climate action which goes beyond the Pacific.

The Copernicus Marine Service Atlas for the Pacific Ocean States goes beyond the unique compilation of CMIP3 climate model projections and data tools compiled by the Pacific Climate Change Science Program (PCCSP 2011, 2014). A complete overview of tropical Pacific observing network is available in the WMO publication library (GCOS 2014a, 2014b). Our study focuses on the application of the available CMEMS products to the Pacific domain defined by PCCSP.

The data sets available through the Copernicus Marine Service provide a valuable window on the underobserved Pacific Ocean and help build a foundation for providing ocean services, including food security, essential biological variables and indicators of ocean health, data to inform early warning systems and climate adaptation. To begin, we address the following key ocean variables:

* sea surface temperature, a much-needed variable for assessing coral reef health and bleaching and tropical cyclone forecasting

* ocean heat content trends for the upper 700 m depth, to monitor ocean warming and thermal volume changes contributing to sea level rise; to track changes in stratification, ocean currents, as well as marine ecosystems and human livelihoods. Moreover, this indicator is linked to the ocean's role as a major heat source for the global atmospheric circulation and has important implications for regional and global climates, including severe events.

* sea level trends from 1993 to 2017 to better inform climate adaptation and coastal planning. Sea level trend values in the entire Pacific Islands domain (Box 3) range between -0.5 mm y⁻¹ and +7.2 mm y⁻¹, illustrating the non-uniformity of the sea level rise in this region.

* near-surface chlorophyll concentrations – as linked to phytoplankton populations – to assess ocean productivity and health as their changes can imply major impacts on ecosystem processes and biogeochemical cycling, which in turn can have significant implications for economy productivity and food availability.

To develop the Pacific case study, the domain was defined to encompass the 15 P-ACP (African, Caribbean and Pacific) countries defined by the EU Cotonou agreement and served by the EU's ACP secretariat (http://www.acp.int/) including: Cook Islands, Federated States of Micronesia, Fiji, Kiribati, Nauru, Niue, Palau, PNG, RMI, Samoa, Solomon Islands, Timor-Leste, Tonga, Tuvalu, and Vanuatu. The domain corresponded to that used for the Pacific Islands in the Pacific Climate Changes Science Program (Australia Bureau of Meteorology and CSIRO 2011). To explore the dynamics inside and outside the Western Pacific Warm Pool, the domain was further subdivided.

Global mean sea surface temperature, upper ocean heat content and mean sea level all show pronounced increasing trends over the last decades and with strong evidence that the positive trend is related to the increase in greenhouse gas concentration (Rhein et al. 2013). Nevertheless, the global surface and subsurface warming and global mean sea level rise is not spatially uniform (Meyssignac et al. 2016). Long-term warming of the western Pacific is a well-documented consensus in literature (Cane et al. 1997; Cravatte et al. 2009; Deser et al. 2010). Accordingly, the Copernicus Marine Service Atlas for the Pacific Islands area shows a significant surface and subsurface warming trend and sea level rise (Figures 3.1.1 and 3.1.2) at values close to and even exceeding the global mean warming and sea level rise rates (see Table 3.1.1, see also Chapter 1). The Western Pacific Islands area shows strong variability over various time scales (Merrifield et al. 2012; Han et al. 2014 Sun et al. 2017). At inter-annual time scales, western tropical Pacific surface and subsurface temperatures, and sea level vary in synchrony with the modes of the El Niño Southern Oscillation (Figure 3.1.2; e.g. Wang et al. 1999; Ablain et al. 2017). The near-surface layers warm in the easternmost Box 2 during the 1997/1998 and 2015/2017 El Niño phase (shown in red) and cool during the 1998–2000, 2007/2008 and 2010/2011 La Niña phase (shown in blue, Figure 3.1.2(a)). Moreover, year-to-year changes for western tropical Pacific sea level are thermogenic driven: sea level rises as ocean temperatures rise (Figure 3.1.2(b,c)).
<figure><img src="image_100.png" /></figure>

<figure><img src="image_101.png" /></figure>

**Figure 3.1.1.** Regional trends of (a) sea surface temperature and (b) upper ocean (0–700 m) ocean heat content over the period 1993–2017 for the western tropical Pacific. The results are derived from a multiproduct approach (ensemble mean of product 3.1.2–3.1.4). Black dots indicate areas where the noise exceeds two times the signal. (c) Regional sea level trend (in mm y⁻¹) for the western tropical Pacific over the period January 1993–May 2017 (note to reviewer: will be up-dated until December 2017) as derived from re-processed satellite altimetry data (product reference 3.1.1). No Glacial Isostatic Adjustment correction is applied on the altimeter data. (d) Map of regional chlorophyll trend (September 1997–December 2017) in the western tropical Pacific as observed by remote sensing. Only statistically significant (*p* < .05) trends are shown, and are based on the CMEMS product 3.1.5. See Table 3.1.1 for the definition of the dataset, and access to related documentation. Regions for analysis are indicated in (a), i.e. Western Pacific Islands (blue, Box 1; 130° E–165°E; 10°S to 10°N to encompass the Western Pacific Warm Pool); Central Pacific Islands (yellow, Box 2: 165°E–150°W; 10°S to 10°N); and Entire Pacific Islands domain (green, Box 3 is consistent with domain used for the Pacific Climate Change Science Program).