<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /><figcaption>**Figure 3.1.2.** Averaged time series over the full area (green line, Box 3, full map area of Figure 3.1.1(a)), and the western (dark blue line, Box 1) and eastern (yellow line, Box 2) tropical Pacific Ocean for (a) sea surface temperature, (b) ocean heat content, (c) sea level and (d) Chlorophyll-a. The seasonal cycle had been removed from all time series (1993–2014 for (a)–(c), 1997–2014 for (d)). Details on box areas and data use is given in the caption of Figure 3.1.1. The Multivariate El Niño Southern Oscillation Index is used to describe the ENSO phase with the El Niño phase shown as red (positive) shaded areas and La Niña shown as blue (negative) shaded areas (Wolter and Timlin 2011, downloaded: https://www.esrl.noaa.gov/psd/enso/mei/).</figcaption></figure>

We introduce chlorophyll concentration (Figure 3.1.1 (d) and Figure 3.1.2(d)) to provide a measure of the amount of phytoplankton present with its corresponding marine productivity on which the entire marine

ecosystem depends, directly or indirectly. Ocean chlorophyll could be used by decision-makers to assess the health and productivity of natural resources and marine life that depend on phytoplankton. Reduced chlorophyll

<table><caption>**Table 3.1.1.** Trend values and their uncertainties (90% confidence interval) of area mean Sea Surface Temperature (SST) and Ocean Heat Content (OHC) over the period 1993–2017 (ensemble approach from products 3.1.2–3.1.4), Sea Level over the period January 1993 to December 2017 (product ref. 3.5.1) and Chlorophyll concentration over the period September 1997–December 2017 (product ref. 3.5.5). Areas for Boxes 1 and 2 are given in Figure 3.1.1(a), and full domain covers the western tropical Pacific as shown in the maps of Figure 3.1.1. Note that the uncertainties related to the sea level internal variability are not included in the sea level trend uncertainties.</caption><thead><tr><th rowspan="2">Variable</th><th>Western Pacific Islands</th><th>Central Pacific Islands</th><th>Entire Pacific Islands domain</th></tr><tr><th>Box 1<br>1993–2017 trend</th><th>Box 2<br>1993–2017 trend</th><th>Box 3<br>1993–2017 trend</th></tr></thead><tbody><tr><td>SST<br>°C y<sup>-1</sup></td><td>+0.02 ± 0.01</td><td>+0.01 ± 0.02</td><td>+0.02 ± 0.01</td></tr><tr><td>OHC (0–700 m)</td><td>+1.9 ± 1.5</td><td>+0.8 ± 0.7</td><td>+1.2 ± 0.7</td></tr><tr><td>W m<sup>-2</sup></td><td></td><td></td><td></td></tr><tr><td>Sea level<br>mm y<sup>-1</sup></td><td>+4.8 ± 2.5</td><td>+2.8 ± 2.5</td><td>+3.5 ± 2.5</td></tr><tr><td>Chlorophyll<br>% y<sup>-1</sup></td><td>-0.4 ± 0.02</td><td>-0.7 ± 0.001</td><td>-0.4 ± 0.001</td></tr></tbody></table>
concentration and associated decrease in primary pro-
duction may negatively impact fish and marine life,
important for food security and economic health of the
Pacific Islands countries that dependent on fisheries.

Significant trends, both positive and negative, have
been reported on a regional basis, for both chlorophyll
concentration and primary production (Mélin et al.
2017; Racault et al. 2017), and some of the most pro-
nounced trends have been reported for the eastern and
western Pacific region. In the western Pacific area
(Box 1 of Figure 3.1.2(d), blue line), large variations
from year-to-year characterise the chlorophyll concen-
trations, highlighting the need for regular monitoring
services. River run off coincident with ITCZ and SPCZ
dynamics in the equatorial coastal areas like that of
northern Papua New Guinea explains the greater high-
frequency variability than open ocean regions. In the
central Pacific (Box 2 of Figure 3.1.2(d), yellow line), a
reduction of chlorophyll concentration since the year
1997 is recorded at a rate of ~-0.7%/year which is either
linked to decadal or longer variability (as observed in the
northern Pacific e.g. Sun et al. 2017) or the impact of cli-
mate change. Chla is strikingly well correlated with the
Central Pacific El Niño signal with high Chla associated
with the negative (La Niña) phase of ENSO as seen in
1998, 1999, 2007 and 2010, and a lower Chla associated
with the positive (El Niño) phase of ENSO as seen in
2015. For the entire Pacific Islands (green line, Box 3,
Figure 3.1.2(d)), Chla mimics that of the western
Pacific (blue line, Box 1) with a lower amplitude due to
the weaker correlation with Central Pacific ENSO events
(yellow line, Box 2). In the Fijian Archipelago, with the
inverse, Chla concentration is increasing between 0%
and 2% per year (Figure 3.1.1(c)), indicating a high posi-
tive response of phytoplankton, or/and a shift in phyto-
plankton composition (Dupouy et al. 2018).

Given that time series from remote sensing used here
is only 18 years long, and the dominant signature is dec-
adal scale variability (Gregg et al. 2017), this time series is
admittedly too short to disentangle the effect of inter-
annual variability and longer-term climate change.
Nevertheless, this series demonstrates (1) the correlation
between Chla and ITCZ in the western Pacific due to
coastal areas in large Pacific Islands in the Western
Pacific (Box 1), (2) the strong correlation between Chla
and La Niña phase due to the equatorial upwelling
enrichment in the Central Pacific (Box 2) and (3) less
correlation for the entire region due to smoothing of
the ENSO or ITCZ effects as demonstrated by the
reduced variability (Box 3). The variability around the
long-term trend appears visually to be inversely related
to the MEI (Figure 3.1.2(d), yellow line).

The State of the Pacific Ocean case study is a demon-
stration of how Copernicus Marine Service products
might be used to inform decision-making in a region
that regards itself as data poor, especially for ongoing
monitoring of biological variables. The perception of
data poverty results from limited capacity to access, dis-
play and analyse data (Holland 2018). The Copernicus
Marine Services State of Pacific Ocean challenges those
perceptions of data poverty by demonstrating the rich-
ness of the data available to inform decision-making in
the Pacific Islands.

Our hope is that the State of the Pacific Ocean atlas
serves as a springboard to begin stakeholder engagement
and dialogue on how to use the available data to inform
decision-making. In the 2018 Pacific Island Forum Lea-
ders meeting, the Pacific leaders of the 16 Forum member
countries prioritised climate change and blue economies.
Optimising utilisation of the available data requires
further dialogue at the science policy interface to generate
the robust products required to inform decision-making.

The Pacific Islands Forum Marine Sector Working
Group (MSWG) has prioritised the need to document
data available for the Pacific Islands domain. A first
step will be to show the products to the MSWG and
other stakeholders to engage them in collaborative dis-
cussions about how the data might be used. The long-
term goal is to produce data products that would inform
decision-making for the blue economies of the Pacific.
The data is available to inform decision-making on an
annual basis through the World Ocean Atlas, and
could become useful on more refined time scales, quar-
terly to weekly to inform climate and ocean outlooks.
One step might be to transform the data products pro-
vided here into real time data available to the Forum
Fisheries agency, Honiara, Solomon Islands for display
in their fisheries monitoring facility. Another step for
the MSWG would be developing real time data displays
that provide the more than 20 years of context for phys-
ical ocean monitoring (GCOS 184)

Future needs for the Copernicus data include refining
the approach to finer time scales with the eventual goal of
providing real time data and information services and
short-term two-week forecasts. The data products are
available through CMEMS, but (i) regionalisation (e.g.
downscaling) and (ii) cross-validation between products
(e.g. link the consolidated products with the non-conso-
lidated ones) are required. The data products shown are
subset of the data available for the Pacific Islands to
launch the much-needed dialogue with the key stake-
holders and champions. With the successful launch of
this Pacific Ocean Atlas, other ideas and innovations
will emerge for research products and applications.
<figure><img src="image_104.png" /></figure>

The Copernicus Marine Services State of Pacific Ocean analysis of available data demonstrates that the ocean surrounding the Pacific Islands is warmer, has higher heat content, with sea level rising at rates higher than the global mean and a decline in chlorophyll content.

## 3.2. Review of the use of ocean data in European fishery management and monitoring applications

**Authors:** Mark R. Payne, Patrick Lehodey

**Statement of main outcome:** Operational oceanographic data is potentially of great value for use in the monitoring and management of marine living resources due to the close coupling between the physiology of marine organisms and their environment. However, while oceanographic data is invaluable in understanding the processes governing the dynamics and behaviour of these organisms from a historical perspective, it has generally not been used in the day-to-day management of fisheries resources. We discuss the reasons for this situation and highlight emerging results, such as dynamic ocean management and marine ecological forecasting, that are starting to reverse this tendency. Finally, we discuss what can potentially be done to improve the uptake of this information.

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>3.2.1</td><td>SST_GLO_SST_L4_NRT_ OBSERVATIONS_010_001<br/>SST_GLO_SST_L4_REP_ OBSERVATIONS_010_011</td><td>PUM:<br/>http://marine.copernicus.eu/documents/PUM/CMEMS-OSI-PUM-010-001.pdf<br/>QUID:<br/>http://marine.copernicus.eu/documents/QUID/CMEMS-OSI-QUID-010-001.pdf</td></tr><tr><td>3.2.2</td><td>GLOBALenger_001_024_MONTHLY<br/>Model</td><td>PUM:<br/>http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-024.pdf<br/>QUID:<br/>http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-024.pdf</td></tr></tbody></table>

Marine organisms are coupled to their physical environment in a way that, as terrestrial mammals, is hard for us to comprehend. Most marine organisms are ectotherms ('cold blooded'), meaning that their body is at the same temperature as the surrounding environment: as a consequence, their metabolisms, and therefore their food requirements, growth rates, reproductive development and activity rates are all directly modulated by the temperature of their surroundings (Pörtner 2002). In addition, the concentration of dissolved oxygen at the surface is strongly temperature-dependent (higher temperature waters contain less oxygen) while below the surface the consumption of oxygen by other organisms can

lead to areas of critical oxygen depletion (Breitburg et al. 2018). Salinity can play an important role in limiting the fitness and distribution of organisms, particularly in and around regions of transition between fresher and saltier waters e.g. from the high salinity North Sea (surface salinity of approximately 33–35) to the low salinity Baltic Sea (surface salinity 2–15) (Pecuchet et al. 2016). Variations in seawater pH and its resulting impacts on carbonate concentration have also been shown to affect both shell-forming organisms (e.g. coccolithophores, shellfish) and higher organisms (e.g. fish) (Dupont and Pörtner 2013).

This tight linkage between the physical environment and marine organisms has long been recognised within both the science and management of living marine resources. Oceanographic data therefore can potentially play an important role in informing these activities (Tommasi et al. 2017). Here we review how operational oceanographic products are currently used in these fields, with a particular focus on the CMEMS product catalogue. We focus on three different applications of this information, according to the timescale in question, i.e. understanding the historical perspective, evaluating the current state of the system and looking towards the future. Finally, we examine potential future directions and how collaboration between these two fields can best be fostered.

Firstly, historical oceanographic data play a key role in developing our scientific understanding of marine organisms and ecosystems: indeed, an entire sub-discipline of oceanography ('fisheries oceanography') has evolved at the interface of fisheries science and physical/chemical/biological oceanography that focuses on resolving these questions. One of the most prominent applications of such historical data is in cataloguing and understanding changes in the context of climate change and climate variability: a review performed in the lead up to the last IPCC report (AR5) identified 1700 such examples of observed responses to climate change in marine systems, including around 800 in European waters (Poloczanska et al. 2013). Large-scale indices have been used to link climate variability to ecological consequences in the ocean, including the North Atlantic Oscillation, the Atlantic Multidecadal Oscillation (Nye et al. 2014), the North Atlantic Subpolar Gyre intensity (Hátún et al. 2009), the El Niño Southern Oscillation (Chavez et al. 2002) or the Indian Ocean Dipole (Saji et al. 1999). The oceanographic data sets underlying these studies are diverse in nature and often reflect what is available to the authors, rather than being selected from either the global marketplace or from a systematic catalogue such as the CMEMS portal. In particular, the most impactful and important results are those that are based on long time series of both biological and physical observations (e.g. Boyce
et al. 2010). Nevertheless, the satellite record is now start-
ing to be of sufficient length to drive analyses on its own
to, for example, link temperature to changes in fish dis-
tributions (e.g. MacKenzie et al. 2014) (Figure 3.2.1) or
to study changes in lower trophic levels based on ocean
colour (Racault et al. 2012).

Historical environmental data are also key inputs to
develop empirical statistical (correlative) models with
direct applications for marine management and conser-
vation. Amongst the most common of these are the so-
called species distributions models (also known as
environmental niche models or bioclimatic envelope
models) that link the distribution of a target species to
environmental variables such as temperature, salinity,
bathymetry, chlorophyll and primary productivity (e.g.
Brunel et al. 2018, Raudsepp et al. 2019: Section 2.5 of
this report). Other biological responses are also com-
monly correlated with environmental variables, in par-
ticular recruitment (the number of juveniles produced
by a fish stock each year), growth and phenology (the
timing of key biological events). Despite the limitations
associated with correlative approaches these models
have been used in two ways. Firstly, they help to under-
stand the processes that are controlling the response of
interest. Secondly they can be used to both project
changes into the future under climate change e.g.
Bruge et al. (2016), and, more recently, to drive near-
term predictions (e.g. Figure 3.2.2). In all cases, long
time series of synoptic oceanographic data, matching at
least the temporal range of the biological observations,
are critical to developing this work, and their lack can
often represent a bottleneck in the analysis.

While oceanographic data is critical to scientific
investigations of historical changes in marine popu-
lations and ecosystem, this data has seen little uptake
on the near-term timescale and in the management of

living marine resources. A review of the management
practices of around 1250 fish populations globally
(Skern-Mauritzen et al. 2016) showed that just 24 used
ecosystem drivers to inform their short-term decision-
making about setting fishing quotas: of these, 15 popu-
lations (1.2%) used oceanographic variables while 10
(0.8%) used the abundances of either the species’ preda-
tors or prey. In essence, oceanographic information is
simply not used in setting quota.

This result may seem surprising to the reader, given the
prior discussion about the importance of the environment
for the dynamics of biological systems. However, it reflects
the current state of the art in this field. In spite of aspira-
tions to move towards the more holistic ‘ecosystem-
approach to fisheries management’ and ‘ecosystem-based
management’ (Rice 2011), the majority of fisheries man-
agement systems today remain firmly rooted in the tra-
ditional single-stock paradigm that considers one
population largely in isolation from both all other species
and the environment (Skern-Mauritzen et al. 2016).
Nevertheless, many fisheries are managed effectively
today based on these simple approaches, supplemented
with regular monitoring of changes in the populations
and their productivity (Daan et al. 2011).

Why is this? An important factor limiting the uptake
of oceanographic data is the paucity of quantitative,
reliable and robust relationships between the environ-
ment and biological responses that can be used in a man-
agement context. This point is particularly well
illustrated by a review paper from the late 1990s, that
revisited published relationships between the environ-
ment and recruitment, to see whether they were still
being used in a management context (Myers 1998). Of
the 49 published relationships reviewed, the author
found just one that was in use 10 years later: results
that were, in his own words, ‘dismal’. While many factors

<figure><img src="image_105.png" /><figcaption>**Figure 3.2.1.** Using remote sensing data to understand changes in the distribution of Atlantic bluefin tuna (*Thunnus thynnus*). In August 2012 bluefin tuna were caught in Denmark strait (location marked by a star on both plots) for the first time in recorded human history. Bluefin tuna are generally restricted to waters warmer than 11 degrees. The plot here shows the proportion of years where August SST > 11°C for (a) 1985–1994 and (b) 2007–2011, while the contour line shows location of the 11°C isotherm for 2012. A clear expansion in the amount of thermally suitable habitat is seen in recent years, providing a corridor whereby tuna can access Denmark strait. From MacKenzie et al. (2014). Reprinted by permission of John Wiley & Sons, Inc. SST Data source is ref. 3.2.1.</figcaption></figure>
<figure><img src="image_106.png" /></figure>

<figure><img src="image_107.png" /></figure>

**Figure 3.2.2.** Forecasts of the spawning distribution of blue whiting (Micromesistius poutassou) in March 2019, issued in January 2019. The forecast is based on a species distribution model (Miesner and Payne 2018) linking spawning distribution of this species to salinity at 300-600 m and a range of geographical covariates. Distribution is represented here as the probability of observing blue whiting larvae and is plotted as (a) the value and (b) the anomaly relative to the climatological probability (1960-2010). Probabilities > 0.4 can be considered as the core spawning habitat. The 1000 and 2000 m isobaths are added for reference. The EN4 data product (Good et al. 2013) provides the basis for the historical development of the model, while the CMEMS PSY4 reanalysis provides the most recent estimates of the state of the system (product ref. 3.2.2). The original model of Miesner and Payne (2018) has been applied here to show the most recent estimates of spawning distribution.

contribute to this situation, the essence of the problem is the sheer complexity of marine biological dynamics together with a lack of appropriate data (e.g. predator and prey fields) that means that the majority of processes that impact recruitment are not and cannot be parameterised in models. This results in the phenomenon of ‘non-stationarity’, where the apparent relationships between the environment and the biological response appears to shift over time, leading to the abandonment of published correlations that Myers (1998) uncovered.

While this situation may appear bleak for the providers of operational oceanographic products, it is important to remember that there are other important uses of this information beyond setting quota. Both the commercial fishing industry and recreational fishers are acutely aware of the link between physics and biology, and have the flexibility and profit/enjoyment motive to take advantage of this information: indeed, commercial services have sprung up providing this information to end-users for a fee (e.g. ‘Roffers Ocean Fishing Forecasting Service’, www.roffs.com). Similarly, the scientific monitoring of the abundance and distribution of marine species also operates within a different framework to that of fisheries management, and can and at times does take advantage of oceanographic information to design their surveys. Changes in fish distribution and productivity, often associated with trends in the physical environment, have consequences for geographically linked fisheries management plans and international quota agreements. Furthermore, the new field of dynamic ocean management (Maxwell et al. 2015) places a high weight on

oceanographic data. Rather than fixing marine protected areas in time and space, this new paradigm makes them dynamic, following the movement and distribution of protected species. Oceanographic data is key to dynamically defining these habitats and regions of interest. The first such tools that implement this approach are now emerging e.g. EcoCast (Hazen et al. 2018) and have shown tremendous potential for improving the way that the ocean is managed.

The tendency of oceanographic data getting greater uptake away from the goal of setting fisheries quota can also be seen when looking into the future. Advances in the ability to observe and forecast the ocean over recent years have paved the way for the creation of marine ecological forecasts for use in the management of living resources. A recent review of these forecast systems globally (Payne et al. 2017) revealed around 10 operational forecast products (Figure 3.2.3). The majority of these forecasts were of the spatial distribution of species, while only one, for salmon on the US West Coast, was directly related to setting quota, highlighting again the point that there are valuable uses for oceanographic observations and forecasts beyond setting quota (Tommasi et al. 2017). A second key point from this review was that Australia and the USA are currently leading the world in the development of such products, while there were no such marine ecological forecast products available in Europe, even though European waters, and particularly the NE Atlantic, are amongst the most predictable waters in the world (Langehaug et al. 2017). However, since that review the first such
<figure><img src="image_108.png" /><figcaption>**Figure 3.2.3.** Distribution of marine ecological forecasts products globally. (1) Return rates of salmon along US West Coast. (2) Whalewatch – distribution and numbers of blue whales in California Current. (3) Turtlewatch – spatial areas where there is a high risk of loggerhead turtle bycatch. (4) Distribution of southern bluefin tuna in the Great Australian Bight. (5) Forecasts of areas closed to the SE Australian long-line tuna fishery. (6) Distribution of California Sardine. (7) Timing of Gulf of Maine lobster landings. (8) Timing of salmon run on the Columbia River. (9) Coral Reef Watch's Heat Stress Outlook. Reproduced from Payne et al. (2017).</figcaption></figure>

European product has come online (Payne 2018) (Figure 3.2.2) and we can expect to see more of these types of products develop in the future.

The final time scale we consider is that of future climatic change. Here, oceanographic and climatic data clearly plays a critical role, as it forms the basis for future projections. Much of the work referred to above that is performed in a historical context can also be used to inform projections of future change (Drinkwater et al. 2010). As the focus of such work is primarily to develop scenarios about the impacts of climate change, rather than trying to make specific estimates or predictions for use in management, weak biological knowledge is only one of a number of other sources of uncertainty that need to be considered (Payne et al. 2016). Much debate exists around the usefulness and reliability of these biological projection models (e.g. Cheung et al. 2012; Brander et al. 2013) and this field is still maturing.

In spite of the poor historical uptake of operational data, the field is emerging rapidly. A particularly promising example of what can be done is provided by the recent implementation of a regional operational model for the Indonesian Ministry of Fisheries. The system couples ocean circulation model and primary production derived from satellite ocean colour data with a model of intermediate trophic levels on the top of which are simulated the spatial dynamics of three exploited tuna species

(Tranchant et al. 2016; Lehodey et al. 2018). Boundary conditions of the regional model are provided by the CMEMS global operational model. The biological component of the model is a complete population dynamics model allowing monitoring of the effects of environmental variability on the stocks (e.g., related to the Indian Ocean Dipole), and the estimation of the fishing impacts. The success of this application relies on the strong multidisciplinary approach adopted covering physical and biogeochemical oceanography, numerical modelling and engineering, marine biology, fisheries oceanography and fish population dynamics modelling. In addition, the low and mid trophic levels (zooplankton and micro nekton) simulated from realistic ocean physics and primary production directly derived from, or assimilating satellite data appear promising potential key explanatory variables to develop other applications, including recruitment indices or species habitat models.

In conclusion, the direct use of operational oceano-graphic data in the management of living marine resources is currently very limited. Nevertheless, oceano-graphic data have an important role to play in the science that currently supports management, particularly in understanding the changes that have taken place in the past, and in projecting the response to climate change in the future. Furthermore, emerging fields such as dynamic ocean management and marine ecological
<figure><img src="image_109.png" /></figure>

forecasting lean heavily on oceanographic data as their foundation. There is also a clear desire to rectify the underutilisation of environmental information in the management of both fisheries and the entire ecosystem, with a move towards more integrated and environmentally informed management approaches. It therefore seems reasonable to expect significant increases in the uptake of operational oceanographic data in the future for use in the management of living marine resources.

**Box:**

What can operational oceanographers do to support fisheries management?

There is little doubt that operational oceanography can make an important contribution to fisheries management: as noted above, the relationship between physics and biology in the ocean is well recognised. However, there are numerous hurdles that need to be overcome to help these two closely related fields work together more effectively.

Perhaps the most important barriers are the simple differences between the practitioners of the two fields. It is easy to overlook the fact that the background, training, skill sets, tools and often the motivations and career paths of the people working in each of these fields can be wildly different (e.g. Berx et al. 2011). These differences in the way of working can limit both the uptake of data and the quality of science and decision-making: clear, effective and continued communication between the fields is therefore essential.

Re-evaluating the way oceanographic data are presented to the rest of the science community can help the communication. Data portals such as CMEMS have tremendous potential to both guide and educate end-users in making choices about the appropriate data product for their needs, but need to take the end-users perspective. For example, product descriptions are often primarily oriented towards the technically literate user and can therefore be difficult to penetrate for the non-specialists. Further developments in the guidance to finding and selecting the appropriate dataset (e.g. NCAR's Climate Data Guide (Schneider et al. 2013) could be highly beneficial for many end-users.

Providing tools and products that are compatible with the wide variety of end-users skills and requirements is also critical. For example, subsetting and online processing tools that allow the users to drive the data extraction and processing process themselves (e.g. by defining areas/polygons of interest, calculating averages and statistics on the server side and providing outputs in a variety of formats) are particularly useful for those not used to handling large datasets.

It is however important that these tools work well with the current toolsets of users: while fisheries scientists work primarily in Excel and R, the current script-based subsetting tools provided by CMEMS are based on Python and NetCDF. Fortunately, technological developments, such as the emergence of robust standards for metadata and web access services following the 'FAIR' principles of Findable, Accessible, Interoperable, and Reusable data (Wilkinson et al. 2016) are helping to pave the way and ease the challenge of moving data between scientific disciplines.

Finally, and most importantly, there is a need for greater understanding and collaboration between the fields. Education and outreach activities, including training courses, are critical but also need to work in both directions, not just to increase the skill-set of potential end-users, but also to educate operational oceanographers in the principles of marine science and fisheries management. Co-development of new products by oceanographers and marine biologists working together is a particularly important and productive approach that can help to bring the two fields closer. Indeed, much can be learnt from the newly developing climate services community, for example, where co-development and co-production of climate services helps to overcome the challenges associated with differences between the producers of climate data and the information needs of end-users (Bruno Soares and Dessai 2016). In the same way, it is hoped that a greater dialogue between operational oceanographers and fisheries scientists can lead to improvements in the state of the marine ecosystem and benefit those that depend on the ocean, whilst at the same time realising the tremendous potential offered by operational oceanographic products.

### 3.3. Synergy between CMEMS products and newly available data from SENTINEL

**Authors:** Joanna Staneva, Arno Behrens, Gerhard Gayer, Lotfi Aouf

**Statement of main outcome:** This study explores the synergy between the CMEMS Monitoring and Forecasting Centres model products and the newly available satellite data. Working with these complementary sources of reliable information is useful not only for validation and assimilation purposes but also to explore in depth both the temporal and spatial scales of variability in European seas. The quality of the newly available Sentinel-3A (S3) data is assessed in comparison with data from Jason-3 (J3) at regional scales. The general performance of the wave products is very good and fairly
<figure><img src="image_110.png" /></figure>

similar compared to both satellite products. The assimilation of these satellite data in the global Monitoring and Forecasting Centres has indicated the skill of the system during storms generating high waves (Aouf 2018). The joined satellite and model analyses also demonstrates the capabilities of CMEMS as a whole, and the potential benefits of merging observational and modelled Copernicus products (Wiese et al. 2018 Behrens et al. 2019;).

Products used:

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>3.3.1</td><td>WAVE_GLO_WAV_L3_SWH_NRT_014_001</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-001-002.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-WAV-QUID-014-001.pdf</td></tr><tr><td>3.3.2</td><td>INSITU_GLO_NRT_013_030</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf</td></tr><tr><td>3.3.3</td><td>GLOBALenger_001_027</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-027.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-027.pdf</td></tr><tr><td>3.3.4</td><td>BLKSEA_007_003</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-BS-PUM-007-003.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-BS-QUID-007-003.pdf</td></tr></tbody></table>

3.3.1. Black Sea

The Black Sea Near Real Time (NRT) in-situ data, systematically maintained at regional level and quality controlled is considered as a drawback with respect to the other MFCs for improving data assimilation capabilities and for enforcing the validation of the CMEMS products (Staneva et al. 2015). The best way for systematic validations of wave model results in such a regional area is the use of satellite data. In the frame of COPERNICUS Marine Evolution System for the Black Sea that needs to provide improved wave predictions, the third-generation spectral wave model WAM is used. The quality of the wind and wave data provided by the new satellite Sentinel-3A is evaluated. The focus is brought to the regional scales where altimeter data are of lower quality than in the open ocean (Wiese et al. 2018; Behrens et al. 2019). Satellite data of Sentinel-3A and Jason-3 are assessed in a

comparison with the WAM data. The comparison between the radar altimeter measurements and the model results have been done for all satellite tracks of Sentinel-3A and Jason-3 that are available for the considered two years' period. QQ-Scatterplots for the comparisons between model versus Jason-3 (top panels) and versus Sentinel-3A (bottom panel) are shown on Figure 3.3.1. On the left hand side the analysis with the new results with the improved wave growth, on the right hand side the old model results. The model simulations with the old MS-MFC version (right panels of Figure 3.3.1) converges to the satellite observations for wave heights below about 2 m, somewhat underestimates the observations for wave heights between 2 and 4 m, and tends to overestimate Hs for higher waves. Due to change in the parameterisation of the wave growth in the source term for the wind input on the quality of the wave NRT products has been improved (left panels of Figure 3.3.1). The statistical analysis of the comparisons between model results and measurements shows reduced bias for the considered time period. Especially for higher waves, the agreement between model and altimeter data is improved significantly for all satellites.

The wave heights in the Black Sea are usually moderate and the differences between measured and computed data are small several interesting situations of the period between 1 December 2015 and 30 November 2017 are discussed in Behrens et al. (2019). On the left side, it is the descending path on 20170210 00:12:52–00:14:20 UTC that touches the area of maximum wave height whereas the ascending path on 20170314 09:25:06–09:26:46 UTC on the right side of Figure 3.3.2 directly crosses the area of maximum wave height.

Additionally two examples for comparisons between wave model data and measured data recorded by the radar altimeter of Sentinel-3A are presented in Figure 3.3.3. The descending satellite track on 20170216 08:25:46–08:27:07 UTC (left) crosses in that case an area of moderate wave height up to 2 m and the corresponding time series along the satellite path show a very good agreement between measurements and model data. The ascending track on the right in Figure 3.3.3 (20170110 19:05:58–19:06:48 UTC) passes the area of maximum wave heights in the east with a good agreement of the wave heights up to 2.8 m as well.

Due to the way satellite altimeter data are processed, the data quality can deteriorate in the vicinity of coastlines, particularly for passes from land to ocean. To test how much the satellite measurements over the study area are affected by this problem, the flights are separated into onshore and offshore flights, with onshore flights passing from the ocean to the shore and offshore flights passing from the shore to the ocean and the statistics are compared to the ones by using the all data. Additional
<figure><img src="image_111.png" /><figcaption>**Figure 3.3.1.** QQ-Scatterplots for the comparisons between model versus Jason-3 (top panels) and versus Sentinel-3A (bottom panel) data for the first quarter of 2017. On the left hand side the analysis with the new results with the improved wave growth, on the right hand side the old model results. QQ-plot (black line), 45° reference line (blue line), least-squares best fit line (red line). The colour bar shows number of entries. Data source are Ref. 331 and 3.3.4.</figcaption></figure>

analyses are performed by subsampling the track data that are 15 km off the coast (off cost row). When comparing the all data with the off-coast ones, it can be concluded that the data quality by the significant wave from Sentinel-3A height is not affected by the coastline. The general performance of WAM for the Black Sea is very good and similar in comparison with S3 and J3 data; only in the bias differences are observed for J3 is 0.017 m and for S3 0.030 m (see Table 3.3.1). When comparing the statistical values for Sentinel-3A and Jason-3 for both onshore and offshore flights, no substantial differences are found, and the statistics are very similar (Wiese et al. 2018). Therefore, the transition from land to water does not influence the quality of the newly available CMEMS satellite observations over our study area.

### 3.3.2. Global Ocean

Satellite wave data play an important role to improve the wave products provided by the global Monitoring

Forecasting Center (MFC) of the Copernicus Marine Environment and Monitoring Service (CMEMS). An upgraded level 2 processing of the Sentinel-3 Radar Altimeter (SRAL) has been implemented operationally by ESA and EUMETSAT in December 2017. This has improved significantly the quality of significant wave height (Aouf 2018). The assimilation of these data in the global CMEMS-MFC has indicated the skilfulness of the system during storms generating high waves. The North-Atlantic storm Carmen in early January 2018 induced a very strong winds of 140 km/h near the French north-western coast. High waves of more than 10 m have been recorded by the Brittany buoys during this event, as illustrated in Figure 3.3.4. Figure 3.3.5 shows a snapshot of difference of significant wave heights with and without assimilation of S3A wave data on 3 January 2018 at 0:00 UTC. This clearly reveals an impact of difference of more than 1.2 m near the Brittany coast, which corrects the overestimation induced by the wave model.
<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /><figcaption>**Figure 3.3.2.** Left: distribution of Hs on 20170210 (00 UTC) and the ascending Jason-3 satellite track 20170210, 00:12:52–00:14:20. Right: distribution of Hs on 20170314 (09 UTC) and the descending Jason-3 satellite track 20170314, 09:25:06–09:26:46. Data source are Ref. 3.3.1 and 3.3.4.</figcaption></figure>

<figure><img src="image_114.png" /><figcaption>**Figure 3.3.3.** Left: distribution of Hs on 20170216 (08 UTC) and the descending Sentinel-3A satellite track 20170216, 08:25:46–08:27:07. Right: distribution of Hs on 20170110 (19 UTC) and the ascending Sentinel-3A satellite track 20170110, 19:05:48–19:06:48. Data source are Ref. 3.3.1 and 3.3.4.</figcaption></figure>
<figure><img src="image_115.png" /><figcaption>**Table 3.3.1.** Comparison of the data quality during the first quarter of 2017 for all satellite data (All), outside the first 15 km off the coast (Non_coast), subsampling by onshore and offshore flights, for Sentinel-3A (S3) and Jason-3 (J3); for bias [in m], root mean square error [in m] (RMSE), Scatter Index (SI), Correlation coefficient (CORR).</figcaption></figure>

<table><thead><tr><th rowspan="2"></th><th colspan="2">All</th><th colspan="2">Off coast</th><th colspan="2">Oncoast</th><th colspan="2">Onshore</th><th colspan="2">Offshore</th></tr><tr><th>J3</th><th>S3</th><th>J3</th><th>S3</th><th>J3</th><th>S3</th><th>J3</th><th>S3</th><th>J3</th><th>S3</th></tr></thead><tbody><tr><td>RMSE</td><td>0.280</td><td>0.278</td><td>0.280</td><td>0.277</td><td>0.279</td><td>0.301</td><td>0.271</td><td>0.299</td><td>0.283</td><td>0.303</td></tr><tr><td>BIAS</td><td>0.017</td><td>-0.032</td><td>0.017</td><td>0.030</td><td>0.028</td><td>-0.060</td><td>0.052</td><td>-0.077</td><td>0.016</td><td>0.044</td></tr><tr><td>SI</td><td>0.245</td><td>0.239</td><td>0.245</td><td>0.237</td><td>0.264</td><td>0.293</td><td>0.279</td><td>0.282</td><td>0.258</td><td>0.304</td></tr><tr><td>CORR</td><td>0.879</td><td>0.888</td><td>0.880</td><td>0.891</td><td>0.858</td><td>0.784</td><td>0.843</td><td>0.778</td><td>0.864</td><td>0.792</td></tr><tr><td>Slope</td><td>1.012</td><td>0.970</td><td>1.012</td><td>0.972</td><td>1.023</td><td>0.924</td><td>1.062</td><td>0.902</td><td>1.009</td><td>0.945</td></tr><tr><td>RV</td><td>0.706</td><td>0.748</td><td>0.708</td><td>0.754</td><td>0.634</td><td>0.518</td><td>0.464</td><td>0.530</td><td>0.673</td><td>0.504</td></tr></tbody></table>

<figure><img src="image_116.png" /><figcaption>**Figure 3.3.4.** Time series of significant wave heights at Brittany buoy location during the storm Carmen in early January 2018. Black, red and blue lines indicate significant wave heights from the buoys, the model MFWAM with and without assimilation, respectively.</figcaption></figure>

<figure><img src="image_117.png" /><figcaption>**Figure 3.3.5.** Difference of significant wave height (in metres) from the wave model MFWAM (Météo-France WAve Model) with and without assimilation on 3 January 2018 at 0:00 UTC. Positive and negative analysis increment stand for an overestimation and underestimation of SWH of the model, respectively. Data source is from 3.3.1.</figcaption></figure>

The assimilation of S3A wave data corrects the misfit of the wave model mostly because of uncertainties related to the wind forcing provided by the atmospheric system. Figure 3.3.5 illustrates the time series of significant wave heights during Carmen at Brittany buoy which is located at 47.5°N and 8.7°W. The validation of the assimilation during the Carmen storm with Brittany buoy has showed an improvement of the normalised scatter index of significant wave height from 12.2% to 10.1%. The assimilation of five altimeters including S3A enhances the impact during storm Carmen and reduces the normalised scatter index of significant wave height to 9.7%. This indicates that such performance of global CMEMS-MFC will ensure better wave products for user applications in many ocean basins.

### 3.4. Joint Monitoring Programme of the EUtrophication of the NOrthSea with SATellite data user case

**Authors:** Dimitry Van der Zande, Marieke Eleveld, Heloise Lavigne, Francis Gohin, Silvia Pardo, Gavin Tilstone, Anouk Blauw, Stiig Markager, Lisette Enserink

**Statement of main outcome:** During the second cycle of the MSFD assessment, incomparability of monitoring methods for chlorophyll-a (CHL) was identified as a main issue hampering a coherent assessment of the eutrophication state of the North Sea. Operational satellite-based ocean colour products provide a solution as they are neutral, transparent and provide cross-boundary information on the CHL state of the North Sea. However, to this day, satellite-based CHL is generally not used in official MSFD reporting. We present the technical steps needed to generate a harmonised CHL indicator map from publicly available CHL products and bridge the gap between the ocean colour community and national monitoring teams/policy makers. This harmonised CHL indicator product enables the progression from point-by-point and country-by-country analyses, to basin-wide analysis of the eutrophication state. Finally, we evaluate the quality of the satellite-based CHL products by a comparison analysis with *in situ*
datasets for different assessment areas in the Greater North Sea.

Products used:

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>3.4.1</td><td>OCEANCOLOUR_ATL<br>CHL_L3_REP<br>OBSERVATIONS_009_067</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-009-066-067-068-069-088-091.pdf</td></tr><tr><td>3.4.2</td><td>OCEANCOLOUR_ATL<br>CHL_L3_REP<br>OBSERVATIONS_009_098</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-009-033-037-082-098.pdf</td></tr><tr><td>3.4.3</td><td>OCEANCOLOUR_ATL<br>OPTICS_L3_REP<br>OBSERVATIONS_009_066</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-009-066-067-068-069-088-091.pdf</td></tr><tr><td>3.4.4</td><td>OCEANCOLOUR_ATL<br>OPTICS_L3_NRT<br>OBSERVATIONS_009_034</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-009-034-036-046-047-087-089-090-092.pdf</td></tr><tr><td>3.4.5</td><td>OCEANCOLOUR_BAL<br>CHL_L3_REP<br>OBSERVATIONS_009_080</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>3.4.6</td><td>OCEANCOLOUR_BAL<br>CHL_L3_NRT<br>OBSERVATIONS_009_049</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-009-048-049.pdf</td></tr><tr><td>3.4.7</td><td>OCEANCOLOUR_BAL<br>OPTICS_L3_NRT<br>OBSERVATIONS_009_048</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-009-048-049.pdf</td></tr><tr><td>3.4.8</td><td>OCEANCOLOUR_BAL<br>OPTICS_L3_REP<br>OBSERVATIONS_009_097</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>

The Marine Strategy Framework Directive (MSFD) is currently one of the most important drivers for

monitoring the coastal and offshore waters in Europe with the objective of reaching a 'good environmental status' (GES) by 2020 (Gohin et al. 2008). It is a crucial legal instrument of the European Commission to protect the marine environment including its ecosystems and biodiversity. Human-induced eutrophication is one of the criteria for assessing the extent to which GES is being achieved. Eutrophication can be defined as the enrichment of water by nutrients causing an accelerated growth of algae and higher forms of plant life to produce an undesirable disturbance to the balance of organisms present in the water and to the quality of the water concerned, and therefore refers to the undesirable effects resulting from anthropogenic enrichment by nutrients (OSPAR 2017).

The eutrophication status is established by monitoring of nutrients, and CHL concentration as a proxy of phytoplankton biomass. More specifically, the indicator of choice is the 90-percentile of the CHL concentrations (CHL-P90) over the phytoplankton growing season (i.e. March–September incl.) for a period of six years expressed in µg l⁻¹ or mg m⁻³. CHL-P90 represents the CHL level such that 90% of the observations are equal to or less than this value. While *in situ* data acquisition is still considered as the main monitoring tool, the European Commission highlighted the need for greater coherence with related EU legislations (Water Framework Directive and Habitats and Birds Directive) and for more coherent and coordinated approaches within and between marine regions and sub-regions (European Commission 2014). While preparing for the second cycle of MSFD assessment, various OSPAR groups (Intersessional Correspondence Group on Eutrophication (ICG-EUT) and the Hazardous Substances and Eutrophication Committee (HASEC)) have identified incomparability of monitoring methods for CHL as a main issue hampering a coherent assessment of the common indicator CHL in the Greater North Sea. Moreover, the assessment levels for CHL, based on background concentrations, have been determined with different methods between member states. This results in different GES determinations across national borders that cannot be explained by differences in water quality (Figure 3.4.1). Additionally, the budgets for marine monitoring are decreasing in many European countries forcing them to efficiently use monitoring resources.

During recent years, there has been a growing tendency to use optical remote sensing as a supporting tool to achieve the monitoring requirements because of severe resource constraints of available ship time and personnel and the need for a coherent assessment of CHL between all OSPAR member states bordering the North Sea. Satellite data of CHL combine cheaper data
<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /><figcaption>**Figure 3.4.1.** Map of problem areas for eutrophication for the North Sea region produced by the OSPAR Common Procedure by evaluating the primary indicators (nutrient concentrations, chlorophyll-a and dissolved oxygen) and one secondary criteria (Phaeocystis). For the problem areas measures need to be taken to reduce or eliminate the anthropogenic causes of eutrophication (OSPAR 2017).</figcaption></figure>

collection with a much improved geographical and temporal coverage compared to traditional *in situ* data.

The two-year EU-project Joint Monitoring Programme of the EUtrophication of the NOrth-Sea with SATellite data (JMP-EUNOSAT, Feb. 2017-Feb. 2019) aims at developing a coherent set of assessment levels and a cost-effective GES assessment for eutrophication in the Greater North Sea. The consortium consists of 14 partners¹ from all countries bordering the North Sea.

Satellite data from ocean colour sensors (i.e. SeaWiFS, MODIS, MERIS, VIIRS, Sentinel-3) can provide spatially coherent data on CHL concentrations using CHL retrieval algorithms. There has been considerable success with blue/green-ratio algorithms such as OC4 (O'Reilly et al. 1998) and OC5 (Gohin et al. 2002) in case 1 waters where the variation of optical properties (absorption and scattering) is dominated by phytoplankton and associated material. In contrast, the optical complexity in coastal waters often poses many challenges to the accurate retrieval of biogeochemical parameters using satellite remote sensing (Sathyendranath 2000; Lee 2006). CHL

retrieval by blue/green-ratio algorithms tend to fail when applied to coastal waters whose optical properties are strongly influenced by non-covarying concentrations of suspended particulate matter (SPM) and coloured dissolved organic matter (CDOM). Such waters are defined as case 2 waters. Several constituent retrieval algorithms for use in case 2 waters have been developed: (1) red-edge algorithms (Gons et al. 2005) taking advantage of the CHL absorption peak near 670 nm and (2) artificial network approaches trained to varying parameter concentrations and optical property ranges specifically developed for use with MERIS data, such as the MERIS Ground Segment Processor (MEGS; Doerffer and Schiller 2007) and the FUB/WeW (Schroeder et al. 2007).

The technical objective of JMP EUNOSAT is to evaluate publicly accessible satellite-based CHL products available from CMEMS, ODESA and IFREMER, and determine their validity for different water types, e.g. clear, turbid or CDOM-rich waters, so that the choice of satellite product is determined by environmental conditions per (cross-border) assessment area, rather than national boundaries of the member states.

We started from a collection of well-validated operational satellite-based CHL products for the Greater North Sea: (1) CMEMS OC5-CI (product 3.4.1), (2) CMEMS GSM (product 3.4.2), (3) CMEMS OC4 adapted to Baltic waters (product 3.4.5 and 3.4.6), (4) OC4 applied to CMEMS remote sensing reflectance products (product 3.4.3 and 3.4.4) and (5) MEGS 7.5 applied to the MERIS archive obtained from ODESA online (http://www.odesa-info.eu/). For each of these products it was determined for which water types, described in terms of remote sensing reflectance (RRS) spectra (product 3.4.3, 3.4.4, 3.4.7 and 3.4.8), they provided the most accurate CHL estimations (i.e. relative error < 50%) based on a variety of reference datasets from the CoastColour Round Robin (CCRR) project² (Nechad et al. 2015). These reference data sets were specifically designed to test algorithms and assess their accuracy for retrieving water quality parameters and comprise 5000IRRCS of CHL concentrations and hyperspectral RRS-spectra covering a wide range of water types in terms of CHL, SPM and CDOM concentrations. The RRS-spectra were used as input to the considered CHL algorithms and their resulting CHL estimates were compared to the reference values allowing the development of a pixel-based quality assessment.

Figure 3.4.2(A,B) shows the application of this approach on satellite observations for the 8th of April 2010 for the OC4 and OC5 products showing an algal bloom in the Belgian and Dutch coastal waters. Figure 3.4.2(C,D) shows a classification map indicating the water types where the OC4 and OC5 algorithms
<figure><img src="image_120.png" /></figure>

**Figure 3.4.2.** (A and B) CHL products generated using the OC4 and OC5 algorithms for the 8th of April 2010 showing an algal bloom in the Belgian and Dutch coastal waters. (C and D) Water type classification map indicating the water types where the OC4 (C) and OC5 (D) algorithms are applicable indicating that OC5 can be applied in more situations than OC4. These products are merged on a pixel by pixel basis with a priority rule given to OC4, then OC5 and finally MEGS 7.5 filling up the map with the most appropriate algorithms available for different water types (based on validation analysis, data not shown): clear waters with OC4, moderately turbid waters with OC5 and highly turbid waters with MEGS 7.5. OC4 is inaccurate in the English Channel and South-East UK due to high concentrations of suspended matter (SPM). Cloud cover is presented in white, i.e. no data available. In case no clouds are present, but both OC4 and OC5 are not applicable, the MERIS MEGS 7.5 product is used. If that product is also not suitable we have no reliable data for that specific day. The main goal of this approach is to eliminate erroneous data from the process.

are applicable indicating that the OC5 algorithm can be applied in more situations than the OC4 algorithm. The OC4 algorithm is inaccurate in the English Channel and southeast UK due to high SPM concentrations.

In the next phase of the JMP-EUNOSAT project, a blending process was developed to join CHL datasets based on best suited algorithm/water type combination, with special attention to the transition zones between different water types to ensure a gradual merge. This step enabled the progress from point-by-point and country-by-country analyses, to basin-wide analysis with data that covers gradients in the ecosystem system. This enabled a definition of cross-border assessment areas based on ecosystem characteristics. The blended JMP-EUNOSAT CHL product was compared to available *in situ* datasets for all assessment areas (data not shown).

This regional intercomparison will quantify the suitability of used standard products and blending approach for eutrophication assessment. Figure 3.4.3 shows different quality-controlled CHL products (i.e. OC4, OC5, MEGS_7.5) for the 8th of April 2010. These products are merged on a pixel by pixel basis with a priority rule given to OC4, then OC5 and finally MEGS 7.5 filling up the map with the most appropriate algorithms available: clear waters with OC4, moderately turbid waters with OC5 and highly turbid waters with MEGS 7.5.

*Intercomparison of satellite products with ship-based observations*

The quality-controlled and merged satellite-based CHL observations are compared to *in situ* observations that have been collected in national monitoring programs.
<figure><img src="image_121.png" /></figure>

<figure><img src="image_122.png" /><figcaption>**Figure 3.4.3.** Blending process of different quality-controlled CHL products on a pixel per pixel basis. The different quality-controlled CHL products (i.e. OC4, OC5 and MEGS_7.5) for the 8th of April 2010 are presented in the top row.</figcaption></figure>

Figure 3.4.4 shows 90-percentile map of CHL for the growing season (March-Sept incl.) of 2003 providing a spatial interpretation of the intensity of the algal blooms in the North Sea. Additionally, CHL time series are provided for the national monitoring stations Stonehaven (Scotland), Rottumerplaat 50 (The Netherlands), 330 (Belgium) and Boulogne (France) for the year 2003 showing the ability of the satellite data to capture the temporal CHL dynamics. The *in situ* measured CHL was analysed using the HPLC-method. For the time

series of satellite data, we extracted a 3 × 3 macro-pixel and the 1 × 1 km centre pixel containing the monitoring station location. The resulting time series are presented in monthly bins as *in situ* data is mostly collected on a monthly basis in these stations. The satellite data is presented as boxplots to demonstrate the increased availability of satellite data compared to *in situ* sampling, i.e. 20–50 observations per growing season depending on the location, cloud cover and water conditions.
<figure><img src="image_123.png" /></figure>

<figure><img src="image_124.png" /></figure>

**Figure 3.4.4.** Map of the 90th percentile of the blended and quality-controlled CHL product for the growing season (March–September incl.) of 2003 providing a spatial interpretation of the intensity of the algal blooms in the North Sea. Additionally, a direct comparison of CHL time series, presented as boxplots, is provided for the national monitoring stations Stonehaven (Scotland), Rottumerplaat (The Netherlands), 330 (Belgium) and Boulogne (France) for the year 2003 showing the ability of the satellite data to capture the temporal CHL dynamics. Black dots represent the mean monthly in-situ CHL concentration, the boxplots show the monthly satellite-based CHL concentration with box extending from the lower to upper quartile values of the data, with a line at the median and the whiskers showing the 10- and 90-percentiles.

*Towards operational collaboration between North Sea Countries*

For efficient monitoring of eutrophication, it is advised to combine all available monitoring platforms, i.e. dedicated monitoring surveys taking water samples, Ferry-boxes mounted on ‘ships of opportunity’ and satellite observations. In this way, the strengths and weaknesses of one platform can be compensated by another in terms of spatial and temporal resolution, sampling depth, ability to measure different variables, analytical precision and costs. To enable such a combined use of different data sources, there is a need for a scientifically sound procedure to feed data collected with different methods into one common indicator for the assessment (e.g. CHL) describing both the state and the development of the pelagic environment. Data distribution centres such as CMEMS play a key role in this endeavour as they provide validated ocean colour products as input

for the JMP-EUNOSAT processing chain. With the Copernicus program guaranteeing a reliable source of data to at least 2036, special efforts are made to ensure future integration of Sentinel-3/OLCI data into the processing chain. Sentinel-3/OLCI has a similar spectral bandset as MERIS which is useful to provide more reliable results in turbid coastal waters. Additionally, the full resolution data (300 m spatial resolution) will provide more robust CHL estimates close to the coast.

**3.5. Regional mean time series for the Northwest European Shelf Seas**

**Authors:** Jonathan Tinker, Richard Renshaw, Rosa Barciela, Richard Wood

**Statement of main outcome:** We have developed a set of regional mean time series to aid Copernicus Marine Environmental Monitoring Service (CMEMS)
<figure><img src="image_125.png" /></figure>

Northwest European Shelf Seas reanalysis product users (product number 3.5.1). Consultation with end-users has highlighted the appetite for pre-computed regional mean time series on a number of standardised regions masks, for variables such as Sea Surface and Near Bed Temperature (SST, NBT). We consider these regional mean time series to describe and summarise the behaviour of the CMEMS Northwest European Shelf Seas Reanalysis (which is extensively evaluated). We use the CMEMS Northwest European Shelf Seas Reanalysis evaluation from its Quality User Information Document (http://marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-009-011.pdf) to suggests qualitative error bounds for the regional mean time series, of ±0.5°C and ±0.5 PSU. We also provide time series of the regional spatial variance for users who want an estimate of the distribution of values within a region. Regional summaries are used in many monitoring and management activities, and providing a ready-made integrated product that will aid utilisation for a wide range of end-users.

Products used:

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>3.5.1</td><td>NORTHWESTSHELF_REANALYSIS PHYS 004 009</td><td>PUM:<br>http://marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009-011.pdf<br>QUID:<br>http://marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-009-011.pdf</td></tr></tbody></table>

The Northwest European Shelf Seas face many overlapping pressures, and so are subject to significant statutory management, including the Marine Strategic Framework Directive (MSFD) and the Common Fisheries Policy (CFP). Both of these policies require an accurate knowledge of the current state of the sea to aid and inform decisions. However, *in situ* measurements are often underfunded and relatively sparse on the Northwest European Shelf Seas (relative to the local Rossby radius), and so there is scope to complement these with ocean model reanalyses. Reanalyses synthesise models and observations into a statistical 'best guess' of the ocean state - ocean reanalyses potentially provide more skill than either the model or observations alone (Balmaseda et al. 2015), with the model physics providing continuous 3-d fields, and the observations to anchor them in reality. The model reanalyses and near real time forecast analyses provided by the CMEMS are therefore an ideal dataset to help inform these management processes.

Regional summary statistics are used in a wide range of applications. Many monitoring and management processes report on such predefined standard regions. For example, International Council for the Exploration of the Sea (ICES) provides fisheries management advice to multiple governments and regional fisheries management organisations (according to ICES Advisory regions) advising on multiple aspects, including MSFD and CFP. The UK Marine Monitoring and Assessment Strategy (UKMMAS) gathers much of its evidence on the state of the UK waters in the Charting Progress 2 biogeographic regions (DEFRA 2010), which feeds into the MSFD. The UK's Marine Climate Change Impact Partnership reports observed and projected climate impacts on the Charting Progress 2 regions (e.g. MCCIP 2013).

Pre-computing regional means for useful regions may simplify the use of CMEMS data for some users, and allow for a wide range of new CMEMS users. As part of the Horizon 2020 funded AtlantOS project, work package 8.6 assessed user requirement for CMEMS Northwest European Shelf Seas data within three major European organisations, involved with the MSFD and CFP (Barciela et al. 2019). In-depth interviews were undertaken with scientists at the European Environment Agency (EEA, MSFD), ICES (CFP and fisheries management) and Cefas (the UK government agency responsible for the UK's MSFD and CFP reporting). Each organisation was asked a number of questions, including 'How would you like the products/service to evolve in the future?'. Both ICES and EEA asked for pre-computed time series of temperature, salinity and stratification in predefined regions - specifically in regions that they were already using (ICES Ecoregions). In response, we are therefore releasing the regional mean time series as a CMEMS product. In addition, there are many marine organisations with less technical resource than the EEA and ICES (and no expertise in working with spatial data in netCDF files) that may also benefit from such a simplified product.

The choice of appropriate region masks is important for uptake for the regional mean product. We initially built on the Northwest European Shelf Seas region mask of Wakelin et al. (2012). To identify region masks important to users, we undertook informal user engagement through the UK's Marine Climate Change Impact Partnership steering group, which is a partnership between scientists, the UK government and its agencies, and non-governmental organisations with wide engagement. For example, over 150 scientists from more than 50 leading science organisations contributed to the 2013 full report card
Region Masks for CMEMS NWS regional mean time series

<figure><img src="image_126.png" /><figcaption>**Figure 3.5.1.** Sets of standardised regions used for time series: (A) the UK Charting Progress 2 (CP2) regions; (B) regions based on Wakelin et al. (2012). The northern North Sea (blue) and Celtic Sea (red) region, which are used in Figure 3.5.2, are highlighted; (C) regions delimiting the shelf, by combining regions in (B); (D) combined regions in (B), used in model evaluation by Tinker et al. (2015); (E) ICES Advisory Areas (ICES 2004); and (F) ICES Ecoregions (ICES 2004).</figcaption></figure>

(MCCIP 2013). These discussions supported the ICES Ecoregions and ICES Advisory Areas (ICES 2004), and advocated inclusion of the UK government Charting Progress 2 regions (DEFRA 2010). These region maps are presented in Figure 3.5.1. Wakelin et al. (2012) divided the NWS according to geographical region and characteristics (Figure 3.5.1(b)), and into shelf and oceanic regions (Figure 3.5.1(c)). Tinker et al. (2015) joins some of these to create larger regions for model evaluation (to increase the numbers of observations within each region Figure 3.5.1(d)). The UK government Charting Progress 2 divides the UK sea area into eight regions, principally based on physical and biological features such as tidal fronts and seabed flora and fauna (Figure 3.5.1(a)). The ICES regions (Figure 3.5.1(e)) and Ecoregions

(Figure 3.5.1(f)) are based on biogeographic and oceanographic features and existing political, social, economic, and management divisions. With sufficient user support, additional region masks may be included in future versions of the CMEMS Northwest European Shelf Seas reanalysis.

The regional means provide a simple overview of the conditions within the region. For example, the SST and SSS from the Celtic Sea and the northern North Sea (respectively) can be plotted with no, or little, processing as in Figure 3.5.2. Some regions may include different water masses (for example, coastal water and regions of freshwater influence) which can lead to a wide range of values within the region – for some applications this may be important, and should be considered when interpreting the regional
<figure><img src="image_127.png" /></figure>

<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

**Figure 3.5.2.** Example regional mean time series for the Celtic Sea ((A–D); red; upper two rows) and the northern North Sea ((E–H); blue; lower two rows) for SST ((A, C, E, G); left column) and SSS ((B, D, F, H); right column). Absolute daily mean SST/SSS (light shading) averaged over the regions (as defined in Figure 3.5.1(B), with colouring matching the region outline), with a 30-day running mean (dark line), a 365-day running mean (grey), and a linear trend line (dotted coloured line) are presented in the first and third rows (Celtic Sea SST and SSS (A, B) and northern North Sea SST and SSS (E, F)). Regional mean anomalies (relative to a climatological seasonal cycle calculated between 1992 and (end of) 2017 are presented in the second and fourth row (Celtic Sea SST and SSS (C, D) and northern North Sea SST and SSS (G, H)).
means. We also include the regional (spatial) variance, which, together with the mean, gives an estimate of the distribution of values within the region – this may be useful for users concerned about how representative the regional mean is.

There are only a few variables on the NWS that are sampled with complete spatial coverage (SST being the exception). Therefore it is difficult to robustly validate the regional mean product. For sea surface temperature, it is possible to evaluate the regional mean time series with a spatially complete data set, such as the (largely satellite based) OSTIA analysis (Operational Sea-surface Temperature and sea-Ice Analysis; Roberts-Jones et al. 2012), however, as the reanalysis assimilates SST, this is circular. Instead, the regional mean time series are considered descriptors of the behaviour of the NWS reanalysis, rather than describing reality. The NWS reanalysis has been extensively evaluated in the Quality Information Documents (QUID), and given the data assimilation of observations, provides the statistical ‘best guess’ of the state estimate for the Northwest European Shelf Seas (Tinker et al. 2018). Here we use the relevant parts of the QUID to inform the user of the scale of the model errors and biases that will be propagate into the regional mean time series product.

The reanalysis temperature biases are generally smaller than ±0.5°C at all depths over the shelf. Reanalysis salinity biases are generally of magnitude less than ±0.5 PSU. In the coastal regions of the Southern Bight of the North Sea and around the Norwegian Trench, the reanalysis is typically too fresh, and is typically too saline in the Irish Sea, and further offshore in the Norwegian Trench. These errors are captured within the regional mean time series, and so provide qualitative error bounds of ±0.5°C and ±0.5 PSU for regions on the Northwest European Shelf Seas.

As these regional mean time series have only just been produced they have not been available to end-users, and so no examples of their use can be cited. However, similar Northwest European Shelf Seas regional mean time series and summary statistics have been used in a number of recent studies. Tinker et al. (2018) used regional mean time series of the previous NWS reanalysis, calculated on the Wakelin et al. (2012) regions (Figure 3.5.1(B)) to investigate seasonal predictability on the Northwest European Shelf. Tinker et al. (2015, 2016) used regional mean time series calculated from their Northwest European Shelf Seas climate projection to aid model evaluation, and to summarise their findings. The CMEMS Northwest European Shelf Seas regional mean time series product

will be released by Autumn 2019, and will include surface and bed (and surface-minus-bed) temperature and salinity, for the regional mean and its associated spatial variance. Once the new regional mean time series (introduced here) are released, work will continue to help raise awareness with MCCIP partners, and other policy users.

### Acknowledgements

The authors would like to thank Enda O'Dea and Pat Hyder (Met Office), Stephen Dye and Paul Buckley (CEFAS) and Matt Frost (MBA) for advice in preparing this section. This publication has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no 633211 (AtlantOS).

## 3.6. Using CMEMS and the Mediterranean Marine Protected Areas sentinel network to track ocean warming effects in coastal areas

**Authors:** Nathaniel Bensoussan, Emma Cebrian, Jean-Marie Dominici, Diego Kurt Kersting, Silvija Kipson, Zafer Kizilkaya, Oscar Ocaña, Marion Peirache, Frédéric Zuberer, Jean-Baptiste Ledoux, Cristina Linares, Mikel Zabala, Bruno Buongiorno Nardelli, Andrea Pisano, Joaquim Garrabou

**Statement of main outcomes:** Systematic and sustained in situ sampling effort is being conducted in a growing number of Mediterranean Marine Protected Areas to track and assess climate change effects in marine coastal ecosystems. Considering the need for accurate observation at large geographical scale, we conducted joint analysis of CMEMS satellite derived high-resolution foundation sea surface temperature with T-MEDNet database of multi-year in situ temperature acquired nearshore in Mediterranean Marine Protected Areas. Statistical analysis of the match-up database (multi-year, 22 sites) demonstrated the applicability of the CMEMS satellite data to the nearshore areas and further documented associated uncertainties across temperature and variability gradients. Rapid and accelerated warming of sea surface temperature in the northwestern Mediterranean during the past decade is reported and compared to the 1982–2011 period (0.047 vs. 0.029°C/year respectively). Elevated and consistent warming rates were calculated at local scale from in situ and satellite observations. Combining CMEMS remote sensing and in situ monitoring systems, as shown in this study, is a pillar to enhance our understanding on climate change impacts in coastal areas.
<figure><img src="image_135.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>3.6.1</td><td>SST_MED_SST_L4_REP_<br/>OBSERVATIONS_010_021<br/>Remote sensing</td><td>PUM:<br/>http://marine.copernicus.eu/documents/PUM/CMEMS-OSI-PUM-010-021-022.pdf<br/>QUID:<br/>http://marine.copernicus.eu/documents/QUID/CMEMS-OSI-QUID-010-021-022.pdf</td></tr><tr><td>3.6.2</td><td>T-MEDNet consolidated in situ<br/>temperature time series. www.t-mednet.org (DOI pending)<br/>In situ</td><td>www.t-mednet.org/T-Database</td></tr></tbody></table>

The coastal ocean is among the most dynamic and biologically diverse areas on Earth and supports a wide range of key marine ecosystems, which are providing goods and services to our societies. Climate change is one of the major threats for the conservation of marine coastal ecosystems through direct but also cumulative effects with other stressors (e.g. Coll et al. 2010; Hughes et al. 2017). Understanding how ocean warming is affecting the structure and functioning of marine ecosystems is crucial to support sound management and conservation policies and strategies. In the Mediterranean Sea, the occurrence of mass mortality events affecting the coastal macrobenthic biota and shifts in species distribution are the major effects of ongoing warming trend (e.g. Garra-bou et al. 2009; Kersting et al. 2013, Bianchi et al. 2017). However, studies considering appropriate large-spatial and long-term scales on different climate change indicators are scarce. Representative data across biogeographic gradients are essential to enhance our capacity to evaluate current changes and impacts as well as to explore, through novel modelling approaches, future trajectories of ecosystems under different scenarios. Such information is required to establish vulnerability assessments and develop adaptation plans for the different management frameworks (from local to regional scales).

A systematic and sustained observation effort of Essential Climate and Ocean Variables (ECV/EOV), among which sea surface temperature and subsurface temperature, is essential to analyse changes and trends in marine ecosystems (IPCC, see Wong et al. 2014). Building sound knowledge of climate change impacts in the complex 3D marine realm might push requirements for sustained and accurate environmental data acquisition at relevant resolution both in time and space like never before (e.g. Bates et al. 2018).

Sea surface and subsurface temperature can be measured with different sensors and instrumental platforms, which have intrinsic differences, making it

necessary to carefully assess errors and biases between them before merging data sets (Smale and Wernberg 2009). While satellites provide good spatial and temporal coverage of the surface layer of the ocean, the ability of gridded or operational satellite observations products in retrieving accurate sea surface temperature information in coastal areas has been repeatedly challenged (e.g. Smale and Wernberg 2009; Smit et al. 2013; Brewin et al. 2017, 2018). *In situ* measurements are needed to document the temperature variations beneath the surface, and they provide more reliable and accurate source of information on local conditions but show spatial and temporal limitations. In particular, one can note the scarcity of long-term series suited for climate change studies in the coastal zone. Indeed, long-term coastal observation is at the confluence of several challenges, among which are investment and maintenance costs, fieldwork constraints, data management, qualification and reporting.

Understanding the processes driving the ecological responses to climate change across different biological organisation levels (from the genes to ecosystems) is essential to address sound adaptation measures. High-resolution *in situ* measurements provide key information to characterise the variability of thermal regimes in which marine organisms thrive. In particular, these data have been key to characterise the thermal environment associated with the onset of mass mortality events that have affected the benthic biota during the past decades (e.g. Bensoussan et al. 2010; Crisci et al. 2011),

<figure><img src="image_136.png" /><figcaption>**Figure 3.6.1.** Thermotolerance response function of the red gordonian *Paramuricea clavata*. The response curve shows the exposure duration to different temperature treatments (experimental T°C) leading to the first signs of tissue necrosis. Results were obtained from the compilation of different thermotolerance experiments and from *in situ* observations, combining local (at depth) information on thermal environment and necrosis. Figure modified from the data presented in Crisci et al. (2011, 2017) and Pairaud et al. (2014), integrating new experimental results.</figcaption></figure>
and for the design of tailored field and laboratory exper-
imental studies on the biological responses to warming
of sensitive species (e.g. Ledoux et al. 2015; Crisci et al.
2017). For instance, comprehensive assessment of the
thermotolerance response function of *Paramuricea clavata*, a habitat forming species, has been conducted
from thermotolerance experiments and field obser-
vations (Figure 3.6.1). Non-linear response was evi-
denced, showing (sub-) lethal impacts across a range of
temperature conditions, from sustained periods
(months) of warm temperature to few days of extreme
hot conditions. Such conditions might be detected
using different and complementary approaches, like
anomalies, extreme warm or hot conditions, and their
integrative over different time scales, from event to sea-
son or year (see also sections 4.4 on Marine Heat
Waves). This kind of information can provide empirical
response functions to thermal stress that can be used to
develop early warning systems and explore the risk of
onset of mass mortality events under different climatic
scenarios (e.g. Bensoussan et al. 2013; Pairaud et al.
2014; Galli et al. 2017).

In this study, we perform a joint analysis of multi-year
to decadal high-resolution temperature time series acquired
*in situ* from T-MEDNet network (www.t-mednet.org,
product reference 3.6.2) with CMEMS remote sensing
product of daily optimally interpolated foundation sea
surface temperature at 4 km spatial resolution/smooth-
ness (product reference 3.6.1) in the Mediterranean
coastal zone. This joint analysis focuses on two main
topics: characterisation of thermal regimes and warming
trends. The results obtained advocate for the comple-
mentarity of these valuable date sets, from local to
large scales in the coastal zone, by evaluating representa-
tiveness, uncertainties and limitations from which an
improved framework on the assessment of climate
change effects can be designed.

Instrumental development and a new generation of
temperature data loggers now allow for deployment in
multiple locations and depths over periods from months
to years for high frequency (minutes to hours) character-
isation of seawater temperature in a cost-effective man-
ner. The origin of T-MEDNet network was set during
the late nineties, when Mediterranean marine ecologists
interested in climate change impacts on marine coastal
ecosystems started to implement a standardised strategy
to obtain *in situ* temperature data. This strategy con-
sisted in temperature acquisition at high frequency
(1 h) and high-resolution across the seasonal thermo-
cline, using data loggers deployed every 5 m from surface
to 40 m depth or more. Since then systematic and sus-
tained sampling effort has been conducted in a growing
number of sites, mainly Marine Protected Areas (Figure

3.6.2(a)). Same data loggers are being used at standard
depth (Hobo U22, accuracy 0.21°C, resolution 0.02°C),
are attached to rocky walls or moorings exposed to
dominant winds and currents, and retrieved every 6–12
months by scuba divers. At present over 40 sites are
being monitored, mainly in the north and central wes-
tern Mediterranean but also in the Alboran Sea, south-
western Mediterranean, Tunisian, Adriatic and Aegean
sub-basins (Figure 3.6.2(a)). T-MEDNet temperature
monitoring sites span across a large range of Mediterra-
nean Sea surface temperature (more than 7°C, from 2nd
to 92th percentile of surface variability, Figure 3.6.2(b)).
The monitoring strategy has proved its efficiency, with
high return rates on observations (median return rate
above 80%). Field surveys were complemented by the
launch, in 2009, of a collaborative platform (www.
t-mednet.org) for rigorous data management and quality
check, allowing the building of unified consolidated data-
base on *in situ* temperature in coastal waters consisting
in more than 13 × 10⁶ samples acquired at high fre-
quency and standard depth levels. The development of
this regional observation network was possible through
sustained partnership between research institutions,
Marine Protected Areas management bodies, Non-Gov-
ernmental Organisations (e.g. IUCN, MEDPAN) and
regional organisations (SPA/RAC). The ultimate goal is
to contribute to a representative sentinel network on cli-
mate change effects in the Mediterranean Sea with the
aim to maintain and enhance monitoring effort, also in
terms of representativeness across the different sub-
basins.

In Figure 3.6.2(c), we show an example of the time
series acquired in the Marine Protected Area ‘Reserve
Naturelle de Scandola’ (Corsica, France) over the period
2004–2018. From such long-term and high-resolution
time series, robust baselines on coastal thermal regimes
and seasonal stratification dynamics can be obtained
(Figure 3.6.2(d)). The Marine climatology obtained
from multi-year continuous monitoring shows some
typical features of North Western Mediterranean ther-
mal regimes. The annual cycle of the water column is
governed by the seasonal cycle, which originate in seaso-
nal vertical temperature stratification, and can display
important variability depending on the area, due to the
local wind regimes, bathymetry and topography (see
for instance Bensoussan et al. 2010). The minimum
temperature is observed in February–March (13.2°C)
when the water column can become fully mixed. Spring
and Summer surface warming induce seasonal vertical
temperature stratification with the development and
progressive deepening of the surface mixed layer. Maxi-
mum temperature occurs in August and generally, elev-
ated daily temperatures are observed in the upper 15 m
<figure><img src="image_137.png" /><figcaption>a)</figcaption></figure>

<figure><img src="image_138.png" /><figcaption>b)</figcaption></figure>

<figure><img src="image_139.png" /><figcaption>c)</figcaption></figure>

<figure><img src="image_140.png" /><figcaption>d)</figcaption></figure>

<figure><img src="image_141.png" /></figure>

**Figure 3.6.2.** (a) Location map of T-MEDNet temperature monitoring sites in the Mediterranean Sea (Product reference 3.6.2). The sites retained for statistical comparison with satellite data are shown in orange, in the Alboran (Alb.), Balearic-Catalan (Bal.-Cat.), Provence-Corsica (Pro.-Cor.), Adriatic (Adr.) and Aegean (Aeg.) sub-regions. (b) Yearly mean sea surface temperature at the T-MEDNet sites and corresponding percentile relative to distribution over the entire Mediterranean Sea. Calculations were conducted over the period 1982–2011 from product reference 3.6.1. (c) Example of the high-resolution and long-term time series acquired *in situ* using data loggers set at standard depth levels (every 5 m from 5 to 40 m depth) in the Marine Protected Area of Scandola (Corsica, France) since 2004. (d) Climatological mean seawater temperature between the surface and 40 m depth at Scandola Marine Protected Area. The climatological mean was calculated over the period 2004–2017, for each depth and day of year using a 11-day moving window and additional smoothing on 31 days.

of the water column (mean maximum values ranging between 23°C and 26°C). The temperature difference between 5 and 40 m depth is 8°C on average in August. In late summer and fall, surface cooling and important (wind induced) vertical mixing occur, with subsequent deepening of the mixed layer depth to the bottom in late October or November. Benthic ecosystems dwelling between the surface and 40 m depth are thus exposed to contrasted environmental conditions, with strong differences in the magnitude and phasing of their hydrological cycle (e.g. mean amplitude of 12°C at the surface vs. 6°C at 40 m depth, maximum observed in August and October respectively). From the data series available, the different patterns of stratification and temporal variability were characterised across the network.

Regarding the *in situ* T-MEDNet network temperature database (product reference 3.6.2), a first subsample was obtained, retaining maximum depth of 5 m (*in situ*$_{5m}$) and more than one full year of measurements, for joint analysis with sea surface temperature from CMEMS (product reference 3.6.1). Out of these criteria,

22 time series were considered (median length 7.4 years), from the western and eastern Mediterranean basins where contrasted hydro-climatic conditions occur (Figure 3.6.2(a,b)). A matchup database of co-located satellite (nearest pixel) and *in situ*$_{5m}$ multi-year daily time series was built for statistical analysis of the observed differences over the annual cycle. Comparisons between remote sensing and *in situ*$_{5m}$ data were carried out using classical descriptors and analysis for bias, standard deviation, correlation and root mean square difference and results were synthetised in Taylor diagrams (Taylor 2001).

Important differences were evidenced from the 22 sites (Figure 3.6.3(a,b)). Overall, high correlation (>0.97) and low bias (<0.4°C) were shown for most sites (group A, N = 15) while lower agreement was evidenced for six sites distributed along 60 km of coastline in Provence (group B1) and on the southern coast of Gibraltar Straight (B2, Ceuta, Spain). From the observed variability in the correlation and standard deviation patterns, the 22 sites were clustered in six groups (groups A1
<figure><img src="image_142.png" /></figure>

<figure><img src="image_143.png" /><figcaption>**Figure 3.6.3.** (a) Taylor diagram showing comparison between multi-year time series of satellite derived daily sea surface temperature from CMEMS (Product reference 3.6.1) and T-MEDNet *in situ* measurements at 5 m depth (product reference 3.6.2) considering all available samples over the 2004–2017 period. (b) Zoom on the box shown in panel a showing sites clustering along the standard deviation and correlation axes. Different symbol colours were used for sites from the Balearic-Catalan (red), Provence-Corsica (blue), Adriatic (black) and Aegean (green) sub-regions. (c–d) Linear regression analysis of daily temperature data in two sites from subgroups A1 and B1 (Columbretes Islands and Marseille-Riou respectively). (e) Average of the mean monthly bias for the different subgroups of stations shown in the panels a–b.</figcaption></figure>

to A4, B1 and B2). They were further analysed in order to quantify typical uncertainties and monthly bias during the annual cycle (Table 3.6.1, Figure 3.6.3(e)) and to better understand the determinants of the observed variability (e.g. Figure 3.6.3(c,d)).

Interestingly, highest and near perfect agreement in correlation (R = 0.996) and amplitude pattern (point lying on the unit standard deviation arc) was shown for two sites located off mainland coast, nearshore small islands in the Balearic Sea (group A1, Columbretes and Mallorca-S, distant by ca. 200 km, Figure 3.6.3(b,c)). The root mean square difference calculated over more than eight years of data was 0.44°C, i.e. comparable to typical accuracy of satellite measurement in non-nearshore area (0.5°C, Table 3.6.1, Pisano et al. 2016). These results demonstrate the high consistency and accuracy of multi-year local time series obtained from

the optimally interpolated satellite data in such near open sea conditions (product reference 3.6.1).

Comparatively, two to five-fold increase in root mean square difference was evidenced in typical coastal zone (mostly Marine Protected Areas, Table 3.6.1). Generally, low bias occurred in winter and fall but significant warm bias was shown in June (0.7°C for group A2) or during summer (Figure 3.6.3(e)). This pattern, consistent with the seasonal cycle, was amplified in sites within the clusters showing lower agreement, with summer bias >2°C to 4°C in groups B1 and B2 respectively (Figure 3.6.3(b,e), Table 3.6.1). However, a distinct seasonal pattern was shown in Gokova Koremen (A4, STD = 0.90, Figure 3.6.3(e), Table 3.6.1) where effect from the nearby Akyaka river is strongly suspected, which might explain the seasonal inversion in vertical temperature gradients in the top 5 m of the water column.
<figure><img src="image_144.png" /><figcaption>**Table 3.6.1.** Summary of the satellite – *in situ* matchup data over the Mediterranean Sea in the near-shore and off-shore as a function of sensor type.</figcaption></figure>

<table><thead><tr><th>Data Type</th><th>Depth (m)</th><th>Bias (°C)</th><th>RMSD (°C)</th><th>Correlation</th><th>Standard deviation (Normalized)</th><th>Number of samples (and sites)</th><th>Reference period (median duration in year)</th></tr></thead><tbody><tr><td colspan="8">Nearshore HF time series using benthic data loggers, coastal moorings - multi-year data sets from T-MEDNet - this study</td></tr><tr><td>A1</td><td>5.0</td><td>-0.11</td><td>0.44</td><td>0.996</td><td>1.01</td><td>6 768 (2)</td><td>2007-2017 (9.7)</td></tr><tr><td>A2</td><td>5.0</td><td>0.16</td><td>0.75</td><td>0.984</td><td>1.04</td><td>23 960 (9)</td><td>2004-2017 (7.2)</td></tr><tr><td>A3</td><td>5.0</td><td>0.09</td><td>0.81</td><td>0.983</td><td>1.14</td><td>7 539 (3)</td><td>2012-2017 (3.7)</td></tr><tr><td>A4</td><td>5.0</td><td>-0.21</td><td>0.99</td><td>0.970</td><td>0.91</td><td>985 (1)</td><td>2015-2017 (2.7)</td></tr><tr><td>B1</td><td>5.0</td><td>0.59</td><td>1.64</td><td>0.910</td><td>1.14</td><td>16 232 (6)</td><td>2004-2017 (7.4)</td></tr><tr><td>B2</td><td>5.0</td><td>0.92</td><td>2.1</td><td>0.798</td><td>2.15</td><td>1 008 (1)</td><td>2014-2017 (2.8)</td></tr><tr><td>Total</td><td></td><td></td><td></td><td></td><td></td><td>56 222 (22)</td><td></td></tr><tr><td colspan="8">Source: Pisano et al. 2016</td></tr><tr><td>Drifter</td><td>0.2</td><td>-0.23</td><td>0.52</td><td></td><td></td><td>102 164</td><td>2004-2012</td></tr><tr><td>TSG</td><td>4.0</td><td>-0.25</td><td>0.58</td><td></td><td></td><td>57 369</td><td>1990-2012</td></tr><tr><td>CTD</td><td>3.0</td><td>-0.29</td><td>0.58</td><td></td><td></td><td>6 649</td><td>1982-2012</td></tr><tr><td>XBT</td><td>3.0</td><td>-0.29</td><td>0.58</td><td></td><td></td><td>5 927</td><td>1990-2012</td></tr><tr><td>ARGO Float</td><td>3.0</td><td>-0.18</td><td>0.54</td><td></td><td></td><td>769</td><td>1999-2012</td></tr><tr><td>Total</td><td></td><td>-0.24</td><td>0.55</td><td>0.992</td><td></td><td>172 878</td><td>1982-2012</td></tr></tbody></table>

From group A2, a high agreement between the satellite and *in situ*$_{5m}$ data was achieved for nine sites from the different sub-basins, from cold to warm Mediterranean Sea surface temperature: in the Catalan, Province-Corsica, Adriatic and S-Aegean Seas. The satellite data typically explained 96% of the variance observed *in situ*, with a root mean square difference $0.75 \pm 0.18^{\circ}\text{C}$ (Figure 3.6.3(b), Table 3.6.1). Figure 3.6.4(a–d) shows how satellite and *in situ*$_{5m}$ data track each other during the entire annual cycle of year 2015 in Columbretes Islands (from group A1), and in three sites from group A2 accounting for large-spatial temperature gradients in the Mediterranean Sea (*in situ* data completeness index 99.6%). The high agreement allows fine analysis of the local subsurface annual temperature cycle and of such inter-site differences using satellite data.

highest bias between satellite and *in situ*$_{5m}$ daily temperatures occurred during periods of strong sea surface warming (e.g. in June and warm summer events) which might be a sign of vertical temperature gradients, or during cold episodic events that were smoothed in the satellite data (up to $1^{\circ}\text{C}$ in Columbretes, $2-3^{\circ}\text{C}$ in other sites).

To take into account how complex coastal hydrological dynamics can affect satellite data, we considered a group of six T-MEDNet *in situ* monitoring sites (group B1), from the Gulf of Marseille to the East, to Cap Sicié to the West (Figure 3.6.5(c,d)). These sites displayed comparatively lower agreement between *in situ*$_{5m}$ and satellite data than other sites from the A groups. Since the hydrology in the area is under the influence of coastal upwelling cells triggered by

<figure><img src="image_145.png" /><figcaption>**Figure 3.6.4.** (a–d) Satellite and *in situ* temperature time series for year 2015 for sites from the Western and Eastern basin (Columbretes from group A1 and Medes, Scandola and Kas, from group A2). (e–h) Spatial maps of RMSD for match-ups (same day) of *in situ*$_{5m}$ data for all satellite sea surface temperature data from pixels in 1 × 1° box. Reference of the products used: 3.6.1 and 3.6.2.</figcaption></figure>
<figure><img src="image_146.png" /></figure>

<figure><img src="image_147.png" /><figcaption>**Figure 3.6.5.** (a) Time series of satellite derived sea surface temperature and *in situ*<sub>5m</sub> in Marseille Riou (Provence France). (b) Wind data from local observations at Cap Couronne (Source Météo France). (c–d) spatial maps of sea surface temperature and *in situ* measurement during the set up and relaxation phase of the upwelling event observed at the end of July 2015 (dashed vertical lines in panel A).</figcaption></figure>

alongshore Mistral winds (Millot and Wald 1981; Ben-soussan et al. 2010), we related the oceanographic time series with independent wind data (Meteo France). We found that the degradation of matching patterns of group B1 could be attributed to satellite data which did not accurately resolve the surface temperature during the frequent upwelling events (Figure 3.6.5(a,b)). In fact, from their setup, when temperature decreased at rates up to 7°C/day, to their relaxation. Although most upwelling events were captured by the satellite data, time lag of few days was observed for their set up and their intensity was systematically underestimated. This was also illustrated in the spatial maps of sea surface temperature for the strong upwelling event that occurred at the end of July 2015. Local bias up to 8°C occurred on 27th of July 2015 (Figure 3.6.5(c)), while higher spatial agreement between satellite and *in situ* data was shown during the relaxation phase of this specific event whose influence was not limited to the nearshore but concerned broader areas (Figure 3.6.5(d)). These discrepancies are likely due to the satellite data quality check and interpolation procedures, which result in smoothing of such cold events.

Similarly, satellite smoothing of the summer high frequency variability was evidenced for sites of group A3. These three sites from the N-Adriatic and N-Aegean Seas share common features such as complex topography on large and shallow continental shelves and important wind forcing which drive episodic cooling events (Bora and Meltem winds). Contrarily in Ceuta (B2) warm bias was consistently observed during summer and the satellite derived sea surface temperature over the pixel area was not representative of local nearshore conditions, which might be interpreted as sign of spatial variability due to complex hydrodynamics along the southern coast in the Gibraltar straight.

Finally, in order to analyse the spatial representativeness of the *in situ* point measurements, statistics were

computed considering all satellite data from pixels within boxes of 1° longitude and 1° latitude centred on the monitoring sites shown in Figure 3.6.4(a–d). The spatial maps of root mean square difference for year 2015 (Figure 3.6.4(e–h)) indicate smooth gradients, with highest agreement between satellite and *in situ* data for closest and adjacent pixels, though not necessarily alongshore (Figure 3.6.4(f–h)). Coherent spatial patterns were observed, for instance for Capes vs. Gulfs that further illustrate the interest of combining satellite and *in situ* data to enhance analysis of the spatial variability over coastal areas. In Columbretes, owing to the near open sea conditions and low horizontal sea surface temperature gradients (0.6°C annual difference over the S-Catalan Sea map, data not shown), high agreement concerned vast areas (100's km²), south and east of the nearshore monitoring site (Figure 3.6.4(e)).

Warming can vary regionally and locally, with potential impacts on ecosystems. In order to evaluate the applicability of satellite data for representing subsurface trends in the nearshore area, nine time series from sites in the northwestern Mediterranean were selected from T-MEDNet database. The sites selection was based on the availability of a minimum number of seven years covered by data over the 2007–2016 period (Figure 3.6.6). The monthly average temperature, climatology and anomalies at each site were computed, out of which few were excluded from analysis when based on less than 15 days of observation. Finally, the average completeness index on monthly anomalies was 84 ± 9% (mean ± std), ranging from 73% to 96%. Warming rates (in °C per year) were calculated using the Sen's method to estimate the slope of the monthly anomalies time series (Sen 1968) over the 2007–2016 period. Warming trends over the northwestern Mediterranean Sea were calculated from satellite data (product reference 3.6.1) over the past decade (2007–2016) and at
<figure><img src="image_148.png" /></figure>

<figure><img src="image_149.png" /></figure>

**Figure 3.6.6.** Sea surface warming trends over the past decade (period 2007–2016) in the northwestern Mediterranean Sea. (a) Trends over the north western Mediterranean Sea from high-resolution satellite data and combined *in situ* and satellite data from nine coastal sites (see panel (c)). Results show accelerated warming over the area compared to the 1982–2011 period (0.047 ± 0.031°C/year vs. 0.029 ± 0.003°C/year respectively) (b) Comparison of surface warming trends in the 9 coastal sites shown on map calculated from *in situ* or satellite sea surface temperature data. The effect of missing data on trends calculation is also shown (grey shaded area). (c) Best estimates of surface warming trends in the nine coastal sites obtained by combining *in situ* and satellite data (black curve). Trends from satellite data at the coastal sites and over the entire area are indicated by the grey curve and blue line respectively.

climatological time scale (1982–2011). For comparison at local scale, satellite data from nearest pixel to the nine T-MEDNet sites were considered, retaining the complete data sets over the 2007–2016 period, and also sub-samples by retaining only matching dates with *in situ*$_{5m}$ in order to evaluate the potential incidence of missing data.

Analysis conducted on satellite data from CMEMS (product reference 3.6.1) attest to rapid and accelerated warming of sea surface temperature in the northwestern Mediterranean during the past decade compared to the 1982–2011 period (0.047 ± 0.031°C/year vs. 0.029 ± 0.003°C/year respectively, Figure 3.6.6(a)). Strong spatial variability is obvious and elevated warming rates (>0.1°C per year) occurred in the Balearic Sea and the Provence-Corsica Sea, mostly offshore. Rapid warming was also evidenced from the analysis of the T-MEDNet *in situ* time series to the coast (Figure 3.6.6(b)). Local warming rates calculated from *in situ*$_{5m}$ and satellite data showed comparable values on average (0.065 vs. 0.061°C/year respectively), but important differences arose when comparing pair of values at each site (mean of absolute difference 0.024 ± 0.014°C/year). We must also note the stronger inter-site variability of warming rates, at the various spatial scales, from *in situ* time series when compared to satellite data (Figure 3.6.6(b)).

We further evaluated the effect of missing data which is a characteristic of most, if not all, *in situ* time series, on estimation of trends. Trend calculation is known to be highly sensitive to the length of the time window, as well as the start and end dates considered. Here we showed

that even minor change in the completeness index of the data set (e.g. for Columbretes, retaining 96% vs. 100% of the satellite data over the 2007–2016 period) can result in significant variability/uncertainty in warming trends estimates (grey shaded area, Figure 3.6.6(b)). Overall, we assume that missing data may account for a 50% or more of the observed differences in the warming rates obtained using satellite and *in situ*$_{5m}$ data. Considering the statistical analysis conducted above, *in situ*$_{5m}$ data gaps were filled by satellite data with high confidence for all sites (from group A1 and A2), except for Marseille-Riou (upwelling area as discussed above).

Interestingly, the new estimates combining *in situ* and satellite data showed fair agreement with trends derived from the complete satellite data (Figure 3.6.6(c)). These results showed overall elevated warming rates (0.043° C/year in Marseille to 0.077°C/year in the Catalan Sea) among which most are higher than the average warming at sub-regional scale (blue dotted line, Figure 3.6.5(c)). These results further demonstrate the interest of combining satellite and *in situ* observations to enhance and validate analysis on thermal regimes and warming trends in the vital coastal and nearshore area.

Climate change is expected to have significant impacts in the coastal areas. Integrated Coastal Zone Management and Ecosystem Based Approach strategies are being implemented to deal with current and long-term climate change as well as other drivers of change. Through these strategies, integrated and adaptive approach to coastal zone planning and management are developed in order to achieve Good Environmental
Status and sustainable development of coastal areas. Marine Protected Areas are one of the main instruments being implemented in this framework. Besides Marine Protected Areas are recognised as nature-based solutions to cope with climate change in many frameworks (e.g. Convention on Biological Diversity, Sustainable Development Goal 14). In this study we showed the relevance of joint effort with Marine Protected Areas in tracking and informing on ongoing changes associated to climate change in coastal areas.

The statistical analysis of the satellite and *in situ* matchup database (22 sites, multi-year) firstly determined that high agreement in terms of correlation (0.98), RMSD (0.5–0.8°C) and bias (year = 0.3°C, month < 0.8°C) was attained in most Marine Protected Areas in the different sub-basins, from cold to warm Mediterranean Sea surface temperature. Secondly the large variability in matching patterns, with higher uncertainty in the nearshore compared to offshore and significant warm bias during summer, largely reflects the underlying coastal oceanographic processes that determine the local seawater temperature variability. Our results, consistent with the literature (e.g. Smale and Wernberg 2009; Smit et al. 2013; Brewin et al. 2018), further document uncertainties associated to such approach in the Mediterranean Sea and highlight potential limitations, especially in upwelling areas, that may inform user uptake and future product improvement. These results also demonstrate the interest of considering multi-year time series acquired nearshore using benthic data loggers in complement to other sensors platforms/classically used for satellite data validation (surface drifter, thermosalinograph, CTD, XBT, Argo float, Table 3.6.1, Pisano et al. 2016). Future work could focus on the validation of different sea surface temperature products but also high-resolution models and reanalysis, by considering the hourly and high vertical resolution of T-MEDNet *in situ* measurements. This analysis also informed on the spatial representativeness of *in situ* point measurements from which adaptive and cost-effective sampling strategy could be designed across environmental gradients in the Mediterranean coastal regions and potentially offshore, considering small islands. We advocate that in coastal areas where poor knowledge on oceanographic features, systematic *in situ* measurements over a complete annual cycle should be considered in order to maximise the potential of use and interpretation of satellite derived sea surface temperature at local and broader spatial scales relevant for coastal management (e.g. Gulfs, Marine Protected Area and coastal networks). Indeed, the long-term remote sensing data series (since 1982) can be of particular relevance for broad scale ecological studies (e.g. for computation of climatological

means), analysis of extreme warm or hot events based on anomalies to the climatological mean (see for instance Sections 4.4 on Marine Heat Waves) and warming trends.

Combining remote sensing and *in situ* monitoring systems as shown in this study is a pillar to enhance our understanding of climate change impacts and improve 3D modelling approach in hydrologically complex coastal areas. Supporting the development of a representative coastal monitoring network at Mediterranean scale, e.g. across the network of Marine Protected Areas, while pursuing the enhancement and delivery of CMEMS products to provide accurate and high-resolution information offers a unique opportunity to address vulnerability and adaptation plans to climate change over broad ecological and economic settings.

## Acknowledgements

This study was partially funded by the Prince Albert II de Monaco Foundation (MIMOSA project n° 1983) and the project MPA-ADAPT Interreg MED Programme (Grant number Pr MPA-Adapt 1MED15_3.2_M2_337) 85% co-funded by the European Regional Development Fund. The authors wish to warmly thank T-MEDNet network members and teams involved in field work and management. D.K.K. is supported by the DFG (German Research Foundation).

## 3.7. Combined analysis of Cryosat-2/SMOS sea ice thickness data with model reanalysis fields over the Baltic Sea

**Authors:** Urmas Raudsepp, Rivo Uiboupin, Ilja Maljutenko, Stefan Hendricks, Robert Ricker, Ye Liu, Doroteaciro Iovino, K. Andrew Peterson, Hao Zuo, Thomas Lavergne, Signe Aaboe, Roshin P. Raj

**Statement of outcome:** The satellites Cryosat-2 and SMOS provide a new insight for accurate estimation of the sea ice thickness in the Baltic Sea, a heavily trafficked seasonally ice-covered boreal sea. In this study, we demonstrate that combined Cryosat-2/SMOS ice thickness product correlates with the high-resolution model ice thickness values with correlation coefficient of 0.41 and root mean square difference of 0.30 m. Model and Cryosat-2/SMOS data accuracy is good during the ice growth period when ice thickness is below 0.6 m. Cryosat-2/SMOS data captures inter-annual variations of ice thickness, volume and concentration as well as regional differences between Baltic Sea basins. Moreover, Cryosat-2/SMOS data provide added value to the ice thickness estimations based solely on the model during the ice melting period. Therefore, including Cryosat-2/
<figure><img src="image_150.png" /></figure>

SMOS ice thickness product as a member in the existing product family (numerical model reanalysis product and operational ice charts) enables multiproduct ice thickness estimation with reduced uncertainties.

Products used:

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>3.7.1</td><td>BALTICSEA_REANALYSIS PHY_003_011 Reanalysis</td><td>PUM: http://marine.copernicus.eu/ documents/PUM/CMEMS-BAL- PUM-003-011.pdf<br>QUID: http://marine.copernicus.eu/ documents/QUID/CMEMS-BAL- QUID-003-011.pdf</td></tr><tr><td>3.7.2</td><td>CS2SMOS Remote sensing</td><td>http://epic.avi.de/41602/ Ricker et al. (2017)</td></tr><tr><td>3.7.3</td><td>SEAICE_BAL_SEAICE_L4_NRT _OBSERVATIONS_011_011 Remote sensing</td><td>PUM: http://marine.copernicus.eu/ documents/PUM/CMEMS-OSI- PUM-011-004-011.pdf<br>QUID: http://marine.copernicus.eu/ documents/QUID/CMEMS-OSI- QUID-011-001to007-009to012.pdf</td></tr><tr><td>3.7.4</td><td>C3S ERA5 Model reanalysis</td><td>ECMWF (2019)</td></tr><tr><td>3.7.5</td><td>BALTIC_OMI_SI_volume Observations</td><td>PUM: in production<br>QUID: in production<br>http://marine.copernicus.eu/ science-learning/ocean- monitoring-indicators/catalogue/ PUM: in production<br>QUID: in production<br>http://marine.copernicus.eu/ science-learning/ocean- monitoring-indicators/catalogue/</td></tr><tr><td>3.7.6</td><td>BALTIC_OMI_SIIRExtent Observations</td><td></td></tr></tbody></table>

Knowledge of accurate sea ice thickness has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation facilitating planning of ice-breaking activity and operation of the icebreakers (Valdez Banda et al. 2015; Boström and Österman 2017). In the seasonally ice-covered Baltic Sea, in situ sea ice thickness measurements outside the fast ice zone (ice attached to the coastline, not drifting) are difficult to perform. Air- and shipborne electromagnetic soundings are considered to be the most accurate method to measure ice thickness in the drift ice zone where the contribution of the fractions of level and deformed ice thickness is included (Ronkainen et al. 2018). The shortcomings of electromagnetic measurements are limited spatio-temporal coverage and uncertainties related to snow on the ice and porous ridge keels. Operational sea ice charts (Karvonen et al. 2003; product reference 3.7.3), which have been produced by combining in situ measurements, visual observations and Synthetic Aperture Radar data, are considered as the standard method for spatial mapping of the sea ice thickness in the Baltic Sea. Still, production of these maps includes a high degree of expert knowledge and these maps represent the typical thickness of level ice (Ronkainen et al. 2018; Gegiuc et al. 2018). Moreover, numerical model simulations of ice

thickness, ice concentration and therefore ice volume have been improved over time, but have not reached sufficient accuracy at seasonal time scales (Vihma and Haapala 2009; Herman et al. 2011; Pemberton et al. 2017).

Copernicus Marine Environmental Monitoring Service provides ice thickness and volume estimates based on operational sea ice charts (product reference 3.7.3) and numerical model simulations (product reference 3.7.1). Considering the limited number of in situ ice thickness measurements and uncertainties in the existing estimates of the sea ice thickness and volume in the Baltic Sea, any new data source should be included in the estimation of seasonal and inter-annual sea ice thickness and volume variations with reduced uncertainties in the Baltic Sea, e.g. product reference 3.7.5.

Previous studies have shown that SMOS data could be used for estimating sea ice thickness in the Baltic Sea regional scale (Maaß et al. 2015; Kaleschke et al. 2016), while the merged Cryosat-2 and SMOS data product has proven to be valuable at global scale (Ricker et al. 2017). Thus, in principle, the combined ice thickness product of Cryosat-2 and SMOS (CS2SMOS) complemented with auxiliary ice concentration data from the Ocean and Sea Ice Satellite Application Facility (OSI-401-b) (Tonboe et al. 2018) could provide information about ice thickness in the open part of the Baltic Sea.

Therefore, a general purpose of the study is to assess the potential of CS2SMOS ice product for monitoring/forecasting the ice conditions (thickness, volume) in the Baltic Sea. Ice thickness values of two products are compared: sea ice thickness from CS2SMOS (product reference 3.7.2) and from a numerical ocean/sea ice model based on the NEMO-LIM3.6 modelling system for the Baltic Sea (product reference 3.7.1). No sea ice data of any kind has been assimilated into the NEMO-LIM3.6 modelling system for the Baltic Sea due to insufficient confidence on the ice thickness observations, which are derived from digitised hand-drawn ice charts based on ship observations and various other sources (Pemberton et al. 2017). Both products are considered to have errors which are difficult to quantify beforehand. The sources of the errors could be related to (1) dynamic/variable ice conditions, (2) relatively low ice thickness and (3) coarse resolution (25 km) of the CS2SMOS product (product reference 3.7.2) compared to the basin scale. Moreover, the ice thickness in the northern Baltic Sea is often in the range of 0.4–1 m which is the ‘transition zone’ in terms of sensitivity of the two sensors in the CS2SMOS product (Ricker et al. 2017). The comparison of ice thickness from the model and CS2SMOS product with operational ice charts (product reference 3.7.3) is limited to the year 2016, only,
because of the availability of the data from Copernicus Marine Environment Monitoring Service catalogue.

Spatial resolution of satellite data is 25 × 25 km and return period of the satellite is about one week. Model data has 2 × 2 nmi (3.7 × 3.7 km) grid resolution at hourly frequency. Operational ice charts have spatial resolution of 0.5 × 0.5 km data and updated daily (product reference 3.7.3). Satellite data covers open sea area only (Figure 3.7.1). To compare satellite data and model results the model values are transferred to the satellite data grid and comparison is made for the points where satellite data has ice concentration and thickness values higher than zero. Hence, ice concentration and ice thickness from model are averaged over the spatial domain of 25 × 25 km and time interval of one week. To retrieve the ice volume, the product of three components – (i) ice concentration, (ii) ice thickness and (iii) model grid area – is calculated for each model gridpoint and time

<caption>**Figure 3.7.1.** Example of overlaid ice concentration product (product reference 3.7.2) in the Baltic Sea indicating the resolution and coverage of the products on 24 January, 2011. Also, the borders of sub-basins and the stations for which the comparison between model and remote sensing product was carried out are shown. The dots show model and squares the CS2SMOS data points.</caption>

instant which fall into the spatial area and time interval of the satellite data resolution.

The Bothnian Bay represents the region with thicker ice (up to 1 m) and high ice concentration values (usually between 0.6 and 1.0). The Bothnian Sea, the Gulf of Finland and the Gulf of Riga represent regions with thinner and more dynamic ice conditions where ice thickness is usually below 0.5 m except for cold winters like 2011.

Correlation (R) and root mean square difference (RMSD) were calculated for assessing the overall agreement of the two ice thickness products in the gridpoints that are not contaminated by the coastline and the islands. In total we have N = 6133 comparison points over the six-year time span, 2011–2016. A general agreement between the model and remote sensing ice thickness (R = 0.41, RMSD = 0.30 m) is in the same range as in the satellite validation experiments in the Arctic (Ricker et al. 2017). Considerable difference is that Ricker et al. (2017) compare CS2SMOS ice thickness with airborne electromagnetic thickness measurements, but we compare CS2SMOS with numerical model ice thickness. The latter contains a high uncertainty itself. Due to peculiarities of each considered sub-basin of the Baltic Sea regional statistics for each sub-basin were calculated separately (Figure 3.7.2).

We have compared each satellite measurement with corresponding area and time averaged model value by calculating a mean and standard deviation of the model/satellite ice thickness relative to the predefined satellite/model ice thickness intervals. A general tendency of the means for each subregion is that for thicker ice from the model, the satellite ice thickness is lower. For instance, in the Bothnian Bay for each model ice thickness interval starting from 0.3–0.4 m to 1.4–1.5 m, interval mean satellite ice thicknesses vary between 0.3 and 0.5 m (Figure 3.7.2(a)). In the other basins, among the few model values with the ice thickness in excess of 0.6, corresponding satellite ice thickness values are mainly lower than the model values. The same tendency is true for the mean model ice thickness relative to predefined satellite ice thickness intervals. This is especially pronounced for the Bothnian Sea and the Gulf of Riga, where for the ice intervals from 0.4 m to 1.2 m, the mean ice thickness is between 0.2 and 0.4 m (Figure 3.7.2(b,d)). The scatters around the means are large with STDs up to 0.35 m and without significant differences between model and satellite, or ice thickness intervals. In the Bothnian Bay (Figure 3.7.2(a)) and in the Gulf of Finland (Figure 3.7.2(c)), the satellite mean ice thickness is close to the mean ice thickness from the model for the predefined model ice thickness intervals up to 0.6 and 0.7 m, respectively, but the scatter is still large (STD is up to 0.25 m). The model mean ice thickness,

<figure><img src="image_151.png" /></figure>
<figure><img src="image_152.png" /></figure>

<figure><img src="image_153.png" /></figure>

**Figure 3.7.2.** Scatter plots of CS2SMOS and model ice thickness in the Bothnian Bay (a), the Bothnian Sea (b), the Gulf of Finland (c) and the Gulf of Riga (d). Data from product references 3.7.1 and 3.7.2. Red triangles denote average model ice thickness for the ranges of CS2SMOS ice thicknesses with interval of 0.1 m. Vertical red lines denote the ±STD of corresponding model ice thicknesses relative to mean, but plotted on 1:1 line. Likewise, the blue triangles denote average CS2SMOS ice thickness for the ranges of model ice thickness with interval of 0.1 m. Horizontal blue lines denote the ±STD of corresponding CS2SMOS ice thicknesses relative to mean, but plotted on 1:1 line.

relative to the satellite, is overestimated by up to 0.15 m for the intervals up to 0.5–0.6 and by 0.3–0.4 for the Bothnian Bay and Gulf of Finland, respectively, but underestimated thereafter. In the Bothnian Sea and the Gulf of Riga the model mean ice thickness relative to the satellite is underestimated over the almost whole range of satellite ice thickness intervals.

In the Bothnian Bay and Bothnian Sea, the correlation coefficients ($R = 0.44$ and $R = 0.39$) and root mean square differences between CS2SMOS and model ice thickness values ($RMSD = 0.33$ m and $RMSD = 0.34$ m) are comparable. Relatively high RMSD values in comparison to the range of ice thickness values are caused by different reasons. In the Bothnian Bay, very low ice thickness in CS2SMOS which coincide with any of the ice thickness value in the model (Figure 3.7.2(a)). In the Bothnian Sea, unusual discrepancy is observed in the range of CS2SMOS ice thickness from 0.7 to 1.2 m where model values start to decline and correlation is even negative.

The correlation between CS2SMOS and model ice thickness is the strongest ($R = 0.5$) and the difference is the lowest ($RMSD = 0.23$ m) in the Gulf of Finland (Figure 3.7.2(c)). However, there are too few comparison points with ice thickness in excess of 0.5 m. Gulf of Finland is a narrow gulf perpendicular with the satellite track and has a staggered coastline with a number of small islands some of which are in the middle of the gulf. Because of the geometry of the Gulf of Finland there is a limited number of CS2SMOS data points that could be used for comparison (see Figure 3.7.1). The correlation between CS2SMOS and model ice thickness is the weakest in the Gulf of Riga (Figure 3.7.2(d)).

In addition, a time series analysis has been performed at a selected location in the Bothnian Bay and Bothnian Sea (see Figure 3.7.1) aiming to compare the seasonal course and inter-annual variations of model and remote sensing-based ice concentration, thickness and volume.
<figure><img src="image_154.png" /></figure>

<figure><img src="image_155.png" /></figure>

**Figure 3.7.3.** Time series of ice concentration (a), ice thickness (b) and ice volume (c) in the Bothnia Bay. Red dots show the CS2SMOS values. Shaded area represents the range of model values over the week centred at the CS2SMOS data derived time instant. Data from product references 3.7.1 and 3.7.2. Local (65.05°N, 23.23°E) air temperature time series are plotted on panel (b) (product reference 3.7.4). Blue line corresponds to 3-hourly data, thick black line corresponds to temperature time series smoothed using 30-day moving average filter.

In the Bothnian Bay, ice concentrations of both products (remote sensing and model) follow the same general seasonal pattern (Figure 3.7.3(a)). Bias is around 0.1 with model values usually higher (Table 3.7.1), and RMSD is about 10% of the maximum possible value, except for 2014 and 2015. In case of ice thickness, bias is negative (i.e. model values are higher) and RMSD is about 50% of maximum ice thickness of CS2SMOS product in 2011–2014 (Figure 3.7.3(b)). In 2015 and 2016, RMSD is about 100% of maximum ice thickness of CS2SMOS product. Visual comparison of model and CS2SMOS data indicates that model ice thickness follows the CS2SMOS ice thickness during the ice formation period, and continues to increase during the ice melting period as detected from CS2SMOS data and air temperature (Figure 3.7.3(b)). Statistically, bias and RMSD are slightly higher during the ice growth period than during ice melting period: bias is -0.18 and -0.14 m, RMSD is

0.40 and 0.46 m, respectively. In the ice volume comparison (Figure 3.7.3(c) and Table 3.7.1), the large discrepancy between model and CS2SMOS values is slightly suppressed, RMSD remains around 30% of maximum CS2SMOS ice volume values, except for the year 2016.

Inter-annual changes of the ice parameters are more pronounced in the Bothnian Sea (Figure 3.7.4), than in the Bothnian Bay (Figure 3.7.3). At the selected location in the Bothnian Sea, durable ice is formed in 2011 and 2013. In 2015, ice is completely absent. The results are consistent with the inter-annual changes of air temperature. Maximum ice extent and ice volume for the entire Baltic Sea calculated from operational ice charts by SMHI (Samuelsen et al. 2018; product references 3.7.5 and 3.7.6) and mean ice thickness for the Bothnian Bay (Ronkainen et al. 2018) confirm the results of inter-annual variations of ice thickness and ice volume which are calculated from CS2SMOS product at selected
<figure><img src="image_156.png" /><figcaption>**Table 3.7.1.** Yearly root mean square difference (RMSD) and bias between CS2SMOS and model values (CS2SMOS minus Model): ice thickness, ice concentration and ice volume for the selected locations in Bothnian Bay and Bothnian Sea (see Figure 3.7.1 for locations). Data from product references 3.7.1 and 3.7.2.</figcaption></figure>

<table><thead><tr><th></th><th>Year</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th><th>2015</th><th>2016</th></tr></thead><tbody><tr><td colspan="8"><strong>Bothnian Bay</strong></td></tr><tr><td rowspan="2">Ice concentration [1]</td><td>RMSD</td><td>0.05</td><td>0.11</td><td>0.11</td><td>0.28</td><td>0.24</td><td>0.11</td></tr><tr><td>BIAS</td><td>-0.01</td><td>-0.11</td><td>-0.08</td><td>-0.05</td><td>0.05</td><td>-0.14</td></tr><tr><td rowspan="2">Ice thickness [m]</td><td>RMSD</td><td>0.35</td><td>0.27</td><td>0.20</td><td>0.36</td><td>0.17</td><td>1.01</td></tr><tr><td>BIAS</td><td>-0.12</td><td>-0.16</td><td>-0.10</td><td>-0.29</td><td>-0.11</td><td>-0.87</td></tr><tr><td rowspan="2">Ice volume [km³]</td><td>RMSD</td><td>0.17</td><td>0.14</td><td>0.11</td><td>0.15</td><td>0.04</td><td>0.60</td></tr><tr><td>BIAS</td><td>-0.04</td><td>-0.10</td><td>-0.07</td><td>-0.15</td><td>-0.02</td><td>-0.55</td></tr><tr><td colspan="8"><strong>Bothnian Sea</strong></td></tr><tr><td rowspan="2">Ice concentration [1]</td><td>RMSD</td><td>0.14</td><td>0.19</td><td>0.17</td><td>0.35</td><td>n/a</td><td>0.33</td></tr><tr><td>BIAS</td><td>-0.06</td><td>0.05</td><td>-0.08</td><td>-0.11</td><td>n/a</td><td>-0.28</td></tr><tr><td rowspan="2">Ice thickness [m]</td><td>RMSD</td><td>0.28</td><td>0.11</td><td>0.20</td><td>0.07</td><td>n/a</td><td>0.09</td></tr><tr><td>BIAS</td><td>0.21</td><td>-0.01</td><td>-0.06</td><td>-0.11</td><td>n/a</td><td>-0.11</td></tr><tr><td rowspan="2">Ice volume [km³]</td><td>RMSD</td><td>0.15</td><td>0.01</td><td>0.07</td><td>0.03</td><td>n/a</td><td>0.04</td></tr><tr><td>BIAS</td><td>0.11</td><td>0.00</td><td>0.00</td><td>-0.02</td><td>n/a</td><td>-0.04</td></tr></tbody></table>

locations in the Bothnian Bay and Bothnian Sea. No con- clusive differences between the bias and RMSD in the Bothnian Sea and Bothnian Bay could be derived

(Table 3.7.1), although ice conditions in the Bothnian Sea are more dynamic due to larger and variable open water area.

<figure><img src="image_157.png" /><figcaption>**Figure 3.7.4.** Time series of ice concentration (a), ice thickness (b) and ice volume (c) in the Bothnia Sea. Red dots show the CS2SMOS values. Shaded area represents the range of model values over the week centred at the CS2SMOS data derived time instant. Data from product references 3.7.1 and 3.7.2.</figcaption></figure>
<figure><img src="image_158.png" /></figure>

**Figure 3.7.5.** Ice thickness time series from CS2SMOS (product reference 3.7.2), Model (product reference 3.7.1) and ice charts (product reference 3.7.3) data sources in the Bothnian Bay (location shown on Figure 3.7.1) in 2016. Shaded area represents the range of model values over the week centred at the CS2SMOS data derived time instant. The difference between the ice charts and Model/CS2SMOS ice thicknesses is shown with blue/red bars.

A comparison of multiple ice thickness products CS2SMOS (product reference 3.7.2), model (product reference 3.7.1) and operational sea ice charts (product reference 3.7.3) in the Bothnian Bay for 2016 (Figure 3.7.5) shows that during the ice formation period in January and the first half of February ice thickness from the model and ice charts compare better than between the CS2SMOS and ice charts. Thereafter, model ice thickness increases beyond reasonable values (see Figure 3.7.3(b)). The ice thickness from the ice charts show a slow monotonic increase until the beginning of April, while sea ice thickness from CS2SMOS increases to a maximum value in the first 10 days of March, then drops below the ice thickness values from the ice charts and remains 0.1–0.15 m lower until the beginning of April. Based on the air temperature data (Figure 3.7.3(b)) only, we can not conclude whether the sea ice thickness from ice charts or from CS2SMOS is more accurate, but the model results are erroneous during this time.

In the Baltic Sea, sea ice thickness estimates include model data with 3.7 × 3.7 km grid resolution at hourly frequency and Synthetic Aperture Radar based operational ice charts with spatial resolution of 0.5 × 0.5 km updated daily. The validation of these estimates is fragmented due to sparse in-situ measurements and the accuracy has high uncertainties. Therefore, all new datasets that become available for the estimation of sea ice thickness in the offshore area of the Baltic Sea are valuable contributors for the improvement of the sea ice monitoring. The assessment of CS2SMOS ice thickness product shows that it can be used for the Baltic Sea. Indeed, spatial resolution of CS2SMOS data is 25 × 25 km, return period of the satellite is about one week and CS2SMOS data are usable for open sea area only. Still, we suggest that CS2SMOS data could be used

within Copernicus Marine Environmental Monitoring Service for increasing the accuracy of sea ice thickness estimates either by assimilating the data into a model or by including the data in multiproduct ice thickness estimations.

**3.8. Chlorophyll-a evolution during the last 21 years and its relation with mussel growth and optimal repartition for aquaculture and fishery**

**Authors:** Philippe Bryère, Antoine Mangin and Philippe Garnesson

**Statement of main outcome:** The aim of this study is to estimate the chlorophyll-a evolution effect over the past 21 years on mussel growth and optimal distribution for aquaculture and fishery. The results showed in this paper identify a decrease of chlorophyll-a concentration in the English Channel, the North Sea and the Irish Sea. This decrease in chlorophyll-a concentration begins around 2003. It is clearly observed in 2018 and is affecting mainly the higher concentrations (>10 mg m⁻³). This might have an impact on the living organisms feeding on phytoplankton such as bivalves. Our results show that the chlorophyll-a decrease doesn't affect significantly the mussel growth seeing that they need less than 2 mg m⁻³ (annual mean) for optimal growth. However we observed that the most impacted areas for mussel farming are off Only the Normando Breton gulf is an important are for mussel farming. Fortunately, the impacted areas are offshore the farming sites. So even if the chlorophyll-a concentration has a tendency to decrease, it doesn't appear to impact the coastal mussel farms during the studied period. Offshore mussel farming and fishery areas, where the chlorophyll-a concentration is lower than in coastal areas, could be more affected in the future and should be further studied.
<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /></figure>

**Figure 3.8.1.** a) Relationship between the annual average of chlorophyll-a and the weight reached by mussels after 19 months of growth. Vertical axis: mean final weight (grams), Horizontal axis: annual mean chlorophyll-a concentration (mg m⁻³). b) Mean final weight (grams) of mussels estimated applying the relationship for the period of 2003–2004 in the Bay of Mont Saint-Michel, using the annual averages of satellite estimated chlorophyll-a (Thomas et al. 2011).

<figure><img src="image_161.png" /></figure>

**Figure 3.8.2.** P90 of chlorophyll-a calculated over the productive period (March to October) on four periods of six years (a) 1998–2003, (b) 2003–2008, (c) 2008–2013 and (d) 2013–2018. The most impacted regions by the decrease are the English Channel and the Irish Sea.
<figure><img src="image_162.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>3.8.1</td><td>OCEANCOLOUR_ATL_CHL_L4_REP_009_098<br>Plankton Remote sensing</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-OC-PUM-009-ALL.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-030-032-033-037-081-082-083-085-086-098.pdf</td></tr><tr><td>3.8.2</td><td>OCEANCOLOUR_ATL_CHL_L4_NRT_008_037<br>Plankton Remote sensing</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-OC-PUM-009-ALL.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-030-032-033-037-081-082-083-085-086-098.pdf</td></tr><tr><td>3.8.3</td><td>IBI_REANALYSIS_WAV_005_006<br>Wave Hindcast</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-IBI-PUM-005-006.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-IBI-QUID-005-006.pdf</td></tr><tr><td>3.8.4</td><td>IBIenger_005_005<br>Wave Forecast</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-IBI-PUM-005-006.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-IBI-QUID-005-006.pdf</td></tr></tbody></table>

For many years the decrease of the riverine nutrient influx into the North-East Atlantic Basin, (mainly into the North Sea, the English Channel and the Bay of Biscay), has impacted the annual concentration of chlorophyll-a. Recently, Capuzzo et al. (2018), using in situ data, identified the sea surface warming and reduced nutrient riverine inputs as the major cause for the North-Sea primary productivity decline over the period 1998–2013. Using satellite data in the frame of the JMP EUNOSAT project (Joint Monitoring Program of the Eutrophication of NOrth Sea with SATellite data, founded by European commission, https://www.informatiehuismarien.nl/uk/projects/algae-evaluated-from/, see also chapter 3.4), Gohin et al. (2019) show an evolution of chlorophyll-a since 1997 with a negative trend in the English Channel. The estimation chlorophyll-a concentration by remote sensing has been successfully used for more than 20 years (SeaWiFs/NASA, Modis/NASA, Meris/ESA, Viirs/NASA) for global (Maritorea and Siegel 2005; Morel et al. 2007) and coastal applications (Gohin et al. 2002; Gohin 2011; Lapucci et al. 2012; Novoa et al. 2012; Loisel et al. 2017). In the Bay of Mont-Saint-Michel, Thomas et al. (2011) established a relationship between the annual averages of satellite estimated chlorophyll-a and the weight reached by mussels caught after 19 months of growth (Figure 3.8.1). For this reason, over the 2003–2004 period, they used the chlorophyll-a estimated with the OC5 algorithm from IFREMER (Gohin et al. 2002; Gohin 2011) as input in a dynamic energy budget (DEB) model.

This decrease of chlorophyll-a concentration might thus have an impact on the living organisms feeding on phytoplankton such as the bivalves. The estimation of chlorophyll-a concentration by remote sensing with satellite imagery has been used to obtain a spatial analysis of this impact. The goal of this study is to

<figure><img src="image_163.png" /></figure>

<figure><img src="image_164.png" /><figcaption>**Figure 3.8.3.** Differences of P90 of chlorophyll-a calculated over the productive period (March to October) on four periods of six years (a) 1998–2003 vs. 2003–2008, (b) 2003–2008 vs. 2008–2013, (c) 2008–2013 vs. 2013–2018. (d) 1998–2003 vs. 2013–2018. The results confirm that the most impacted regions by the decrease are the English Channel, the Irish Sea and the North Sea.</figcaption></figure>
<figure><img src="image_165.png" /></figure>

show the potential impact that the chlorophyll-a decrease might have on Atlantic Mussel (Mytilus edulis) growth and spatial repartition in the North-East Atlantic Basin using CMEMS products: satellite chlorophyll-a estimation for growth and farming (products 3.8.1 and 3.8.2), and modelled wave height for farming operations (products 3.8.3 and 3.8.4). In order to study the spatial and temporal evolution of chlorophyll-a, images of the 90-percentile (P90) calculated over the productive period (March to October) and over a period of six years (i.e. period used for reporting for European Framework Directives, Belin et al. 2014) have been generated for 4 periods (i.e. 1998-2003, 2003-2008, 2008-2013 and 2013-2018). The results show that the decrease of chlorophyll-a concentration is observed in the last 2 periods (Figure 3.8.2(c,d)) and impact mainly the Irish Sea and the English Channel (i.e. Bay of Seine, Bay of Mont-Saint-Michel). The southern regions like the Bay of

Biscay, the Portuguese and Spanish coasts don't seem to be significantly impacted by the chlorophyll-a decrease and remain stable.

Using the difference of P90 of chlorophyll-a between the four periods (Figure 3.8.2(a-c)) we can observe its spatio-temporal evolution. During the first two periods (1998-2003 vs. 2003-2008, Figure 3.8.3(a)) we observe an increase in the North Sea in coastal areas and a slight decrease in the East English Channel except off the Bay of Somme. A slight increase is also observed in the coastal area of the Bay of Biscay. Over the second and third periods (2003-2008 vs. 2008-2013, Figure 3.8.3 (b)) the decrease is observed in the North Sea except off the Rhine/Scheldt mouth where there is a high increase. Moreover, a decrease is observed in the Eastern part of the English Channel, the Bay of Mont-Saint-Michel and in the North of Ireland. Finally, during the last two periods (2008-2013 vs. 2013-2018, Figure 3.8.3

<figure><img src="image_166.png" /><figcaption>**Figure 3.8.4.** Mussel Final weight expected after two years (from 4 to 20 g), estimated from the mean of chlorophyll-a concentration calculated on a period of two years: (a) 1998-1999, (b) 2003-2004, (c) 2008-2009, (d) 2017-2018. Deep blue for mussels < 4 g and deep brown for mussels > 20 g. The grey shaded areas indicate the regions with not enough chlorophyll-a for mussel growth.</figcaption></figure>
(c)) a high decrease is observed in the coastal areas of Belgium and Netherlands and in the French coasts of the East English Channel. The decrease is also observed all around Ireland and in the English coast of the Irish Sea. The difference between the first and the last period (1998–2003 vs. 2013–2018, Figure 3.8.2(d)) confirm a chlorophyll-a P90 decrease during the last 21 years that mostly impacted the English Channel (mainly French coasts), the Irish Sea and an area of the North Sea (off the North Coasts of the Netherlands).

These results confirm the observations of Gohin et al. (2019), i.e. the evolution of chlorophyll-a since 1997 with a negative trend in the English Channel while the Bay of Biscay was not impacted by this decrease. Moreover, these results are obtained using two different sources of chlorophyll-a data: products 3.8.1 and 3.8.2 were used for this study while Gohin et al. (2019) used the level 4 IFREMER products (Saulquin et al. 2011). Now that the decrease of chlorophyll-a is confirmed in the North of the IBIROOS region (Ireland-Biscay-Iberia Regional Operational Oceanographic System), we can study the impact on the mussel growth and then define the optimal farming locations in this area.

Initially, we applied, on the studied area, the relationship between chlorophyll-a concentration and mussel growth established by Thomas et al. (2011) as described

above. In summary, mussel growth is strongly correlated with the mean concentration of chlorophyll-a during its two years of growth. Biannual means of chlorophyll-a were computed from 1998 to 2018 using the satellite products 3.8.1 and 3.8.2. The relationship was applied to each pixel of the area to determine the mean final weight of the mussel after two years of growth. The resulting maps are shown below (Figure 3.8.4). Between the periods 1998–1999 and 2003–2004 (Figure 3.8.4(a,b)) an increase of the high growth areas can be observed in the English Channel, North Sea and the Irish Sea while others regions do not differ significantly. Then, from 2003–2004 to 2017–2018 (Figure 3.8.4(b–d)) we observe a significant decrease of the high growth areas in the North part of the studied regions. The results show that the main impacted areas are the North Sea, the English Channel (including the Bay of Seine and the Bay of Mont Saint-Michel) and the Irish Sea. In these regions, the decrease of chlorophyll-a concentration can be linked to the decrease of nutrients influx from rivers, mainly the Phosphorus (Romero et al. 2013).

Comparing the difference of expected weight between the periods 2003–2004 vs. 2017–2018 (Figure 3.8.5) we can observe that the coastal areas most impacted by the decrease of chlorophyll-a (deep blue) are the English Channel and the Irish Sea (Irish Coasts). The coastal

<figure><img src="image_167.png" /><figcaption>**Figure 3.8.5.** Mussel growth difference between 2003–2004 vs. 2017–2018. The most impacted zones by the decrease of chlorophyll-a (deep blue) are the central part at the south of the North Sea, the English Channel, the Irish Sea and off the north-west of Ireland.</figcaption></figure>
<figure><img src="image_168.png" /></figure>

areas with a very high chlorophyll-a concentration are not impacted (North Sea, French Eastern part of the English Channel, Western English coast, Bay of Biscay). The north East of England and the south of the Bay of Biscay are the two regions that show a higher growth potential.

To finalise this case study we will analyse the delimitation of optimal sites for mussel farming using chlorophyll-a (product ref. 3.8.1 and 3.8.2) as well as wave product (product ref. 3.8.3 and 3.8.4). The chlorophyll-a concentration directly impacts the mussel growth while the wave height impacts the farming operation conditions. In prospecting sites for mussel culture, well-protected or sheltered coves and bays are preferred over unprotected open areas. Sites affected by heavy wind conditions and big waves could damage the stock and farming materials and, therefore, must be avoided. Thus, protected areas rich in plankton should be selected.

For this purpose, we have used the mean of chlorophyll-a concentration for two-year periods from 1998 to 2018 and the P90 of the maximum significant wave

height (HSMAX) calculated from 1998–2018. Then, thresholds are applied to each parameter to exclude the areas that do not correspond to the suitability criteria (that could be adjusted depending on the studied area). The data combination, based on the environmental conditions, has been used to exclude the areas which do not comply with the following criteria (used in the EU FP7 SAFI project: http://www.copernicus.eu/projects/safi-safi Consortium (2016)):

* To avoid the areas too exposed to high waves: P90 of HSMAX < 3 m

* To select areas with enough chlorophyll-a for optimal mussel growth, but without eutrophication risks: 1.2 < Mean chlorophyll-a < 25 mg m⁻³

* Coastal distance < 10 km to optimise the exploitation costs

The results show (Figure 3.8.6) that between 1998–1999 and 2017–2018 there is an increase of optimal sites for

<figure><img src="image_169.png" /><figcaption>**Figure 3.8.6.** The differences between optimal sites (in red): (a) from 2017–2018 comparing to 1998–1999. Stable areas in blue, new sites in green, disappeared sites in red.</figcaption></figure>
mussel farming in the North-West coasts of Ireland, the North Coast of Cornwall, the West Coast of Brittany and the Southern Coast of the French part of the Bay of Biscay. However during the same time there is a decrease of optimal sites for mussel farming in the Bay of Exeter, the Normano-Breton Gulf and off the Rhone river mouth.

In conclusion, these results confirm the observation of Gohin et al. (2019), showing a decrease of chlorophyll-a concentration in the English Channel and the North Sea. Moreover, we observe a similar negative trend in the Irish Sea. This decrease in chlorophyll-a concentration begins around 2003 and is clearly observed in 2018. This phenomenon concerns mainly the higher concentrations (>10 mg m⁻³). The main cause seems to be the nutrients influx decrease from rivers over the last years, and the decline in phosphorus is certainly the major factor driving the evolution of the phytoplankton biomass (Romero et al. 2013).

As mentioned above, this negative trend might have an impact on the living organisms feeding on phytoplankton, as for instance the bivalves. Hence, it could impact the farming and fishery of commercial bivalves such as mussels in which growth is strongly correlated with chlorophyll-a concentration (Thomas et al. 2011). Our results show that the chlorophyll-a decrease impacts mainly the higher concentrations (>10 mg m⁻³) and does not appear to significantly impact the mussel growth since they need less than 2 mg m⁻³ (annual mean) to achieve an optimal growth. However, we observed that the most impacted areas for mussel farming are the Bay of Exeter, the Rhone mouth and the Normano-Breton Gulf. Although the Normano-Breton Gulf is a high spot for mussel farming, fortunately, until now, the actual farms have not been affected

Although chlorophyll-a concentration has a tendency to decrease, the coastal mussel farms are not yet affected. However, the most impacted areas must be monitored to follow the evolution of the chlorophyll-a over the next years. Mussel offshore farming and fishery may be affected in the near future, therefore additional studies will be needed. It should be noted however that other external parameters, such as harmful algal blooms, fishing pressure, human pressure, have not been taken into account in this study, and may have a significant impact on local ecosystem.

## Notes

1. The JMP-EUNOSAT project consortium consists of: RWS (co-ordinator), Deltares (NL), RBINS (BE), IFREMER (FR), PML, Cefas, MSS (UK), IMR, NIVA (NO), SMHI (SE), AU (DK), UBA, NLWKN, BSH (DE, all self-funded).

2. Coast Colour Round Robin (CCRR) data set published by Nechad et al. (2015). The CCRR project (http://www.coastcolour.org) funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was developed to help end users of remote sensing products to select the most accurate algorithms for their coastal region.

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## Section 3.8. Chlorophyll-a evolution during the last 21 years and its relation with mussel growth and optimal repartition for aquaculture and fishery

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# Chapter 4: Specific events 2017

## 4.1. The Weddell Sea Polynya

**Authors:** Céline Heuzé, Gilles Garric, Thomas Lavergne

**Statement of main outcome:** During the year 2017 a large hole in the winter sea ice cover (polynya) appeared in the Weddell Sea, a region of the Antarctic Ocean. It stayed open for almost three months and was the first reappearance of such an event since 1976. Deep vertical oceanic mixing started after the polynya opened but was stopped after just two months, probably as sun-induced sea ice melting released enough freshwater to stabilise the water column. Such deep mixing is crucial for the global ocean circulation, deep ocean ventilation, and carbon and heat storage. The Weddell Polynya must therefore be actively monitored to understand why this deep mixing starts and stops.

**Products used:**

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>4.1.1</td><td>SEAICE_GLO_SEAICE_L4_REP_ OBSERVATIONS_011_009<br>Global Ocean Sea Ice Concentration<br>Time Series Reprocessed from EUMETSAT OSI SAF</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-OSI-PUM-011-009.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-OSI-QUID-011-001to007-009to012.pdf</td></tr><tr><td>4.1.2</td><td>GLOBAL_REANALYSIS PHY_001_026<br>Global ocean ensemble physics reanalysis</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-026.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-026.pdf</td></tr></tbody></table>

Every winter at both poles, strong winds push the sea ice away from the coast (Smith and Barber 2007), opening small holes in the sea ice or so-called 'coastal polynyas'. Via these holes, the comparatively warm ocean is in direct contact with the cold atmosphere, which results in strong oceanic heat loss, sea ice formation and dense water production (e.g. Cavalieri and Martin 1994; Kusahara et al. 2010). In stark contrast with these small coastal polynyas, in austral winter 2017 a very large hole opened unexpectedly in the sea ice in the open ocean in the Weddell Sea – the Atlantic sector of the Southern Ocean. That hole, the Weddell Polynya, has been a modelling mystery for the last decades, occurring regularly in climate projections (e.g. Heuzé et al. 2013) yet only twice to date in the observational record. It is hence crucial to study this second occurrence, not only

to help improve global climate models but also due to the potential large role of the Weddell Polynya on the global oceanic circulation (Orsi et al. 2001) and on local ecosystems (Smith and Barber 2007).

The first satellites to routinely observe the Antarctic sea ice detected the Weddell Sea polynya, a huge hole that opened three winters in a row, from 1974 to 1976 and reached up to 350,000 km² (Carsey 1980). Then, nothing happened for forty years. A small 'halo' with sea ice concentrations never exceeding 90% (Lindsay et al. 2004) was regularly observed, but the polynya itself did not re-open until 27 July 2016 and closed shortly after on 17 August 2016. In 2017 however, the Weddell Polynya stayed open continuously from mid-September until the seasonal retreat of sea ice early December, and at its maximum reached approximately 300,000 km² (Figure 4.1.1).

There have been too few observed events to determine the exact reasons why the Weddell Polynya only opens occasionally. The Weddell Polynya is a latent heat polynya, meaning that it opens because sea ice is melted locally from below by upwelled warm waters (Morales-Maqueda et al. 2004). The sea ice halo indicates that this upwelling happens often, most likely because of the presence of the underwater seamount Maud Rise (Holland 2001). However, additional processes are required to trigger a full polynya opening but there is no consensus as to which they are, owing notably to a lack of in-situ measurements. The trigger could be an event in the atmosphere, e.g. persistent anomalous winds (Gordon et al. 2007; Cheon et al. 2014); or a weakening of the oceanic stratification (Comiso and Gordon 1987; Heuzé et al. 2015; Kjellsson et al. 2015); or even low frequency variations in the Weddell Sea heat content (Martin et al. 2013; Dufour et al. 2017).

The Weddell Sea and the other seas surrounding Antarctica are vast and cannot be sampled effectively with conventional automated in-situ observations (drifters, profilers, etc), and ship-based campaigns are too seldom to allow for the monitoring of such rapidly evolving phenomena as the polynya. The main direct observation of the polar ocean and its sea ice cover is thus made using satellite-based products. Figure 4.1.1 shows the temporal evolution of the Weddell Sea polynya in austral spring 2017 (15 September, 25 October, 15 November, and 1 December). Daily maps of sea-ice concentration are computed from passive microwave radiometer observations from the Special Sensor Microwave Imager/ Sounder (SSMIS). Raw brightness temperature measurements are converted to sea-ice concentration by the EUMETSAT Ocean and Satellite Application Facility (OSI SAF, http://www.osi-saf.org/) and redistributed by CMEMS as SEAICE_GLO_SEAICE_L4_NRT_observations_011_001 (product reference 4.1.1).
<figure><img src="image_173.png" /></figure>

<figure><img src="image_174.png" /></figure>

**Figure 4.1.1.** Evolution of the Weddell Sea Polynya in austral spring (15 September, 25 October, 15 November, and 1 December) 2017 as monitored by product 4.1.1. Yellow box highlights the area used for the output averaging of GREP (product 4.1.2) shown on Figures 4.1.2 and 4.1.3.

In the following section, we use the ensemble mean global reanalysis product GREP (product reference 4.1.2). GREP consists of GLORYS2V4 from Mercator Ocean (Fr), ORAS5 from ECMWF, FOAM/GloSea from the UK Met Office (UK) and C-GLORS from CMCC (It). All these reanalyses assimilate sea ice concentration from passive microwave observation data and the vertical mixing is parameterised according to a turbulent closure model adapted by Blanke and Delecluse (1993).

Observations (Martinson et al. 1981) and coupled models (e.g. Heuzé et al. 2013) both show a deepening of the mixed layer during a polynya event. The GREP product correctly reproduces this phenomenon (Figure 4.1.2): in 2017, the monthly mixed layer depth (MLD) departs from an average value in August to peak at 470 m in October 2017, 350 m deeper than the mean value of 1993–2016, and returns to a normal value in December. The deep mixed layers, by bringing warm water up, are thought to keep the polynya open. GREP in contrast shows that in November, as the polynya grew exponentially, the MLD had decreased again (Figure 4.1.2), suggesting that from November onwards the polynya continued growing because of melting of sea ice by the atmosphere, not by further upwelling of warm oceanic water.

The evolution of both temperature and salinity with time and depth in the polynya region (Figure 4.1.3) confirms these findings. After the opening of the polynya,

<figure><img src="image_175.png" /></figure>

**Figure 4.1.2.** Monthly mixed layer depth over Maud Rise (yellow box, Figure 4.1.1) from product 4.1.2. Black line indicates the mean of 1993–2016 (individual dotted lines). Red line is 2017, with the polynya opening in September. Based on product 4.1.2 (GREP).

notably in September 2017, the surface waters are warmer and saltier than previously (resp. 0.05°C and 0.01, thick black line on Figure 4.1.3(b,d)) whereas from 70 m depth onwards they are colder and slightly fresher than before the polynya opened. This pattern of warming of the surface waters in winter coincident with a cooling of the subsurface, all while the MLD deepens, is indicative of convection. From November 2017 onwards however, the surface signal is dominated by the large freshening induced by the growth of the polynya (Figure 4.1.1) while at subsurface temperatures around 100 m increase again as the mixed layer shoals, further suggesting that the convection was limited to September–October 2017. It is worth noting that the hydrography from GREP is remarkably similar to that measured by the autonomous SOCCOM floats that accidentally sampled the 2017 polynya (see Figure SB6.2 of Swart et al. (2018) compared to Figure 4.1.3(a,c) here). Since modelling approaches are routinely used in the ice-covered Southern Ocean where observations are lacking, it is crucial that reanalysis such as the four members of the GREP product remain as accurate as possible, even when having to react to an unexpected polynya event.

In summary, although the opening of the Weddell Polynya in winter 2017 was unexpected, it was accurately detected by the passive microwave observations (product 4.1.1), which were then assimilated by GREP (product 4.1.2) resulting in GREP's hydrography matching that opportunistically observed (Swart et al. 2018). The GREP results presented here suggest that the 2017 Weddell Polynya had two different stages of development: first, in September and October, the mixed layer
<figure><img src="image_176.png" /></figure>

<figure><img src="image_177.png" /><figcaption>**Figure 4.1.3.** Evolution of the monthly hydrographic properties in GREP (product 4.1.2) with depth and time over Weddell Polynya region (yellow box, Figure 4.1.1): (a) Potential Temperature from January 2015 to December 2017; (b) change in that temperature after the opening of the polynya in September (S, dashed black), October (O, plain dark grey), November (N, dashed grey) and December (D, plain light grey) 2017 when compared with August 2017; (c) and (d) same as (a) and (b) respectively for the salinity. On (a) and (c), yellow dashed line is the monthly MLD from product 4.1.2; vertical purple dashed line and associated arrow, the opening of the polynya in September 2017; vertical dashed black lines, the turn of the year.</figcaption></figure>

deepened and heat was redistributed through the water column, indicative of convection bringing warm water to the surface to further melt the sea ice from below; then, in November, the water column was stabilised, convection stopped, and the polynya grew only because of seasonal surface melting from above, hence producing even more freshwater to stabilise the water column. It is however too early to say whether the dynamics in the model are correct. Longer observational time series with more openings of the large Weddell Polynya than just the 1970s and 2017 are needed, along with measurements of its effect on the rest of the climate system in terms of heat and carbon fluxes and volumes of deep water formation. Since sea ice has been struggling to close over the polynya region in May and June 2018, we may be able to extend our analysis by one year soon.

## 4.2. Temperature and salinity anomalies in the North Atlantic subpolar gyre

**Authors:** Jérôme Gourrion, Julie Deshayes, Mélanie Juza, Tanguy Szekely

**Statement of outcome:** A regional cold and fresh anomaly appeared in the North Atlantic in 2014. It is associated with the onset of a positive North Atlantic Oscillation phase and intense deep convection in the Labrador Sea. The previous Ocean State Report indicates

that in 2016, this cold and fresh anomaly was located to the south of Iceland and primarily associated with an eastward migration of the eastern boundary of the subpolar gyre. In 2017, a similar cold and fresh anomaly is detected further to the north-east with temperature anomalies of smaller amplitude and salinity anomalies of similar amplitude as observed previously.

**Products used:**

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>4.2.1</td><td>INSITU_GLO_TS_OA_REP_ OBSERVATIONS_013_002_B<br/>INSITU_GLO_TS_OA_NRT_ OBSERVATIONS_013_002_A<br/><strong>In situ TS data</strong></td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-002-ab.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-002b.pdf<br/>http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-002a.pdf</td></tr><tr><td>4.2.2</td><td>GLOBAL_REANALYSIS PHY_001_030<br/><strong>Reanalysis</strong></td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-030.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-030.pdf</td></tr></tbody></table>

The North Atlantic subpolar gyre is a region characterised by substantial variability on interannual to decadal
<figure><img src="image_178.png" /></figure>

timescale, with cold and fresh anomalies, such as in the 1970s (Dickson et al. 1988; Belkin et al. 1998) but also in the 1980s and early 1990s, alternating with warm and salty anomalies, e.g. in the late 1990s (Robson et al. 2012). It is crucial to understand better the drivers of this regional variability, because (i) the presence of cold and fresh anomalies may conceal the long-term increase in ocean heat uptake (Levitus et al. 2009), (ii) sea surface temperature anomalies in the North Atlantic have a direct impact on European climate (e.g. Czaja and Frankignoul 2002) and (iii) it is expected to have consequences on the regional to global ocean and climate system through their potential impact on dense water formation (Hátún et al. 2005). In 2016, Gourrion et al. (2018) reported that cold and fresh anomalies are observed in the subpolar gyre, to the south of Iceland, from the surface down to 1000 m. The authors suggest that these anomalies were associated with an eastward migration of the eastern boundary of the subpolar gyre, and resulted from a regional adjustment of the basin-scale dynamical structures subsequent to dense water formation in the Labrador Sea. As these cold anomalies coincide with general warming trends elsewhere, there has been numerous studies focussing on those and other hypotheses have

been advanced, for example anomalous air-sea fluxes (e.g. Grist et al. 2016; Zunino et al. 2017), large scale changes in horizontal (Piecuch et al. 2017) and/or overturning (Robson et al. 2016) circulations, among others (see review of Josey et al. 2018). In this section, we analyse the temperature and salinity anomalies observed in the subpolar North Atlantic in 2017 and discuss their relationship with those observed previously.

In 2017, the global CMEMS *in situ* observation reprocessed dataset (product reference 4.2.1, hereafter designed as the CORA dataset) suggests that upper ocean anomalies in the subpolar North Atlantic are cold and fresh compared to the mean over the period 2003–2014 (Figure 4.2.1, left), similarly to 2016. Anomalies of maximum amplitude (around -1°C and -0.2 psu when integrated over 100–400 m depth) are located more to the north east (approximately at 21°W, 59°N) than previous year. To the south of the subpolar gyre and offshore Grand Banks (40°W, 48°N), as well as to the north of the Labrador Sea, upper ocean anomalies are warm and salty during 2017. In order to determine which upper ocean anomalies are robust, similar diagnostics from the GLO-MFC reanalysis product (product reference 4.2.2, hereafter designed as GLORYS12V1) are also produced

<figure><img src="image_179.png" /></figure>

**Figure 4.2.1.** Temperature (in °C, top) and salinity (in psu, bottom) anomaly fields integrated within the 100–400 m depth layer in 2017 from the CORA observational dataset (product reference 4.2.1) and the GLORYS12V1 model (product reference 4.2.2). The reference period for anomaly computation is the mean over 2003–2014. The white box corresponds to the control box used in Figure 4.2.2 to compute the depth-time diagram. The white contours correspond to the patterns of temperature and salinity anomaly maxima in 2016 (delimited by the values of -1.3°C and 0.17 psu, respectively).
(Figure 4.2.1, right). The latter also offers the unique opportunity to examine coincident ocean circulation anomalies, as will be done later. In GLORYS12V1, upper ocean anomalies are cold and fresh to the south of Iceland, and warm and salty (although they are more patchy) to the south of the subpolar gyre, as obtained from CORA (product reference 4.2.1).

The CORA dataset is used to determine the extent at depth of upper ocean cold and fresh anomalies (Figure 4.2.2). By averaging those anomalies over a box centred on the anomalies of maximum amplitude (displayed in Figure 4.2.1), we notice that they have a signature reaching 1000 m depth, similar to what was already observed in 2016. Notwithstanding, temperature

<figure><img src="image_180.png" /><figcaption>**Figure 4.2.2.** Depth-time diagram of temperature (in °C) and salinity (in psu,) anomalies averaged over the control box (defined in Figure 4.2.1) from CORA (product reference 4.2.1) and GLORYS12V1 (product reference 4.2.2). The reference period for anomaly computation is the mean over 2003–2014.</figcaption></figure>