<figure><img src="image_181.png" /></figure>

anomalies in 2017 have weaker amplitude compared to those in 2016, while salinity anomalies have similar amplitude. This is also visible in depth-time diagram computed from GLORYS12V1 (Figure 4.2.2). As a consequence, cold and fresh anomalies detected in 2017 share similarities with those observed in 2016, except that they are located further to the north east, and that temperature signal has been attenuated.

Local anomalies in air-sea fluxes have the potential to drive upper-ocean anomalies, and we investigate their role in our region of interest in 2017, by computing net heat and freshwater fluxes from GLORYS12V1 over the white box displayed in Figure 4.2.1 (Figure 4.2.3, plain lines). As for previous variables, monthly anomalies are computed from the corresponding climatological seasonal cycles calculated over the period 2003–2014. In 2016 and 2017, heat flux anomalies are positive over the region of maximum cold anomalies, which suggests that 2017 upper ocean temperature anomalies have not been generated locally by anomalous atmospheric heat fluxes during 2017 winter. In addition, the fact that heat flux anomalies are positive in 2017 is consistent with a weakening of the negative ocean temperature anomalies, and suggests that air-sea interactions have had a damping influence on the cold anomalies of 2016. Freshwater flux anomalies

are weak on average in 2017, as they are negative in the first part of the year, and positive later. Figure 4.2.3 also suggests that those flux anomalies are very similar when averaged over a slightly bigger spatial box (dashed lines), which then includes the area of maximum amplitude cold and fresh anomalies in 2016. As a result, cold and fresh anomalies in 2017 have not been generated locally by anomalous air-sea fluxes, but result from pre-existing anomalies created elsewhere and advected into the area of interest (see Gourrion et al. 2018).

GLORYS12V1 also provides useful information about the ocean circulation. The region of maximum amplitude anomalies in 2017 lies in the north-eastern part of the cyclonic subpolar gyre, where the barotropic currents are oriented to the north-east (Figure 4.2.4). This is compatible with the north-east migration of the maximum amplitude anomalies from 2016 to 2017, as advected by the mean barotropic currents, and so confirms that the cold and fresh anomalies observed in 2017 are the continuation of those previously described in 2014, 2015 and 2016 (see Gourrion et al. 2018). Besides, anomalies in the barotropic streamfunction calculated every year as deviations from the 2003–2014 climatology. Results from Figure 4.2.5 suggest that the cyclonic subpolar gyre circulation is more intense over the last four years.

<figure><img src="image_182.png" /><figcaption>**Figure 4.2.3.** Net heat fluxes (in W/m²) and freshwater fluxes (in kg/m²/s) anomalies averaged over the control box (defined in Figure 4.2.1) from the GLORYS12V1 model (product reference 4.2.2). The fluxes are defined positive from the atmosphere to the ocean. The reference period for anomaly computation is the mean over 2003–2014.</figcaption></figure>
<figure><img src="image_183.png" /></figure>

**Figure 4.2.4.** Mean barotropic streamfunction in 2017 from GLORYS12V1 (product reference 4.2.2). The white box with plain lines corresponds to the area used to compute depth-time anomalies in Figure 4.2.2. The white box with dashed lines represents the area of maximum amplitude cold and fresh anomalies in 2016 (see Gourrion et al. 2018).

<figure><img src="image_184.png" /></figure>

**Figure 4.2.5.** Barotropic streamfunction anomalies in 2014 (a), 2015 (b), 2016 (c) and 2017 (d) from GLORYS12V1 (product reference 4.2.2). The reference period for anomaly computation is the mean over 2003–2014.
<figure><img src="image_185.png" /></figure>

Indeed, although anomalies in batrotropic circulation are very patchy to the south of 54N, which corresponds to the highly turbulent North Atlantic Current (this region is discussed below), they are overall negative to the north of 56N from 2014 to 2017. Previous studies underlined that intensifications of the subpolar gyre in the last 40 years have been associated with eastward migrations of the eastern boundary of the gyre (Flatau et al. 2003; Hátún et al. 2005; Deshayes and Frankignoul 2008), and that has actually been observed in 2016 through an eastward migration of 35.1 isohaline (Figure 4.2.3 in Gourrion et al. 2018). Hence, this suggests that the cold and fresh anomalies observed from 2014 to 2017 are the result of dynamical changes in the subpolar gyre, which drivers are discussed at length in Gourrion et al. (2018). Note that this is actually in agreement with the conclusions of Piecuch et al. (2017) although they only investigate anomalies in ocean heat content.

Figure 4.2.1 also highlights warm and salty anomalies observed to the south of the subpolar gyre during 2017. The yearly anomalies in barotropic streamfunction (Figure 4.2.5) are instrumental in speculating about their origin. Those anomalies are very patchy with alternating positive and negative patterns, which is not surprising as this is a highly turbulent region with intense mesoscale activity. Yet, there seems to be an overall intensification in the amplitude and occurrence of positive patterns from 2014 until 2017, which could reflect the interannual adjustment of the ocean to the positive North Atlantic Oscillation phase that began in 2014 (Barrier et al. 2014). Ultimately, this dynamic adjustment is expected to favour the northward penetration of warm and salty subtropical water masses, which could explain the observed warm and salty anomalies to the south of the subpolar gyre in 2017.

### 4.3. Anticyclonic Eddy Anomaly: impact on the boundary current and circulation in the western Mediterranean Sea

**Authors:** Eva Aguiar, Mélanie Juza, Baptiste Mourre, Ananda Pascual, Evan Mason, Aida Alvera-Azcárate, Joaquín Tintoré

**Statement of main outcome:** An intense anticyclonic eddy anomaly event was observed in fall-winter 2017 north of the island of Mallorca in the western Mediterranean Sea. Similar long-lived eddies were reported during 1998 and 2010. The eddy alters the general cyclonic circulation of the Balearic Sea and the regional water mass properties. In particular, glider data in 2017 showed an anomalous strong inflow through the Ibiza Channel of recent Atlantic Water. These changes significantly affect the heat, salt and nutrient distributions in the

area with implications for climate and primary production. The monitoring of these events is thus essential for both science and society in this area.

**Products used:**

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>4.3.1</td><td>SEALEVEL_MEDPHY_L4_REPOBSERVATIONS_008_051<br>SEALEVEL_MEDPHY_L4_NRT OBSERVATIONS_008_050<br>Sea level</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-SL-PUM-008-032-051.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-SL-QUID-008-032-051.pdf</td></tr><tr><td>4.3.2</td><td>INSITU_MED_TS_REPOBSERVATIONS_031_041<br>INSITU_MED_NRT_ OBSERVATIONS_031_035<br>In situ TS data</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-041.pdf<br>http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf</td></tr></tbody></table>

Mean flows, as part of the general circulation, are responsible for heat, salt and nutrient redistribution along our coasts, affecting climate and primary production. Their interactions with eddies produces exchanges of energy and momentum that can induce changes in the general ocean circulation (e.g. Kang and Curchitser 2015). Hence, it is essential to better understand the processes that favour eddy generation and their interaction with the mean flow. Previous theoretical studies have shown that instability processes associated with mean flows can lead to eddy generation and that these eddies, in turn, play an important role driving changes in the large-scale circulation (Holland and Lin 1975; Holland 1978; Hogg and Stommel 1985; Greatbatch 1987; Dengler et al. 2004; Marshall 2006).

In the southern part of the western Mediterranean Sea (Figure 4.3.1), the circulation in the Alboran Sea is mainly driven by the Atlantic Jet entering the basin at the Strait of Gibraltar (Parrilla and Kinder 1987; Viúdez and Haney 1997) bringing fresh Atlantic Water with salinity values around 36.5. In the northern part of the western Mediterranean, the circulation is characterised by a strong boundary current (the so-called Northern Current), which flows south-westward through the Balearic Sea, before splitting into two branches (Font et al. 1988): one deviating along the northern slope of the Balearic Islands forming the Balearic Current (Ruiz et al. 2009; Mason and Pascual 2013) and the other flowing southward through the Ibiza Channel (Pinot et al. 2002). This channel is often considered to be a choke point of meridional exchanges between the northern and southern basins (Heslop et al. 2012; Juza et al. 2013). The intense meso-scale activity
<figure><img src="image_186.png" /></figure>

**Figure 4.3.1.** Mean geostrophic velocities from the product reference 4.3.1 over the period 1993–2012 in the western Mediterranean Sea. White arrows represent the main regional circulation pattern in the Balearic Sea. The black contour delimits the anticyclonic eddy anomaly influence-area where the climatological index is calculated (see text for more details). The black point denotes the deflection location of the Northern Current, the black line is the section where the transports are extracted to monitor that deviation, and the red line corresponds to the glider transect in the Ibiza Channel.

observed in the western Mediterranean Sea is associated with a small Rossby radius (approximately 10 km; Robinson et al. 2001). Moreover, the high number of available observations (Tintoré et al. 2013) converts this oceanic sub-basin into a small ocean laboratory where eddy-mean flow interactions can be monitored, detected, and studied.

In this study, we use sea level anomalies from the product reference 4.3.1 over the period 1993–2017. First, the eddy anomaly is characterised based on monthly mean sea surface height maps (adding the sea level anomaly maps to the mean dynamic topography; Rio et al. 2014) (Figure 4.3.2(a)). Then, the influence area of the anticyclonic eddy anomaly is defined (Figure 4.3.1) by calculating the mean contour of all the anticyclonic eddies detected by an eddy identification and tracking algorithm (Mason et al. 2014) north of Mallorca in 2017. A climatological index is derived from the spatially-averaged sea level over the influence area. This index is seasonal and low-pass filtered using a 30-day moving average to detect only long-lived eddy anomalies (Escudier et al. 2016) which are our focus in this study. The index enables us to define the anomalous character of the reported events (Figure 4.3.2(b)). Finally, the Northern Current is monitored at two locations as displayed in Figure 4.3.1 (Figure 4.3.2(c,d)). Geostrophic velocity time series have been computed from sea level anomalies using the equation for geostrophic equilibrium. Associated transport time

series have been calculated from the perpendicular component of the velocities projected onto the defined section. High-resolution glider data in the Ibiza Channel as part of the product reference 4.3.2 are also used over their available period 2011–2017 to indicate the presence or not of anomalous Atlantic Water of recent origin south of the position of the eddy anomaly.

The anticyclonic eddy anomaly first occurs during September 2017 and persists until February 2018, with a radius varying between 32 and 90 km (Figure 4.3.2 (a)). The climatological index (Figure 4.3.2(b)) confirms the formation and persistence of this eddy, and also highlights the two previous events in 1998 and 2010 (Pascual et al. 2002; Mason and Pascual 2013). The maxima of the index typically occur in the fall-winter months. They are higher in 1998 and 2017 (16 and 20 cm, respectively) compared to 2010 (12 cm), and correspond to long-lived anticyclonic eddies with durations of four to five months in 1998 and 2017, and two months in 2010. Other anticyclonic eddies in the same area as reported in the literature were not persistent enough (shorter than 30 days) to be detected by the index. These include, for example, the eddies in fall 2001 (Rubio et al. 2009) and in April 2008 (Bouffard et al. 2010). The anticyclonic eddy analysed by Amores et al. (2013) in 2010 occurred in a different area further south. The 2007 anticyclonic eddy studied by Garreau et al. (2011) in this area was not stationary, and was migrating along the Catalan continental slope into the Balearic Sea during a period of two months and a half.

The time series of the zonal geostrophic component of the Northern Current off Cap de Begur from 1993 to 2017 clearly indicates the eastward deviation of the current downstream from Creus Cape (black point, Figure 4.3.1) during the years associated with the anticyclonic eddy anomaly (Figure 4.3.2(c)): 1998, 2010 and 2017. During these three events, the lags between the maxima of the northern current zonal component and of the climatological index suggest that the Northern Current deviates first. The anticyclonic eddy anomaly reaches its maximum two months later in 1998 and 2017 and one month later in 2010. The maximum transport monitored further south east is produced one week after the maximum of northern current deviation in 1998, and around two months later in the case of the 2010 and 2017 events (Figure 4.3.2(d)). These delays are associated with slightly different evolutions of the eddy anomaly.

Water mass transports are computed using data from gliders in the Ibiza Channel (as in Heslop et al. 2012) (section shown in Figure 4.3.1) starting from January 2011 (product reference 4.3.2). The time series of meridional geostrophic transports show an intensification of the northward flow associated with the entrance of
<figure><img src="image_187.png" /></figure>

<figure><img src="image_188.png" /><figcaption>**Figure 4.3.2.** (a) Monthly sea surface height maps and associated geostrophic currents in September, October, November and December 2017 from the product reference 4.3.1, (b) Anticyclonic eddy anomaly climatological index, (c) Northern Current zonal component extracted at the point displayed in Figure 4.3.2(a), and (d) Geostrophic transport through the black section shown in Figure 4.3.2(a).</figcaption></figure>

recent Atlantic Water into the Balearic Sea since the end of 2015 (Figure 4.3.3). Strong northward transports of this water mass are captured by the gliders, in particular in October 2016 (with values higher than 0.5 Sv) and in October 2017 when the maxima are reached (1 Sv). During these two autumnal glider missions, the inflow of recent Atlantic Water represents a large amount of the total transport (41–56% in 2016 and 48–59% in 2017) leading to an unusual and strong positive net inflow. During the anticyclonic eddy anomaly event of 1998, Pascual et al. (2002) also reported the anomalous presence of recent Atlantic Water in the Balearic Sea, which was explained by the possible weakening of the Northern Current that leads to the entrance of this water mass from the Alboran Sea through the Balearic channels.

The mechanisms involved in the process of the eddy generation are related to barotropic instabilities, due to

horizontal shear perturbations of the ocean currents, and/or to baroclinic instabilities, induced by major changes in the stratification. Concretely, in this area, Pascual et al. (2002) argue that the presence of warm water in the Balearic Sea interfered with the negative curl from the northwesterly Mistral wind shear downstream of the Pyrenees (Herbaut et al. 1997) could generate the strong eddy in fall 1998. Rubio et al. (2009) related the generation of the anticyclonic eddy in September 2001 to the separation of the coastal current downstream of Cape Creus that was induced by strong Mistral wind events. However, our empirical orthogonal function analysis of the winds in the north-western Mediterranean Sea indicate that the zero curl-isoline on the northern edge of the eddy is a necessary but not a sufficient condition to produce the strong and long-term eddy (not shown). Consequently, we suggest a combination of several factors are responsible
<figure><img src="image_189.png" /></figure>

<figure><img src="image_190.png" /><figcaption>**Figure 4.3.3.** Northward (positive) and southward (negative) geostrophic transports in the Ibiza Channel integrated over the full water column (grey) and only considering recent Atlantic Water (red) obtained from product reference 4.3.2 during the glider missions from 2011 to 2017.</figcaption></figure>

for the formation of the eddy event in 2017. These include the intense Mistral wind jets which could be responsible for the coastal detachment of the Northern Current. This in turn would favour the recent Atlantic Water coastal intrusion, and which would then gain negative vorticity due to the negative curl caused by wind in this area. High-resolution numerical model simulations will be used in the future to analyse this hypothesis and to improve our knowledge of the generation and permanence of these mesoscale eddies and their interaction with the mean flow.

## Acknowledgements

This study is supported by La Caixa fundation through the MedClic project (LCF/PR/PR14/11090002).

## 4.4. Insights on 2017 Marine Heat Waves in the Mediterranean Sea

**Authors:** Nathaniel Bensoussan, Jacopo Chiggiato, Bruno Buongiorno Nardelli, Andrea Pisano, Joaquim Garrabou

**Statement of main outcomes:** Overall, 99.6% of Mediterranean Sea surface experienced at least one Marine Heat Wave event during year 2017. Strong Marine Heat Wave events occurred at regional scale, in June, July and August. Analysis of sea surface temperature from CMEMS revealed unprecedented Marine Heat Wave

total duration in the north-western sub-region (up to 225 days locally in the north Catalan Sea) and exceptionally long single event (entire summer) in the Eastern Levantine Sea. In all sub-regions examined, a long-term increasing trend in annual Marine Heat Wave duration is obvious over the 1982–2017 period. As for previous significant Marine Heat Wave events (e.g. summer 2003), mass mortality events affected the benthic biota in the north-western Mediterranean Sea in 2017. Unprecedented large-scale and long-lasting benthic mucilaginous bloom also occurred in the north Catalan Sea. Analysis of in situ temperature time series in Scandola Marine Protected Area showed sub-surface intensification of Marine Heat Wave events (both in intensity and duration) which could not be inferred from surface data only. Enhancing the monitoring framework on physical and biological indicators is thus required for good evaluation of Marine Heat Wave and their impacts on Marine Coastal Biodiversity at local and regional scale.

## Products used:

<table><thead><tr><td>Ref. No.</td><td>Product name and type</td><td>Documentation</td></tr></thead><tbody><tr><td>4.4.1</td><td>SST_MED_SST_L4_REP_ OBSERVATIONS_010_021 Sea surface temperature data</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-OSI-PUM-010-021-022.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/</td></tr></tbody></table>

(Continued)
<figure><img src="image_191.png" /></figure>

Continued.

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>4.4.2</td><td>T-MEDNet consolidated in situ temperature time series.<br>www.t-mednet.org<br>In situ temperature data</td><td>CMEMS-OSI-QUID-010-021-022.pdf<br>www.t-mednet.org/T-Database</td></tr></tbody></table>

Ocean warming is associated to large changes in daily temperature distribution with increase in the occurrence of conditions presently perceived as extreme (known as climate intensification, see for instance section 2.2). Extreme warm sea temperature events observed around the world have been termed Marine Heat Waves. The interest in the characterisation of Marine Heat Waves has increased in the recent years (Hobday et al. 2016, 2018; Frölicher and Laufkötter 2018). For this study we applied the approach proposed by Hobday et al. (2016) in which Marine Heat Waves can be qualitatively defined as prolonged periods (five consecutive days or more) of anomalously warm water conditions when compared to the climatological mean (Hobday et al. 2016, 2018; see below for more information). Their mean duration and frequency have increased significantly over the past century, resulting in a 54% increase in sea surface annual Marine Heat Wave days globally (Oliver et al. 2018).

Increasing thermal stress is already having a range of important impacts on marine ecosystems and the goods and services they provide (Frölicher and Laufkötter 2018). Increasingly, mass mortality events on the benthic biota are reported in both tropical and temperate ecosystems (e.g. Garrabou et al. 2009; Wernberg et al. 2016; Hughes et al. 2017). In the north-western Mediterranean Sea, anomalously warm conditions during summers 1999, 2003 and 2006 have been associated to unprecedented mass mortality events, which have affected macrobenthic engineer species along tens to thousands of km of the coastline of Spain, France and Italy (Garrabou et al. 2009; Crisci et al. 2011; Marbà et al. 2015). Since 1999, several Marine Heat Waves and new mass mortality events occurred, particularly during recent years, as was the case during year 2017. However, the linkage between surface Marine Heat Wave metrics and biological impacts in subsurface marine habitats is not straightforward, firstly due to seasonal stratification and the influence of wind on coastal hydrodynamics (Bensoussan et al. 2010; Schaeffer and Roughan 2017). Furthermore, relating the thermal anomalies to biological observations might be seen as a highly context-dependent issue (e.g. species depth distribution, life cycle, differential response to thermal stress) and overall relies on biological responses to temperature (among other climatic and non-climatic stressors) which

are most often poorly constrained due to the lack of observation at the appropriate spatial and temporal scales.

In this context, we must note the importance of (i) a remote-sensing observing system to track the evolution of Marine Heat Wave events, (ii) sustained in situ observation over the long-term in marine coastal habitats, on both physical (temperature) and biological indicators, and (iii) a common analysis framework for comparison across temporal and geographic scales. Here, we analyse how such framework, when available, can allow good evaluation of Marine Heat Wave and better assessment of their ecological impacts. We provide insights on the 2017 Mediterranean marine heatwave, first with a view from the surface at regional and sub-regional scale in the Mediterranean Sea, considering CMEMS high resolution (4 km) satellite sea surface temperature (product reference 4.4.1). Then an inside view at local scale combining satellite and multi-year in situ data retrieved from T-MEDNet temperature series (product reference 4.4.2, see Section 3.6) recorded in a Marine Protected Area where biological impacts have been observed.

### Identification and classification of MHW events

Marine heat waves have mostly been studied from the analysis of long-term satellite data sets over climatic time scale (30 years). Two different approaches can be conducted for their identification with respect to the long-term climatological mean for each location and day of year (Hobday et al. 2016) or with respect to an elevated temperature threshold (e.g. species specific thermotolerance threshold or upper percentiles of site temperatures, Marbà et al. 2015; Galli et al. 2017). While the later approach focuses on extreme hot events, the former allows identification of strong anomalies (extreme warm events) throughout the entire annual cycle, which can prove relevant for a range of biological impacts, considering different biological processes and life stages of life cycle of marine organisms.

Relying on the definition of Hobday et al. (2016), Marine Heat Wave events, are identified as discrete and prolonged period of time (at least five consecutive days) with temperature above the site/day climatological 90th percentile. The method allows comparison of events duration and intensity across time and space from sites with different thermal regimes and we first analysed satellite sea surface temperature daily data from CMEMS (product reference 4.4.1). For each pixel, climatological mean and 90th percentiles were calculated over the 1982–2011 period, using a 11-day centred window and additional smoothing on the climatology with a 30-day running mean (Hobday et al. 2016). The following primary metrics were used to
<figure><img src="image_192.png" /></figure>

describe the events: duration, maximum daily tempera-
ture (Tmax), maximum intensity (imax = Tmax – climato-
logical mean), mean intensity and cumulative intensity
(i cum = mean intensity × duration, in C° day). Total
duration and total cumulative intensity were also calcu-
lated at different time scales, by aggregating duration or
i cum by month, season or year. The spatial extent of
Marine Heat Wave events was computed at monthly
and seasonal time scale from the satellite data of year
2017. In order to analyse the long-term trends at
sub-regional scale, average daily temperature time
series were calculated over three boxes shown in
Figure 4.4.1(a). The Marine Heat Wave analysis was
conducted over these spatially averaged time-series.

Classification of Marine Heat Wave events can be
conducted by scaling their maximum intensity with
respect to the degree to which temperature exceed the
local climatology (Hobday et al. 2018). Categories of
Marine Heat Waves are based on multiples of the value
represented by the local difference between the climato-
logical mean and the climatological 90th percentile. Mul-
tiples of this local difference describe different categories
of Marine Heat Waves: defined as moderate (1–2×, Cat-
egory I), strong (2–3×, Category II), severe (3–4×, Cat-
egory III), and extreme (>4×, Category IV), based on
their maximum intensity (i max) at each point in space
(Hobday et al. 2018).

The method is also suited for *in situ* time series (Hob-
day et al. 2016). Long-term (14 years) continuous
(hourly) temperature time series has been acquired
using vertical array of data loggers set at standard
depth levels (every 5 m, between 5 and 40 m depth) in
the no take zone of the Réserve Naturelle de Scandola
(Parc Régional de Corse, France). Such oceanographic
time series was obtained in the frame of sustained T-
MEDNet network monitoring effort conducted jointly
with Mediterranean Marine Protected Areas (see Section
3.6). The climatological mean and 90th percentile, were
calculated for each depth over all daily averaged data
available over the 2004–2016 period.

The 2017 Mediterranean MHW over the satellite record
Statistics computed from the CMEMS sea surface temp-
erature data over the Mediterranean Sea show that 2017
was the sixth warmest year (mean SST = 19.86°C,
anomaly + 0.65°C), and warmest spring on average
since 1982 (Figure 4.4.1). During this warm year, signifi-
cant Marine Heat Wave events occurred at regional scale
in June, July and August (Figure 4.4.2) and at local to
sub-regional scale across all four seasons, resulting in
elevated total number of Marine Heat Wave days
(Figure 4.4.2(a)). Overall, 99.6% of Mediterranean Sea
surface experienced at least one Marine Heat Wave
event and significant variability was evidenced at the var-
ious space scales (Figure 4.4.2). Marine Heat Wave days

<figure><img src="image_193.png" /><figcaption>**Figure 4.4.1.** Time series of yearly and Spring (April, May and June) Mediterranean Sea surface temperature (SST) over the period 1982–2017. The black dots show results for year 2017. Reference number of the product used: 4.4.1.</figcaption></figure>
<figure><img src="image_194.png" /></figure>

<figure><img src="image_195.png" /></figure>

**Figure 4.4.2.** Maps showing different MHW metrics (left) and categorisation of MHW events by month (right). (a) Annual number of Marine Heat wave days in 2017, (b) MHW maximum intensity ($i_{max}$) during June 2017, (c) June to August 2017 MHW cumulative intensity ($i_{cum}$). (d, e, f) Highest MHW category by month, during the regional MHW events of June, July and August. Locations with no heatwave days are blanked out. Reference number of the product used: 4.4.1. Location of the three boxes considered for sub-regional analysis and of the Scandola Marine protected Area are shown in panel a.

were on average twice higher in the western than in the eastern basin (88 ± 31 vs. 43 ± 25 days, mean ± std). Total duration exceeded 6 months (max 225 days) in the northern Catalan-Balearic Sea and 4 months over 13% of the western basin surface, mainly from Algerian to Catalan Sea and from Sardinia to Ligurian Sea (Figure 4.4.2(a)). Similar amount of Marine Heat Wave days were not observed in the eastern basin, except locally in the eastern Levantine Sea.

A significant event at regional scale took place in June 2017, with a single long-lasting Marine Heat Wave (depending on the location, up to 20-25 days) in the western Mediterranean Sea. Figure 4.4.2(b) shows the very large spatial extent and elevated peak anomaly during this event. Maximum daily sea surface temperature anomalies up to 6°C were detected north of the Balearic-Corsica front but, in general, 4°C anomaly was reached in most part of the sub-basin and in the northernmost part of the eastern Mediterranean. We also analyse the cumulative intensity at the seasonal scale (here grouping June, July and August in order to account for different timing of observed events in the basin during

the warm season). Combining both duration and intensity, Figure 4.4.2(c) shows overall elevated thermal stress in the western basin and adjacent Sicily channel, in the Levantine and northern Adriatic sub-basins. Significant spatial variability was observed with local extremes along the North Catalan coast, in the Algerian and Levantine seas which could be associated to seasonal or permanent circulation features, like eddy intensification in the Balearic-Catalan Sea (see Section 4.3) and permanent gyres (e.g. Ierapetra and Cyprus gyres), locally exacerbating the regional warm signal. It should be noted, on the other hand, that elevated $i_{max}$ in the Aegean Sea (Figure 4.4.2(b)), when combined with locally short duration of the Marine Heat Wave, leads to a low cumulated intensity (Figure 4.4.2(c)). This example is suggestive of the relevance of considering both scores together with maximal temperature when interpreting Marine Heat Wave intensity. Adoption of a Marine Heat Wave scale was also found useful to communicate and raise scientific and public awareness on these extreme warm events (Hobday et al. 2018). Under this scheme, peak intensity of category II (strong)
<figure><img src="image_196.png" /></figure>

took place over most of the western Mediterranean in June, the Aegean-Levantine basin in July and the Adriatic-Ionian in August (Figure 4.4.2(d,e,f)).

## Spatial and temporal variability of MHW events

We further focus our analysis to the Catalan-Balearic, northern Adriatic and eastern Levantine Sea, where relatively long event of category II occurred (Figure 4.4.3). In the north Catalan-Balearic area (Box 1, Figure 4.4.3(a)), sea surface temperature was overall warmer than average except in September. A sequence of eight Marine Heat Wave events took place between January and December, among which, the strong events of April and June ($T_{max}$ 15 vs. 25°C) and the moderate but notably long fall event (78 days by the end of year, $i_{cum}$ = 113°C day). This long event can be related to the intense anticyclonic eddy anomaly detected in fall-winter 2017 north of Mallorca Island, which blocked the general cyclonic circulation and enhanced the prevalence of Marine Heat Wave over the area (see Section 4.3).

In the warmer eastern Levantine Sea (Box 2, Figure 4.4.3 (b)), positive sea surface temperature anomaly prevailed from March to December and a notably long summer single event (97 days) occurred from 27 June to 1 October ($T_{max}$ = 29°C in July, $i_{cum}$ = 133 C°day). In the northern

Adriatic, six Marine Heat Wave events took place between late March and August (Figure 4.4.3(c)), out of which four were synchronous with events in the western Mediterranean (Box 1). Elevated $T_{max}$ (28°C) was observed during the strong Marine Heat Wave event from 2 to 10 August.

Further analysis of the 36 years of satellite data attest of the unprecedented duration of the 2017 events (Figure 4.4.3(d,e,f)). In the western Mediterranean Sea (Box 1, Figure 4.4.3(d)), 2017 was the year with the highest annual number of Marine Heat Wave days since 1982. They were observed throughout the four seasons, as in 2015, but with only few days in summer compared to the years 2003, 2006 and 2015 (7 vs. 36–60 days respectively). Also, summer long event was evidenced in the eastern Levantine Sea for the second time since 1982 (as in 2012, Figure 4.4.3(e)). Interestingly in all areas, long-term increasing trend in Marine Heat Wave total duration (yearly) is obvious and longest total duration (>170 days per year) occurred during the past decade (2010 in the Levantine, 2014 in the Adriatic and 2017 in the Western). Splitting the observation period in two highlights contrasted Marine Heat Wave regimes. Since late nineties, Marine Heat Wave events have occurred every year in at least one season (except in 2005 in the N-Adriatic) and they last longer. This regime shift attest of the

<figure><img src="image_197.png" /></figure>

**Figure 4.4.3.** (a–c) Spatially averaged time series of daily sea surface temperature for the three boxes shown in Figure 4.4.2(a). The climatological mean (blue curve) and thresholds used to define MHW categories from moderate (dotted grey line) to strong (grey line) were calculated over the 1982–2011 period. MHW events are identified and filled with colours by categories (orange = moderate, red = strong). (d–f) Time series of the total number of marine heatwave days by year and by season since 1982, the arrow indicates the 2017 data. Reference number of the product used: 4.4.1.
<figure><img src="image_198.png" /></figure>

important increase in the prevalence of extreme warm (upper decile) daily temperature under current warming trends (see Figure 4.4.1). Our results are in agreement with analysis conducted at global scale, which have been related to the acceleration of warming trends (Oliver et al. 2018).

### Observed biological impacts

In 2017, concomitant with the thermal context, a range of biological impacts were observed. Firstly an unprecedented large-scale and long-lasting mucilaginous benthic algal bloom occurred along the French and Spanish coast of the northern Catalan Sea from spring till early fall (Figure 4.4.4). The mucilaginous algal blooms, mainly the Ectocarpal *Acinetospora crinita*, could cover all benthic habitats from 10 m down to 30 m depth. The cover of thick layers of mucilaginous algae dramatically affected even gorgonians which grow arborescent colonies up to 1 m in height depending on the species (Figure 4.4.4(b)). Subsequently, mass mortality on gorgonian species occurred in late Summer and Fall 2017 (mainly the red and white gorgonian *Paramuricea clavata* and *Eunicella singularis*, respectively). While in other areas of the north-western Mediterranean Sea mortality of gorgonians as well as other macrobenthic biota was observed, also in late summer. For instance, at the

Réserve Naturelle de Scandola (Corsica) the yellow gorgonian *Eunicella cavolini* and several sponges species (*Spongia spp. Petrosia ficiformis, Ircina spp.*) dwelling between 10 and 25 m depth suffered from moderate to severe mortality impacts.

### Surface and subsurface MHW in nearshore coastal waters (Scandola Marine Protected Area)

Proper knowledge on changes in environmental conditions, including marine heat waves, is needed to better assess potential impacts on ecosystem structure and function. Owing to the limited availability of long *in situ* data sets, it is not clear how accurate information can be obtained in the near-shore using satellite data (but see section 3.6). As satellite are restricted to the surface, *in situ* observations are required to cope with the important depth variability occurring during the seasonal stratification period, typically from April-May to Fall convective events. For instance the summer 2003 mega atmospheric heatwave over Europe was associated to extreme sea surface temperature in the central Ligurian Sea in August while cold anomaly prevailed below 10 m depth (Sparnocchia et al. 2006). Here we conduct further analysis from satellite data retrieved at local scale and *in situ* measurements conducted along the depth gradient from 5 to 40 m depth.

<figure><img src="image_199.png" /><figcaption>**Figure 4.4.4.** (a–b) Mucilaginous blooms covering different benthic assemblages (rocky infralittoral habitats and coralligenous) and species (the red gorgonian *Paramuricea clavata*) along the Catalan coast between Spring and Fall 2017, and (c) white gorgonian *Eunicella singularis* displaying signs of very recent necrosis (white tissue peeling off from the axis leaving denuded axis).</figcaption></figure>
<figure><img src="image_200.png" /></figure>

Local satellite data were retrieved from the closest pixel to the monitoring site considering the common years of data available (2004–2017). From the statistical analysis of the local satellite and *in situ*<sub>5m</sub> multi-year matchup database, high agreement was evidenced for Scandola, both in terms of correlation (0.988), bias (0.17°C) and round

mean square difference (0.7°C). These results are in agreement with results shown in section 3.6 from analysis conducted in several Marine Protected Areas member of the T-MEDNet monitoring network. Figure 4.4.5(a,b) shows results from the Marine Heat Wave analysis conducted from June to October 2017 on the satellite and *in situ*<sub>5m</sub>

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>

<figure><img src="image_203.png" /></figure>

<figure><img src="image_204.png" /></figure>

<figure><img src="image_205.png" /></figure>

**Figure 4.4.5.** Information on MHW events in Scandola Marine Protected Area nearshore waters (Parc Régional de Corse, France). (a–c) Time series of daily satellite sea surface temperature and *in situ* temperature measured at 5 and 15 m depth. Climatology (blue line) and MHW thresholds (grey lines) calculated over the period 2004–2016 are also shown. MHW events are identified and filled with colours by categories (orange = moderate, red = strong). (d) *In situ* temperature anomalies relative to the multi-year mean at 10, 25 and 40 m depth. (e) Total number of MHW days from satellite (0 m) and *in situ* data (5–40 m depth). Reference number of the products used: 4.4.1 and 4.4.2.
<figure><img src="image_206.png" /></figure>

data. The climatological mean and 90th percentile were slightly higher for the surface when compared to *in situ*<sub>5m</sub> (by typically <1°C). Also some differences arose regarding the number and category of detected events. Considering the strong June event however, high agreement was evidenced with negligible differences in terms of duration (17 days), *i*<sub>max</sub> (3.5°C) and *i*<sub>cum</sub> (46 C°day). This event reached deeper marine habitats, in fact, down to 15 m depth, with a time lag of ca. 10 days (Figure 4.4.5 (c)) while warm pulses occurred deeper (shorter than five days, not shown). Overall, over these 4 months, total number of Marine Heat Wave days was higher at 15 m (38 days) depth compared to 5 m (22 days) and surface (17 days), which can be seen as subsurface intensification of Marine Heat Wave, and largely depends on the seasonal stratification dynamics (Bensoussan et al. 2010; Schaeffer and Roughan 2017). Such anomalies were observed in late September and October at 40 m depth, as vertical mixing occurred and propagated the warm signal to depth (Figure 4.4.5(d)). Meanwhile, one might notice that cold to neutral anomaly prevailed at the surface.

Considering the 2004–2017 period (Figure 4.4.5(e)), elevate total number of Marine Heat Wave days was calculated over the depth range examined (325–429 days, maximum in the upper 15 m) from which a significant fraction occurred in year 2017. Interestingly, we can note the fair agreement between Marine Heat Wave days calculated from the local satellite and *in situ*<sub>5m</sub> data (0 vs. 5 m depth difference < 10%). Together with the high agreement evidenced for the June 2017 event, these results open new and interesting perspectives for satellite based analysis of Marine Heat Waves over climatic time scales (back to 1982) in the coastal zone.

Understanding the biological responses to warming and how changes in the thermal environment are driving changes in the marine biota is an active research field (see for instance Section 3.2 for fishes and Section 3.6 for the benthic biota). As introduced earlier in the section, different approaches might be used complementarily in order to assess such responses/impacts. In fact, metrics used to characterise Marine Heat Wave events (duration, *i*<sub>max</sub>, *i*<sub>cum</sub>) could usefully be adapted to quantify more largely the positive anomalies (Figure 4.4.5(d)) occurring over given periods of interest, from days to month(s) and thus integrate the strong dynamics and variability modes of subsurface conditions (e.g. inertial oscillations, Bensoussan et al. 2010).

Mass mortality events are particularly strong disturbances affecting benthic habitats since they affect a wide range of macrobenthic species over large geographical scales (tens to thousands of kilometres of

coastlines) as was the case in the 2017. However, mass mortality is only one of the impacts of climate change in the benthic coastal habitats. Overall climate change is severely modifying the structure and functions of marine coastal ecosystems to new configurations, which might no longer support the goods and services to people. In this context, we must note the importance of sustained observation effort on sub-surface temperature and biological indicators for comparison across temporal and geographic scales to enhance our understanding of ongoing changes and our forecasting abilities.

### 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*.

## 4.5. Reversal of the Northern Ionian circulation in 2017

**Authors:** Giulio Notarstefano, Milena Menna, Jean-François Legeais

**Statement of main outcome:** A reversal of the surface circulation in the Northern Ionian Sea was observed in 2017. This changing of the circulation pattern seems to be periodic and it is critical for the redistribution of salt between the Eastern Mediterranean and the Northern Ionian – Southern Adriatic Seas. The periodicity of this reversal occurs on a quasi-decadal scale and, when it happens, it constitutes a special event because this phenomenon can deeply impact on the deep water formation sites and hence can affect the water masses distribution between the different Mediterranean sub-basins. Moreover, the shift of circulation mode can contribute to trigger abrupt events like the Eastern Mediterranean Transient that impacted the Mediterranean regional oceanography, shifting the deep water formation site from the South Adriatic to the Aegean/Cretan Sea.

### Products used:

<table><thead><tr><td>Ref. No.</td><td>Product name and type</td><td>Documentation</td></tr></thead><tbody><tr><td>4.5.1</td><td>SEALEVEL_MED PHY L4 NRT_ OBSERVATIONS_008_050<br>SEALEVEL_MED PHY L4_REP_ OBSERVATIONS_008_051<br>Remote sensing</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-SL-PUM-008-032-051.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-SL-QUID-008-032-051.pdf</td></tr></tbody></table>
<figure><img src="image_207.png" /></figure>

The surface circulation in the Northern Ionian Sea is mainly influenced by quasi-decadal reversal of the Northern Ionian Gyre over the last 40 years even though a real periodicity is still not clear (Pinardi et al. 2015). This circulation system (Gačić et al. 2011; Pinardi et al. 2015; Simoncelli et al. 2016) is evident also at intermediate depth and strongly impacts on the distribution of salinity in the South Adriatic Sea (Gačić et al. 2010, 2011; Kokkini et al. 2017). Thermohaline changes in the South Adriatic Sea are of considerable importance for the Mediterranean Sea climate due to the deep water convection and dense water formation that takes place in this area (Gačić et al. 2014; Simoncelli et al. 2017). The anticyclonic circulation mode is associated with the northward deflection of the Atlantic Ionian Stream that brings fresh Atlantic water in the Northern Ionian and South Adriatic seas; consequently, the intrusion of the Levantine salty water in the South Adriatic is reduced (Figure 4.5.1, left panel). During the cyclonic circulation mode this water mass exchange is strongly modified (Figure 4.5.1, right panel): the eastward intense flow of the Atlantic Ionian Stream brings fresh Atlantic water in the Cretan Passage and, at the same time, more saline waters at the intermediate level from the Levantine basin flow into the Northern Ionian and South Adriatic seas. Currently, four reversals are documented by numerical and experimental studies: two of them, from cyclonic to anticyclonic mode, took place around 1987–1988 (Demirov and Pinardi 2002) and in 2006 (Gačić et al.

2010; Bessiéres et al. 2013); other two, from anticyclonic to cyclonic mode, occurred in 1997 (Pujol and Larnicol 2005) and in 2011 (Bessiéres et al. 2013; Gačić et al. 2014), respectively.

Satellite altimetry, available since 1993, is a powerful product to observe the ocean variability at global and regional level (Legeais et al. 2016, 2017), since it provides sea level anomaly measurements continuously in time. In particular, the absolute dynamic topography, obtained by adding the mean dynamic topography (Rio et al. 2014) to the sea level anomaly, and the related absolute geostrophic velocities, were used to define the temporal phases of cyclonic/anticyclonic Northern Ionian Gyre. The relative vorticity field (ζ) of the absolute geostrophic velocities was evaluated as the vertical component of the velocity field curl:

$$ \zeta = \frac{\partial vg}{\partial x} - \frac{\partial ug}{\partial y} $$

where ug and vg are the components of the absolute geostrophic velocities.

Monthly means of the geostrophic current vorticity fields were spatially averaged in the region of northern Ionian Sea (white rectangles in Figure 4.5.1) and the time series of this parameter (blue line in Figure 4.5.2) was filtered using a 13-month moving average (red line in Figure 4.5.2), in order to remove the seasonal and intra-annual variations and focus on the interannual fluctuations. The geographical area of the spatial vorticity

<figure><img src="image_208.png" /><figcaption>**Figure 4.5.1.** Summary of the water masses distribution in the Ionian Sea during the anticyclonic (left) and cyclonic (right) modes of the Northern Ionian Gyre (adapted from Lavigne et al. 2018), superimposed with the yearly maps of absolute dynamic topography (colours) in 1996 and 1999, respectively. The acronyms in the figure are: Atlantic Water (AW); Levantine Surface Water (LSW); Levantine intermediate water (LIW). White squares show the area used to estimate the time series of Figure 4.5.2. The reference number of the products used is 4.5.1: SEALEVEL_MED PHY L4 NRT OBSERVATIONS 008 050, SEALEVEL_MED PHY L4_REP OBSERVATIONS 008 051.</figcaption></figure>
<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /><figcaption>**Figure 4.5.2.** Time series of the spatially averaged current vorticity (blue line) and low-pass filtered (13 months) current vorticity (red line) computed in the Northern Ionian Sea (37–39°N; 17–19.5°E), during the period January 1993–November 2018. The temporal phases of the Northern Ionian Gyre are defined cyclonic when the vorticity field is positive and anticyclonic when the vorticity field is negative. Reference number of the product used: 4.5.1.</figcaption></figure>

mean (37–39°N and 17–19.5°E: white rectangles in Figure 4.5.1) was selected according to the shape of the Northern Ionian Gyre (a similar choice was made in Shabrang et al. 2016). During the anticyclonic mode (Figure 4.5.1(a)) the Northern Ionian Gyre is located in the centre of the basin and is surrounded by recurrent mesoscale cyclonic eddies (e.g. along the eastern coast of Sicily, at the entrance of the Gulf of Taranto, along the eastern Ionian flanks), whereas during the cyclonic mode the whole northern Ionian Sea is involved in the counterclockwise circulation (Figure 4.5.1(b)) and the northern Ionian meander moves very close to the coasts. In order to identify the interannual variability of the Northern Ionian Gyre vorticity field, avoiding contaminations by the mesoscale structures located along the edges of the basin-scale meander, we focus on the area statistically less influenced by the mesoscale activity.

The vorticity field associated with the surface geostrophic circulation in the Northern Ionian Gyre is positive/negative during cyclonic/anticyclonic phases (Figure 4.5.2). Time series of the current vorticity shows that seasonal oscillations (blue line) do not influence the decadal variability and the reversals of Northern Ionian Gyre. Even Poulain et al. (2012) shows that the basin wide circulation in the Northern Ionian Sea remains invariant with the seasons, whereas a significant seasonality is evident in the central Ionian related to the Atlantic-Ionian Stream variations.

The inversion to Northern Ionian Gyre cyclonic mode in 1997 (Figure 4.5.2) is widely shared in literature (Pujol and Larnicol 2005; Bessiéres et al. 2013; Gačić et al. 2014; Pinardi et al. 2015). On the other hand, the inversion to anticyclonic mode in 2006 is still a controversial aspect.

It is supported by altimetry data (Gačić et al. 2014; see also Figure 4.5.2) and ocean state indices (Bessiéres et al. 2013; Reale et al. 2017), but not sustained by the MEDREA reanalysis products that document only a weakening of the cyclonic circulation in the period 2006–2016 (Simoncelli et al. 2014; Simoncelli et al. 2017). As pointed out by Bessiéres et al. (2013), there are many intermediate states between the two main circulation modes, during which transition phases exist and strengthening or weakening of the main modes can be observed. However, they also argue that the weak anticyclonic phase between 2006 and 2011 cannot be considered a temporary inversion or a weakening of the cyclonic circulation mode, since it lasted almost five years. In 2011, altimetry data show another reversal of Northern Ionian Gyre to cyclonic mode, prematurely and temporary interrupted between May 2012 and March 2013 due to the climatic variability induced by the severe winter 2012 which caused the formation of very dense Adriatic waters, flooding Ionian flanks in May and inverting the bottom pressure gradient (Gačić et al. 2014).

The mean maps of absolute dynamic topography and absolute geostrophic velocities show a new weakening of the Northern Ionian Gyre cyclonic mode in 2016 (Figure 4.5.2; Figure 4.5.3(a,b)). The anticyclonic meander located in the Southern Ionian Sea (south of 36°N; Figure 4.5.3(a)) protrudes northward (Figure 4.5.3(b)), promoting the gradual break-up of the sub-basin scale cyclone. Then, in the first part of 2017, the sea level in the centre of the northern Ionian becomes slightly higher than the flanks (Figure 4.5.3(c)) and finally, in the second part of 2017, the anticyclonic circulation is
<figure><img src="image_211.png" /></figure>

<figure><img src="image_212.png" /><figcaption>**Figure 4.5.3.** Mean maps of absolute dynamic topography (colour-coded in the figures, where warm colours/red means relative highs and cold colours/blue means relative lows) and absolute geostrophic velocities (white arrows) for the periods January–June 2016 (a), July–December 2016 (b), January–June 2017 (c) and July–December 2017 (d). Reference number of the product used: 4.5.1</figcaption></figure>

established (Figure 4.5.3(d)). This change of the circulation mode should not be attributed to seasonal variability that rarely impacts the basin wide circulation (Gačić et al. 2014). Moreover, the low-pass filtered (13 months) current vorticity spatially averaged in the Northern Ionian Sea indicates that a shift of the surface circulation from cyclonic to anticyclonic has occurred in 2017, irrespective of the seasonal signal. Further studies, additional datasets and the support of numerical models are needed for a better comprehension, a robust description and a deeper analysis of the Ionian reversal phenomenon, not only for the year 2017 but also for the future years. It is of fundamental importance to keep on monitoring this circulation pattern that deeply impacts on the surface and intermediate water masses distribution and water masses formation. Indeed, the anticyclonic circulation mode is one of the main catalyst for abrupt events like the Eastern Mediterranean Transient (Gačić et al. 2011; Pinardi et al. 2015; Simoncelli et al. 2016) in which the South Adriatic deep water

formation site was substitute by the Aegean/Cretan Sea (Roether et al. 1996).

## 4.6. 'Silent' storm surge extremes in the western Baltic Sea on 4 January 2017

**Authors:** Jun She and Jacob Woge Nielsen

**Statement of main outcome:** Most storm surge events in the western Baltic Sea are caused by local gale winds. On 4 January 2017, sea level in the western Baltic Sea reached a 50 to 100 year high. However, the local wind speed over sea did not exceed 7 Beaufort (17.1 m/s). This is far from sufficient to generate such a rare surge event. By combining in-situ sea level and wind measurements, satellite observed wind speed, modelled sea level and water transport, mechanisms of the event are analysed. We find that wind conditions during two weeks prior to the event generated a 'U-shaped' sea level pattern, with high sea level in Kattegat and the central Baltic,
<figure><img src="image_213.png" /></figure>

and low sea level in the western Baltic. A large scale, prevailing strong northeasterly on 4 January over the central Baltic Sea, shifted the water mass southwestward. It was found that, during the event, the Baltic surge propagated westwards with a speed of around 8.5 m/s. The southward flow from the Kattegat through the Danish Straits contributed 42% of the sea level rise, with 58% due to westward transport from the open Baltic Sea.

Products used:

<table><thead><tr><th>Ref. No.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>4.6.1</td><td>WIND_GLO_WIND_L4_NRT_ OBSERVATIONS_012_004<br>Remote sensing</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-OSI-PUM-012-004.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-OSI-QUID-012-004.pdf</td></tr><tr><td>4.6.2</td><td>BALTICSEAengerSTASOFTHEFORECAST<br>_PHY_003_006<br>Model</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-BAL-PUM-003-006.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-BAL-QUID-003-006.pdf</td></tr><tr><td>4.6.3</td><td>INSITUYEASTHORSEREP<br>_OBSERVATIONS_013_042<br>In-situ product</td><td>PUM: http://marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-042.pdf<br>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-042.pdf</td></tr></tbody></table>

Storm surges in the Baltic Sea may cause substantial loss of lives and property. To implement efficient safety measures, both accurate real time forecast and estimates of the coastal sea level 50, 100 and 1000 year return periods are required. Due to gaps in existing sea level observations in the coastal area, such estimates have to rely on both long-term observations and calibrated ocean models. It is therefore very important to understand the mechanisms forming the extreme sea level events. Many existing studies have focused on the

long-term change and return periods of extreme sea level in the western Baltic Sea (Sztobryn et al. 2005; Gräwe and Burchard 2011; Wolski et al. 2014; Madsen et al. 2017). A comprehensive study was done by Sztabryn et al. (2005) on extreme storm surge events in the southern and western Baltic Sea during 1976–2000. It was found that all of the surges studied were caused by gale-force onshore winds of short or long duration, with a prevailing northerly component in the wind direction. However, occasionally an alternative mechanism may generate an extreme storm surge event. For example, in November 1993, a sea level high of 1.52 m was recorded at Korsør, located in the Great Belt. The return period exceeded 50 years. However, no local high winds were observed during the event. The calm weather conditions led to the event being referred to as a ‘silent’ storm surge. On 4 January 2017, a similar event was recorded in the western Baltic Sea. Although these ‘silent’ surges do not happen often, they significantly contribute to the history of sea level extremes in the western Baltic Sea (Madsen et al. 2017; Kystdirektoratet 2018). As there is still a lack of mechanism study and documentation on such kind of events, in this paper we will use comprehensive model and observation data from CMEMS to study the mechanisms of the event.

During 4 January 2017, sea level in a large part of the western Baltic Sea rose by 1.2–1.8 m during a relatively short period of 14–16 h. At Gedser, maximum sea level reached 1.67 m, which is the second highest recorded during the past hundred years (the highest being 1.68 m, recorded in 1954). At Bagenkop Havn sea level peaked at 1.77 m, which is 7 cm higher than the estimated 100 year event (Kystdirektoratet 2018). Table 4.6.1 shows the maximum sea level and corresponding UTC time, recorded at Danish and German

<table><caption>**Table 4.6.1.** Sea level peaks and corresponding UTC time, recorded at western Baltic Sea stations (see Figure 4.6.1), based on the product ref. 4.6.3.</caption><thead><tr><th>Danish stations</th><th>Longitude (deg.)</th><th>Latitude (deg.)</th><th>Max. Sea level (m)</th><th>Peak time (2017.1.4)</th></tr></thead><tbody><tr><td>Køge</td><td>12.20</td><td>55.45</td><td>1.57</td><td>17:00</td></tr><tr><td>Rødvig</td><td>12.3728</td><td>55.2542</td><td>1.66</td><td>17:10</td></tr><tr><td>Hesnæs</td><td>12.1333</td><td>54.8167</td><td>1.70</td><td>17:50</td></tr><tr><td>Gedser</td><td>11.9256</td><td>54.5728</td><td>1.67</td><td>19:30</td></tr><tr><td>Rødby</td><td>11.35</td><td>54.55</td><td>1.68</td><td>20:20</td></tr><tr><td>Fåborg</td><td>10.25</td><td>55.10</td><td>1.68</td><td>22:30</td></tr><tr><td>Bagenkop</td><td>10.7528</td><td>54.7778</td><td>1.77</td><td>22:50</td></tr><tr><td>Fynshav</td><td>9.9869</td><td>54.995</td><td>1.66</td><td>23:10</td></tr><tr><td>Sønderborg</td><td>9.7833</td><td>54.9167</td><td>1.72</td><td>23:40</td></tr><tr><td>German stations</td><td></td><td></td><td></td><td></td></tr><tr><td>Warnemünde</td><td>12.1033</td><td>54.1697</td><td>1.46</td><td>19:30</td></tr><tr><td>Wismar</td><td>11.4581</td><td>53.8989</td><td>1.68</td><td>20:15</td></tr><tr><td>Travemünde</td><td>10.8722</td><td>53.9581</td><td>1.73</td><td>21:00</td></tr><tr><td>Heiligenhafen</td><td>11.0056</td><td>54.3731</td><td>1.6</td><td>21:15</td></tr><tr><td>Kiel</td><td>10.2733</td><td>54.4997</td><td>1.64</td><td>22:45</td></tr><tr><td>Kalkgrund</td><td>9.8881</td><td>54.8247</td><td>1.67</td><td>23:45</td></tr><tr><td>Eckernförde</td><td>9.8361</td><td>54.4747</td><td>1.69</td><td>00:15 + 1day</td></tr></tbody></table>
<figure><img src="image_214.png" /></figure>

**Figure 4.6.1.** Subareas of the Baltic Sea and location of sea level stations. Tide gauge stations are marked with red dots and numbers – 1: Sønderborg; 2: Bagenkop Havn; 3: Gedser; 4: Eckernförde; 5: Warnemünde; 6: Rødvig; 7: Landsort Norra; 8: Degerby. Moorings are marked with green dots. Transects for transport calculation are indicated by red lines.

coastal stations in the western Baltic Sea (see Figure 4.6.1 for locations). The Danish stations cover an area from 12.37°E (Rødvig) to 9.78°E (Sønderborg) around 55.0°N while German stations cover 12.10°E (Warnemuende) to 9.84°E (Eckernfoerde) around 54.5°N. The maximum sea level was first reached at the easternmost stations, and then propagated westward.

The observed sea level time series are displayed in Figure 4.6.2 for the four Danish stations Rødvig, Gedser, Bagenkop Havn and Sønderborg. Sea level at those stations begins to drop around noon 2 January and reaches a low early 4 January. From then on, sea level increases during the next 16 h by about 2 m. It is noticed that there is a phase shift both in sea level highs and lows. Sønderborg is the first one to start dropping on 2 January, but the last one to reach its later minimum and maximum both on 4 January. Conversely, Rødvig is

the last one to start dropping, but the first one to reach its minimum and later maximum sea level. This phenomenon will be analysed in detail in the subsection 'propagation of surge'.

*Wind and sea level conditions.* The initial sea level before the surge event has a 'U-shaped' pattern, i.e., high sea level in the Kattegat and open Baltic Sea, and low sea level in the western Baltic Sea, as shown in Figure 4.6.3 (left). This creates a favourable condition both for southward water movement from the Kattegat and westward movement from the southern Baltic Sea to the western Baltic Sea on 4 January. By the late evening on 4 January, the sea level has changed dramatically (Figure 4.6.3 right). The western Baltic sea level increased by up to 1.8 m while sea level decreased in the Kattegat and the open Baltic.

The building up of the Baltic Sea high mean sea level is illustrated by the 17-day sea level time series in the

<figure><img src="image_215.png" /></figure>

**Figure 4.6.2.** Hourly sea level before and during the 4 January 2017 storm surge observed at Rødvig, Gedser, Bagenkop Havn and Sønderborg, based on the product ref. 4.6.3.
<figure><img src="image_216.png" /></figure>

<figure><img src="image_217.png" /><figcaption>**Figure 4.6.3.** Modelled sea level at 2300 3 January 2017 (left) and 20-h sea level change, between 2300 3 January 2017 and 1900 4 January 2017 (right) (product ref. 4.6.2).</figcaption></figure>

central Baltic Sea stations Landsort and Degerby, as shown in Figure 4.6.4. A gradual sea level rise in the central Baltic Sea is observed during 24 December 2016–3 January 2017. Then a sudden drop is recorded during 4 January, as the event takes place.

Wind conditions before the surge event are important for establishing the ‘U-shaped’ pattern of the sea level. The buoy measurements (Figure 4.6.5) show that, at Darss Sill, the winds are westerly and northwesterly during 02/01 to mid-04/01 with speeds increasing up to 18 m/s; at ÖlandÖst, the temporal pattern of winds are similar except for that the northwesterlies last 6 h less than Darss. The westerlies move water from Skagerrak to the Kattegat and also from the western to the open Baltic, which is important for building up the ‘U-shaped’ pattern of the sea level. Further analysis indicates that the persistent westerlies prevail during two weeks before the event and are responsible for building up the rising sea level in the central Baltic Sea, as shown in Figure 4.6.4.

Wind conditions change dramatically during the surge event. At the Darss Sill buoy, the wind shifts from westerly to northeasterly, accompanied by a wind speed decrease from 15 to 10 m/s during the second half of 4 January. At Öland Öst, the wind shift from westerly to northeasterly occurs 6 h earlier, and wind speed is much stronger (20–35 m/s). The strong northeasterly in the central Baltic Sea during the surge event is essential for piling up water in the western Baltic Sea. This analysis is further supported by the six-hourly wind fields from CMEMS (product ref. 4.6.1). In the morning of 4 January, the strong northeasterly wind (up to 23 m/s) in the central Baltic Sea and the northwesterly wind in southern Baltic Sea trigger a convergence of water in the southern Baltic Sea (Figure 4.6.6 left). Later the same day, northeasterly winds prevail in large parts of the Baltic Sea (Figure 4.6.6 right). This displaces water mass to the western Baltic Sea from the southern Baltic Sea.

**Propagation of surge.** The sea level peaks propagate westwards in the western Baltic Sea (Table 1 and

<figure><img src="image_218.png" /><figcaption>**Figure 4.6.4.** Observed sea level in the central Baltic Sea, calculated as an average of sea level at two central Baltic stations (Landsort Norra and Degerby) as derived from product ref. 4.6.3.</figcaption></figure>
<figure><img src="image_219.png" /></figure>

<figure><img src="image_220.png" /><figcaption>Figure 4.6.5. Hourly winds during the storm surge period at Darss Sill (54.7°N, 12.7°E, left) in the western Baltic Sea and Öland Öst station in the Oland East (56.92°N, 17.08°E, right). Note: the wind speed scale is different on the two graphs (product ref. 4.6.3).</figcaption></figure>

Figure 2). This is further illustrated in Figure 4.6.7 using a sea level peak time vs. longitude plot. The Danish and German station pairs coincide well in their phases: Gedser and Warnemünde (at 12°E), Rødby and Wismar (at 11.4°E), Fåborg and Kiel (at 10.25°E) reach the peaks at the same or similar time (Table 4.6.1), which means that the propagation of the surge is westward.

Assuming a homogeneous wind impact in the area, the temporal lag of the maximum sea level is mainly caused by the propagation of a surface gravity wave. With shallow water assumption in the western Baltic Sea, the phase speed of free surface gravity wave and energy propagating speed should be the same, therefore referring as ‘propagation speed’ hereafter. The actual propagation speed of the sea level peaks can be estimated. For the German stations, a linear regression between the longitude distance between a station and Warnemünde ($L_g$, in metres) and corresponding sea level peak time difference $T_g$ (in seconds) gives:

$$L_g = 8.5354T_g + 6.1204$$

with square of correlation coefficient $R^2 = 0.9537$. Hence the speed of the sea level peak phase propagation is about 8.5 m/s. Similarly, a linear regression can be obtained for the Danish stations west of Gedser, resulting in a propagation speed of the sea level peak of 8.4 m/s.

The main part of the water body west of Gedser-Warnemünde transect has a depth of 15–20 m, suggesting a propagation speed of 12–14 m/s for free surface gravity waves. This means that the actual propagating speed is just 54–61% of the free surface gravity wave velocity. The difference is mainly attributed to bottom friction in the shallow waters.

*Transport through the Danish Straits and the Arkona Basin.* The analysis above shows the importance of the initial sea level condition (the ‘U-shaped’ pattern) in generating the high sea level in the western Baltic Sea. However, there is a lack of explanation on how this sea level was formed. Furthermore, prevailing wind conditions during 4 January suggest that flow from Kattegat to the western Baltic Sea through the Danish Straits (sections B24, B25 and B26) also

<figure><img src="image_221.png" /><figcaption>Figure 4.6.6. Sea surface wind conditions at 0600 4 January 2017 (left) and 1800 4 January 2017 (right). (product ref. 4.6.1)</figcaption></figure>
<figure><img src="image_222.png" /></figure>

<figure><img src="image_223.png" /><figcaption>**Figure 4.6.7.** The longitude and sea level peak time at the western Baltic Sea stations (as shown in Table 1) (product ref. 4.6.3)</figcaption></figure>

contributes to the high sea level. However, the amount and relevant importance of this transport has not been quantitatively assessed. This subsection will address these issues.

Accurate estimation of the transport through given transects requires high frequency and full model resolution data. Since the BAL MFC ocean forecast product only provides 25 out of 122 vertical layers of the forecast, the product is not suitable for transport calculation. Instead, we use an operational BOOS (Baltic Sea Operational Oceanography System) water transport product. The water transport values for 35 Baltic Sea transects are calculated daily, and results are shown on the BOOS website http://www.boos.org in near real time. For transports between Kattegat and western Baltic Sea, transects B24 (Øresund), B25 (Little Belt) and B26 (Great Belt) are added and used. For transport between the southern and western Baltic Sea, transect B29 (north-southward across the Arkona Basin) is used (Figure 4.6.1).

Time series of daily net transport was calculated from DMI (Danish Meteorological Institute) operational forecast product for the period 15 December 2016–6 January 2017 (Figure 4.6.8). Most of the time, transports through transect B29 (red line) are negatively correlated with transports through the Danish Straits (green line), which means that when water flows into the western Baltic Sea from the Kattegat, there is also water flowing out from the western Baltic Sea to the open Baltic Sea, and vice versa. Before 22 December, the transport features a net outflow from the Baltic to the Kattegat, with little or no impact on the Western Baltic (black line in Figure 4.6.8). During 22 December 2016–3 January 2017, the flow pattern reverses, with significant inflow through the Danish Straits, together with outflow from the Arkona Basin to the open Baltic Sea. As a result the sea level in the open Baltic Sea increased during 22 December 2016–3 January 2017 (cf. Figure 4.6.4). The simultaneous outflow from the western Baltic through both transects 2–3 January is essential in generating the low sea level in the western Baltic Sea. At the same time, North Sea westerlies transport water into the Kattegat from the Skagerrak. This forms the ‘U-shaped’ pattern of sea level on 3 January, as shown in Figure 4.6.3 (left).

The ‘U-shaped’ pattern of sea level is a necessary preface for the storm surge event on 4 January 2017. Generated by large scale wind forcing conditions, flow finally convergences in the western Baltic Sea on 4 January, by inflow not only from the Arkona Basin side but also from the Danish Straits. The westward transport from the open Baltic Sea through transect B29 amounts to about 58%, while the remaining 42% stem from southward transport through the Danish Straits.

<figure><img src="image_224.png" /><figcaption>**Figure 4.6.8.** Daily net transport during 15 December 2016–6 January 2017, positive value means flow into the western Baltic Sea. Green: transport across transects B24–B26; red: transport across transect B29 and black: total net transport into the western Baltic Sea.</figcaption></figure>
<figure><img src="image_225.png" /></figure>

## 4.7. The 2017 coastal El Niño

**Authors:** Florent Gasparin, Vincent Echevin, Alexandre Mignot, Coralie Perruche, Marie Drévillon

**Statement of main outcome:** While the Tropical Pacific was rather in neutral El Niño-Southern Oscillation conditions during 2017, a significant surface warming with similar amplitude to typical eastern Pacific El Niños was found locally along the coast of Peru and Ecuador at the beginning of the year. Triggered by an anomalously low along-shore wind, the surface warming stopped the coastal upwelling and generated strong interannual precipitation over the coastal land in the north of Peru. This warm event, named ‘coastal El Niño’, was not anticipated by climate forecasting centres and left local authorities totally unprepared, regarding floods and landslides generated by persistent heavy rains from January to March. Given the strong consequences for the local populations, these very rare coastal El Niños (only two previously reported) therefore require further investigations.

**Products used:**

<table><thead><tr><th>Ref. no.</th><th>Product name and type</th><th>Documentation</th></tr></thead><tbody><tr><td>4.7.1.</td><td>GLOBAL_REANALYSIS_<br/>PHY_001_025</td><td>PUM: http://marine.copernicus.eu/<br/>documents/PUM/CMEMS-GLO-<br/>PUM-001-025.pdf<br/>QUID: http://marine.copernicus.eu/<br/>documents/QUID/CMEMS-GLO-<br/>QUID-001-025.pdf</td></tr><tr><td>4.7.2.</td><td>ECMWF Era-Interim<br/>reanalysis wind product<br/>Reanalysis (atmosphere)</td><td>Dee et al. (2011), downloaded from<br/>the website http://data.ecmwf.int/data/</td></tr><tr><td>4.7.3.</td><td>OCEANCOLOUR_GLO_<br/>CHL_L4_REP_<br/>OBSERVATIONS_009_082<br/>OCEANCOLOUR_GLO_CHL_<br/>L4_NRT_009_033</td><td>PUM: http://marine.copernicus.eu/<br/>documents/PUM/CMEMS-OC-<br/>PUM-009-ALL.pdf<br/>QUID: http://marine.copernicus.eu/<br/>documents/QUID/CMEMS-OC-<br/>QUID-009-033-037-082-098.pdf<br/>http://marine.copernicus.eu/<br/>documents/QUID/CMEMS-OC-<br/>QUID-009-030-032-033-081-082-<br/>083-085-086.pdf</td></tr><tr><td>4.7.4.</td><td>GLOBAL_REANALYSIS_<br/>BIO_001_018</td><td>PUM: http://marine.copernicus.eu/<br/>documents/PUM/CMEMS-GLO-<br/>PUM-001-018.pdf<br/>QUID: http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-018.pdf</td></tr></tbody></table>

<figure><img src="image_226.png" /><figcaption>**Figure 4.7.1.** Temperature (shading, in °C) and winds (arrows, in m/s) anomalies, from the 1993–2014 climatology, time-averaged for the periods (b) January–March 2016 and (c) October–December 2016 (products reference 4.7.1, 4.7.2).</figcaption></figure>
<figure><img src="image_227.png" /></figure>

As the dominant interannual climate signal on Earth, the El Niño-Southern Oscillation includes a wide variety of local and large-scale atmospheric and oceanic phenomena, but is typically characterised by two anomalous basin-wide patterns in the tropical Pacific (e.g., Guilyardi et al. 2009; Wang et al. 2017 for a review). At the beginning of 2017, while the tropical Pacific conditions in 2017 were not marked by significant anomalous basin-wide El Niño-Southern Oscillation conditions such as during the 2015/2016 El Niño (Gasparin et al. 2017), a localised warm event, associated with anomalously strong precipitation, occurs in the southeastern Pacific along the northwestern coast of South America. This type of event, named ‘coastal El Niño’, is very rare and the mechanisms are not well known, as only two coastal El Niños were previously reported in 1891 and 1925 (Takahashi and Martínez 2017).

As seen in Figure 4.7.1, the January/March 2017 sea surface temperature is characterised by a strong warm anomaly of more than 4°C in the eastern equatorial Pacific off the coasts of Peru and Ecuador. This anomaly was similar in shape and intensity to anomalies typical of eastern Pacific El Niño conditions, with the major difference being the absence of El Niño conditions in the central-eastern Pacific during this period. Although a relatively weak downwelling equatorial Kelvin wave may have contributed to the warm sea surface temperature anomaly along the Peru coasts in February–March 2017 (through the deepening of the thermocline), the main forcing triggering the 2017 event was potentially a strong large-scale relaxation of the southeasterly trades in the eastern south Pacific (Figure 4.7.1). The large-scale mechanism which generated the wind decrease could be an enhanced deep convection over north Australia, triggering an atmospheric teleconnection between the western equatorial Pacific and the eastern South Pacific, as evidenced by Garreaud (2018).

The intense local ocean warming, which peaked during March 2017, resulted in enhanced local precipitation rate in the northern Peru and Ecuador. In Figure 4.7.2, the precipitation rate time series, area-averaged off the coasts of Peru (red box in Figure 4.7.2(a)), shows that the March 2017 precipitation rate was more than 4 times higher than normal, exhibiting larger amplitude as for the 1997/1998 El Niño. This impacted on the surface ocean in favouring the development of a negative sea surface salinity anomaly along the coast of Peru (Figure 4.7.2(a)). In addition to the coastal area, this strong event caused high inland precipitation over the nearby desert land inducing devastating floods and ‘huacos’ (rivers of mud) in northern Peru and Ecuador (Fraser 2017). Further investigations would require to

quantify the dominant terms of the freshwater balance in the surface layer, including both atmospheric inputs and oceanic dynamics.

To further investigate how this event impacted on the phytoplankton biomass and the production of organic carbon through photosynthesis, the surface chlorophyll concentration (used as a proxy of phytoplankton biomass) is shown from independent estimates deduced from satellite observations and from a numerical model (Figure 4.7.3). A strong negative anomaly (<2.2 mg/m³) clearly appears along the coast of Peru on both estimates. The model estimate suggests that this negative anomaly is extended down to about 30 m depth. The coastal upwelling system off Peru is a place of enhanced level of primary production due to high nutrient supply by wind-driven upwelling (Pennington et al. 2006). In March 2017, a decrease of the nearshore wind-driven upwelling along the coast of Peru (Figure 4.7.3(c)), associated with Ekman pumping changes (Echevin et al. 2018), probably reduced the inputs of nutrients to the surface layer, therefore decreasing the production of organic carbon and phytoplankton biomass.

<figure><img src="image_228.png" /><figcaption>**Figure 4.7.2.** (a) Salinity anomaly (shading) and precipitation rate (contour, in mm/day), for the month of March 2017 (products reference 4.7.1, 4.7.2). (b) Precipitation rate is area-averaged in the red box of (a) (84°W–80°W, 3°S–10°S). Anomaly is calculated from the 1993–2014 climatology.</figcaption></figure>
<figure><img src="image_229.png" /></figure>

**Figure 4.7.3.** (a, b) Surface chlorophyll concentration anomalies (in mg/m³), from the 2007–2017 climatology, for the month of March 2017 from (a) GLOBCOLOUR, and (b) the FREE-BIORYS product (products reference 4.7.3, 4.7.4). (c) Vertical section of chlorophyll concentration anomaly (shading) and current velocity anomaly (arrows), latitude-averaged over 10°S–13°S (dashed lines in b) in the upper 50 m. Anomalies are calculated from the climatological cycle. The current velocity has been vertically interpolated every 8 m.

Thus, the large-scale atmospheric variability in the eastern Pacific has led to significant modification of the local oceanic/land conditions (i) by warming coastal surface waters and (ii) by enhancing precipitation in the northern Peru and Ecuador, and (iii) by decreasing the upwelling-driven primary production in the coastal ocean. Unlike the very strong 2015/2016 El Niño, the 2017 coastal El Niño was not anticipated by climate forecasting centres and left local authorities totally

unprepared (Ramírez and Briones 2017; Garreaud 2018). Although the combination of oceanic/atmospheric observation and model products allows a detailed description of the 2017 coastal El Niño event, the rare occurrence of these coastal El Niños can make difficult the understanding and the prediction of these extreme events, which thus require further investigation.

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