Future evolution of Marine Heatwaves in the Mediterranean Sea

Abstract

Extreme ocean warming events, known as marine heatwaves (MHWs), have been observed to perturb significantly marine ecosystems and fisheries around the world. Here, we propose a detection method for long-lasting and large-scale summer MHWs, using a local, climatological 99th percentile threshold, based on present-climate (1976–2005) daily SST. To assess their future evolution in the Mediterranean Sea we use, for the first time, a dedicated ensemble of fully-coupled Regional Climate System Models from the Med-CORDEX initiative and a multi-scenario approach. The models appear to simulate well MHW properties during historical period, despite biases in mean and extreme SST. In response to increasing greenhouse gas forcing, the events become stronger and more intense under RCP4.5 and RCP8.5 than RCP2.6. By 2100 and under RCP8.5, simulations project at least one long-lasting MHW every year, up to three months longer, about 4 times more intense and 42 times more severe than present-day events. They are expected to occur from June-October and to affect at peak the entire basin. Their evolution is found to occur mainly due to an increase in the mean SST, but increased daily SST variability also plays a noticeable role. Until the mid-21st century, MHW characteristics rise independently of the choice of the emission scenario, the influence of which becomes more evident by the end of the period. Further analysis reveals different climate change responses in certain configurations, more likely linked to their driving global climate model rather than to the individual model biases.

Full text

Vol.:(0123456789)
1 3
Climate Dynamics (2019) 53:1371–1392
https://doi.org/10.1007/s00382-019-04661-z
Future evolution ofMarine Heatwaves intheMediterranean Sea
SoaDarmaraki1· SamuelSomot1· FlorenceSevault1· PierreNabat1· WilliamDavidCabosNarvaez2·
LeoneCavicchia3· VladimirDjurdjevic4· LaurentLi5· GianmariaSannino6· DmitryV.Sein7,8
Received: 28 June 2018 / Accepted: 1 February 2019 / Published online: 21 February 2019
© The Author(s) 2019
Abstract
Extreme ocean warming events, known as marine heatwaves (MHWs), have been observed to perturb significantly marine
ecosystems and fisheries around the world. Here, we propose a detection method for long-lasting and large-scale summer
MHWs, using a local, climatological 99th percentile threshold, based on present-climate (1976–2005) daily SST. To assess
their future evolution in the Mediterranean Sea we use, for the first time, a dedicated ensemble of fully-coupled Regional
Climate System Models from the Med-CORDEX initiative and a multi-scenario approach. The models appear to simulate
well MHW properties during historical period, despite biases in mean and extreme SST. In response to increasing green-
house gas forcing, the events become stronger and more intense under RCP4.5 and RCP8.5 than RCP2.6. By 2100 and
under RCP8.5, simulations project at least one long-lasting MHW every year, up to three months longer, about 4 times more
intense and 42 times more severe than present-day events. They are expected to occur from June-October and to affect at
peak the entire basin. Their evolution is found to occur mainly due to an increase in the mean SST, but increased daily SST
variability also plays a noticeable role. Until the mid-21st century, MHW characteristics rise independently of the choice
of the emission scenario, the influence of which becomes more evident by the end of the period. Further analysis reveals
different climate change responses in certain configurations, more likely linked to their driving global climate model rather
than to the individual model biases.
Keywords Marine Heatwaves· Mediterranean Sea· Coupled regional climate models· Future scenario· Extreme ocean
temperatures· Med-CORDEX· Climate change· Climate simulations
1 Introduction
Episodes of large-scale warm temperature anomalies in the
ocean may prompt substantial disruptions to marine ecosys-
tems (Frölicher and Laufkötter 2018; Hobday etal. 2016)
and major implications for fisheries as well (Mills etal.
2013). Known as marine heatwaves (MHW), these extreme
events describe abrupt but prolonged periods of high sea
surface temperatures (SST) (Scannell etal. 2016) that can
occur anywhere, at any time, with the potential to propa-
gate deeper to the water column (Schaeffer and Roughan
2017). They have received little attention until improved
observational systems revealed adverse consequences ema-
nating from them. Their occurrence is likely to intensify
under continued anthropogenic warming (Frölicher etal.
2018; Oliver etal. 2018a), engendering the need for a more
comprehensive examination of their spatiotemporal distribu-
tion and underlying physical causes.
In the Mediterranean area, a well-known“Hot Spot”
region for climate change (Giorgi 2006), the annual mean
basin SST by the end of the 21st century is expected to
increase from +1.5 °C to +3 °C relative to present-day
levels, depending on the greenhouse gas (GHG) emission
scenario (Somot etal. 2006; Mariotti etal. 2015; Adloff
etal. 2015). This significant rise in SST is expected to
accelerate future MHW occurrence, in congruence with
projections for GHG-induced heat stress intensification of
200–500% throughout the region (Diffenbaugh etal. 2007).
The Mediterranean area’s sensitivity to increased GHG
forcing is mainly attributed to a significant mean warming
and increased interannual warm-season variability, along
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s0038 2-019-04661 -z) contains
supplementary material, which is available to authorized users.
* Sofia Darmaraki
sofia.darmaraki@meteo.fr
Extended author information available on the last page of the article
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1372 S.Darmaraki et al.
1 3
with a reduction in precipitation (Giorgi 2006). A recent
study has already identified significant increases in MHWs
globally over the last century, including the Mediterranean
Sea (Oliver etal. 2018a).
In fact, one of the first-detected MHWs worldwide
occurred in the Mediterranean in the summer of 2003:
Surface anomalies of 2–3
◦C
above climatological mean
lasted for over a month due to significant increases in air-
temperature and a reduction of wind stress and air-sea
exchanges (Grazzini and Viterbo 2003; Sparnocchia etal.
2006; Olita etal. 2007). These factors seem to have also
triggered an anomalous SST warming in the eastern Medi-
terranean area during the heatwave of 2007, at the order
of +5 °C above climatology (Mavrakis and Tsiros 2018).
Since then, numerous studies have explored the modu-
lating factors behind individual events around the world.
For instance, a combination of local oceanic and large-
scale atmospheric forcing was suggested for the Australian
MHW of 2011 (Feng etal. 2013; Benthuysen etal. 2014)
and the persistent, multi-year (2014–2016) “Pacific Blob”
(Bond etal. 2015; DiLorenzo and Mantua 2016). Other
events have been attributed to mainly atmosphere-related
drivers, such as the 2012 Atlantic MHW (Chen etal. 2014,
2015) and the extreme marine warming across Tropical
Australia (Benthuysen etal. 2018), or to ocean-dominat-
ing forcing like the 2015/2016 Tasman Sea MHW (Oliver
etal. 2017). The importance of regional influences was
further noted in coastal MHWs in South Africa (Schlegel
etal. 2017a) and during subsurface MHW intensification
around Australia (Schaeffer and Roughan 2017).
As a result of these events, severe impacts on marine
ecosystems have been documented worldwide, including
biodiversity die-offs and tropicalisation of marine com-
munities (Wernberg etal. 2013, 2016), extensive species
migrations (Mills etal. 2013), strandings of marine mam-
mals and seabirds, toxic algal blooms (Cavole etal. 2016)
and extensive coral bleaching (Hughes etal. 2017). In the
Mediterranean Sea in particular, unprecedented mass mor-
tality events and changes in community composition due
to extreme warming were reported in the summers of 1999
(Perez etal. 2000; Cerrano etal. 2000; Garrabou etal.
2001; Linares etal. 2005), 2003 (Garrabou etal. 2009;
Schiaparelli etal. 2007; Diaz-Almela etal. 2007; Munari
2011), 2006 (Kersting etal. 2013; Marba and Duarte 2010)
and 2008 (Huete-Stauffer etal. 2011; Cebrian etal. 2011),
affecting a wide variety of species and taxa (e.g. 80 % of
Gorgonian fan colonies and seagrass Posidonia oceanica).
MHWs can be especially lethal for organisms with reduced
mobility that are usually limited to the upper water col-
umn; Their severity is determined by both temperature and
duration (Galli etal. 2017). Finally, cascading effects have
also been observed in fisheries, resulting in huge financial
losses and even economic tensions between nations (Mills
etal. 2013; Cavole etal. 2016; Oliver etal. 2017).
However, despite the growing body of MHW-related
literature, systematic examination of MHWs as distinct
exceptional events with intensity, frequency and duration
has only just emerged. Although marine extremes have been
investigated before, only a few studies have analysed past
trends in extreme ocean temperatures (e.g. Scannell etal.
2016; MacKenzie and Schiedek 2007) and even fewer have
dealt with their future evolution. For instance, past trends
of extreme SST have been investigated in coastal regions
(Lima and Wethey 2012) and through thermal-stress-related
coral bleaching records (Lough 2000; Selig etal. 2010;
Hughes etal. 2018). Using a more standardised framework,
past MHW occurrences have been studied in the Tasman
Sea (Oliver etal. 2018b) and the global ocean (Oliver etal.
2018a). For the 21st century, MHW projections have been
performed so far on a global scale, with the use of multi-
model setups from CMIP5 (Frölicher etal. 2018) and CMIP3
(Hobday and Pecl 2014) and under different GHG emission
scenarios. On a regional scale though, ocean extremes have
been assessed in Australia (King etal. 2017) and the Tasman
Sea (Oliver etal. 2014).
The above-mentioned studies used different definitions
for extreme warm temperatures, with some adopting a recent
standardised MHW approach proposed by Hobday etal.
(2016). The set of dedicated statistical metrics developed in
this framework allows for a consistent definition and quan-
tification of the MHW properties. A MHW is now described
as a “discrete, prolonged, anomalously warm water event
at a particular location” . Using this definition, Schlegel
etal. (2017b), for example, identified an increase in MHW
frequency around South Africa for the period 1982–2015,
while Schaeffer and Roughan (2017) demonstrated sub-
surface intensification of MHWs in coastal SE Australia
between 1953–2016. A linear classification scheme was also
proposed by Hobday etal. (2018), where MHWs are defined
based on temperature exceedance from local climatology.
In the case of the Mediterranean Sea, however, little is
known about past or future MHW trends and their under-
lying mechanisms. The MHW-related research has mostly
been focused on local ecological impacts without system-
atically assessing MHW occurrence. According to Riv-
etti etal. (2014) and Coma etal. (2009), most of the mass
mortalities documented in the basin were related to posi-
tive thermal anomalies in the water column that occurred
regionally during the summer. Although they have been
reported with increased frequency since the early 1990s,
their occurrence has been observed as early as the 1980s.
Meanwhile, the evolution of extreme Mediterranean SST
in the 21st century has so far been examined in relation
to the thermotolerance responses of certain species. For
instance, Jordà etal. (2012) used an ensemble of models
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1373Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
under the moderately optimistic scenario for GHG emissions
A1B and suggested an increased seagrass mortality in the
future around the Balearic islands due to a projected rise of
the annual maximum SST by 2100. Similarly, Bensoussan
etal. (2013) evaluated the thermal-stress related risk of mass
mortality in Mediterranean benthic ecosystems for the 21st
century, based on the average warming estimated between
2090–2099 and 2000–2010, under the pessimistic future
warming scenario A2. Finally, Galli etal. (2017) showed an
increase in MHW frequency, severity and depth extension
in the basin, assuming exceedances from species-specific
thermotolerance thresholds under the high-emission IPCC
RCP8.5 scenario. [The A1B and A2 emission scenarios cor-
respond to projections of a likely, mean temperature change
of 1.7–4.4 °C and 2.0–5.4 °C respectively by the end of
the 21st century (IPCC 2007), whereas RCP2.6, RCP4.5
and RCP8.5 to a likely change of 0.3–1.7 °C, 1.1–2.6 °and
2.6–4.8 °respectively, by the end of the period (Kirtman
etal. 2013)].
In addition, our understanding of the Mediterranean Sea’s
response to future climate change to date mostly relies on
ensembles of low resolution GCMs (CMIP5) (e.g. Jordà
etal. 2012; Mariotti etal. 2015) or on numerical experi-
ments carried out with a single regional ocean model under
different emission scenarios (i.e Somot etal. 2006; Ben-
soussan etal. 2013; Adloff etal. 2015; Galli etal. 2017).
Consequently, the various sources of uncertainty related to
the choice of the socio-economic scenario, choice of climate
model and natural variability have not been properly taken
into account by climate change impact studies on Mediter-
ranean Sea ecosystems and maritime activities. Since there
is an evident link between distinctive climate anomalies and
notable ecosystem effects (e.g in the Mediterranean Sea,
Crisci etal. 2011; Bensoussan etal. 2010), it is important to
adress these uncertainties by considering different possible
climate futures through multi-model, multi-scenario set ups
when possible.
In this context, the aim of this study is to provide a robust
assessment of the future evolution of summer MHWs in the
Mediterranean Sea using an ensemble of high-resolution
coupled regional climate system models (RCSM), driven
by GCMs and a multi-scenario approach (RCP2.6, RCP4.5,
RCP8.5). The RCSM’s ability to reproduce Mediterranean
SST features is first evaluated against satellite data. Then,
a MHW spatiotemporal definition, based on SST and on
Hobday etal. (2016)’s recommendations, is developed and
applied to study the response of extreme thermal events to
future climate change. For the first time, changes in sum-
mer Mediterranean MHW frequency, duration, intensity
and severity are investigated with respect to an envelope of
possible futures.
This paper is organised as follows: in Sect.2 we pre-
sent the ensemble of RCSMs along with the methodology
proposed for the detection and characterisation of sum-
mer MHWs. Model evaluation against observed mean and
extreme SST is performed in Sect.3, using daily SST data.
We also describe the future evolution of Mediterranean SST
and MHW properties under different greenhouse gas emis-
sion scenarios from 1976–2100. A discussion and summary
of the results are presented in Sects.4 and 5.
2 Material andmethods
2.1 Model data andsimulations
An ensemble of six coupled RCSMs (CNRM-RCSM4,
LMDZ-MED, COSMOMED, ROM, EBU-POM, PRO-
THEUS) with different Mediterranean configurations is
employed in this study. Participant members are provided
by six research institutes from the Med-CORDEX initiative
[Ruti etal. (2016), https ://www.medco rdex.eu/] and each
simulation will be herein referred to by the name of the cor-
responding institute, as mentioned in Table1 (e.g. simula-
tions with the CNRM-RCSM4 model will be referred as
CNRM, etc.). Med-CORDEX can be considered as a multi-
model follow-up to the CIRCE project (Gualdi etal. 2013),
which studied the Mediterranean Sea under a single scenario
(A1B) with most of the simulations stopped in 2050.
One novel aspect of the Med-CORDEX ensemble is that
all models have a high-resolution oceanic (eddy-resolving)
and atmospheric component as well as high coupling fre-
quency (see Table1). The free air-sea exchanges offered by
their high-resolution interface is also an advantage for the
MHW representation, which depends on ocean-atmosphere
interactions. The domains cover the entire Mediterranean
and a small part of the Atlantic, while the Black Sea and Nile
river are respectively parametrized or represented with cli-
matologies (except for AWI/GERICS, in which the oceanic
component is global and explictly simulates the Black Sea).
Boundary conditions come from 4 different general circu-
lation models of CMIP5. Information about each coupled
system is summarised in Table1. To avoid biasing results
towards one or more members of the ensemble, only the
realization with the highest resolution is selected for each
model.
All the numerical simulations produced daily SST data
(3D temperatures were stored at a monthly scale) between
1950–2005 for the historical experiment (HIST) and for
2006-2100 under the Representative Concentration Pathway
RCP8.5 (high-emission scenario), RCP4.5 (moderate-emis-
sion scenario), RCP26 (low-emission scenario) IPCC sce-
narios. As the models use boundary conditions from CMIP5,
which are not in phase with the observed variability, simula-
tion chronology does not represent the actual conditions that
correspond to each calendar year. Instead, they are expected
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1374 S.Darmaraki et al.
1 3
to represent the climate statistics of each period (e.g. aver-
age, standard deviation) well. We use SST instead of deeper
layer temperatures, as both the models’ behaviour and the
MHW identification technique can be evaluated at a larger
scale using satellite data. A total of 17 simulations were used
from six models with variable resolution (Table1). For the
purposes of our analysis, we define 30-year periods from
the HIST run between 1976–2005 (from this moment on
referred as HIST), the near future (2021–2050) and the far
future (2071–2100).
In the case of ENEA, the HIST run span from 1979–2005
due to different simulation initialization, while CMCC and
AWI/GERICS simulations reached 2099. The spin-up strat-
egy of the Med-CORDEX ensemble was not prescribed,
therefore it was different for every configuration. The lack
of a long spin-up (e.g. U.BELGRAD, ENEA) could be det-
rimental for temperatures at deeper layers but not so relevant
for the SST evolution. For the CNRM model, a constant
monthly flux (atmosphere to ocean) correction was applied
to minimise identified biases, with no significant influence
on the climate change signal. Also, a slightly intense SST
signal in the Alboran Sea was noted in the U.BELGRADE
configuration for 2021–2050 under RCP8.5 and is prob-
ably linked to the simple representation of the connection
between the Mediterranean Sea and the Atlantic Ocean in
the model: the open boundary condition, as defined in the
POM model, was applied in single model point defined on
the strait of Gibraltar, without any buffer zone and with
prescribed boundary conditions in the Atlantic Ocean; this
is, on the other hand, a common approach in many mod-
els. Finally, an error has been recently reported concerning
the CNRM-CM5 GCM files that were used as atmospheric
lateral boundary conditions for CNRM and ENEA (http://
www.umr-cnrm.fr/cmip5 /spip.php?artic le24), but this likely
has no significant effect on the long-term climate change
signal.
Working from the hypothesis that MHWs are usually
confined close to the surface, in this study we consider that
the model SST data of the 1st layer depth represent surface
temperatures between 1-16 m, depending on the model. We
acknowledge, however, that MHWs may penetrate deeper to
the water column under certain conditions, but assume for
Table 1 Characteristics of the Med-CORDEX coupled regional climate system models (RCSM) and the simulations used in this study
More information on MEDATLAS initial conditions can be found in Rixen etal. (2005)
INSTITUTE CNRM LMD CMCC AWI/GERICS U.BELGRADE ENEA
Model characteristics
RCSM name CNRM-RCSM4 LMDZ-MED COSMOMED ROM EBU-POM PROTHEUS
Driving GCM CNRM-CM5 IPSL-CM5A-MR CMCC-CM MPI-ESM-LR MPI-ESM-LR CNRM-CM5
Med.Sea Model NEMOMED8 NEMOMED8 NEMO-MFS MPIOM POM MedMIT8
Ocean Res. 9–12 km 9–12 km 6–7 km 10–18 km 30 km 13 km
Num. of z-Lev-
els (Ocean)
43 43 72 40 21 42
SST (1st layer
depth)
6 m 6 m 1.5 m 16 m 1.8 m 10 m
Timestep
(Ocean)
1200 s 1200 s 480 s 900 s 360 s 600 s
Atmosphere
model
ALADIN-climate LMDZ CCLM REMO Eta/NCEP RegCM
Atmosphere
Res.
50 km 30 km 50 km 25 km 50 km 30 km
Coupling fre-
quency
Daily Daily 80 min 60 min 6 min 6 h
Numerical Simulations
SPIN UP 130 years 40 years 25 years 56 years 5 years No Spin Up
Initial condi-
tions
MEDATLAS MEDATLAS MEDATLAS MEDATLAS MEDATLAS MEDATLAS
HIST 1950–2005 1950–2005 1950–2005 1950–2005 1950–2005 1979–2005
RCP8.5 2006–2100 2006–2100 2006–2099 2006–2099 2006–2100 –
RCP4.5 2006–2100 2006–2100 2006–2099 2006–2099 – 2006–2100
RCP2.6 2006–2100 – – – – –
References Sevault etal. (2014) L’Hévéder etal.
(2013)
Cavicchia etal.
(2015)
Sein etal. (2015) Djurdjevic and
Rajkovic
(2008)
Artale etal. (2010)
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1375Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
the time being that SST is a reliable sign of possible harmful
conditions for deeper layers.
2.2 Reference dataset
In order to evaluate the model’s capability to simulate trends
in regional extreme thermal events, we first perform com-
parisons with satellite data (OBS) provided by the Coperni-
cus Marine Service and CNR - ISAC ROME. More specifi-
cally, the Mediterranean Sea high-resolution L4 dataset is
employed, providing daily, reprocessed SSTs on a 0.04 °grid,
an interpolation of remotely sensed SSTs from the Advanced
Very High Resolution Radiometer (AVHRR) Pathfinder Ver-
sion 5.2 (PFV52) onto a regular grid (Pisano etal. 2016).
They are obtained over a 30-year period of January 1982
to December 2012 and are used as a reference for the mod-
els’ performance in the mean and extreme climate in the
Mediterranean Sea. With the aim of validating the “present-
day” climate, we choose the 30-year period (1976–2005)
in the model HIST runs that has the greatest overlap with
the observed 30-year period (1982–2012). Prior to perform-
ing any calculations and in order to compare the results
between the models and observations, we first interpolated
every dataset to the NEMOMED8 grid, already in use by 2
RCSMs, by implementing the nearest neighbour method.
2.3 Defining marine heatwaves
As for their atmospheric counterparts, there is no univer-
sal definition for MHWs. However, certain metrics can be
applied to compare different events in space and time. In
this research, the qualitative MHW definition proposed by
Hobday etal. (2016) is followed. We use it as a baseline for
developing a quantitative method that will identify MHWs,
namely in the summer months, based on the climatology
and the geographical characteristics of the area. Although
we recognise that heatwaves in colder months might also be
essential for certain species, we choose to focus on extreme
events related to the highest annual SSTs, when organisms
may be beyond their optima, as seen by previous mass mor-
tality events in the Mediterranean (e.g. MHW of 1999 and
2003).
According to Hobday etal. (2016) a MHW is a “pro-
longed, anomalously warm water event at a particular loca-
tion” and it should be defined relative to a 30-year period.
In our case, a subset of the HIST experiments (1976–2005)
and the 1982–2012 period for the observations are chosen,
representing the average climate in the latter half of the 20th
century. In order to achieve a homogenised yet area-spe-
cific temperature diagnostic, for every year of the reference
period (HIST) we first compute the 99th quantile of daily
SST (
SST99Q
) for every grid point. Then we average these 30
years of extreme values, constructing a 2D threshold map.
Note that individual threshold maps were created for each
dataset separately, accounting for the different model char-
acteristics (e.g SST bias). An “anomalously warm day” at
every grid point is then any given day when the local
SST99Q
threshold is exceeded. However, in order to be classified as a
“prolonged” event, we set the minimum duration of a MHW
to 5 days, following Hobday etal. (2016). Further, we aim to
identify long-lasting events, since most of the previous mass
mortalities in the basin occurred during thermal anomalies
that lasted for more than 5 days (e.g. (Garrabou etal. 2009;
DiCamillo and Cerrano 2015; Cerrano etal. 2000; Cebrian
etal. 2011). In addition, the average present-day MHW dura-
tion in the basin was found around 10 days (not shown).
Therefore, a 3-day or 7-day minimum definition threshold
would not change significantly the MHW characteristics in
the future (see Sect.4.)
The discrete nature of MHWs also necessitates a well-
defined starting and ending day, but gaps with temperatures
close to threshold values can also be found, as a result of
day-to-day SST fluctuations. At this point, our definition dif-
fers slightly from that of Hobday etal. (2016). More specifi-
cally, gaps of up to 4 consecutive days or less are allowed
inside a local MHW (considered as warm days). This is
true,however, only when both the preceeding and following
6-day mean SST of a gap day (including the gap day in each
mean) are above the local
SST99Q
. For the cool day “neigh-
bourhood” this would represent a tendency to remain above
threshold, even though the SST of that particular cool day
might be below limit. This also reflects the fact that minor
SST deviations from the threshold cannot impact the overall
warm conditions of a MHW. It would most likely not offer
either an “essential” relief to organisms, even to the less
mobile and perhaps less tolerant species, once a MHW has
started. Taking advantage of the default statistical sensitiv-
ity of the mean to outliers (in this case cold temperatures),
we make the assumption that an event with the potential to
interrupt a MHW (e.g wind, current) should cause a consid-
erable drop in daily SST. Therefore, a below-threshold drop
in either of the 6-day SST averages would not allow any cool
day to merge with a MHW, in the same way that a sequence
of five cool days or more would interrupt an event entirely.
The 11-day window around the gap day is chosen since the
minimum duration of a MHW was set to five days.
The spatial coverage of the MHW is then determined
by aggregating grid points that are “activated” in a MHW
state every day but are not necessarily contiguous. In com-
mon with many atmospheric definitions, a minimum 20%
of the Mediterranean surface in
km2
was chosen in order to
detect large-scale events that may have a broad ecosystem
impact but also represent rare occurrence for the average
climate conditions of HIST period. We, therefore, opt for
prolonged, large-scale and extremely warm ocean tempera-
tures that do not occur on a yearly basis in the 20th century,
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1376 S.Darmaraki et al.
1 3
with a view of quantifying their evolution in the 21st century
under different GHG emission scenarios. The advantage of
a percentile-based SST threshold in our case is that spatial
patterns are also identified independently from the differ-
ent extreme temperature levels that characterise sub-basins
in the Mediterranean. We acknowledge that the detection
method is developed based on subjective choices, and the
sensitivity of the climate change results to these changes was
also tested (See Sect.4).
Once a MHW is identified, a subset of MHW metrics
defined in Hobday etal. (2016) are used to characterise
it. We examine the frequency of MHWs (Annual count
of events), and the duration of each event is defined as the
time between the first (
ts
) and last day (
te
) for which a mini-
mum of 20% of Mediterranean Sea surface is touched by
a MHW. Every event is characterised by a mean and max
intensity (mean and spatiotemporal maximum temperature
anomaly relative to the threshold over the event duration)
and a maximum surface coverage. Finally, its severity is
represented by cumulative intensity (spatiotemporal sum of
daily temperature anomalies relative to the threshold over
the event duration) (Fig.1, Table2)
3 Results
3.1 Model evaluation
The first goal of this paper is to evaluate each models’ abil-
ity to simulate mean (
SST
) and extreme Mediterranean Sea
SST (
SST99Q
) correctly. For this reason, pattern correlations
were first performed, using the Pearson product-moment
coefficient of linear correlation between two variables. The
observed annual mean
SST
between 1982–2012 (Fig.2,
OBS) shows a NW-SE pattern of cold-warm temperatures
ranging from
∼15
°C to 23 °C, respectively. Similarly, all
the models demonstrate a warmer Eastern Mediterranean
(EM) between 19 and 23 °C while colder deep water forma-
tion areas (e.g. Gulf of Lions, Adriatic) are captured well
around 15–17 °C. Despite a multi-model mean (MMM) cold
bias of about 0.6 °C, spatial correlations between each model
mean 1976-2005
SST
and observations are high (MMM
∼0.94
). The lowest bias is found in ENEA and the high-
est in the CMCC and AWI/GERICS models (see Table3).
Note that satellite provides skin and night-time SST values,
whereas the model SST represents averaged daily tempera-
tures of the first few meters of mixed layer depth. Part of
the model bias can be therefore explained by this difference
in SST.
More complex spatial patterns are revealed when exam-
ining the 2D threshold maps used as the basis for defin-
ing Mediterranean MHWs (Fig.3). The highest
SST99Q
are
observed in Central Ionian, Gulf of Gabes, Tyrrhenian Sea
and Levantine basin varying from approximately 27–31 °C
and the lowest (20–22 °C) in deep water formation areas
and the Alboran Sea (Fig.3 OBS). In general, all the mod-
els are able to reproduce these patterns, although this time
they share lower spatial correlations with the observations
(MMM
∼0.78
). The ENEA model shows a warm bias
whereas CMCC, U.BELGRAD and AWI/GERICS show a
cold bias larger than 1 °C. The similar behaviour of the latter
three could perhaps be related to the common atmospheric
component (ECHAM) of their driving GCM. On the whole,
the difference between the MMM mean and the extreme
Fig. 1 Schematic of a MHW based on Hobday et al. (2016). The
black line represents daily SST variations of one grid point in a ran-
dom year and red line is the local threshold (
SST99Q
) based on the
30-year average of yearly 99th quantile of daily SST for that point.
The blue line is the daily 30-year climatology for this point. Also
shown here also are the starting day (
ts
) and ending day (
te
) above
SST99Q
, gap days and the different measures of daily intensity. MHW
metrics refer to the total event duration
Table 2 Marine heatwave
(MHW) set of properties and
their description after Hobday
etal. (2016)
Marine Heatwave Metrics Description
Frequency Number of events occurring per year
Duration
(te
–
ts
) +1 (days)
Mean intensity (Imean)
∫
[
∫
(
SST(x,y,t)
–
SST99Q(x,y)
)dxdy]dt/
∫
dxdy
∫
dt (°C)
Max intensity (Imax)
max(x,y,t)
(
SST(x,y,t)
−
SST99Q(x,y)
) (°C)
Severity (Icum)
∫
[
∫
(
SST
(
x,y,t)
−
SST99Q(
x
,
y
)
)dxdy]dt (°
C days km2
)
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1377Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
basinwide SST is found to be
∼6.6
°C, in good agreement
with the observations (7.1 °C). The corresponding MMM
spread is small for the
SST
but higher for the
SST99Q
(
∼1
°C).
Further, the domain-averaged timeseries of SST illustrate
a warming tendency of both the
SST
and annual
SST99Q
(Fig.4; Table3). Even though MMM
SST
and
SST99Q
obtain
similar trends (
∼0.02
°C/year) they seem to underestimate
the corresponding observed trends (0.04/0.05 °C/year).
Particularly for AWI/GERICS and U.BELGRAD, this is a
response likely explained by their common driving GCM
(MPI-ESM-LR). On the other hand, the amplitude of inter-
annual variability is found similar to the observations for
most of the models. On the whole, the observed and most
of the model trends are statistically significant at a level of
95% except for certain cases indicated in Table3. Interest-
ingly, none of the simulations peaked as high as the observa-
tions during the exceptional MHW year of 2003 (20.4 °C for
SST
and 28.4 °C for
SST99Q
). This record basinwide
SST99Q
value is on average 8.7 °C higher than the average
SST
°C of
1982–2012 and 2.8 °C greater than the basin-mean
SST99Q
of that period.
In terms of MHW properties during 1982–2012 (Table3),
observed MHW frequency is found at 0.8 events per year
that last a maximum of 1.5 months and range between July
and September. The mean intensity of MHWs varies from
0.3 to 0.9 °C, covering a maximum of 20–90% of the Medi-
terranean Sea surface, with a maximum intensity of 5.0 °C
(2002) and a maximum severity of
8.5 ×107
°
C days km2
.
The highest values over this period (except from Imax) refer
Fig. 2 Yearly
SST
(°C) for the HIST run of every model (1976–2005) and satellite data during 1982–2012. Note that the HIST run for ENEA is
from 1979–2005
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1378 S.Darmaraki et al.
1 3
to the characteristics of the well-known MHW 2003. More
specifically, they correspond to a Mediterranean-scale event
lasting 48 days (20 July–5 September) by our definition, in
line with Grazzini and Viterbo (2003) and Sparnocchia etal.
(2006). It seems that mainly the phase of the MHW that was
both large-scale and intense was captured here.
On average, the simulated events during HIST are well
within the equivalent observed range of every variable. They
manifest though a slightly lower annual frequency, a poten-
tial for slightly higher maximum durations and starting dates
up to early September. They also appear to underestimate the
upper level of the Imean, Imax and severity range. In particu-
lar, event durations of two months or more are exhibited by
LMD, CMCC and AWI/GERICS models, while the ENEA
model shows the highest Imax of 5.3 °C. Maximum severity,
on the other hand, appears closer to the observed values only
in the LMD and CMCC models. These configurations also
show a MHW maximum spatial coverage above 80%, along
with CNRM and AWI/GERICS. In general, the Med-COR-
DEX ensemble appears to perform well given that this is
the first time, to our knowledge, that Mediterranean RCSMs
have been evaluated for MHWs properties.
To better understand the ensemble variability of the
MHW characteristics in the HIST period, we also com-
bine Intensity-Duration-Frequency (IDF) information for
every dataset separately (see Fig.5). The total number of
events of this period are organised in bins of Imean (every
0.02 °C) and duration (5-day bins progressively increased
to 10-day and 20-day bins). Although some models simu-
late longer events relative to the observed MHW 2003,
Fig. 3 Individual MHW threshold maps of mean
SST99Q
(°C) computed from the HIST run of every model (1976–2005) and satellite data dur-
ing 1982–2012. Note that the HIST run for ENEA is from 1979–2005
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1379Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
only CMCC exhibited equivalent MHWs in terms of
duration and intensity. At the same time, 1–3 events are
detected for most classes of Imean and duration, in both the
observations and the models. There are only a few cases
where 3–7 events appear with Imean below 0.6 °C but fol-
low no specific duration pattern.
3.2 Future Mediterranean SST evolution
In this section we analyze projections of
SST
and
SST99Q
in the 21st century by comparing their evolution against
the reference period and under different GHG emission
scenarios.
Fig. 4 Timeseries of area-averaged, yearly
SST
°C (left) and
SST99Q
°C (right), during HIST for every model and satellite data, represented by a
solid line. Trends are indicated in dashed lines. The different simulations are represented by different colors
Fig. 5 IDF plot; Intensity (Imean in °C), Duration (Days), Frequency (Number of MHW during 1976–2005). Imean is organised in bins of 0.02 °C
while duration is in bins of 5, 10 and 20 days. Red box indicates observed characteristics corresponding to the exceptional MHW of 2003
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1380 S.Darmaraki et al.
1 3
During 2021–2050 an increase is found for the domain-
averaged ensemble mean
SST
and
SST99Q
with respect to
HIST, around 0.8–1 °C and 1–1.2 °C respectively. While
the mid-21st century anomalies appear almost independent
from the greenhouse gas forcing, a more diverse and sub-
stantial warming occurs towards 2071–2100 (see Fig.6).
Table 3 Evaluation of SST and MHW properties during HIST run
Mean annual and threshold SST are indicated with
SST
(°C) and
SST99Q
(°C) respectively. The Mann-Kendal non-parametric test is used to
detect the presence of linear or non-linear monotonic trends (°C/year) in domain-averaged SST timeseries. Trends with statistical significance
lower than 95% level are indicated with star. Spatial correlations (Corr.Coeff) and bias with respect to observations are given for each dataset.
Also shown here, are the range (min and max) of frequency, duration (days), starting day (calendar month), ending day (calendar month), Imean
(°C) Imax (°C), Severity*
107
(°
C days km2
) and maximum surface coverage(%) of MHWs. The multi-model column indicates the ensemble aver-
age values and standard deviation for each variable
Characteristics CNRM LMD CMCC AWI/GERICS U.BELGRAD ENEA Multi-Model OBS
SST evaluation

SST
19.11 19.16 18.80 18.69 19.16 19.41
19.06 ±0.30
19.70

SST
– OBS Corr.Coeff 0.97 0.97 0.95 0.97 0.92 0.89
0.94 ±0.03

SST
-OBS Bias −0.59 −0.54 −0.90 –1.01 −0.54 −0.29
−0.63 ±0.30

SST
Timeseries Trend 0.02 0.04 0.02 0.01* 0.01 0.01*
0.02 ±0.01
0.04

SST99Q
26.23 26.03 24.52 24.43 25.68 26.97
25.64 ±1.00
26.79

SST99Q
-OBS Corr.coeff 0.88 0.84 0.77 0.84 0.70 0.74
0.79 ±0.07

SST99Q
-OBS Bias −0.56 −0.76 −2.27 −2.36 −1.11 0.18
−1.15 ±1.00

SST99Q
Timeseries trend 0.03 0.04 0.04 0.01* 0.00* 0.02*
0.02 ±0.02
0.05
MHW characteristics (HIST)
Frequency 0.7 0.6 0.6 1 0.7 0.7 0.7 0.8
Duration 2–32 4–67 1–74 1–61 4–38 4–44 2.6–52.6 1–48
Starting Day Jul–Sep Jul–Aug Jul–Aug Jul–Sep Jul–Aug Jul–Sep Jul–Sep Jul–Aug
Ending Day Jul–Sep Aug–Sep Aug–Sep Aug–Sep Jul–Sep Aug–Sep Aug–Sep Aug–Sep
Imean 0.4–0.8 0.3–0.7 0.3–0.9 0.2–0.6 0.3–0.6 0.4–0.6 0.3–0.7 0.3–0.9
Imax 1.8–5.5 1.5–4.1 1.4–4.9 1.0–3.0 1.5–3.2 1.8–5.3 1.5–4.3 1.3–5.0
Severity (Icum) 0.04–3.6 0.1–7.8 0.01–8.8 0.01–4.3 0.1–1.6 0.1–2.6 0.05–4.8 0.02–8.5
Max Surface 20.8–82.5 21.5–88.3 20.1–90.8 20.3 –81.9 22.5–59.8 20.7–72.1 21.0–79.2 20.1–90.1
Fig. 6 Area-average, yearly
SST
°C (left) and extreme
SST99Q
°C
(right) anomalies with respect to HIST. Bold colors represent the
multi-model average and lighter colors are the individual simulations.
RCP2.6 scenario has only one simulation (CNRM), HIST run is illus-
trated in grey and observations in dashed black
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1381Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
In particular, the multi-model mean
SST
and
SST99Q
anomalies under RCP8.5 are 3.1 °C and 3.6 °C respec-
tively, exhibiting nearly a doubling of their corresponding
RCP4.5 rise. Similarly, the equivalent increase of CNRM
SST
and
SST99Q
under RCP8.5 is about 3 times as high
as that under RCP2.6 for the same period. Individually,
however, the highest mean and extreme SST anomalies
are demonstrated by the LMD and CMCC models under
every scenario and for every period (see Table4). For
both SST indices, the effects of the different emission sce-
narios become more evident by 2060, with the highest/
intermediate warming occurring for every model under
RCP8.5/RCP4.5 and the lowest under the (mono-model)
RCP2.6 simulation. In the latter, little or no difference is
found between the
SST
and
SST99Q
rise throughout the
century. In contrast, under RCP4.5 and RCP8.5, the multi-
model
SST99Q
increase appears greater than the
SST
rise by
20–25% during 2021–2050 and by 16–18% for 2071–2100
(see Table4 and discussion section). This implies a higher
contribution from
SST
to the warming towards the end of
the century.
The spatial distribution of the corresponding anomalies,
however, appears inhomogeneous. For 2071–2100, some
regions in the Levantine basin, Balearic islands, Tyrrenian
Sea, Ionian Sea and North Adriatic Sea exhibit the high-
est MMM
SST
anomalies in every scenario (Fig.7). In
contrast, the lowest anomalies of that time are located in
the Alboran Sea, where cold waters are advected from the
Atlantic, and depending on the scenario they may range
from
∼0.6
°C (RCP2.6) to
∼2.4
°C (RCP8.5).
Meanwhile, the most pronounced extreme warm anoma-
lies (
SST99Q
) for 2071–2100 under RCP4.5 and RCP8.5 are
projected for the NW mediterranean, Tyrrenian Sea, Ionian
Sea and some parts of North Levantine basin (Fig.8). Under
RCP2.6 though, the greatest
SST99Q
anomalies (>1.2 °C)
are more confined towards the Aegean Sea, Adriatic, Tyr-
rhenian Sea and the area around Balearic islands. In addition
to the highest
SST99Q
rise, the Adriatic Sea, Ionian Sea, Tyr-
rhenian Sea, some parts around the Balearic islands and the
North Levantine basin display also the greatest
SST
rise, for
every scenario during the 2nd half of the 21st century. Dur-
ing 2021–2050, however, they exhibit the highest mean and
extreme warming under RCP26 and RCP4.5 but not under
RCP8.5. The Alboran Sea and the SE Levantine basin, on
the other hand, demonstrate the lowest
SST99Q
anomalies in
every period and every scenario
3.3 Future evolution ofMediterranean MHWs
The MHW climate change response is examined here using
anomalies. These anomalies are computed for the average
MHW characteristics in the future relative to the average
MHW characteristics in HIST run, for each sub-period,
model and scenario (Table5).
The multi-model mean reveals an increase in frequency
of 0.3–0.4 events/year for every period of RCP4.5/RCP8.5
with the mono-model RCP2.6 simulation showing a
Table 4 Future Mediterranean-
averaged, yearly mean (
SST
)
and extreme (
SST99Q
) anomalies
(with respect to HIST) for
the near and far future under
different emission scenarios
The multi-model column indicates the ensemble average values and standard deviation. Values are in °C
CNRM LMD CMCC AWI/GERICS BELGRAD ENEA Multi-model
RCP85 2021–2050

SST
0.9 1.3 1.2 0.7 0.7 –
1.0 ±0.3

SST99Q
1.1 1.7 1.3 1.0 1.0 –
1.2 ±0.3
RCP85 2071–2100

SST
2.7 3.8 3.4 2.7 2.7 –
3.1 ±0.5

SST99Q
2.9 4.5 4.3 3.1 3.1 –
3.6 ±0.7
RCP45 2021–2050

SST
0.7 1.2 1.0 0.6 – 0.6
0.8 ±0.4

SST99Q
0.8 1.4 1.3 0.8 – 0.7
1.0 ±0.5
RCP45 2071–2100

SST
1.6 2.1 2.0 1.1 – 1.2
1.6 ±0.8

SST99Q
1.8 2.6 2.5 1.4 – 1.3
1.9 ±0.9
RCP26 2021–2050

SST
0.8 – – – – – –

SST99Q
1.0 – – – – – –
RCP26 2071–2100

SST
1.0 – – – – – –

SST99Q
1.0 – – – – – –
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1382 S.Darmaraki et al.
1 3
Table 5 Future response
(anomalies with respect
to HIST) of MHW mean
properties for the 6 RCSMs
under RCP8.5, RCP4.5 and
RCP2.6, for the near (2021-
2050) and far future (2071–
2100).
Variables CNRM LMD CMCC AWI/GERICS BELGRAD ENEA Multi-model
Marine heatwave characteristics
RCP8.5 (2021–2050)
Frequency 0.5 0.4 0.5 0.1 0.4 –
0.4 ±0.2
Duration 32.7 47.8 39.3 39.1 37.4 –
39.2 ±16.7
Starting day Jul Jul Jul Aug Jul – Jul
Ending day Sep Sep Sep Sep Sep – Sep
Imean 0.2 0.4 0.3 0.2 0.2 –
0.3 ±0.1
Imax 1 1.4 1.3 1.2 0.9 –
1.2 ±0.5
Max surface 23.5 39.2 29.4 36.7 35.6 –
32.9 ±14.6
Severity (Icum) 3.9 11.6 7.1 6.7 4.7 –
6.8 ±3.8
RCP8.5 (2071–2100)
Frequency 0.3 0.4 0.5 0 0.4 –
0.3 ±0.2
Duration 83.8 105.9 97.7 99.6 83.7 –
94.1 ±9.9
Starting Day Jun Jun Jun Jun Jun – Jun
Ending Day Oct Oct Oct Oct Oct – Oct
Imean 0.9 1.9 1.6 1.3 1.1 –
1.4 ±0.4
Imax 3.1 4.2 4.4 3.8 3.1 –
3.7 ±0.6
Max Surface 51.7 50.7 46.1 58.9 53.5 –
52.2 ±4.6
Severity (Icum) 29.2 73.1 63.1 46 34.1 –
49.1 ±18.7
RCP4.5 (2021–2050)
Frequency 0.6 0.4 0.5 0.1 – 0.2
0.4 ±0.2
Duration 17.7 43.7 38.6 33.9 - 20.6
30.9 ±16.2
Starting day Aug Jul Jul Jul – Jul Jul
Ending day Sep Sep Sep Sep - Sep Sep
Imean 0.1 0.3 0.2 0.2 – 0.2
0.2 ±0.1
Imax 0.5 1.2 1.2 1.1 – 0.9
1.0 ±0.5
Max surface 13.5 34.1 27.9 33.8 - 27.9
27.4 ±13.5
Severity (Icum) 1.9 8 6.7 4.9 – 3.7
5.0 ±3.0
RCP4.5 (2071–2100)
Frequency 0.4 0.4 0.5 0 – 0.2
0.3 ±0.2
Duration 56.4 69,8 67,9 55,4 – 45,4
59 ±10.0
Starting day Jul Jun Jul Jul – Jul Jul
Ending day Sep Oct Oct Sep – Sep Sep
Imean 0.4 0.8 0.7 0.4 - 0.4
0.5 ±0.2
Imax 1.6 2.3 2.3 1.8 - 1.5
1.9 ±0.4
Max surface 44.4 48.5 42.6 51.2 – 39
45.1 ±4.8
Severity (Icum) 10.6 25.3 23.9 11.8 - 8.5
16.0 ±7.7
RCP2.6 (2021–2050)
Frequency 0.7 – – – – – –
Duration 17.2 – – – – – –
Starting day Jul – – – – – –
Ending day Sep – – – – – –
Imean 0.1 – – – – – –
Imax 0.5 – – – – – –
Max surface 15 – – – – – –
Severity (Icum) 2.4 – – – – – –
RCP2.6 2071–2100
Frequency 0.5 – – – – – –
Duration 30.5 – – – – – –
Starting day Jul – – – – – –
Ending day Sep – – – – – –
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1383Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
slightly greater increase of 0.5–0.7 events/year. In fact,
individual simulations of Fig.9 suggest a shift from
a period where years without MHWs were common
(1976–2030) to a period with at least one long-lasting
MHW every year. More specifically, towards 2071–2100,
events can start as early as June and finish as late as Octo-
ber under RCP8.5, whereas for RCP4.5 and RCP2.6, the
MHW temporal extent appears between July–September
(Fig.10). It is clear that the higher the radiative forc-
ing, the broader the window of occurrence. For example,
MHWs during 2071–2100 may last on average 3 months
longer in RCP8.5 than HIST MHWs (
∼21.8
days, not
shown) but almost 2 months longer in RCP4.5 (see
Table5). This is a MMM increase in the duration, almost
double the corresponding increase during 2021–2050
under RCP4.5 (
∼30.9
days) and more than double that
under RCP8.5 (
∼39.2
days). Even under the optimistic
RCP2.6 scenario, MHWs by 2050 may be 17.2 days longer
than today and may become 1 month longer at maximum
by 2100.
Shown here are the average annual event count (frequency), average MHW duration (days), starting day
(calendar month), ending day (calendar month), Imean (°C), Imax (°C), severity (
∗107
°
C days km2
) and
maximum surface coverage (%). The multi-model column indicates the ensemble mean values for each
variable and their standard deviation. Only the CNRM simulation is available for the RCP2.6 scenario, 5
simulations for RCP8.5 and 5 simulations for RCP4.5
Table 5 (continued) Variables CNRM LMD CMCC AWI/GERICS BELGRAD ENEA Multi-model
Imean 0.2 – – – – – –
Imax 0.9 – – – – – –
Max surface 22.7 – – – – – –
Severity (Icum) 3.6 – – – – – –
Fig. 7 Multi-model average anomaly of yearly
SST
(°C) with respect to the corresponding ensemble mean HIST of each scenario, for the near
and far future. The RCP2.6 scenario has only one simulation (CNRM)
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1384 S.Darmaraki et al.
1 3
Long-term projections show analogous changes in the
Imean of future MHWs. They are examined through IDF
plots that display the total number of MHWs identified by
the ensemble during HIST (1976–2005) run, near and far
future (Fig.11). To avoid imbalances in the present-future
comparisons arising from the different sets of models for
Fig. 8 Multi-model average anomaly of extreme
SST99Q
(°C) with respect to corresponding ensemble mean HIST (1976-2005) of each scenario,
for the near and far future. The RCP2.6 scenario has only one simulation (CNRM)
Fig. 9 Annual number of MHWs (Annual Frequency) for RCP8.5
(red) RCP4.5 (blue) RCP2.6 (green) HIST (grey) and observations
(dashed black). Bold colors indicate the multi-model mean and
shaded zones represent individual MHW events identified by the
models. Years without MHWs are also included, with shaded areas
reaching 0. RCP2.6 has only 1 simulation (CNRM)
Fig. 10 Annual earliest starting (solid lines) and latest ending (dashed
lines) day of MHW events for RCP8.5 (red) RCP4.5 (blue) RCP2.6
(green) HIST (grey) and observations (black). Bold colors indicate
multi-model average values while lighter dots represent individual
event dates
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1385Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
RCP4.5 and RCP8.5 (see Table5), all the simulated future
events are pooled for every period and juxtaposed against
the corresponding sets of HIST events. Therefore, we show
3 HIST IDF plots, one for each scenario. As previously dem-
onstrated the stronger the emission scenario, the longer the
duration and the higher the Imean of the events. The MMM
Imean response appears small during 2021–2050 (+0.1°C to
+0.3 °C depending on the scenario) but increases towards
the end of the period with higher radiative forcing. For
instance, MHWs show durations of up to 170 days (Fig.11)
in the far future of RCP8.5 and Imean of 1.8 °C on aver-
age (not shown). For the CNRM model though and under
RCP2.6, the corresponding response towards 2071–2100 has
doubled compared to the mid-21st century, while it becomes
4.5 times higher under RCP8.5 (see Table5). Longer-lasting
MHWs at the end of the period for RCP4.5 and RCP8.5
Fig. 11 IDF (Imean, Duration, Frequency) plots display the total MHW
number of every dataset, for every scenario, over 2021-2050 and
2071-2100. RCP8.5 and RCP4.5 include events from 5 simulations,
while RCP2.6 from only 1 (CNRM) simulation. HIST run contains
MHWs from the corresponding set of models each time. The number
of MHWs is calculated over each 30 year period. For contrast pur-
poses, the red box depicts the observed characteristics of MHW 2003
in the Mediterranean
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1386 S.Darmaraki et al.
1 3
explain the lower frequency of occurrences compared to
RCP2.6. A similar behaviour to Imean displays the MMM
average response of Imax, with the highest anomalies indi-
cated towards 2071–2100 (up to 3.7 °C), whereas during the
mid-21st century they range between 0.5 °C (RCP2.6) and
1.2 °C(RCP8.5).
It should be also noted that for RCP4.5 and RCP8.5,
events with characteristics similar to the observed excep-
tional MHW 2003 (Fig.11, red box) seem to become the
new standard over 2021–2050 and even constitute weak
occurrences for the distant future of RCP8.5. In the more
optimistic RCP2.6, MHWs appear more frequent during
2021–2050 but their number is slightly decreased towards
2071–2100. Their characteristics, however, sustain a lower
increase throughout the period compared to RCP4.5 and
RCP8.5. For example, the response in duration and Imean is
found close to that projected for CNRM during 2021–2050
and under RCP4.5 and RCP8.5 (see Table5). Therefore, the
possibility for an event like the MHW 2003 to occur regu-
larly still features in a scenario close to the Paris Agreement
(RCP2.6) .
Yet, the range of the uncertainty in future projections
evolves not only in time but also throughout the different
models. The severity (Icum) distribution of future MHWs
was determined in that sense using Whisker diagrams.
In these box plots, a specific Icum index is appointed at
each simulated event of every dataset for each period and
scenario (see Fig.12, left). By definition, Icum translates
the total spatiotemporal MHW impact into numbers. It
features an exponential increase from HIST towards the
end of the century from
∼1×10
7
C days km2
to about
∼50 ×107C days km2
for RCP8.5 (2071–2100-not
shown). Moreover, the higher the emission forcing, the
higher the rate the ensemble mean Icum response esca-
lates from its mid- to end-of-century values; for example,
Icum varies from 5 to
33 ×107C days km2
in RCP4.5 and
from 6.8 to
49.1 ×10
7
C days km2
in RCP8.5 (see Table5).
This becomes more evident when comparing the equiva-
lent CNRM severity response under RCP2.6 (2.4–3.6
×
107C days km2
) with the significantly higher response
under RCP4.5 and RCP8.5. Although all configurations
indicate an abrupt escalation through time, there appears
to be a family of models (CMCC and LMD) that share a
stronger climate change response. Those models exhibit
higher changes in Icum, along with higher Imean, Imax,
and duration values than the remaining models (see also
Table5 and Sect.4).
The identified families of MHWs are also associated
with a maximum spatial coverage illustrated through box
plots in Fig.12. It is estimated that events may affect a
maximum of 40% of the Mediterranean Sea, on average,
during HIST but may impact almost 100% of the basin
by 2071–2100 under RCP8.5. Notwithstanding the large
variability found for the mid-21st century, by 2100 the
simulated maximum MHW extent seems to be an unani-
mous projection from every model and under RCP8.5.
Conversely, MHWs under RCP2.6 increase their maximum
coverage throughout the period, but towards 2071–2100
events may impact, on average, a maximum of 70% of the
Mediterranean Sea.
Fig. 12 Whisker diagram of (left) Severity (Icum) and (right) maxi-
mum surface coverage of every observed and simulated MHW during
HIST, 2021-2050 and 2071-2100. Box plots illustrate minimum, 25th
percentile, median, 75th percentile and maximum values of each vari-
able for a given model, scenario and period
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1387Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
4 Discussion
4.1 MHW detection method
Several sensitivity tests performed on the MHW detection
algorithm using only the CNRM model indicate low levels
of uncertainty associated with small perturbations on the
initial definition. For example, definitions with a different
number of gap days, different minimum duration or mini-
mum MHW spatial extent allowed (e.g. 10%) were tested but
did not seem to change significantly the response of future
MHW characteristics with respect to HIST run (see Sup-
plementary Table1S). The use of different quantile thresh-
olds also showed that climate change response of duration,
Imean and Imax with respect to HIST does not differ signifi-
cantly if a lower/higher threshold than the
SST99Q
is cho-
sen. However, the severity and maximum spatial coverage
appear more sensitive to such changes (see Supplementary
Table1S).
However, certain limitations exist: assuming no spatial
connectivity, the detection algorithm provides identification
of large–scale (>20%) and long–lasting events but does not
consider MHW effects during colder months or spatially
smaller events. While it describes surface MHWs in the
summer, it can be also applied to deeper layers and/or winter
season when availability of data allows it.
4.2 Model‑observation discrepancies
The discrepancies on mean and extreme Mediterranean
Sea temperatures with respect to observations on the mod-
els were also evaluated using a shorter but common refer-
ence period of 1982-2005 for both datasets. Values of
SST
,
SST99Q
, their trends and pattern correlations did not change
considerably. However, the multi-model mean bias was
slightly reduced by 28% for
SST
and by 31% for
SST99Q
(see
Supplementary material Table2). Moreover, MHW identi-
fication appeared consistent despite the different SST layer
depth of the observations (
∼
mm) and the models (
∼
m).
4.3 Model uncertainty
By default, the estimate of the uncertainty is given by the
variation of the results across the ensemble members in an
opportunistic way (Knutti etal. 2010). Although the mod-
els we use have a high-resolution representation of the air-
sea interactions, uncertainties are introduced due to their
individual biases but also due to the small number of the
currently available Med-CORDEX simulations (6 RCSMs).
To this purpose, more runs will be added in the future as
part of the Med-CORDEX initiative. Despite this limitation,
the RCSM ensemble seems to explore well the spread of
SST anomalies predicted by earlier studies based on GCMs.
For example, for RCP4.5 we estimate annual area-average
SST
anomalies from 2006–2100 with respect to HIST from
approximately 0.7 to 2.6 °C, depending on the model (Fig.6,
left). This covers a large part of the corresponding anomalies
found by Mariotti etal. (2015) for 2006–2100 with respect to
1980–2005 mean, which were between 0.5 °C to 3.5 °C for
the CMIP5 ensemble of GCMs under the RCP4.5 scenario.
Although our ensemble appears to underestimate the upper
limit of this CMIP5 range, this could also reflect a better
representation of the Mediterranean Sea dynamics by the
regional models. Indeed, at higher resolutions the represen-
tation of air-sea interaction also improves (e.g. Akhtar etal.
2018; Roberts etal. 2016; Hewitt etal. 2017). At the same
time, our results indicate an intensification of MHWs in the
Mediterranean Sea with time, in agreement with the results
obtained by Oliver etal. (2018a) and Frölicher etal. (2018),
which used different MHW definitions.
Albeit some models have demonstrated lower/higher
biases than others, we have chosen not to discard any of the
configurations since their weak performance in some indi-
ces is not related to any specific behaviour of MHW indices
in scenario. This choice also favours the holistic presenta-
tion of the uncertainty spectrum, without a considerable
impact on the climate change response. More specifically,
closer examination of the
SST
and
SST99Q
bias effect on the
anomalies of the average MHW characteristics in RCP8.5
and RCP4.5 with respect to HIST suggested no particular
tendency or outliers affecting the range of the outcome, for
any of the periods and scenarios (Supplementary material
Fig.1S, Fig.2S). It is however notable that LMD and CMCC
have a tendency to show stronger responses in MHW or
SST values. This could be due to the driving GCMs (IPSL-
CMA5-MR and CMCC-CM), which demonstrate a higher
mean surface temperature change over Europe by 2080
compared to CNRM-CM5 and MPI-ESM-LR, according to
McSweeney etal. (2015). In that study, the performance
of all the GCMs driving the Med-CORDEX RCSMs was
characterised as “satisfactory” for downscaling, except that
of LMD, which was found with biases.
4.4 MHW evolution andchanges inSST
Present-day extreme warming at the order of
SST99Q
might
constitute a rare occurrence for the Mediterranean Sea cli-
mate; however, in the future it becomes the new normal. In
2071–2100 in particular, the warming signal is found so high
that almost every day from June to October can experience
such extreme temperatures. This means that future warm-
ing in the Mediterranean Sea is practically able to saturate
what is considered today as a severe MHW. The difference
between the scenarios lies in the fact that under RCP4.5 and
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1388 S.Darmaraki et al.
1 3
RCP8.5 anomalous temperatures appear more persistent and
widespread and therefore fewer but longer and more intense
events occur. By contrast, under RCP2.6, events appear less
persistent, and therefore more “breaks” between MHWs may
occur (frequency of events is increased), since a significant
part of the basin is more likely to fall below the
SST99Q
threshold (Fig.9).
Most of the future changes in MHW characteristics were
seen to increase following the GHG forcing, yet this raises
the question of whether this behavior could be explained
by changes in the mean (shift of distribution) or the day-to-
day SST variability (distribution flattening/narrowing). As
a first indicator, we calculated (for the CNRM model only)
the
SST
difference between RCP8.5 (2071-2100) and HIST
and added it to the current
SST99Q
threshold map (see Sup-
plementary Table1). The resulting climate change response
(future-present) of MHW characteristics was much lower
than the one found when using the initial
SST99Q
threshold
alone. This signifies that the mean SST change alone can
explain a large part, but not all, of the future changes in
MHWs. We estimate that 10–20% of the MHW characteris-
tics are due to changes in day-to-day variability.
To further test our hypothesis, we calculated
the multi-model mean ratio
R=𝛥(SST99QScenario
–
SST99
Q
Hist
)∕𝛥(SST
Scenario
–
SSTHist
) for every scenario
and period (see Fig.13). In regions where
R>1
SST daily
variability contributes to the extreme temperature increase
and only where
R>2
, it dominates the mean SST change
contribution (distribution flattening). For
R<1
, a narrow-
ing of SST daily distribution lowers the mean SST signal,
which makes the dominant contribution when close to
R=1. Overall, model results indicate a higher contribution
from SST daily variability change in the mid-21st century
compared to 2071-2100, when
SST
change becomes more
important (Table4; Fig.13). During 2021-2050 and for
every scenario, basin-mean R
∼1.2
with the Alboran Sea,
some coastal parts of the Aegean Sea, Adriatic Sea and SE
Levantine basin exhibiting a narrowing (
R<1
) or a shift
Fig. 13 Multi-model mean ratio R of
𝛥
SST99Q
(°C) over
𝛥
SST
(°C)
for every period and scenario. Regions where
R>1∕R<1
indicate
regions where flattening/narrowing of SST distribution is detected in
addition to the mean distribution shift. Where R
∼1
the
SST
increase
can be considered as the main factor for MHW changes
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1389Future evolution ofMarine Heatwaves intheMediterranean Sea
1 3
(R=1) in the SST distribution. Towards 2071-2100, how-
ever, and under RCP4.5 and RCP8.5, basin-average R is
between
1<R<1.2
and more areas demonstrate a range
closer to R = 1.
It is worth noting that narrowing of the SST distribution
is a rare situation that appears in small areas only under
RCP2.6 or around the Alboran Sea under RCP85 for the
mid-21st century. Flattening, on the other hand, appears
more common and could possibly reflect the increase in
day-to-day variability of 2 m-air temperature estimated
by Giorgi (2006) for the Mediterranean area in the future.
Finally, a slightly stronger influence of SST daily variability
is seen in the NW Mediterranean area for every scenario
and period (Fig.13). The possible explanations for such a
spatial pattern could be a future mixed layer depth shoaling,
as projected by (Adloff etal. 2015). This would mean that
heat fluxes would be able to change the heat content of a
shallower MLD faster, creating that way an increase in daily
SST variability.
5 Conclusions
The main objective of this study is to investigate the future
evolution (1976–2100) of SST and marine heatwaves in the
Mediterranean Sea, using the best dedicated multi-model
ensemble available. Here we examine six Regional Climate
System Models from the Med-CORDEX initiative, driven
by 4 CMIP5 GCMs under the RCP2.6, RCP4.5 and RCP8.5
scenarios. A quantitative MHW definition and detection
method based on SST and on Hobday etal. (2016) approach
is developed, targeting large-scale and long-lasting events,
mostly in the warmer months. The algorithm uses a climato-
logical 99th percentile threshold based on historical simula-
tions (1976–2005) and takes into account a spatially-varying
threshold. It delivers MHW metrics such as frequency, dura-
tion, mean and maximum intensity along with severity and
maximum spatial coverage.
Spatiotemporal indices under a 1976–2005 (HIST) run
reveal that the Med-CORDEX ensemble simulates the
present MHW characteristics well, although it appears to
underestimate the warming trends of
SST
and
SST99Q
of that
period with respect to observations from 1982–2012. The
latter dataset yields an annual frequency of 0.8 events/year,
with MHWs lasting a maximum of 1.5 months between July
and September, while covering a maximum of 90% of the
Mediterranean Sea surface. The longest and most severe
event of that period corresponded to the MHW of 2003,
which also demonstrates the highest mean intensity and
maximum event coverage.
Analysis of future evolution shows that differences in
the GHG forcing are reflected mostly towards 2071–2100,
whereas uncertainty for the mid-21st century is dominated
by the model uncertainty. Ensemble means by the end of the
century demonstrate the highest
SST
(3.1 °C) and
SST99Q
(3.6 °C) increase under RCP8.5 and lowest under RCP2.6
(mono-model). The corresponding warming for 2021-2050,
however, is less pronounced under RCP4.5 (
∼0.8
°C/1°C )
and RCP8.5 (
∼1
°C/1.2 °C). In contrast, basinwide mean
and extreme SST for RCP2.6 (
∼1
°C) does not differ sig-
nificantly from mid- to end of 21st century.
By 2100, models project at least one long-lasting MHW
occurring every year under RCP8.5 up to 3 months longer,
and about 4 times more intense and 42 times more severe
than today’s events. Their occurrence is expected between
June and October, affecting at peak, the entire Mediterranean
basin. In fact, with respect to the HIST run, MMM MHW
frequency increases by a factor of
∼1.6
for RCP8.5 and
RCP4.5 by 2021–2050 and slightly less than that towards
2071–2100 for both scenario. The equivalent CNRM com-
parison between the scenarios reveals a slightly greater fre-
quency increase during 2071–2100 under RCP2.6 (by fac-
tor of 1.7) than under RCP8.5 and RCP4.5. Multi-model
mean duration, on the other hand, is multiplied by a factor
of 3.7 for RCP4.5 and 5.3 for RCP8.5 during 2071–2100.
MHWs under RCP8.5 may also have an Imean 3.9 times as
high as today’s event, while the equivalent increase under
RCP4.5 and RCP2.6 is significantly lower (see Table5).
For 2021–2050, however, there is a higher convergence
in the factor of increase in frequency (
∼
1.5x) duration (
∼
2.4x–2.7x), Imean (
∼
1.5x) and severity (
∼
5x–7x) between
MMM of RCP4.5 and RCP8.5.
In general, MHWs become stronger and more intense in
response to increasing greenhouse gas forcing and especially
towards the end of the century. RCP2.6, however, shows
a slight increase in MHW signatures with time but lower
than RCP4.5 and RCP8.5. Note here that certain models
demonstrate stronger climate change responses than others,
likely due to the choice of the driving GCM rather than to
the individual RCSM biases. Much of the MHW evolution
is found to occur mainly due to an increase in the mean SST,
but an increase in daily SST variability also plays a notice-
able role. Complementary sensitivity tests also prove that a
mean shift in SST distributions alone cannot be responsible
for the futures changes in MHWs.
Overall, the MHW and SST changes predicted for the
21st century will clearly impact the vulnerable Mediterra-
nean Sea ecosystems. What was encountered as widespread
consequences from the MHW 2003 could become the “new
normal”, since our analysis signified that future MHWs
become longer and more intense than this event in the near
future. Especially under RCP8.5 and 2071–2100, MHWs
can become three times longer than the MHW 2003, with
mean intensities three times higher. While RCP8.5 is the
business-as-usual scenario, RCP2.6 is the closest to Paris
agreement limits, which could offer a relative stability in
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1390 S.Darmaraki et al.
1 3
both the SST increase and MHW evolution in the basin after
the mid-21st century. MHWs exert a strong influence not
only on marine ecosystems but also on marine-dependent
economies and hence society. Therefore, more research is
needed towards an improved mechanistic understanding of
these events and their underlying physical drivers. In a con-
stantly warming world, this information, along with projec-
tions of large-scale future MHW evolution, might help iden-
tify regions with a physical predisposition to these extreme
occurrences. In combination with biogeochemical studies,
more light could be shed on the full extent of the biological
system risks related to MHWs.
Acknowledgements “We would like to thank the anonymous review-
ers for their constructive suggestions. This research was funded by the
MARmaED project, which has received funding from the European
Union’s Horizon 2020 research and innovation programme under the
Marie Sklodowska-Curie grant agreement No 675997. The result of
this publication reflects only the author’s view and the Commission is
not responsible for any use that may be made of the information it con-
tains. This work is also a part of the Med-CORDEX initiative (www.
medco rdex.eu) and HyMeX programme (www.hymex .org). Dmitry
V.Sein was supported by the EC Horizon 2020 project PRIMAVERA
under the grant agreement 641727 and the state assignment of FASO
Russia (theme No.0149-2018-0014)”. V.Djordjevic was partially sup-
ported by the Serbian Ministry of Science, Education and Technologi-
cal Development, under grant No.III43007.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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Aliations
SoaDarmaraki1· SamuelSomot1· FlorenceSevault1· PierreNabat1· WilliamDavidCabosNarvaez2·
LeoneCavicchia3· VladimirDjurdjevic4· LaurentLi5· GianmariaSannino6· DmitryV.Sein7,8
Samuel Somot
samuel.somot@meteo.fr
Florence Sevault
florence.sevault@meteo.fr
Pierre Nabat
pierre.nabat@meteo.fr
William David Cabos Narvaez
william.cabos@uah.es
Leone Cavicchia
leone.cavicchia@unimelb.edu.au
Vladimir Djurdjevic
vdj@ff.bg.ac.rs
Laurent Li
laurent.li@lmd.jussieu.fr
Gianmaria Sannino
gianmaria.sannino@enea.it
Dmitry V. Sein
dmitry.sein@awi.de
1 CNRM, Centre National de Recherches Météorologiques,
UMR 3589, Université de Toulouse, Météo-France, CNRS,
42 Avenue Coriolis, 31057Toulouse, France
2 Department ofPhysics andMathematics, UAH, University
ofAlcalá, Madrid, Spain
3 CMCC, Centro Euro Mediterraneo sui Cambiamenti
Climatici, Lecce, Italy
4 Faculty ofPhysics, University ofBelgrade, Studentski trg 12,
Belgrade, Serbia
5 LMD Laboratoire de Meteorology Dynamique, Centre
National de la Recherche Scientifique (CNRS), Université
Pierre et Marie Curie (Paris 6), Paris, France
6 ENEA, via Anguillarese 301, 00123Rome, Italy
7 Alfred-Wegener-Institute forPolar andMarine Research
(AWI), 27568Bremerhaven, Germany
8 Shirshov Institute ofOceanology, Russian Academy
ofScience, 36 Nahimovskiy Prospect, Moscow117997,
Russia
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