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Abstract

During winter 2012-2013, open-ocean deep convection which is a major driver for the thermohaline circulation and ventilation of the ocean, occurred in the Gulf of Lions (Northwestern Mediterranean Sea) and has been thoroughly documented thanks in particular to the deployment of several gliders, Argo profiling floats, several dedicated ship cruises, and a mooring array during a period of about a year. Thanks to these intense observational efforts, we show that deep convection reached the bottom in winter early in February 2013 in a area of maximum 28±3 109m9. We present new quantitative results with estimates of heat and salt content at the sub-basin scale at different time scales (on the seasonal scale to a ten days basis) through optimal interpolation techniques, and robust estimates of the deep water formation rate of 2.0 ± 0.2Sν. We provide an overview of the spatio-temporal coverage that has been reached throughout the seasons this year and we highlight some results based on data analysis and numerical modeling that are presented in this special issue. They concern key circulation features for the deep convection and the subsequent bloom such as Submesoscale Coherent Vortices (SCVs), the plumes and symmetric instability at the edge of the deep convection area.
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/,
Multi-scale observations of deep convection in the1
northwestern Mediterranean Sea during winter2
2012-2013 using multiple platforms3
Pierre Testor1, Anthony Bosse2, Lo¨ıc Houpert3, F´elix Margirier1, Laurent
Mortier4, Herv´e Legoff1, Denis Dausse1, Matthieu Labaste1, Johannes
Karstensen5, Daniel Hayes6, Antonio Olita7, Alberto Ribotti7, Katrin
Schroeder8, Jacopo Chiggiato8, Reiner Onken9, Emma Heslop10, Baptiste
Mourre10, Fabrizio D’Ortenzio11, Nicolas Mayot11, H´eloise Lavigne11, Orens
de Fommervault11,12, Laurent Coppola11, Louis Prieur11, Vincent
Taillandier11, Xavier Durrieu de Madron13, Francois Bourrin13, Gael Many13,
Pierre Damien14, Claude Estournel14, Patrick Marsaleix14, Isabelle
Taupier-Letage15, Patrick Raimbault15, Robin Waldman16, Marie-Noelle
Bouin16,17, Herv´e Giordani16, Guy Caniaux16, Samuel Somot16, V´eronique
Ducrocq16, Pascal Conan18.
Corresponding author: P. Testor, Laboratoire d’Oc´eanographie et de Climatologie:
Exp´erimentation et Approches Num´eriques (LOCEAN), Universit´e Pierre et Marie Curie, 4 place
Jussieu, 75252 Paris cedex 05, France (testor@locean-ipsl.upmc.fr)
1CNRS-Sorbonne Universit´es (UPMC
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Abstract. During winter 2012-2013, open-ocean deep convection which4
is a major driver for the thermohaline circulation and ventilation of the ocean,5
occurred in the Gulf of Lions (Northwestern Mediterranean Sea) and has been6
thoroughly documented thanks in particular to the deployment of several glid-7
ers, Argo profiling floats, several dedicated ship cruises, and a mooring ar-8
ray during a period of about a year.9
Thanks to these intense observational efforts, we show that deep convec-10
tion reached the bottom in winter early in February 2013 in a area of max-11
imum 28±3 109m2. We present new quantitative results with estimates of12
heat and salt content at the sub-basin scale at different time scales (on the13
seasonal scale to a ten days basis) through optimal interpolation techniques,14
and robust estimates of the deep water formation rate of 2.0±0.2Sv. We pro-15
vide an overview of the spatio-temporal coverage that has been reached through-16
out the seasons this year and we highlight some results based on data anal-17
ysis and numerical modeling that are presented in this special issue. They18
concern key circulation features for the deep convection and the subsequent19
bloom such as Submesoscale Coherent Vortices (SCVs), the plumes and sym-20
metric instability at the edge of the deep convection area.21
Univ. Pierre et Marie Curie, Paris
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1. Introduction
Open-ocean deep convection is a key process that materially exchanges heat and salt, as22
well as momentum, between the surface layers and the deep ocean in localized regions of23
the global ocean and is a major contributor to the thermohaline circulation [Marshall and24
Schott, 1999]. Open-ocean deep convection happens in winter and results in oceanic deep25
water formation. The Mediterranean Sea, the Weddell Sea, the Labrador Sea and the26
Greenland Sea are deep convection areas that are relatively well documented but many27
details about what is occurring during the different phases of convection and what drives28
the vernal bloom that can be observed during the restratification phase are still unclear29
because many scales appear to interplay and the vertical dimension is difficult to observe.30
31
Deep convection in the Gulf of Lion was first described by the MEDOC-Group [1970]32
in three phases:33
the preconditioning of the area by a cyclonic gyre circulation in the whole northwest-34
ern Mediterranean Sea producing a doming of isopycnals toward the surface centered at35
about (42N, 5E), exposing a large body of weakly stratified waters to local cooling and36
evaporation, due to dry and cold Mistral and Tramontane winds blowing over the Gulf of37
Lion;38
the vertical mixing due to buoyancy loss generated by intense surface cooling and39
evaporation reaching about 1000 W/m2for short periods and allowing overturning of the40
water column;41
06)-CNRS-IRD-MNHN, UMR 7159,
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the spreading/restratification phase with newly-formed deep waters propagating42
away from the formation site while stratified waters around invade the deep convection43
area.44
This framework is still commonly used in all studies concerning deep convection pro-45
cesses, in all locations of deep water formation, likely because it clearly depicts the major46
physical drivers. Furthermore, it is well-known winter mixing, and in particular deep con-47
vection, participates to transfers of biogeochemical properties like oxygen, all inorganic48
and organic, dissolved and particulate, matters and is a major contributor to the func-49
tioning of the upper-ocean ecosystem by supplying in particular nutrients from the deep50
ocean to the euphotic layer. Convection is one of the major drivers of the phytoplankton51
phenology [Lavigne et al., 2013] as well as of the deep pelagic and benthic ecosystems52
[Pusceddu et al., 2010; Stabholz et al., 2013; Tamburini et al., 2013]. Satellite ocean color53
images show high phytoplankton abundances at the surface, starting and increasing dur-54
ing the violent mixing periods around a ’blue hole’ where deep mixing occurs and then55
at the sub-basin scale during restratification events, generally in April. This is the onset56
of the most intense bloom in the Mediterranean Sea. As such, it appears to be a major57
phenomenon for the evolution of the Mediterranean Sea that contributes to the evolution58
of this physical-biological system, which is considered as a hot spot for biodiversity and59
climate change [Giorgi, 2006; Coll et al., 2010]. The northwestern Mediterranean Sea is60
well-known to be subject to rapid and drastic responses to climate change [Cacho et al.,61
2002; Somot et al., 2006], and it is today of the ultimate importance to better understand62
Laboratoire d’Oc´eanographie et de
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the response of the Mediterranean water cycle [Adloff et al., 2015] and marine ecosystems63
to external constraints [Herrmann et al., 2013, 2014; Auger et al., 2014].64
From a biogeochemical perspective, the Mediterranean has long been known as an olig-65
otrophic area with relatively low nutrient concentrations, characterized by a general West66
to East gradient of increasing oligotrophy. The elemental stoichiometry in all compart-67
ments (i.e. particulate and dissolved inorganic and organic) reveals an excess of carbon,68
a deficiency in phosphorus relative to nitrogen and a sporadic silicate deficiency [ethoux69
et al., 2002] as compared to other oceanic provinces. It is well known that the elemen-70
tal composition of biotic and abiotic compartments can widely vary with environmental71
conditions (light, temperature, trophic status), or growth rate of living organisms [Conan72
et al., 2007], but the Mediterranean anomalies, though frequently explored, still represent73
open issues for the understanding of the functioning of the marine ecosystem in gen-74
eral. Macro-nutrient concentrations there depend on the exchanges through the Straits75
of Gibraltar and Bosphorus, atmospheric depositions, and river discharges, whereas their76
distributions are controlled by both physical (i.e. dense water formation) and biological77
activities (consumption/mineralization). Continental inputs are characterized by a strong78
variability in terms of quantity and quality, dominated by extreme events (i.e. large river79
floods and dust deposits), due to the climatic specificity of this region. These inputs, lat-80
eral fluxes and the exchanges between the surface and deep layers across the nutriclines,81
are dominant processes for the development of phytoplankton and higher trophic levels.82
From a physical point of view, the violent atmospheric forcing events that trigger deep83
convection in the center of the preconditioned area [Somot et al., 2016; Herrmann and84
Climatologie (LOCEAN), Institut Pierre
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Somot, 2008] produce a Mixed Patch that is unstable. Many studies have shown the85
important role of baroclinic instability for deep convection [Killworth, 1976; Gascard,86
1978; Killworth, 1979; Legg and Marshall, 1993; Visbeck et al., 1996; Jones and Marshall,87
1997; Legg et al., 1998; Testor and Gascard, 2006] because it is a mechanism that could88
occur throughout the deep convection process, from the preconditioning to the spreading89
phase, that can contribute to vertical mixing by inducing vertical velocities order of 1-90
100m/day over periods of days, as well as to lateral fluxes by eddy shedding. At a later91
stage, once the atmospheric forcing had considerably lessened, the Mixed Patch becomes92
highly unstable and there is a general breakup on a time scale of a few weeks [Madec93
et al., 1991]. Many observations of Submesoscale Coherent Vortices (SCVs as introduced94
by McWilliams [1985]) of a scale O(5km) composed of newly-formed waters [Lilly et al.,95
1999; Gascard et al., 2002; Testor and Gascard, 2003, 2006] document the eddy field in96
such areas and this scale likely modulates the variability in the vicinity of the Mixed97
Patch presenting a horizontal scale of O(100km). All these SCVs appear to have similar98
characteristics (small radius, large aspect ratio and long lifetime of the order of a year).99
They are involved in the large scale circulation of the newly formed deep waters (spreading100
phase) and contribute to the deep ventilation. It appears these vortices are numerous,101
can travel 100s of km during their lifetime and can export waters composing their cores102
over long distances and periods of time.103
In the Mixed Patch, intense vertical velocities O(10 cm s1) were observed in cells with104
horizontal and vertical scales of O(1 km) [Schott and Leaman, 1991; Schott et al., 1996]105
at a smaller scale than the observed eddies. Supported by numerical modeling and tank106
Simon Laplace (IPSL), Observatoire Ecce
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experiments [Marshall and Schott , 1999] could explain these so-called plumes resulting107
from hydrostatic instability and earth rotation. The Mixed Patch would result from an108
integral effect of these non-penetrative plumes [Send and Marshall , 1995] balanced by109
lateral buoyancy fluxes. However, these experiments considered a homogeneous ocean110
forced by a heterogeneous atmosphere (disc-shaped atmospheric forcing) and did not111
account for preconditioning effects at large, meso- or even submeso- scales. On the other112
hand, Legg and McWilliams [2001] proposed that the homogenization of the newly formed113
deep waters was likely due to the turbulent geostrophic eddy field, and eddies presenting114
a doming of isopycnals toward the surface could definitely act as local preconditioners115
favoring locally deep convection.116
It is clear that physical and biogeochemical processes act in setting up the Spring bloom117
that is observed after deep convection events. Vertical and horizontal fluxes of particulate118
and dissolved inorganic and organic matters are constrained by physical processes and119
biogeochemical cycles. However, little is known of the scales at which these processes120
interact and most of the questions that are still unresolved concerning Mediterranean121
biogeochemical evolution deal with the temporal variability of the key processes that122
govern the functioning and budgets of the different physical, chemical, and biological123
compartments.124
Observational limits are the principal causes of this uncertainty. The preconditioning,125
violent mixing and restratification/spreading phases do overlap with a preconditioning126
phase starting at least the previous Summer and a spreading phase extending possibly127
over years, while presenting high-frequency variability. The Mixed Patch extends over128
Terra, 4 place Jussieu, F-75005 Paris,
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100 km with modulations at (sub)mesoscale O(5 km) and small scale O(1 km) while129
the bloom seems to extend over the whole northwestern basin with high variability at130
meso/submeso/small scale, often clearly coupled to the physical one. According to [Dur-131
rieu de Madron et al., 2011], bloom and deep convection events result from an ’history’132
of at least 6-8 months beforehand that needs to be characterized. This observational133
challenge motivated a multi-platform experiment aiming at a continuous description of134
the water column at the basin/meso/submeso scales over a year. Building on long-term135
observational efforts in that area, additional observations were carried out in 2012-2013136
to try to achieve this goal.137
In the present paper, we will describe and analyze the results obtained from this 2012-138
2013 DEWEX (DEnse Water EXperiment) experiment coordinating different projects in139
that area, providing a more complete and extended description of the different phases of140
deep convection. We will first describe the sampling strategy of the experiment and the141
area under study, based on all in situ potential temperature, salinity, potential density,142
and fluorescence of chl-a profiles as well as currents and depth-average currents estimates143
that were collected in this framework thanks to ships, gliders, moorings, profiling floats144
and surface drifters. We will provide an overview of the spatio-temporal coverage that145
was achieved during this experiment, describe the evolution of the northwestern Mediter-146
ranean Sea mainly from a physical point of view, and estimate newly-formed deep water147
formation rates and energy fluxes. We will finally discuss the importance of different phys-148
ical processes for the deep convection and subsequent bloom, that were observed during149
France
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our study period based on different studies developed in this framework, before a general150
conclusion.151
2. The multi-platform sampling strategy
Taking advantage of long-term observational efforts (Long-term Observation Period,152
LOP) carried out in the framework of MOOSE (Mediterranean Ocean Observing System153
for the Environment, http://www.moose-network.fr) in this region, additional observa-154
tions (Enhanced Observation Period, EOP and Special Observation Periods, SOPs) were155
carried out in the northwestern Mediterranean Sea to try to achieve the above-mentioned156
goal, thanks to several European and national projects (see Acknowledgments). Thanks to157
numerous research cruises, gliders, profiling floats, moorings and drifters, a very significant158
number of oceanic vertical profiles, could be collected to reach a better characterization159
of deep convection in this region, and the subsequent bloom.160
The approach was to combine the sampling capabilities of R/Vs with autonomous plat-161
forms to reach an adequate spatio-temporal coverage during a period starting in Summer162
to the next, and to be able to capture all the key processes involved in deep convection163
during a year. Our ”study period” was July 1st, 2012 to October 1st, 2013 and the data164
considered here includes gliders, ship CTD, profiling floats, drifters, and moorings. All165
data considered are displayed on Figure 1 together with the temporal sampling strategy166
for each platform.167
2Geophysical Institute, University of
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Figure 2 describes typical ocean color satellite images that were obtained, when the168
sky was clear, and illustrates the different phases of deep convection. Summer-Fall is a169
period of low phytoplankton abundance followed by a Winter period during which high170
phytoplankton abundance can be observed around a ’blue hole’ in the deep convection171
area and then, a Spring period dominated by a planktonic bloom covering the entire172
northwestern basin until it fades away in late Spring.173
To really understand and assess the deep convection and bloom processes, a vertical174
description of the variations that can be observed with satellites was required and an175
optimal combination of the various in-situ platform sampling capabilities has been sought.176
The observational efforts required:177
periods of intensive observation at certain key moments (SOPs), allowing access to178
a full annual cycle for the entire zone. It is indeed essential to monitor the evolution of179
the ocean in the study area over specific periods of the year, so changes related to dense180
water formation can be assessed for both water balances and elements involved in the181
functioning of the ecosystem and the sequestration of matter;182
a sampling strategy compatible with the large, meso- and submeso- scale phenomena183
and which can be used effectively to constrain modeling studies. ;184
a coordination with periods of intensive atmospheric observations of intense events;185
a consistency with observations carried out on the long-term in the area.186
Different models were used in this program in combination with the observational efforts187
presenting different configurations (and in particular horizontal resolution) depending188
Bergen and Bjerknes Center for Climate
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on the different processes under focus (large/small space/time scales) and the sampling189
strategy was designed to provide validation (and initialization) at the sub-basin scale as190
well as at submesoscale taking advantage of the different sampling capabilities of the191
platforms considered here.192
Ship cruises were planned before, during, and after deep convection and bloom events,193
while gliders, profiling floats, moorings (at few locations) and drifters could provide infor-194
mation in-between. Even if this information is more limited in terms of observed variables,195
most of the autonomous platforms deployed during the study period were equipped with196
physical (temperature, salinity, currents) and bio-optical (dissolved oxygen, chl-a fluores-197
cence, turbidity, CDOM, nitrates) sensors and this allows a quasi-continuous description198
of the physical forcing on key biogeochemical variables.199
Research cruises mainly intended to provide a CTD network covering the whole sub-200
basin at different periods of the year. The CTD casts were mainly carried out at relatively201
low horizontal resolution (about 20nm except on the continental slope where the distance202
between the CTD casts was lower in order to sample the boundary circulation) to cover203
the whole sub-basin in about 3 weeks.204
For gliders, the planned sections were designed with a low repeat rate but large spatial205
coverage before and after deep convection events, while repeat-sections at higher repeat206
rate (but smaller spatial coverage) were carried out during the ”deep convection” period.207
During this period, the plan was to make the gliders turn back along their planned repeat208
sections as soon as the gliders were more than about 20km away from the deep convection209
region.210
Research, Bergen, Norway
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Profiling floats were primarily deployed in the deep convection area just before, during,211
and just after the violent mixing events. The aim was to document the evolution of the212
Mixed Patch and to follow its break-up from a quasi-Lagrangian point of view, on even213
longer timescales.214
Drifting buoys were deployed north of the deep convection area and in the deep convec-215
tion area before, during and after the violent mixing events. The aim was to document216
the surface temperature and salinity, and the atmospheric parameters during the period217
of strong surface heat loss.218
One overarching objective with a massive deployment of autonomous platforms was219
to carry out about 40/300 profiles on average per day/week, distributed over the whole220
northwestern Mediterranean Sea, at any time during the whole deep convection/bloom221
period (including preconditioning and spreading/restratification phases) to adequately222
document the water column evolution.223
3. Data
3.1. Ship CTD data
Several, and often basin scale, cruises were carried out in the northwestern Mediter-224
ranean Sea during our study period (see table 1). Since 2010, each of the MOOSE-GE225
cruise, on board R/V Thetys II or R/V Le Suroˆıt, provides a yearly snapshot in summer226
of the open-ocean part of the basin with about 70–100 CTD stations distributed on a227
star-shape array centered on the deep convection zone with branches about perpendic-228
3Scottish Association for Marine Science,
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ular to the continental slope around. A major objective of the MOOSE observatory is229
to monitor the deep waters formation in the Gulf of Lions and to be able to detect and230
identify long-term environmental trends and anomalies of the marine environment and231
ecosystem in response to climate change. The remnants of the convective events happen-232
ing in February are observed at the basin scale and this allows to monitor the deep water233
formation rate as for instance demonstrated by Waldman et al. [2016].234
The DEWEX and DOWEX cruises, on board R/V Le Suroˆıt and R/V Tethys II respec-235
tively, followed the same spatial sampling strategy and intended to cover the seasonal cycle236
with a focus first on the Winter-Spring period when deep convection and bloom occurs237
and second, in September for the preconditioning. They provided very accurate profile238
measurements every 20nm or so, covering the whole basin. CTD casts have also been239
collected during the HyMeX SOP1 cruises (see [Ducrocq et al., 2014; Lebeaupin-Brossier240
et al., 2014]) from R/V Urania and R/V Le Provence, and during the HyMeX SOP2241
cruises from R/V Tethys II and R/V Le Provence. To span the preconditioning period,242
Marisonde and Surface Velocity Program (SVP) drifters were launched from a dedicated243
cruise early September 2012, on a transect off Toulon ( 5E). To deploy Argo floats in244
the Mixed Patch, and re-position the Marisonde buoys for the convection period, support245
cruises were set-up late January and late February 2013. In order to catch an intense246
Mistral wind event and its impact on the convection, R/V Le Provence was chartered to247
enable sampling on alert [Estournel et al., 2016a]. In total, about 400 CTD casts were248
carried out during our study period.249
Oban, Argyll, Scotland.
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The ship CTD data are displayed on Figure 3. This is the reference data set and it250
describes well the different water masses that are present in the area during our study251
period. Because of the number of casts (among the highest numbers of CTD casts ever252
carried out in a year in this area) we certainly have a nice statistical description of all253
kind of profiles that can be observed, having in mind a water mass classification. One can254
identify the Atlantic Water (AW) characterized by a minimum in salinity and its modal255
form, the Winter Intermediate Water (WIW), the Levantine Intermediate Water (LIW)256
below, characterized by a maximum in salinity and in potential temperature, and the257
Western Mediterranean Deep Waters (WMDW) and the newly-formed Western Mediter-258
ranean Deep Waters (nWMDW) generally at greater depths, that are characterized by a259
potential temperature of 12.91-12.94C and a salinity of 38.45-38.48, the highest values260
being typical of the newly formed waters and reciprocally, the lowest ones being typical of261
water formed previously. Figure 3a shows the profiles collected before the deep convection262
events with a narrow distribution around an almost linear relationship between the deep263
and intermediates waters. A white dot indicates the presence of nWMDW formed the264
previous year that cohabits with even older ones. During the winter mixing events (Fig-265
ure 3b) the distribution of Θ-S values is more scattered (with lower probabilities) with a266
number of accumulation points often saltier than before. After a period of mixing, a sig-267
nificant volume of newly formed deep water emerges (around the white dot on Figure 3c).268
Note that this year, cascading was relatively weak compared to intense cascading events269
that can be observed every 6 years or so, as shown by [Durrieu de Madron et al., 2013]270
4ENSTA–Paristech, Laboratoire
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or [Houpert et al., 2016]. Deep water formed by cascading apparently did not propagate271
very deep in 2012-2013. Such a water mass has been detected, as shown by glider sections272
crossing the continental shelf and slope along Cape Creus canyon and at the surface with273
the TRANSMED thermosalinometer ([Taupier-Letage et al., 2016]), but is not visible on274
the Θ-S diagrams presented on Figure 3 and is not considered as a major newly-formed275
deep water mass during this winter.276
3.2. The mooring lines data
The LION mooring line is in the vicinity of the center of the deep water formation zone277
at 4202’N/441’E (bottom depth at 2350m, see Figure 1). It was equipped for the study278
period with eleven SeaBird Microcats SBE37 (conductivity-temperature-pressure sensor),279
ten RBR temperature sensors, and five Nortek Aquadopp current meters measuring hori-280
zontal and vertical currents, spaced along the line from 150 m to 2300 m. The DYFAMED281
mooring line in the Ligurian Sea at 4325’E/754’N was equipped similarly but with fewer282
instruments (four SeaBird Microcats SBE37 at about 200 m, 700 m, 1000 m and 2000 m,283
Nortek Aquadopp current meters at 100 m and 1000 m). These moorings provide rela-284
tively profiles with low resolution along the vertical of the water column but about every285
30 minutes, this rate being the lowest sampling rate of all instruments attached to the286
lines.287
The LION and AZUR M´et´eo-France moored buoys are located at about 4206’N/438’E288
and 4323’N/750’E close to LION and DYFAMED mooring lines, respectively. They289
provided hourly measurements of atmospheric parameters (atmospheric pressure, tem-290
d’Oc´eanographie et de Climatologie
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perature, relative humidity, wind speed and direction, downward radiative fluxes) and291
surface oceanic parameters from a SeaBird Microcat SBE37 during our study period.292
Additionally, at the LION surface buoy, twenty NKE Instrumentation SP2T sensors pro-293
vided hourly measurements of pressure and temperature from the surface down to 250 m294
to complement water column measurements carried out between 150m and the bottom295
by the LION mooring line [Bouin and Rolland, 2011]. Note that no surface turbulent296
heat (sensible and latent) and momentum flux measurement was carried out. Fluxes297
were estimated in this study from surface parameters through the use of turbulent flux298
parameterization from Fairall et al. [2003].299
The LACAZE-DUTHIERS and PLANIER moorings, at about 4225’N/332’E and300
4301’N/448’E respectively, were equipped with CTD sensors (Microcats) and cur-301
rentmeters at 500 m and 1000 m depths. Like DYFAMED and LION/LIONCEAU302
(4201’N/448’E), these two moorings are also equipped with sediment traps to moni-303
tor the fluxes through the canyons but only hydrographical data from these moorings are304
used in this study.305
3.3. Profiling floats data
Profiling floats drift autonomously at a given parking depth for a given time period,306
typically 1-10 days. At the end of their drifting time, they dive to 2000m depth (or307
sometimes 1000m depth) and collect a profile of temperature and salinity subsequent308
ascent to the surface. The collected data are sent in real-time to a data center before309
the floats return to their parking depth. During our study period, 27 floats deployed in310
(LOCEAN), Palaiseau, France.
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the framework of Argo and MedArgo and Bio-ArgoMed, were active in this area. Due311
to the Mediterranean specificity, the MedArgo program has set the interval between the312
successive surfacing of Argo floats to be 4–5 days and their parking depth to 400m,313
the approximate depth of the LIW. During our study period, other float configurations314
provided different results such as casts down to 1000m depth every day with parking315
depths at 1000 m depth for some period of time or casts to 2000m depth every 5 days316
etc. For instance, bio-optical floats were configured to profile everyday when drifting in317
the Mixed Patch to better observe it and then, when atmospheric fluxes reverted, were318
remotely reconfigured to cycles of 5 days to document at a larger scale the spreading of319
the newly-formed deep waters. Profiling floats collected a total of about 2700 potential320
temperature and salinity profiles in the northwestern Mediterranean Sea during our study321
period. Many were equipped with oxygen sensors [Coppola et al., 2017] and others with322
nitrate, fluorescence of Chl-a, fluorescence of CDOM, and turbidity sensors [Mayot et al.,323
2017] to document the ventilation processes and the physical-biogeochemical interactions.324
3.4. Drifter data
Two types of drifting buoys were deployed during the HyMeX SOP1-SOP2 periods.325
SVP drifters provide measurements of atmospheric pressure, SST and SSS (SVP-BS type326
drifters) or water temperature from the surface down to 80 m (SVP-BTC drifters). They327
are attached to a 15-m drogue and follow the surface currents. Five salinity SVP drifters328
and five temperature SVP drifters were deployed before the deep convection period in329
5GEOMAR, Kiel, Germany.
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the north of the Gulf of Lions. They provided a good coverage of the deep convection330
area before and during the mixing period. Marisonde buoys are particular drifters that331
measure the water temperature from the surface down to 250 m. In addition, they record332
atmospheric pressure, temperature and wind. They are however more sensitive to the333
surface wind than to the current and cannot be considered as Lagrangian. Five of them334
were dropped in the north of the deep convection area at the beginning of September 2012,335
five more at the same place in February 2013 during the HyMeX SOP 1 and 2 cruises.336
3.5. Gliders data
Gliders [Testor et al., 2010] are steerable autonomous platforms that sample the ocean337
along saw-tooth trajectories between the surface and a maximum depth of 1000 m today.338
As the slopes of isopycnals (a few degrees) are generally much smaller than the pitch339
angle of the glider (about ±15-30), the glider dives and ascents can be considered as340
vertical profiles to a large extent. Under this assumption, two consecutive profiles down341
to 1000 m are separated by approximately 2–4 km and 2–4 h depending on the currents342
and the sampling strategy of the platform, with sensors being powered on during dives343
and/or ascents. With a horizontal speed of 30–40 km day1relative to the water, gliders344
are perfectly suited to capture balanced circulation features and eddies that propagate345
more slowly. By comparing dead reckoning navigation and GPS fixes at the surface,346
gliders can also deduce a depth-average current between two surfacing. The average of347
the currents over a dive provides a transport estimate, being close to a measure of the348
6Oceanography Center, University of
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average currents between surfacing (generally 2-4 km apart) and between the surface and349
the depth achieved (generally 1000m depth). The gliders used during this experiment were350
equipped with the same sensors as for the profiling floats for measurements of potential351
temperature, salinity, but also oxygen concentration, fluorescence of Chl-a, fluorescence352
of CDOM, and turbidity. They provided about 40 000 profiles over our study period.353
4. Data harmonization and integration
4.1. Temperature and Salinity estimates
Two coupled Seabird SBE 911+ CTD were used during MOOSE-GE/DOWEX/DEWEX354
cruises with pre- and post- calibrations from the manufacturer. The data have also been355
compared to the analysis of the Rosette water samples with a Guideline Autosal. The356
absolute accuracy of this calibration method is estimated to be about 0.005 for the salin-357
ity, and 0.001C for the temperature. These calibrated CTD casts provide a ground truth358
used for the calibration of other instruments such as the deep mooring lines (LION and359
DYFAMED in particular) and the data collected by autonomous gliders, profiling floats360
through alignments on a linear T/S relationship observed at depths (700-1000m) each361
year at the basin scale, and point-to-point intercomparison exercises.362
An intercalibration of the instruments on the LION and DYFAMED mooring lines363
after and before each deployment has been carried out to ensure the consistency of the364
mooring sensors with the ship CTD dataset. Each year, during the mooring maintenance365
operations, microcats are attached to the Rosette and a cast consisting in a 20 minutes366
stop at 1000m depth is carried out with all the instruments. A relative calibration of the367
Cyprus, Nicosia, Cyprus.
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moored instruments with each other and relative to the shipborne CTD probe SBE 911+368
is performed as in [Testor et al., 2016].369
Each glider is equipped with a pumped or unpumped CTD sensor that generally needs370
to be corrected with an offset as a first order correction for each deployment. By compar-371
ing the gliders data in the deep layers (700–1000 m) with nearby calibrated CTD casts372
collected by R/V (<15 km and <3 days), and/or with the calibrated data of the mooring373
lines LION and DYFAMED (<2.5 km and <18 h, about the inertial period in this region),374
we checked the consistencies of the hydrographical data in the deeper layers sampled by375
the gliders, as the variability of the temperature and salinity are relatively small at those376
depths [Bosse et al., 2015, 2016]. The deduced offsets that are applied are on average of377
about 0.01C and 0.01 in Potential Temperature and Salinity respectively. In addition,378
the method of Garau et al. [2011] was used to correct thermal lag issues of the gliders379
pumped and unpumped CTD probes. Note this applies second order corrections every-380
where but in sharp summer thermoclines (order of 1-10C over less than 10 m) where381
salinity measurements can indeed be affected. If no direct comparison with calibrated382
data is possible (30% of the deployments), only salinity is offset to fit the linear θS383
relationship holding between the intermediate and deep layers (700–1000 m) and provided384
by the calibrated data from R/V (see figure 3). Glider time series have been sliced in up-385
and down-casts and interpolated every 1 m along the vertical to provide equivalents of386
vertical profiles located at average up- or down- casts times and locations.387
7Consiglio Nazionale delle Ricerche -
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We applied similar calibration procedures for the Argo profiling floats and drifters388
equipped with thermistor chains below, as for the gliders. The thermal lag issue is a389
known problem for profiling floats too (gliders are equipped with the same probes) but390
when vertical resolution is not high enough to resolve the thermocline (and this is often391
the case for profiling floats not configured to resolve sharp thermoclines), no thermal lag392
correction could be applied and a vertical interpolation just applied. No correction was393
applied on drifters thermistor chain data, timeseries data being just interpolated along394
the vertical on a 1m basis, like mooring data, to estimate profiles.395
This method ensures the autonomous platforms CTD errors in temperature and salinity396
to overall be smaller than respectively 0.01C and 0.01. On the other hand, the variability397
in θ-S characteristics could be estimated with unique platforms at different levels based398
on a water mass identification approach. As illustrated by Figure 4f, differences between399
the nWMDW in 2013 and former WMDW at great depth are about 0.04C in potential400
temperature (and 0.03 in salinity, not shown). Similarly, the differences in potential401
temperature and salinity between nWMDW and LIW (maxima of Potential Temperature402
and Salinity) are about 0.3C (Figure 4e) and 0.3 respectively, in the intermediate layers.403
Finally, the differences between nWMDW and AW (minimum of Salinity) is about 0.4 in404
salinity with a wide range of relatively similar temperatures at any time (prominence of405
the seasonal cycle) in the open sea region (Figure 4d). Therefore the overall corrected406
data set can be considered as consistent in accuracy for studying the evolution of the407
water masses and the deep convection processes, with a reference to high-quality values408
from ship measurements and water samples.409
Istituto per l’ambiente marino costiero
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4.2. Chl-a concentration estimates
During MOOSE-GE, DOWEX, and DEWEX cruises, Chlorophyll-a fluorometers cal-410
ibrated by manufacturers were available on all kind of platforms (i.e. ships, gliders,411
profiling floats). Moreover, water samples were filtered during ship surveys to estimate412
Chlorophyll-a concentration through High Pressure Liquid Chromatography (HPLC) tech-413
nique [Gieskes et al., 1983]. The harmonization of the whole fluorescence data set was414
carried out by using the Lavigne et al. [2012] technique, which provides fluorometer-specific415
calibration coefficients (offset and slope) by comparison with ocean color satellite images.416
Briefly (see Lavigne et al. [2012] for a complete explanation of the method), fluorescence417
profiles are initially corrected for photochemical quenching [Xing et al., 2012]; then an418
offset is evaluated by imposing zero value at depth below the Mixed Layer. Satellite419
match-ups were then generated (+/- 4 hours temporal difference with satellite overpass,420
using daily MODIS level 3, at 4 km spatial resolution products) and used to calculate421
slope coefficients. Slope and offset coefficients were first evaluated on a single profile422
basis. Then, to keep the high spatio-temporal variability measured by autonomous plat-423
forms, a single coefficient was defined for each platform (for floats), for each deployment424
(for gliders) or for each leg (for ships), by using median values. A visual check of the time-425
series of the slope and off-set coefficients allowed to verify there was no significant drift in426
fluorometer during float or glider missions or ship legs. When available (i.e. for most of427
the ship fluorescence profiles, and on some autonomous platforms), a direct comparison of428
the satellite-calibrated fluorescence with HPLC Chlorophyll estimations was carried out429
(not shown). The median error is of 28%, indicating a general good performance of the430
(CNR-IAMC) Oristano, Italy.
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harmonization method applied here. Note that an enhanced calibration of the available431
fluorometers was provided by Mayot et al. [2017], who opted for an improved calibration432
(by directly comparing fluorometers data with HPLC), although a degraded data avail-433
ability (only floats and ships having simultaneous HPLC samples at the float deployment434
or during the ship surveys were used). Mayot et al. [2017] demonstrated, however, that435
the satellite-derived calibration presented here is only slightly less accurate than their436
enhanced method.437
4.3. Depth-average current estimates
Calibrations of the compasses of the gliders have been performed before each deploy-438
ment. The current estimates were corrected using estimates of the angle of attack from439
the flight model used in Margirier et al.. Indeed, the typical angle of attack of a glider440
is about 3(during dives and opposite during ascents) and induces an artificial forward441
oceanic current in the depth-average current estimates, if not taken into account. When442
possible, the depth-average current estimates from gliders where compared to the mooring443
current meters data (at 150 m and 1000m data) and the data were consistent for 1 cm s1
444
when both current meters data were strongly correlated and somewhat representative of445
the 0–1000 m water column. Return points along trajectories allowed comparisons of446
depth-average current estimates within few hours and km. Such a protocol ensures a rela-447
tive accuracy of about 1 cm s1for both components of the estimates of the depth-average448
currents, typically about the expected natural variability of depth-average currents over449
8Consiglio Nazionale delle Ricerche -
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such scales. This allows discarding the few data clearly having a compass bias over a450
whole glider deployment (no deployment was discarded during our study period, but con-451
sidering older data, it looks it is a quality control to apply). Outliers (>1 m s1) certainly452
due to spurious and bad GPS fixes correspond to 0.1% of the data and were discarded453
from our data set. In this study, we consider only 1000 m depth-average currents. This454
includes currents in the open sea but also part of the boundary circulation which flows455
roughly centered above the 1000 m isobath. It excludes depth-average current estimates456
over shallower dives which are not directly comparable to depth-average current estimates457
over 0–1000 m. The currents are generally more intense at the surface than at great depth458
and depth-average currents estimated over shallower dives reflect the baroclinic compo-459
nent in a different way. Keeping only depth-average currents estimates over 0-1000m460
allows having a consistent data set for currents averaged along the vertical over this layer.461
5. Objective analysis
Our objective analysis method consists in extrapolation in 2D along the horizontal462
from several point observations distributed in space and time using a correlation function463
[Le Traon, 1990]. At first order, one can consider a Gaussian correlation function describ-464
ing fluctuations at given spatial and/or temporal scales L: Cov(a, b) = E+SeD(a,b)2/L2,465
D(a, b) being the temporal/spatial distance between two observations ”a” and ”b”. S/E466
is the signal over noise ratio. The error is considered small, about 10% of the estimated467
variance of the signal.468
Istituto di Scienze Marine (CNR-ISMAR),
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To take into account the tendency of oceanic currents to follow f/H,fbeing the469
planetary vorticity and Hthe bottom depth, we can introduce an anisotropy as described470
in Boehme and Send [2005]. The covariance function considered is then: Cov(a, b) =471
E+SeD(a,b)2/L2F(a,b)2/Φ2,D(a, b) and t(a, b) is the spatial distance, F(a, b) is a distance472
in potential vorticity f/H defined as: F(a, b) = |Q(a)Q(b)|/
qQ(a2) + Q(b)2with Q=473
f/H. By taking Φ '0.1, the ocean is relatively isotropic except in the continental slope474
areas where the data are clearly more correlated along-shore than cross-shore.475
For a considered data set, these methods are used with respect to a large scale first guess476
constructed with all data collected over the seasonal cycle. The data are first binned on477
a grid of 10 km x 10 km on a monthly basis and then analyzed with a scale L= 150 km478
corresponding to the basin-scale gradients and relatively high errors of 70%. Then two479
further refinement steps are preceded. The first consists in an analysis at the mixed480
patch scale (L= 75 km) with the observations carried out in a ±10 days period with a481
relative error of 60% in order to capture the large scale and intra-seasonal evolution of482
the mixed patch. Then a second step is performed using a smaller decorrelation scales483
(L= 15 km) and a smaller error of 10% in order to capture the mesoscale variability484
of the deep convection area. An analysis could be done every ten days from January to485
March at the basin-scale with a good data coverage thanks to the intense observational486
effort during that period. Analyses were performed for potential temperature, salinity,487
potential density and chl-a estimates over the whole domain with respect to related first488
guesses and the method provides geometrical error maps.489
Venezia, Italy.
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6. Results
6.1. Evolution of the deep convection area
Figure 4a shows time series of total heat fluxes characterized by a series of storm events490
starting in September with important heat losses from the ocean about 400-800 W/m2.491
The heat fluxes are consistently negative starting in November inducing a clear decrease in492
surface temperature (Figure 4d) but no clear signal in surface salinity except in February493
during which the salinity reaches a plateau of relatively high values (Figure 4c). The494
cascading mentioned above can be observed on Figure 4b but it happens mid-February495
after the mixing has reached the bottom offshore (figure 4g) and there is no signature at496
1000m at Lacaze-Duthiers mooring (not shown).497
Different time series of potential temperature from in-situ profile data are also shown498
in Figure 4 (d, e, f), describing well the evolution of the deep convection area over the499
water column, with respect to the boundary current region where advection dominates500
(time series in grey).501
Figure 4d and Figure 4e shows the evolution of the surface and intermediate waters502
respectively. There is always a contrast in the potential temperature between the convec-503
tion area and the boundary currents where water masses are advected and less modified504
by vertical mixing processes. They also show the vertical propagation of the mixing, the505
temperature averaged over the deepest layer reaching progressively the same values as506
above.507
9Helmholtz-Zentrum Geesthacht,
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The Mixed Layer Depth (MLD) was estimated with the method of Houpert et al. [2016]508
(see Figure 4g). These estimates show a slow deepening starting in October and a rapid509
one starting late January (at about 1000m depth) before the mixing reaches the bottom510
(mid February) and this is consistent with the time series of temperature above. It also511
shows a period of deep mixing from the beginning to the end of February with a rapid512
restratification at the beginning of March. The heat fluxes (see Figure 4a) are positive513
for a short period of time before a second deep convection event triggered by a storm514
Mid March. Deep convection reached the bottom again at that time. This second mixing515
event is quite frequent when ones considers the deep convection from one year to another516
[Houpert, 2013]. The short period of restratification allows to have very few buoyant517
waters on-top of homogeneous ones and such stratification is easily eroded by a storm518
during this period.519
Changes in potential temperature in the deeper layers (see Figure 4f) occur at the520
beginning of February. A CTD cast performed few hours after a storm confirmed the521
winter mixing has reached the bottom by mid-February 2013. It raises sharply from522
12.9 to 12.94 and then significant variations due to the presence of both WMDW and523
nWMDW in the area converge slowly to 12.91 at the beginning of May. At this stage524
old and newly-formed WMDW are relatively well mixed in the convection area and the525
variability returns to a low level, similar as before the rapid rise, but a large volume of526
water has increased in temperature and this corresponds to a significant heat storage.527
The time series on Figure 4h and 4i illustrate how the phytoplankton responds to528
the environment. The amount of estimated chl-a at the surface and on average seems to529
Germany
D R A F T October 23, 2017, 10:16am D R A F T
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increase mid-December when the MLD starts to present values greater than the base of the530
euphotic layer at about 100m depth. At that time the winter mixing reaches waters that531
are nutrient-rich and nutrients being brought to enlighten levels, this participates to the532
growth of phytoplankton as shown in D’Ortenzio et al. [2014]; Pasqueron de Fommervault533
et al. [2015]. When the mixing reaches depths greater than 1000m the surface chl-a drops534
to lower values before a sharp increase mid-March during the restratification period. It535
is likely the surface chl-a has dropped to low values again during the second deep mixing536
event mid-March but unfortunately, very few platforms considered here were equipped537
with a fluorometer at that time. However, enlarging the spatial domain (as in Mayot538
et al. [2017]) the effects of the second event on the chlorophyll distribution could be539
monitored. Surface chl-a values reach even greater values in April before a rapid decrease540
in May once the system has stabilized and the nutrients being consumed in the euphotic541
layer.542
It is interesting to note that the low surface chl-a values observed before the restrat-543
ification may result from dilution as the average chl-a over 0-300m (Figure 4i) presents544
significant values of integrated chl-a compared to what can be estimated from the sur-545
face only. In terms of productivity, the integrated chl-a concentration (reaching about546
100 mg.m2) is about the same during the slow deepening of the mixed layer, the deep547
convection violent events, or the planktonic bloom. The continuous (but slow) introduc-548
tion of nutrients in the surface (mixed) layer during the fall contrasts with the rapid and549
massive introduction of nutrients just after the deep mixing events. Mayot et al. [2017]550
10SOCIB, Mallorca, Spain.
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concluded that the spring bloom is more important than the autumnal one because of a551
dilution effect during the mixed layer deepening. They concluded the higher net accumu-552
lation rate of phytoplankton in spring in this region was not induced by a higher winter553
replenishment of nitrate. The strong and long winter mixing could rather have induced554
a change in zooplankton grazing pressure and silicate availability, leading to a stronger555
phytoplankton spring bloom. Furthermore, a similar autumnal phytoplankton bloom (less556
intense than the spring bloom) between bioregions might be ascribed to a mixing of the557
summer deep chlorophyll maximum, to inputs of nutrients in the surface layer, and/or558
also to photo-acclimation processes.559
6.2. Energy fluxes
Thanks to the depth-average currents measured by the gliders, the evolution of the560
energy content of the basin can also be described. Due to deep convection, newly-formed561
deep waters form a volume of water denser than the surroundings. This increases the562
potential energy of the system and is an energy reservoir that is then transformed into563
kinetic energy, through baroclinic instability as demonstrated by Gascard [1978]; Legg564
and Marshall [1993]; Visbeck et al. [1996]. During the restratification phase, very high565
currents, mainly barotropic, order of 30–40 cm s1can be observed at LION [Houpert566
et al., 2016]. This is consistent with the expected results of baroclinic instability with567
a transfer of Available Potential Energy (APE, here considered as proportional to the568
integral of potential density profiles) into Kinetic Energy (KE) and a barotropisation569
11Sorbonne Universit´es (UPMC Univ.
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of the currents. The kinetic energy (KE) could be estimated from the depth-average570
currents (average over 0–1000 m only) and the kinetic energy due to the fluctuations of571
the currents, the Eddy Kinetic Energy (EKE), by considering those depth-average currents572
minus a large scale depth-average current, low-pass filtered with a scale of 100 km along573
the glider trajectories (Figure 5).574
Noteworthy, the KE and EKE start to increase late January – early February when575
the mixing reaches depths of about 1000 m (see Figure 4g). At this stage, the conversion576
of potential into kinetic energy starts and this will increase until the system reaches a577
maximum in potential energy. This clearly illustrates the violent mixing phase and the578
spreading overlap. The maximum in potential energy is reached by early March. At579
this stage, the heat fluxes at the surface are not able to extract sufficient buoyancy to580
overcome lateral fluxes due to eddies. The maximum in EKE is reached about 2 weeks581
later and this gives evidence to a response time scale for the development of instabilities582
resulting in the break-up of the Mixed Patch. About half of the increase in KE is due583
to eddies while the other half due to larger scale currents (the Northern Current and the584
recirculation associated to the North Balearic Front south of the convection area). Deep585
convection is thus associated with an increase in intensity of these large-scale circulation586
features. This can be due to a large-scale response to the intensification of the lateral587
gradients of density as the water column gets denser and denser through deep convection588
processes in the Mixed Patch.589
The non-filtered data in APE show large variations with a first peak mid-February590
when deep convection first reached the bottom followed by scattered high and low values.591
Pierre et Marie Curie, Paris 06), UMR
D R A F T October 23, 2017, 10:16am D R A F T
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One can observe the same pattern again mid-March during and after the second deep592
convection event. This illustrates the homogenization of the area during the deep mixing593
events, while the area is characterized by both mixed profiles (high APE) and stratified594
ones (lower APE). High values of non-filtered KE and EKE can be observed at the same595
times but also later on, until the APE, KE and EKE reach low values again.596
6.3. Spatio-temporal coverage and budgets estimates
Figure 6 shows analyses of MLD, averaged salinity over the surface layer (0–100 m),597
averaged potential temperature over the intermediate layer (400–600 m), and average chl-598
a profiles over the 0–300 m. Data are considered on the 10 km×10 km grid over periods599
of 1 month with respect to the related first guess. Extrapolated values being estimated600
to have an error of more than 95% (in terms of variance) based on the 75 km analysis are601
shaded. It shows that the amount of collected information provides a convenient spatio-602
temporal coverage and allows to describe the deep convection process on a continuous603
basis at various scales throughout the year.604
Figures 6a, b and 6c show there is a maximum salinity expression in the surface layers605
and a minimum potential temperature expression at intermediate depths on the analyses606
of 14 February concomitant with deep mixed layers (>1000m). Winter mixing actually607
transforms into deep convection at that time, once the winter mixing has eroded the LIW608
layer. Then, the signal fades away, more quickly in the surface layers. Figure 6d presents609
analyses of chl-a estimates averaged over 0-300m and it is consistent with Figure 4h and610
4i. The development of the phytoplankton starts in the deep convection area as early as611
7093, Laboratoire d’Oc´eanographie de
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September when the MLD starts to deepen. Later on, in February, phytoplankton seems612
to develop around the Mixed Patch before the bloom in April. In April, chl-a estimates613
present high values at the scale of the basin, from the Gulf of Lions to the Ligurian Sea,614
and even higher values in the deep convection area. Then, phytoplankton disappears615
rapidly with very low values everywhere in June.616
The very large number of in situ observations harvested between January and May617
allows to solve in a quasi-synoptic way the typical scales of deep convection, and the same618
methodology was applied at a higher frequency. Figures 7 and 8 show high frequency619
(10 days) analyses of the MLD and potential density at 1000 m depth respectively with620
the related first guess being the previously described (monthly) analyses.621
MLDs greater than 1000m depth can be observed starting in mid-January in the western622
part of the Gulf of Lion and the surface of the Mixed Patch increases until the beginning623
of March reaching a maximum extent of 28±3 109m2late February. It then quickly624
restratifies. The analyzed fields are sometimes patchy at the small scale but the general625
evolution emerges well with a break-up starting late March. The deep mixing occurs at626
the end of January with the formation of dense waters (>29.11 kg m3). The density of627
the newly-formed waters increases after it has reached the bottom early February. The628
newly formed deep waters are characterized at that time by a density anomaly of about629
0.01 kg m3and this remains identifiable in the months that follow - in particular in April,630
with a slow ang general movement towards south and west. The amplitude of the density631
anomaly decreases throughout the restratification processes until May, with a progressive632
flattening of the isopycnals at the basin scale. These analyzed fields are consistent with633
Villefranche (LOV), Observatoire
D R A F T October 23, 2017, 10:16am D R A F T
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the time series of Figure 4 and describe the evolution of the area, with a lower time634
resolution but a description of the spatial patterns associated with deep convection.635
The 4D analysis in space and time of the density field in particular, allows us to analyze636
the transformations of the water masses that take place within the deep convection area.637
Figure 9a shows the evolution of the volume of water denser than certain selected potential638
density thresholds, between mid-January and May. These estimates have been made over639
a relatively large area but restricted to the box as displayed on figure 8, for a good coverage.640
The total volume of water presenting potential densities >28.00 kg/m3(σ0) in the area641
under consideration is relatively constant over time, with a volume of 1.6 105km3, the642
volume under consideration being in fact composed quasi-totally by waters denser than >643
28.00 kg/m3. The time series associated to denser waters volumes present increases, the644
denser the later, as a result of transfers between the different isopycnal layers.645
The relatively light waters presenting potential densities <29.11 kg/m3are progressively646
transformed into denser and denser waters during the violent mixing events starting mid-647
January for waters presenting potential densities >29.11 kg/m3and <29.115 kg/m3, and648
later on with the apparition of new waters presenting potential densities >29.115 kg/m3
649
and <29.12 kg/m3early in February, and even denser new waters (>29.12 kg/m3) mid-650
February. During restratification periods, the opposite effect is observed: the volumes of651
dense waters decreases, while they spread out of the area of the Gulf of Lions, mix with652
other waters (with transfers from density classes to others) and light waters reinvest it.653
The increase in volume is generally rapid for the different classes of water >29.11 kg/m3
654
and followed by a general decrease. The fact that all these time series decay at about the655
Oc´eanologique de Villefranche/mer, France.
D R A F T October 23, 2017, 10:16am D R A F T
X - 34 TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS
same rate denotes a general input of lighter waters that can be better observed on Figure656
9b as the volume (averaged over a year and expressed in Sv in order to be compared with657
other numbers that can be found in the literature) increases starting end of February for658
waters presenting densities <29.11 kg/m3. After a transformation in denser waters, the659
volume of this class of density increases from a minimum of 2.0 Sv (volume averaged over660
a year) compared to the situation on 5th January 2013 at a rate opposite and equivalent661
in magnitude to the general decrease of the volume of the denser water masses. At that662
time the volume of waters >29.11 kg/m3is consistently about +2.0 Sv (volume averaged663
over a year). This illustrates that the process of deep water formation by deep convection664
can be considered as a mass transfer that can be quantified, from the surface isopycnal665
layers loosing buoyancy due to air-sea interactions to the deep isopycnal layers.666
The production of the densest waters (>29.12 kg/m3) is estimated at 0.5 Sv (Figure667
9b, volume averaged over a year) and occurs when the mixing reaches the bottom. At668
that time, the atmospheric forcing remains intense for a while allowing to form even669
denser deep waters [Houpert et al., 2016]. This layer presents a volume that increases670
until mid-March and decreases later on, as they spread and mix with lighter waters. The671
volume of the waters presenting potential densities >29.115 kg/m3and <29.12 kg/m3
672
increases up to a maximum of 1.5 Sv (averaged over a year) in mid-March (Figure 9b).673
These deep waters form earlier with an increase in volume starting in early February and674
a first relative maximum in volume in mid-February at the time of the first event of deep675
convection. It then decreases until it increases again around mid-March at the time of676
12Departamento de Oceanografa Fisica,
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 35
the second deep convection event, in a consistent way with Figure 4. The evolution of the677
volume of the waters presenting potential densities >29.11 kg/m3and <29.115 kg/m3
678
shows that they are the first to experience an increase of their volume during the winter. It679
starts to increase in mid-January and reaches a maximum in mid-February. This increase680
is followed by a slow but continuous decrease until May at about the same rate as for the681
densest layers.682
For 2013, we can conclude that deep-water formation has created water with potential683
densities >29.11 kg/m3with a rate of formation which can be estimated to 2.0 ±0.2Sv684
(volume averaged over the year – see Figure 9c). In addition, this volume of deep water685
can be decomposed into two main categories: (1) deep water having a density >29.12686
kg/m3formed around the end of February starting once the mixing layer has reached687
the bottom (25% of volume formed); 2) deep water with a slightly lower density >29.11688
kg/m3formed starting at the beginning of February and composing most of the newly-689
formed deep waters (75% of the volume). During the month of March, the second episode690
of mixing, appears to only generate a second-order formation rate of 0.1 Sv compared to691
the previous maximum observed in mid-February, the period of negative heat fluxes at692
that time being possibly too short to have a real significant impact on the water column.693
These approaches by density classes may suggest there are different types of newly-694
formed deep waters but in reality this is more a continuum of newly-formed deep waters695
presenting densities between 29.11 and 29.123 (the maximum observed density) as illus-696
trated by Figure 9c which inventories the volume (averaged over a year) of the different697
waters formed according to their density properties. Because it shows the dependency of698
Centro de Investigacion Cientfica y de
D R A F T October 23, 2017, 10:16am D R A F T
X - 36 TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS
the change of volume for waters having a greater density than, the production rate must699
be determined by the maximum of the curve and is consistently about 2.0 Sv (volume700
averaged over a year).701
Finally, Figure 9d shows the volumes estimated using the MLD estimates which shows702
that there is instantaneously about 3 times less waters in relatively shallow mixed layer703
(deeper than 500 m) than in the very deep ones (deeper than 1000 m) with volume704
estimates of maximum 71013m3and 51013m3respectively. The overall volume of newly-705
formed deep waters that can be computed late February (when the volume is maximum)706
from this method is about 1.4 Sv (averaged over a year) using MLD>1000m and about707
2.0 Sv (averaged over a year) using MLD>500m.708
7. Discussion
The analyses presented above do not account for small-scale processes, except in the709
’error’ estimated on our 10kmx10km grid. This is so not critical as far as budgets are710
concerned but that somewhat hides a variety of processes at stake. After summarizing711
important results about related numerical studies and discussing the robustness of our712
deep water formation rate estimates, we will highlight in this section several peculiar713
circulation features that could be observed. Our observations bring new knowledge on the714
sub-mesoscale turbulence, the plumes in the Mixed Patch and the symmetric instability715
at the edge of the Mixed Patch that are important to consider when studying with deep716
convection and subsequent bloom because they are responsible for significant fluxes of717
energy and (dissolved and particulate, organic and inorganic) matter – in particular while718
Educacion Superior de Ensenada, Ensenada,
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 37
analyzing/interpreting the various biogeochemical measurements carried out during the719
R/V cruises, and more especially during the DEWEX-1 and DEWEX-2 cruises which720
collected numerous biogeochemical observations based on water samples.721
7.1. Numerical model initialization/validation
The Summer data were used to correct initial conditions for modelling studies. As722
pointed out by Lger et al. [2016], ”L’H´ev´eder et al. [2013] and Somot et al. [2016], numer-723
ical simulations are very sensitive to the initial conditions with regards to winter convec-724
tion and numerical outputs, including operational products like MERCATOR PSY2V4R4725
[Estournel et al., 2016b], have generally serious difficulties to describe well the intermedi-726
ate and deep layers, because stratification is influenced by initial conditions derived from727
smoothed climatologies encompassing decades of observations. Waldman et al. and Es-728
tournel et al. [2016b] showed it is possible to correct the initialization and forcing of their729
model and to significantly improve the realism of the simulations using the DEWEX data730
set both for initial conditions correction in Summer and later validation.731
This data set was then used for validation purposes to assess the realism of numerical732
simulations in particular in terms of timing and geography of the phenomena as well733
as in terms of quantitative estimates of the deep water formation rate [Waldman et al.,734
2016, 2017] and in terms of meso- and submeso- scale processes [Damien et al., 2017;735
Waldman et al.] by performing similar diagnostics in the observations and the simulations,736
and sensitivity studies. They were thus able to reach a better understanding of deep737
convection processes from autumn to winter together with quantitative estimates. They738
Baja California, Mexico
D R A F T October 23, 2017, 10:16am D R A F T
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were able in particular to estimate that lateral advection through the Mixed Patch could739
represent 58% of the destratifying effect of surface fluxes when integrated over the winter.740
This implies restratification must be considered as a major process during, and not only741
after the end of, the violent mixing but not as important as in the theory of Visbeck et al.742
[1996] in which lateral fluxes entirely balance the buoyancy loss through the sea surface,743
certainly because deep convection reached the bottom this year which cast a limit to the744
equilibrium depth solved in this study. The winter 2012-2013 is probably the third in745
buoyancy loss intensity after 2005 and 2012 during the period 1980-2013 [Somot et al.,746
2016] with more than 20 ”stormy days” over the December-March period.747
Another major outcome of this DEWEX experiment concerns the air-sea interactions.748
It must be noted it was impossible to measure directly the air-sea turbulent fluxes and749
that estimates of the total buoyancy losses are dependent on their parameterization. It750
has not been particularly developed for strong winds as one can observe in this region in751
winter and this can introduce some uncertainty on the role of the atmosphere. Thanks752
to this data set, Caniaux et al. [2017] managed to propose an inverse method to estimate753
during one year heat and water fluxes for the whole northwestern Mediterranean basin and754
at a fine scale resolution (i.e. hourly fluxes and 0.04x0.04longitude, latitude) allowing755
to close the heat and freshwater budgets. The comparison of theses adjusted fluxes with756
fluxes estimated at the LION buoy from in-situ meteo-oceanic measurements shows a757
good correlation (r2= 0.96) and provides a validation of the parameterization used for758
13CNRS-Universit´e de Perpignan, Centre
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 39
the estimates of the turbulent air-sea fluxes from the LION buoy (see Caniaux et al.’s759
Figure 9).760
7.2. nWMDW formation rate estimates
de Formation et de Recherche sur les
Environnements Mditerranens (CEFREM),
Perpignan, France.
14CNRS-Universit de Toulouse,
Laboratoire d’Aerologie (LA), Observatoire
Midi-Pyr´en´ees, Toulouse, France.
15Aix-Marseille Universite, Universite de
Toulon, CNRS, IRD, MIO UM 110, 13288,
Marseille, France
16et´eoFrance/CNRS, CNRM, UMR
3589, Toulouse, France.
17Ifremer-CNRS-IRD-UBO, LOPS,
IUEM, Plouzan´e.
18Sorbonne Universit´es (UPMC Univ.
Pierre et Marie Curie, Paris 06), UMR
7093, Laboratoire d’Oc´eanographie
Microbienne (LOMIC), Observatoire
Oc´eanologique de Banyuls/mer, France.
D R A F T October 23, 2017, 10:16am D R A F T
X - 40 TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS
One shortcoming is that the frontier closing the domain used for estimating the deep761
water formation rate (see Figure 8) is relatively close to the Mixed Patch on its south-762
western part. This could lead to underestimations of the volume formed. However, the763
dense water volume formed outside the domain is likely second order compared to our764
estimates. MLD barely >750m (Figure 8) while potential densities <29.10 kg m3(Fig-765
ure 8) are observed along this frontier and the chosen domain likely captures the entire766
deep convection process. In order to assess their robustness, our estimates of 2.0 Sv for767
the production of newly-formed deep waters can be compared with estimates that can be768
made from different methodologies.769
As already pointed out in section 6.3 the volume of water formed could be estimated770
assessing the maximum volume of the mixed layer greater than a given value, enough to771
have mixed the LIW layer lying above the deep waters but this induces some uncertainties772
related to the arbitrary choice of the threshold (see 9d : 1.4 Sv for MLD >1000m, 2.0Sv773
for MLD >500m). Another but similar method is to use satellite ocean color images774
as in Houpert et al. [2016] and Herrmann et al. [2017], when the cloud coverage allows775
exploiting some images of the deep convection area. The strategy is to identify the ’blue776
hole’ associated to Mixed Patch within restratifying waters around. In 2013, using Figure777
2e and estimating the ’Blue Hole’ surface with a threshold value of Chl-a <0.15 mg m3
778
yields to 23 583 km2. Considering an average depth of 2200 m in the convection zone,779
the winter 2013 would thus present a production rate of 1.6 Sv (on average over the780
year). Again, it must be noted this method is very sensitive to the threshold (here in781
chl-a concentration): considering a slightly different threshold in Chl-a concentration of782
<0.25 mg m3would yield in fact to a doubling of the volume of the newly-formed deep783
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 41
waters. There is so a strong need to accurately define the threshold in chl-a concentration784
used for such estimates. The choice of <0.15 mg m3can actually be justified by785
data from gliders crossing the edge of the Mixed Patch at about the date of the satellite786
image [Houpert et al., 2016]. They show that deep mixed layers are associated with chl-a787
concentrations lower than this value this year but the right threshold is not necessarily the788
same every year and it is important to note there is a need to carry out such measurements789
in the long term if one wants to address interannual variability using this method.790
Our estimates from in-situ data based on density classes are similar in magnitude to791
those estimates but still larger by about 0.0-0.6 Sv (on average over a year). On the792
other hand, such estimates are likely to underestimate the deep water formation rate first793
because they do not account for lateral fluxes. Moreover, the process of deep water renewal794
is a process that is not instantaneous and estimates made on the basis of an instantaneous795
image or snapshot inevitably underestimate the volume of newly formed deep waters. The796
dates of analyses of MLD (every 10 days) and the available satellite images (with small797
cloud cover) do not necessarily correspond to the date of the maximum extent of Mixed798
Patch (Blue Hole) and restratification processes are able to quickly recap mixed layers799
possibly hiding volumes of newly-formed deep waters under the surface. Still, it is quite800
appealing that the estimates based on a single analysis or a single ocean color image are801
in such a good agreement with our present ones based on density classes.802
Other estimates were performed by Waldman et al. [2016] using analyses of ship CTD803
data only and the deep water formation rate was estimated to be 2.3±0.5 Sv. Ship data are804
the only data except for the mooring data that characterize the deep layers and the cruise805
plans were designed to estimate such volumes with large-scale surveys. Using an OSSE806
D R A F T October 23, 2017, 10:16am D R A F T
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approach based on the simulation presented in Estournel et al. [2016b], Waldman et al.807
[2016] assessed the capacity of the CTD array to capture seasonal dense water variations,808
in terms of spatial distribution and the results indicate a low uncertainty related to space809
and time undersampling of the observing network because the cruises carried out at large810
scale provide integrated information. Our present estimates of newly-formed dense water811
volumes certainly rely on the same deep data and the estimates are consistently similar.812
The methodologies proposed for estimating the deep convection rate are complementary.813
In particular, Waldman et al. have shown from a modeling study that the Mixed Patch814
volume computed as the volume of MLD>1000m (or from a cold signature (<13C) of815
intermediate waters (400-600m)) reached a lower value by 1.5x104km3(0.4 Sv averaged816
over a year) than the dense water formation rate computed with the volume of waters817
denser than 29.11kg/m3in their run. Both estimates have different physical origins,818
the former resulting exclusively from the intense vertical mixing during the deep water819
formation events and the latter also resulting from lateral advection and mixing with820
surrounding waters.821
Noteworthy, we present here a methodology that allows such estimates to be augmented822
with the data from the numerous autonomous platforms (gliders, profiling floats, moor-823
ings, drifter) that could continuously observe dense waters (Figure 9), sometimes only824
above 1000 m (gliders and floats) or 2000 m (floats) depths but this additional informa-825
tion is very significant, helping to describe the timing of the production at higher frequency826
as well as transfers between different classes of density.Compared to few satellite images in827
months, or 1-6 times a year thanks to MOOSE-GE-like cruises, the 10-days analyses based828
on in-situ data represent a breakthrough for describing the deep convection phenomenon.829
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 43
7.3. SCVs
As described more thoroughly in [Bosse, 2015], glider data revealed for the first time very830
warm (+0.4C) and saline (+0.1) submesoscale and lenticular anticyclones at intermediate831
depth characterized by a small radius (5km) and high Rossby (0.3) and Burger (0.7)832
numbers. Their cores are composed of marked LIW. Figure 10a shows two of them on833
the same glider section and this illustrates how numerous they can be. Roughly ten are834
formed each year contributing significantly to the spreading of the LIW toward the sub-835
basin interior. They have a lifetime order a year and can be quite numerous in the whole836
basin. They would be mainly formed by the combined action of turbulent mixing and flow837
detachment of the northward flow of LIW at the northwestern tip of Sardinia. Upwelling838
conditions along the western coast of Sardinia associated with a geostrophic southward839
surface flow could also play a key role in their formation process. These ”Suddies” contain840
LIW from the formation region that is protected from mixing with the surroundings by841
dynamical barriers due to the high non-linearity of the SCV flow Bosse et al. [2017]. They842
have thus a potential impact on winter mixing because they correspond to salt/heat inputs843
at intermediate depths and are associated with dynamical preconditioning of mixing (local844
doming of isopycnals). About 2-3 (or more?) of these eddies could be present in the deep845
convection area (as suggested by Figure 10a) and expose such LIW (and all associated846
dissolved or particle organic and inorganic matters) to winter mixing. The stratification847
index of such eddies shows they are preconditioning agents and deep convection will848
preferentially develop in these flows. In terms of ecosystem functioning this could be a849
direct route from the SCV formation locations (mainly the northwestern tip of Sardinia)850
to the deep convection area and contact with the atmosphere.851
D R A F T October 23, 2017, 10:16am D R A F T
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In addition, Bosse et al. [2016] identified other SCVs, remnants of wintertime deep verti-852
cal mixing events. Figure 10b shows a transect across the boundary circulation (Northern853
Current and the south recirculation associated with the North Balearic Front) and the854
Mixed Patch with Transition Zones in-between, where SCVs can be observed, just expelled855
from the homogeneous Mixed Patch. Figure 10c also shows two of them (one cyclonic and856
one anticyclonic) on the same glider section, which again illustrates how numerous these857
eddies can be in Spring. This documents the spreading phase of deep convection with dif-858
ferent eddies presenting different characteristics in temperature and salinity. These SCVs859
are though all characterized by a small radius (5–8 km), mostly strong depth-intensified860
orbital velocities (10–20 cm s1) with sometimes a surface signature, high Rossby (0.5)861
and Burger numbers O(0.5–1). Anticyclones are found to transport newly-formed waters862
resulting from vertical mixing characterized by intermediate (300m) to deep (2000 m)863
mixing. Cyclones are characterized by a thick layer (500–2000 m) of weakly stratified864
newly formed deep waters likely extending from the bottom of the ocean (2500 m).865
Cyclones extending from the surface to the bottom have also been observed. All these866
SCVs result from intrusions of mixed fluid parcels into a more stratified environment and867
followed by cyclogeostrophic adjustment. Noteworthy, the formation of cyclonic eddies is868
favored in 2013 once the convection reached the bottom because this implies a limit in869
the adjustment phase and prevents the formation of anticyclones composed of nWMDW.870
Both anticyclonic and cyclonic SCVs have a prominent role in the spreading of the871
newly-formed deep waters away from the winter mixing areas. Since they can survive until872
the following winter, they can greatly populate the basin and also have a great impact on873
the mixed layer deepening through a local preconditioning effect. These SCVs consist in874
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 45
another type of preconditioning agents like the above mentioned Suddies. Moreover, they875
can be formed throughout the deep convection mixing phase and modulate at this scale876
the vertical mixing occurring in Plumes during the violent mixing phase as well.877
As reported by Bosse et al. [2017] they have a significant impact on the distributions878
of biogeochemical properties with clear signatures on the dissolved matter (nutrient and879
dissolved inorganic carbon in particular), compared to the surroundings. SCVs cores con-880
tain concentrations that are very contrasted with the general deep concentrations, being881
composed of waters resulting from a mixing of surface waters with deeper waters. This in-882
troduces a granularity at the SCV scale in the distributions of the biogeochemical variables883
in the basin since SCVs export these waters throughout the basin. Finally, these eddies884
have a peculiar impact on suspended particles distribution. As reported by [Durrieu de885
Madron et al., 2017], there is evidence of bottom thick nepheloid layer formation coin-886
cident with deep sediment resuspension induced by bottom-reaching convection events.887
This bottom nepheloid layer, which presents a maximum thickness of around 2000 m in888
the center of the convection region, can persist within cyclonic nWMDW SCVs that are889
formed during the convection period and can last several months while traveling through890
the basin, still being associated with thick nepheloid layers far from the deep convection891
area. They are thus key mechanisms that control the concentration and characteristics892
of the suspended particulate matter in the basin, and in turn, affect the bathypelagic893
biological activity.894
Waldman et al. [2017] and Waldman et al. and have studied the impact of oceanic895
intrinsic variability on deep water formation with eddy resolving and permitting simula-896
tions. By comparing ensemble results they conclude mesoscale could have a significant897
D R A F T October 23, 2017, 10:16am D R A F T
X - 46 TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS
impact on deep water formation. Resolving mesoscale significantly improves the realism in898
particular of the restratification/spreading phase and the Mixed Patch shape and extent.899
These are first estimates of the impact of such eddies even if the eddy-resolving simulation900
could not really account for SCVs. With a horizontal resolution of 1/36(about 2 km), the901
simulation can actually not produce explicitly circulation features characterized by a ra-902
dius order of 5km but represent them thanks to subgrid parameterizations constrained by903
larger scale, but realist, variability and that allows a first assessment. The large increase904
of ocean intrinsic variability in eddy-resolving, compared to eddy-permitting, simulations905
and of its impact on deep water suggests that SCVs could contribute largely to the chaotic906
ocean variability. Noteworthy, Damien et al. [2017] presented simulations which are the907
first ones to our knowledge that are able to simulate SCVs with similar dynamical char-908
acteristics and lifetimes in fully realistic conditions. A 1 km horizontal resolution and a909
great control of tracers and momentum horizontal diffusion seem to be decisive features to910
accurately resolve SCVs. This numerical study reveals itself particularly useful for refining911
the estimation of their integral effect and tracking them over their entire lifetimes. Further912
studies assessing the role played by SCVs in deep water formation (preconditioning, vio-913
lent mixing and spreading at basin-scale and interannual time-scale) and furthermore, in914
the different biogeochemical cycles that are identified in present biogeochemical numerical915
models forced by physical ones are now possible.916
7.4. Plumes
Margirier et al. present a methodology based on a glider quasi-static flight model that917
was applied to infer the oceanic vertical velocity signal from the glider navigation data.918
Figure 11 shows an example showing the vertical trajectory of the glider being modified919
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 47
by vertical currents, the so-called plumes, and their estimates. Noteworthy, on the first920
apogee, one can see the glider was undergoing strong downward currents. It has nearly921
ended up with the loss of the glider (pressure rated to only 1000 m) but the glider forward922
motion capacity allowed it to cross the vertical stream in about 10 min, and to reach a923
safer area, characterized by upward velocities. This illustrates the vertical currents are924
order of, and fortunately generally lower than, the vertical speed relative to water that925
glider can have, typically about 10 20 cm.s1.926
The data collected during winter 2012–2013 allows a first in situ statistical and 3D927
characterization of the so-called plumes that are important mixing agents. During the928
active phase of mixing, significant oceanic vertical velocities (upward and downward, up929
to 18 cm.s1jostled the gliders. The gliders crossed many downward plumes with a mean930
radius of about 350 m and distant from each other by about 2 km on average. The931
upward part of the plumes is less coherent but apparently organized in crowns around the932
downward plumes. Much higher downward velocities were observed, with a magnitude933
about three times as large as that of the upward ones on average (6.3 cm.s1versus934
+2.3 cm.s1).935
On average, the plumes cover 27% of the convection area and the upward motion as-936
sociated with them covers 71%. The total of 98% provides confidence in coverage of the937
area. These are useful estimates to parameterize deep convection in ocean general circu-938
lation numerical models. A specific parameterization of convection has been introduced939
in atmospheric numerical models long ago but not yet in oceanic ones. Until now, oceanic940
numerical models that would need such a parameterization to represent mixing do use941
artificially increased diffusion instead. These results can now be used for the develop-942
D R A F T October 23, 2017, 10:16am D R A F T
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ment and the testing (with all the data collected during our study period) of a convection943
parameterization in oceanic numerical models, following similar developments in meteo-944
rology for convection in the atmosphere that use the convective fraction of a grid cell as945
a key parameter, and further progress in modeling the deep convection processes can be946
soon expected.947
The structure in temperature and salinity as well as biogeochemical properties (dissolved948
oxygen, fluorescence, turbidity) associated to this plumes is as follows: the downward949
waters are saltier (+0.001), colder (0.005C) and thus denser (0.0015 kg m3) than the950
surrounding upward ones. The downward waters are also slightly richer in oxygen and951
less fluorescent. This confirms the downward plumes participate to the ventilation of952
the waters and a dilution effect on Chl-a estimates (already mentioned previously when953
commenting Figure 4) while in the upward parts of the plumes, phytoplankton would954
benefit from nutrients being brought to the surface layers. On the other hand, there is no955
mean correlation on the turbidity signals despite individual signals in plumes but going956
both directions and this compensates on the average. The role of plumes as mixing agents957
on the suspended material distribution likely results from various factors. There could be958
some passive advection of turbidity signals from the surface (bloom) but also sometimes959
from the nepheloid layer when the mixing reaches it. In the deep convection region,960
intense horizontal currents favor resuspension over thick layers (100s m), with often a961
higher expression in turbidity in that layer than at the surface. In addition, suspended962
material have proper vertical downward speed and that increases the complexity of the963
suspended material fluxes in the presence of plumes tickling this nepheloid layer and964
lateral advection, through SCVs in particular.965
D R A F T October 23, 2017, 10:16am D R A F T
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7.5. Symmetric instability
Figure 12 illustrates the symmetric instability phenomenon presenting interleaving pat-966
terns at the edge of the deep convection area over 0-500 m along the vertical and 20km967
along the glider track. Figure 10 also shows similar patterns north and south of the deep968
convection area, with alternating cold and warm waters circulating respectively down-969
ward/outward and upward/inward of the deep convection area. Figure 12 provides a970
zoom and documents this circulation feature that has a signature on all measured vari-971
ables with tongues of alternating high and low values in temperature and salinity but also972
in dissolved oxygen, chl-a concentration estimates and turbidity. Noteworthy are the high973
chl-a estimates where the interleaving connects to the surface Figure 12d.974
Almost all glider sections across the edge of the Mixed Patch exhibited similar inter-975
leaving patterns during the mixing period as shown in Figure 12. In the ocean, the lateral976
shears, fronts, and preexisting eddies make the horizontal gradients of density in mixed977
layers, thus the thermal wind build up. If the slope of the buoyancy surface is steeper978
than the absolute momentum surface, the slantwise convection will occur to release sym-979
metric instability. That can propagate below the mixed layer and produce circulation980
features responsible for the observed interleaving patterns. As indicated by [Marshall and981
Schott, 1999] the slantwise convection induced by symmetric instability could maintain a982
vertical stratification in the region that is being actively mixed. Using in particular the983
data collected during our study period, Bosse [2015] showed that symmetric instability984
can develop particularly at the edge of the Mixed Patch, mainly where wind and currents985
flow along the same direction, and that it is possibly a major mixing process, like plumes,986
that needs to be taken into account to try to fully comprehend the deep convection phe-987
D R A F T October 23, 2017, 10:16am D R A F T
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nomenon. The data collected by the gliders did allow to estimate the fluid Potential988
Vorticity (PV) and often showed patches of negative PV at the edge of the Mixed Patch,989
presenting a horizontal scale of a few km and a vertical one of hundreds of meters. It990
is noteworthy the negative PV estimates are underestimated in absolute value. In fact,991
the gliders do not always sample the ocean exactly along the density gradients, which are992
thus underestimated, and if negative values could be observed, larger areas are certainly993
characterized by (and even more) negative PV values in reality. These negative patches994
indicate the edge of the Mixed Patch is a region where symmetric instability can develop995
even more broadly than in these local patches.996
The glider data did allow estimates of the vertical velocities associated with plumes but997
not the part associated with symmetric instability. Estimating such vertical velocities998
is actually a major challenge for oceanography today. This type of signal is impossible999
to measure directly by in situ observations because of the weak signals of 1-10 mm/s1000
that are supposed to be associated with such circulations. In addition, these vertical1001
velocities are concentrated in small-scale and rapidly evolving flows that are non-linear1002
and ageostrophic [Mahadevan, 2006; Thomas et al., 2008]. They are weak, but relatively1003
steady and so important in terms of fluxes, compared to oscillating movements due to1004
internal waves that likely mask them with vertical velocities of the order of 1 cm/s and1005
this is even more the case with higher velocities observed in plumes during the violent1006
mixing phase.1007
Analyzing numerical outputs in details can provide a clearer perception of this process.1008
Using the NEMO model, Giordani et al. [2017] shows the edge of the Mixed Patch is1009
a zone where negative PV can be observed and symmetric instability can develop as in1010
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 51
the observations. In the high resolution (1km) SYMPHONIE model as well (see Damien1011
et al. [2017] for a model description), there is a dominant and persistent negative PV1012
frontal region of the Northern Current, where symmetrical instability can develop [Bosse,1013
2015] and Estournel et al. [2016b] showed that destratification of the surface layer in1014
autumn occurs through an interaction of surface and Ekman buoyancy fluxes associated1015
with displacements of the North Balearic front bounding the convection zone to the south.1016
The Ekman buoyancy fluxes appear to be important also in autumn, deepening the mixed1017
layer in the southwestern part of the cyclonic gyre, increasing the size of the preconditioned1018
area, and possibly feeding such symmetric instability processes throughout the year when1019
the wind is blowing down front.1020
The phenomenon can be described as follows. When the wind blows in the down front1021
direction, the Ekman transport carries denser waters towards less dense waters. This1022
induces not only a buoyancy flux but also the development of the symmetric instability1023
phenomena with an associated steepening of isopycnals and increase of horizontal currents.1024
This generates a potentially large turbulent mixing compared to the effect of surface1025
buoyancy losses. This mechanical effect is important as indicated by Giordani et al. [2017]1026
who estimated it is order of 4000 W m2, about 4 times the maximum buoyancy losses1027
at surface. The PV shows negative values when the front is particularly steep (steeper1028
than momentum surfaces) and this indicates where/when the flow is unstable. The region1029
of negative PV is characterized by a marked ageostrophy which tends to accentuate the1030
destabilization of the fluid and to induce vertical motions trying to bring the fluid back1031
to a geostrophic balance. At the interface between negative and positive PV, vertical1032
velocities of about 100 m day1can develop tending to bring fluid particles of positive1033
D R A F T October 23, 2017, 10:16am D R A F T
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vorticity towards the surface and negative vorticity to greater depth. Thereafter, the1034
front would evolve rapidly towards a more stable situation with less inclined isopycnals1035
and a wider frontal area. In both observations and numerical simulation, the effect of this1036
instability can be observed over great depths, much deeper than the mixed layer above.1037
The negative PV regions tend to fade away after about 24 hours in the model simula-1038
tions. Consequently, the frequent physical and biogeochemical observations carried out1039
by gliders that suggest strong vertical motions, because of the observed interleaving of the1040
different physical and biogeochemical observed variables and negative PV estimates, may1041
be only observations of remnants of vertical motions due to symmetric instability. Though1042
they provide clear evidence of the prominence of this phenomenon, higher repeat rates1043
for glider observations would be required to actually resolve it. Crossing the northern1044
Current and the frontal area (about 30-50km width) takes about 1 or 2 days for a single1045
glider and more gliders along the same repeat-sections would be required to increase the1046
repeat rate if one wants to really capture this phenomenon.1047
Overall, symmetric instability appears to be a major process in deep convection inducing1048
water masses mixing during the three deep convection phases as suggested by the high1049
number of occurrences of glider observations of this phenomenon throughout the year1050
and the numerical simulations. Vertical motions can be indeed induced during any down1051
front wind event. This could be active at high temporal frequency and participate to a1052
significant part of the water formed by intermediate and deep convection during winter1053
and more indirectly throughout the year by participating to the preconditioning of the1054
area. This could also explain why the mixing seems to occur preferentially during the1055
D R A F T October 23, 2017, 10:16am D R A F T
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first stages in the western part of the Gulf of Lions (see Figure 7), where northerly winds1056
blow down front, above a southward ocean general circulation.1057
8. Conclusions and outlook
In this review we have attempted to draw together results of observations and numeri-1058
cal experiments in the context of 2012-2013 DEWEX field campaigns, to summarize our1059
current understanding of the underlying hydrodynamic processes at work before, during1060
and after deep ocean convection events in the northwestern Mediterranean Sea and the1061
interplay between the large scales, meso-scales, submeso-scales and convective scales. This1062
interplay is complex since it involves scales, ranging from the scale of the general circula-1063
tion, right down to the plumes at scales of <1 km, through eddies about the deformation1064
radius (O(5km) during winter period in the mixing area). As Marshall and Schott (1999)1065
pointed out, a major challenge is to transform the obtained insights into parametric repre-1066
sentations that address the complex 3–D nature of the processes at work. We have made1067
a major step forward in that direction, about 15 years later, with a better description1068
of the processes thanks to the autonomous platform technology, and can now consider1069
not only some qualitative but also some quantitative aspects concerning deep convection.1070
Deep convection is very difficult to observe due to its multi-scale variability and because1071
it happens during severe weather events that generally prevents the use of ships. We1072
have demonstrated that the massive –and artful– deployment of autonomous platforms1073
in combination with more classical research cruises, can change the way we perceive the1074
oceanic environment, allowing us to reach a much better spatio-temporal coverage. There1075
is a paradigm change with the use of mobile platforms, such as gliders and profiling1076
floats. Although, this concerns a limited number of physical and biogeochemical variables1077
D R A F T October 23, 2017, 10:16am D R A F T
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(the ones measured by miniaturized sensors that can equip such platforms: temperature,1078
salinity, currents, oxygen concentration, chl-a concentration, turbidity estimates, etc.),1079
this allows to better comprehend the deep convection and subsequent bloom phenomena1080
at various scales.1081
Deep convection and subsequent bloom have revealed ever greater complexity. Note-1082
worthy are key elements that appear to be prominent for deep convection and subsequent1083
bloom. The summer stratification is certainly key as it will be eroded continuously un-1084
til the vertical mixing reaches great depths. Horizontal inhomogeneities in density in1085
the mixed layer modulate its deepening, while fronts sharpen and (baroclinic) instability1086
processes develop and produce a mesoscale turbulence. When the vertical mixing has1087
eroded the LIW layer, it can reach quickly great depths (in about 1-2 weeks) and produce1088
nWMDW resulting from mixing of the underlying WMDW with the water resulting from1089
the mixing of AW and LIW above. Plumes develop with a downward plume radius of1090
about 350m over a turbulent flow presenting a scale of about 5km embedded in the gen-1091
eral circulation an ultimately forming the long-lived SCVs. The location of such intense1092
vertical mixing is mainly due to preconditioning effects at various scales (gyre, mesoscale,1093
submesoscale) as sketched in Figure 13, that is interesting to consider together with Fig-1094
ures 6, 7 and 8 for the large scale aspects and Figures 10 and 12 for the smaller ones.1095
Submesoscale turbulence and horizontal transfers shape a deep mixing area in the center1096
of the basin gyre circulation that is surrounded by a Transition Zone where lateral ex-1097
changes are prominently located between the Mixed Patch and the boundary circulation1098
(Northern Current and its recirculation along the North Balearic Front). In about 1-21099
weeks, several storms induce several mixing events and restratification ones in-between1100
D R A F T October 23, 2017, 10:16am D R A F T
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that ultimately produce a water column that is mixed from the surface to the bottom.1101
The SCV phenomenology appears to be key for understanding the deep convection pro-1102
cess because of their role in preconditioning and lateral exchanges. In addition, symmetric1103
instability develops along fronts under down front winds, which vertically and horizon-1104
tally mixes the waters from each side of the fronts and make typical interleaving pattern1105
emerge. The preconditioning and the spreading occur during the violent mixing phase.1106
When the buoyancy loss stops, much of the flow and the spreading of water masses is1107
eddy-dominated and highly variable while serious recapping processes concur due to both1108
heat (and freshwater) gain and oceanic instabilities. Herein lies the reason why deep con-1109
vection is such an interesting phenomenon from a theoretical point of view and why it is1110
such a challenging and demanding process to observe and model.1111
Our multi-platform approach allowed to have more synoptic observations and provided1112
new results on deep convection. This can be considered as a major step forward com-1113
pared to previous studies limited to very few in situ observations of the water column.1114
Our observations allow performing first budgets and assessments with a continuity and1115
accuracy that was never reached before in terms of potential temperature, salinity, MLD,1116
APE, KE, EKE, formation rates but also estimates of chl-a based on in situ data. They1117
also provide a new and nice description of several types of the SCVs, especially along1118
the vertical, including new (or first time identified as such) circulation features like the1119
long-lived cyclonic SCVs. They also allowed a first statistical description of plumes and1120
provided a first in-situ indication of the importance of symmetric instability, all around1121
the deep convection area, down front winds in meanders in the South and in a more1122
pronounced way along the Northern Current where the topographic constraint orients1123
D R A F T October 23, 2017, 10:16am D R A F T
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more generally the flow along Mistral and Tramontane winds. Not only the processes1124
are becoming clearer from a physical point of view but also their prominent impact and1125
significance for biological processes.1126
The budgets and diagnostics presented in this paper can be made in numerical models1127
as well and we advocate that models should be able to produce the same results as1128
presented here, to be considered as presenting a high realism in simulating the deep1129
convection process (and subsequent bloom) and as able to provide relevant conclusions on1130
particular processes and climate projections. The observations carried out in 2012-20131131
could be considered as a first benchmark and a lot of further progress in the (physical and1132
biogeochemical) modelling of deep convection, and subsequent bloom phenomena can be1133
expected by further comparing these observations and numerical simulations.1134
Moreover, the data set collected from ships and autonomous platforms (gliders, profiling1135
floats, moorings, surface drifters) offers an invaluable context for observations based on1136
water samples from ship data. While ship surveys allowed delayed-mode quality control for1137
data collected by autonomous platforms, they were augmented by a better spatio-temporal1138
coverage for a few physical/bio-optical variables. Noteworthy, this could be extended to1139
estimate budgets for other variables with conditional objective analyses methods and work1140
is in progress to estimates budgets for biogeochemical variables that are more scarcely1141
observed. Furthermore, with the addition of numerical modeling and data assimilation, a1142
further insight of the deep convection and subsequent bloom phenomena can be reasonably1143
expected. The DEWEX framework has already motivated many studies based on both1144
observations and modeling and this will undoubtedly furthermore developed, in particular1145
with respect to biogeochemical theory and modeling. Many studies have already used this1146
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 57
wonderous data set and many others can be legitimately anticipated. There is still a lot1147
to investigate and we dare anticipate this will go beyond this special issue.1148
It was urgent and timely to carry out this experiment, in such a way a first spatio-1149
temporal coverage (from and in situ observing point of view) providing adequate initial-1150
ization information is available for 2012-2013, while embedded in the less intense but on1151
the long term observational framework of MOOSE. While the fluxes (from atmospheric1152
models) are more and more validated, the monitoring of some of the resultant changes in1153
the system is now feasible with modern techniques, and this must be done from now in a1154
more global and fit-for-purpose Mediterranean GOOS (Global Ocean Observing System)1155
programme encompassing the whole Mediterranean Sea that can address critical societal1156
issues at this scale. In the future, the knowledge will narrow and more frequent (spatio-1157
temporal) data set will be possible and required to further investigate and monitor the1158
processes. There must be concerted efforts in developing both the spatio-temporal cover-1159
age of the in-situ observing systems (in combination with satellites) and the number of1160
variables that can be observed in an autonomous way. The long-term observations will1161
serve as a backbone for further understanding at the process level on an interannual basis1162
while one can anticipate further and more intense process studies will be developed. As the1163
miniaturization of sensors will increase, the number, the diversity of platforms and sensors1164
on-board will likely unlock our knowledge on many processes/cycles, and transports of1165
energy and various matter in the ocean.1166
We presented an approach that was not only quite successful but especially scalable,1167
and this motivates to develop the same multi-platform/multi-scale strategy for other ar-1168
eas/processes. What has been learned about how to operate such a complex program is1169
D R A F T October 23, 2017, 10:16am D R A F T
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that preparation, coordination and funding are key aspects and it was only possible to1170
achieve it building on several national and European infrastructures and several research1171
programs. No call for proposals could be solely solicited to achieve such an experiment and1172
we hope this will change in the future for the sake of simplicity and continuous knowledge1173
improvements.1174
D R A F T October 23, 2017, 10:16am D R A F T
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EOP gliders
proling oats
LOP gliders
MOOSE moorings
MOOSE cruises
D(E/O)WEX cruises
LION
LACAZE-DUTHIERS
DYFAMED
Corsica
Spain
Italy
France
Sardinia
Menorca
Ibiza
Mallorca
PLANIER
Ligurian
Sea
Gulf of Lions
Pyrenees
Alps
Figure 1. All observations carried out between 1st July 2012 and 1st October 2013.
Gliders surface positions (red dots) and measured depth-average currents (yellow arrows).
Profiling floats surface positions and trajectories (green). CTD casts from research cruises
(blue). Surface drifters trajectories (grey). Positions of the LION, LACAZE-DUTHIERS,
PLANIER, and DYFAMED moorings (white dots). The two selection areas ”Boundary
Current” and ”Mixed Patch” used in Figure 4 are displayed in white.
D R A F T October 23, 2017, 10:16am D R A F T
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Figure 2. (top) Spatial coverage during the so-called ”preconditioning” (Sep 1–Dec
15, 2012), ”mixing” (Dec 15, 2012–Mar 31, 2013) and ”restratification” (Apr 1–May 31,
2013) phases of deep convection. The number of profiles respectively collected by gliders,
Argo profiling floats and R/V is indicated. (bottom) Surface chlorophyll-a concentration
retrieved by satellite (L3 MODIS product) and averaged on November 1–2, 2012 (left),
February 13–21, 2013 (middle), April 12–14, 2013 (right) that correspond to cloud-free
periods during each phase.
D R A F T October 23, 2017, 10:16am D R A F T
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Cruises names Ships Dates Reference
MOOSE-GE 2012 R/V Le Suroˆıt July 2012 [Testor et al., 2012]
DOWEX 2012 R/V Tethys II September 2012 [Mortier , 2012]
HyMeX SOP1 R/V Urania, September 2012 [Ducrocq et al., 2014]
R/V Le Provence and October 2012 [Taupier-Letage, 2013]
DEWEX-1 R/V Le Suroˆıt February 2013 [Testor, 2013]
HyMeX SOP2 R/V Tethys II, January, [Estournel et al., 2016a]
R/V Le Provence February, [Taupier-Letage and Bachelier, 2013]
March,
and May 2013
DEWEX-2 R/V Le Suroˆıt April 2013 [Conan, 2013]
MOOSE-GE 2013 R/V Tethys II June 2013 [Testor et al., 2013]
DOWEX 2013 R/V Tethys II September 2013 [Mortier and Taillandier , 2013].
Table 1. List of cruises carried out in the framework of the DEWEX experiment.
D R A F T October 23, 2017, 10:16am D R A F T
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38.3 38.4 38.5 38.6 38.7
12.9
13
13.1
13.2
13.3
13.4
13.5
13.6
13.7
13.8
13.9
28.9
29
29
29.1
29.2
150m
300m
500m
750m
1000m
2500m
θ[ °C]
Salinity
Preconditionning 2012: Sep 1Dec 15, 2012
38.46 38.47 38.48 38.49 38.5 38.51
12.85
12.9
12.95
13
13.05
29.1
29.11
29.12
29.13
28.9
29
29
29.1
29.2
surface 150m
300m
500m
750m
1000m
2500m
Convection 2013: Dec 15Mar 31, 2013
38.46 38.47 38.48 38.49 38.5 38.51
12.85
12.9
12.95
13
13.05
29.1
29.11
29.12
29.13
Salinity
38.3 38.4 38.5 38.6 38.7
12.9
13
13.1
13.2
13.3
13.4
13.5
13.6
13.7
13.8
13.9
28.9
29
29
29.1
29.2
150m 300m
500m
750m
1000m
2500m
Postconvection 2013: Apr 1Sep 31, 2013
38.46 38.47 38.48 38.49 38.5 38.51
12.85
12.9
12.95
13
13.05
29.1
29.11
29.12
29.13
nWMDM (θ=12.912°C, S=38.49)
Salinity
LIW
AW
WIW WMDW
Figure 3. Probability density function in the θ/S space of all CTD casts data during
the MOOSE-GE 2013, DOWEX 2012, HyMeX-SOP1 2012, HyMeX SOP2 2013, DEWEX
2013-1, DEWEX 2013-2, MOOSE-GE 2013, and DOWEX 2013 cruises, the 1% less fre-
quent values being not shown. The dashed lines with depth labels represent the mean θ/S
profile over each time period. The bottom panels focuses on the deep waters and shows
the transformation of the deep waters during the convection event.
D R A F T October 23, 2017, 10:16am D R A F T
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Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
0
100
200
Chl [mg m−2]
(0−300 m)
(i)
0
1
2
3
Chl [mg m−3]
surface
(h)
0
1000
2000
MLD [m]
(g)
12.9
12.92
12.94
θ [°C]
1500−2000 m
(f)
13
13.2
13.4
θ [°C]
400−600 m
(e)
12
14
16
18
20
θ [°C]
0−100 m
(d)
37.8
38
38.2
38.4
SSS
(c)
12
13
14
θ [°C]
500 m, Lacaze−Duthiers Canyon
(b)
−600
−400
−200
0
200
Qnet [W m−2]
(a)
Figure 4. Timeseries of: (a) Estimated net heat fluxes at the LION buoy; (b) poten-
tial temperature at 500 m recorded in Lacaze-Duthiers canyon (Gulf of Lions shelf); (c)
sea surface salinity at the LION buoy; (d) potential temperature average over the layer
0–100 m; (e) potential temperature averaged over the layer 400–600 m; (f) potential tem-
perature average over the layer 1500–2000 m; (g) Mixed Layer Depth (MLD) estimates
as in Houpert et al. [2016]; (h) estimates of chl-a at surface; and (i) estimates of chl-a
integrated over 0–300 m. Light colors correspond to the ”Boundary Current” selection
area while darker colors correspond to the ”Mixed Patch” one (see white delineated areas
on Figure 1).
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−1
−0.8
−0.6
−0.4
−0.2
0
Buoy. flux [m
2 s−2]
(a)
0
20
40
60
80
100
APE [kJ m−2]
(b)
0
100
200
300
400
KE [cm2 s−2]
0
20
40
60
80
100
KE [kJ m−2]
(c)
0
50
100
150
200
EKE [cm2 s−2]
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
0
10
20
30
40
50
EKE [kJ m−2]
(d)
Figure 5. Timeseries of: (a) Integrated buoyancy flux estimated at the LION me-
teorological buoy (blue dots indicate negative net heat flux, and red positive ones); (b)
Available Potential Energy (APE) integrated from the surface down to 1000 m from all
glider density profiles; (c) total Kinetic Energy (KE); and (d) Eddy Kinetic Energy (EKE)
estimated for 0–1000 m layer from the glider depth-average currents. The black line shows
the mean signal binned into 5 days period and smoothed with a moving average of 30 days.
The gray shaded area represents the standard deviation in each 5 days bin.
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 65
]
468
39
40
41
42
43
44
Latitude
Longitude
01−Aug−2012
4 6 8
Longitude
12−Sep−2012
468
Longitude
14−Feb−2013
4 6 8
Longitude
15−Apr−2013
4 6 8
39
40
41
42
43
44
Latitude
Longitude
26−Jun−2013
MLD [m]
0
500
1000
1500
2000
468
39
40
41
42
43
44
Latitude
Longitude
01−Aug−2012
4 6 8
Longitude
12−Sep−2012
468
Longitude
14−Feb−2013
4 6 8
Longitude
15−Apr−2013
4 6 8
39
40
41
42
43
44
Latitude
Longitude
26−Jun−2013
S0−100
38
38.1
38.2
38.3
38.4
38.5
468
39
40
41
42
43
44
Latitude
Longitude
01−Aug−2012
4 6 8
Longitude
12−Sep−2012
468
Longitude
14−Feb−2013
4 6 8
Longitude
15−Apr−2013
4 6 8
39
40
41
42
43
44
Latitude
Longitude
26−Jun−2013
θ400−600 [°C]
12.9
12.95
13
13.05
13.1
13.15
13.2
13.25
13.3
468
39
40
41
42
43
44
Latitude
Longitude
01−Aug−2012
4 6 8
Longitude
12−Sep−2012
468
Longitude
14−Feb−2013
4 6 8
Longitude
15−Apr−2013
Chl [mg m−2]
0
50
100
150
200
4 6 8
39
40
41
42
43
44
Latitude
Longitude
26−Jun−2013
Figure 6. Objective analyses of a) MLD estimates as in [Houpert et al., 2016], homoge-
neous profiles over more than 1000m were extrapolated to the bottom along the vertical
thanks to LION mooring data b) surface Salinity (averaged over 0–100 m), c) potential
temperature at intermediate depth (averaged over 400–600 m), d) chl-a estimates aver-
aged over 0–300 m. Extrapolated values being estimated to have an error of more than
95% in terms of variance of the analyzed field at 75km are shaded. Data points within
±10 days from the date of the analysis are superimposed (thin black circles filled with
colors coded with values).
D R A F T October 23, 2017, 10:16am D R A F T
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468
39
40
41
42
43
44
Latitude
05−Jan−2013
4 6 8
15−Jan−2013
468
25−Jan−2013
4 6 8
04−Feb−2013
468
39
40
41
42
43
44
Latitude
14−Feb−2013
4 6 8
24−Feb−2013
468
06−Mar−2013
4 6 8
16−Mar−2013
MLD [m]
0
500
1000
1500
2000
468
39
40
41
42
43
44
Latitude
26−Mar−2013
4 6 8
Longitude
05−Apr−2013
468
Longitude
15−Apr−2013
4 6 8
Longitude
25−Apr−2013
Figure 7. Objective analysis of MLD similar as in figure 6 computed on a 10-day
basis. Continuous and dashed black contours indicate MLD greater than 1000m and
500m respectively.
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 67
468
39
40
41
42
43
44
Latitude
15−Jan−2013
4 6 8
25−Jan−2013
468
04−Feb−2013
4 6 8
14−Feb−2013
468
39
40
41
42
43
44
Latitude
24−Feb−2013
4 6 8
06−Mar−2013
468
Longitude
16−Mar−2013
4 6 8
Longitude
26−Mar−2013
σ900−1000 [kg m−3]
29.1
29.105
29.11
29.115
29.12
468
39
40
41
42
43
44
Latitude
Longitude
05−Apr−2013
4 6 8
Longitude
15−Apr−2013
468
Longitude
25−Apr−2013
4 6 8
Longitude
05−May−2013
Figure 8. Objective analysis of potential density averaged over 900-1000m depth on a
10-day basis. The convection area used to assess deep water formation rates is delineated
in black.
D R A F T October 23, 2017, 10:16am D R A F T
X - 68 TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS
Jan Feb Mar Apr May
0
2
4
6
8
10 x 1013
Jan Feb Mar Apr May
0
0.5
1
1.5
2x 105
Date
Volume [km3]
(a)
Date
MLD [m3]
(d)
Jan Feb Mar Apr May
2.5
2
1.5
1
0.5
0
0.5
1
1.5
Date
Formation Rate [Sv]
(b)
29.09 29.1 29.11 29.12 29.13
0
0.5
1
1.5
2
σref [kg m3]
Production rate, σ> σref [Sv]
(c)
Figure 9. (a) Temporal evolution of the volume for waters presenting greater densities
than 28.3 kg/m3(blue) corresponding to the minimum density observed in the deep
convection area shown in figure 8, 29.11 kg/m3(green), 29.115kg/m3(yellow) and 29.12
kg/m3(red). Error bars in gray result from the optimal interpolation error. (b) Temporal
evolution of the volume of water between consecutive isopycnals calculated by comparison
to the situation on 5th January and reduced to Sv (volume averaged over one year) for
waters presenting densities lower than 29.11 kg/m3(blue), between 29.11 kg/m3and
29.15kg/m3(green), between 29.115kg/m3and 29.12kg/m3(yellow) and greater than
29.12kg/m3(red). Error bars result from those of panel (a). (c) Volume of water denser
than a given isopycnal produced between the 5th January and 24th of February. Error bars
are computed from the volume error of each density class of the optimal interpolation.
For clarity, they are only plotted for waters undergoing a net volume increase during
considered period. (d) Convective volume defined as the volume-integrated mixed layer.
The continuous line represents this quantity for MLD greater than 1000 m, the dashed
line for MLD greater than 500 m. Error bars in gray result from the optimal interpolation
error.
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 69
0 50 100 150 200 250 300 350
0
200
400
600
800
1000
28.7
28.7
29
29
29.09
29.09
29.1
29.1
29.1
29.1
29.1
29.105 29.105
29.105 29.105 29.105
29.105
Glider along−track distance [km]
Depth [m]
(a) Preconditionning (17 Sep−06 Oct 2012)
θ [°C]
12.7
12.8
12.9
13
13.1
13.2
13.3
13.4
0 50 100 150 200 250 300 350 400
0
200
400
600
800
1000
28.7
29
29
29.09
29.09
29.1 29.1
29.1
29.105 29.105 29.105
29.11
29.11
29.11
29.11
29.11
29.11
29.11
29.11
29.11
29.11
Glider along−track distance [km]
Depth [m]
(b) Deep mixing (17 Feb−06 Mar 2013)
θ [°C]
12.7
12.8
12.9
13
13.1
13.2
13.3
13.4
0 20 40 60 80 100 120 140 160
0
200
400
600
800
1000
28.7
29
29.09
29.1
29.1
29.1
29.1
29.105 29.105 29.105
Glider along−track distance [km]
Depth [m]
(c) Spreading phase (21 Jun−28 Jun 2013)
θ [°C]
12.7
12.8
12.9
13
13.1
13.2
13.3
13.4
4 6 8
39.5
40
40.5
41
41.5
42
42.5
43
43.5
44
Longitude
Latitude
(d)
Figure 10. Glider potential temperature sections across the northwestern basin il-
lustrating the role of SCVs during the (a) preconditioning, (b) violent mixing and (c)
spreading phases. White circles indicate locations of SCVs. White triangles indicate
interleaving at the edge of the deep convection area.
D R A F T October 23, 2017, 10:16am D R A F T
X - 70 TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS
Figure 11. Vertical trajectory of a glider evolving in the Mixed Patch during violent
mixing events color-coded with potential temperature, salinity and potential density and
estimates (black arrows) of oceanic vertical velocities based on the glider flight model
presented in Margirier et al..
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 71
0 20 40 60 80 100 120
0
200
400
600
800
1000
29
29.09
29.09
29.09
29.1 29.1
29.1
29.1
29.1 29.1
29.105
Depth [m]
Glider along−track distance [km]
(c)
DO [µmol L−1]
150
160
170
180
190
200
210
0 20 40 60 80 100 120
0
200
400
600
800
1000
29
29.09
29.09
29.09
29.1 29.1
29.1
29.1
29.1 29.1
29.105
Depth [m]
(b)
Salinity
38.2
38.3
38.4
38.5
38.6
0 20 40 60 80 100 120
0
200
400
600
800
1000
29
29.09
29.09
29.09
29.1 29.1
29.1
29.1
29.1 29.1
29.105
Depth [m]
(a)
θ [°C]
12.9
13
13.1
13.2
13.3
13.4
13.5
13.6
0 20 40 60 80 100 120
0
200
400
600
800
1000
29
29.09
29.09
29.09
29.1 29.1
29.1
29.1
29.1 29.1
29.105
Glider along−track distance [km]
Depth [m]
(e)
TU
0
0.02
0.04
0.06
0.08
0.1
0 20 40 60 80 100 120
0
200
400
600
800
1000
29
29.09
29.09
29.09
29.1 29.1
29.1
29.1
29.1 29.1
29.105
Depth [m]
(d)
Chl [mg m−3]
0
0.1
0.2
0.3
0.4
0.5
Figure 12. Glider sections across the Transition Zone between the Northern Cur-
rent and the Mixed Patch of a) potential temperature, b) salinity, c) dissolved oxygen
(uncalibrated), d) chl-a fluorescence and e) turbidity (uncalibrated).
D R A F T October 23, 2017, 10:16am D R A F T
X - 72 TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS
Figure 13. Schematic diagram of the evolution of the convection area during the violent
mixing phase in a period of 1-2 weeks. Underlying stratification/outcrop is shown by
selected isopycnals (continuous black lines). The volume of fluid just mixed by convection
is shaded and color coded according to potential density classes.
D R A F T October 23, 2017, 10:16am D R A F T
TESTOR ET AL.: MULTI-SCALE DEEP CONVECTION OBSERVATIONS X - 73
Acknowledgments. Hydrographical data were collected, and made freely available by the1175
Coriolis project (http://www.coriolis.eu.org) and programmes that contribute. We would like to1176
acknowledge the staff of the French National Pool of Gliders (DT-INSU/CNRS-CETSM/Ifremer)1177
for the sustained gliders deployments carried out in the framework of MOOSE, as well as the1178
intensive deployments during this 2012-2013 DEWEX experiment and to warmly thank Captains1179
and crews of R/V Le Tethys II (CNRS/INSU, France), R/V Le Provence (Phares et Balises,1180
France), and R/V Le Suroˆıt (Ifremer, France) and R/V Urania (CNR, Italy), as well as all sci-1181
entists, engineers, technicians and students who participated to the MOOSE-GE, HyMeX/SOP11182
and SOP2, DEWEX, and DOWEX different cruises and autonomous platforms deployments.1183
Support was provided by the French ”Chantier M´editrran´ee” MISTRALS program (HyMeX and1184
MERMeX components), the ANR ASICSMED project (ANR 12-BS06-0003), the MOOSE long-1185
term observatory (SOERE/AllEnvi-SNO/INSU), the Bio-Argo project (CNES-TOSCA) and the1186
ANR NAOS project (ANR J11R107F), as well as the NATO STO-CMRE NOMR12 experiment.1187
Support was also provided by the EU projects FP7 GROOM (Grant Agreement No. 284321),1188
FP7 PERSEUS (Grant Agreement No. 287600), FP7 JERICO (Grant Agreement No. 262584)1189
and the COST Action ES0904 ”EGO” (Everyone’s Gliding Observatories).1190
1191
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... This geostrophic jet flows along the Anatolian coast, the eastern Cretan Arc, and progressively veers northeastward at the entrance of the Cretan Passage, overall designing the RCG rim current (Figure 2). Similar Mediterranean cyclonic gyres have been quantitatively studied, in the Gulf of Lions (Testor et al., 2018), the Ligurian Sea , in the Adriatic Sea (Poulain, 2001), which bears a resemblance to winter intensification of the RCG and the seasonal enhancement of vertical circulations. In the present observation, the AMC flows further west, its thickness reaches 300 m depth, its isopycnals sharpen and outcrop along a density front separating the light and salty waters of the current with denser central waters, overall designing a doming structure (Figures 2 and 5). ...
... This is a dominant process in the other Mediterranean cyclonic gyres; however such formation events can occur intermittently in the RCG (e.g., Gertman et al., 1994;Kubin et al., 2019;Sur et al., 1992). The reported glider transect (Figure 5a) crossed one sub-mesoscale coherent vortex, formed among the mixed patch during a short-term event of moderate convection, as previously described in similar Mediterranean cyclonic gyres (Bosse et al., 2016;Testor et al., 2018). On the other hand, it is important to stress that LIW type waters are formed in the periphery of the RCG (e.g., Özsoy et al., 1989). ...
... This study confirms the efficiency of in-situ multiplatform approaches for accurately characterizing the dynamics of dense water formation zones. As previously carried out over the northwestern Mediterranean Sea, they have revealed the underlying sub-mesoscale processes of newly formed water spreading (Testor et al., 2018), and documented trends to salinification of the Mediterranean water source regions (Margirier et al., 2020). Contextualized by regional-scale field surveys based on cruises and a glider, the timeseries collected by profiling floats gave access to intra-seasonal changes in the hydrographical state of the RCG, from the preconditioning phase until the stratification phase, thus allowing a non-equivocal determination of the LIW, their properties, their origins and their fate. ...
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Climatic changes and interannual variability in the Mediterranean overturning circulation are crucially linked to dense water formation in the Levantine Sea, namely the Levantine Intermediate Water whose formation zone, comprising multiple and intermittent sources, extends over fluctuating pathways. To probe into the variability of this water formation and spreading, a unique dataset was collected during the winter of 2019 in the western Levantine Sea, via oceanographic cruises, profiling floats and a glider, at a spatio‐temporal distribution suited to resolve mesoscale circulation features and intermittent convection events. This study highlights the competition between two source regions, the Cretan Sea and the Rhodes Cyclonic Gyre, to supply the Mediterranean overturning circulation in Levantine Intermediate Water. The Cretan source was estimated as the most abundant, supported by increasingly saltier water masses coming from the Levantine Sea under the pumping effect of a water deficit caused by strong western outflow toward the Ionian Sea.
... Advective processes prevail in some sub-regions. Other subregions are mostly affected by convective mixing, in particular winter convection that plays a major role in vertical water column homogenization such as in the Gulf of Lion, South Adriatic, Cretan Sea and Rhode Gyre (Pinardi et al., 2015;Testor et al., 2018). These sub-regions also allow taking into consideration the spatial variability of the surface ocean warming rate in response to global change ( Figure 1A). ...
... They could complement observations in poorly or unevenly sampled dynamical regions (e.g., deep convection areas, upwelling and downwelling zones, permanent currents or mesoscale eddies) to better characterize the ocean processes involved in the MHW propagation (both vertical extent and horizontal advection), to estimate their contributions and thus to enhance the predictability depending on the sub-regional dynamics. This model-based analysis could be supported by observational platforms deployed in specific dynamical areas such as the fixed mooring and gliders in the deep convection area in Gulf of Lion (MOOSE network 10 ; Testor et al., 2018), the SOCIB gliders in the Ibiza Channel (Heslop et al., 2012) or the fixed mooring in the Sicily Strait (Schroeder et al., 2017). ...
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Society is facing climate-related challenges and impacts, such as marine heat wave (MHW) events that adversely affect ecosystems, threaten economies and strengthen storms by warming ocean waters. MHWs are substantially increasing in intensity, duration and frequency worldwide, particularly in the Mediterranean Sea, which responds rapidly to climate change. This study proposes a comprehensive analysis of MHWs in the different sub-regions of the Mediterranean, where the strong spatial variability requires focused attention, from surface to sub-surface and from open to coastal oceans. At surface, the MHW indices have dramatically increased over the last four decades from 1982 to 2020, with an unprecedented acceleration rate in recent years in all sub-regions. Besides the sub-regional features of surface MHWs, the propagation of such events into the ocean interior is also examined highlighting sub-regional and seasonal variability in the sub-surface ocean response. The resulting upper-ocean density stratification to these extreme events is enhanced in all sub-regions which would increase the degree of decoupling between surface and deep oceans causing changes in water masses and marine life. Finally, extremely warm events in coastal waters are also addressed through a case study in the Balearic Islands showing their higher intensity and occurrence in near-shore environment as well as the different response from surface to sub-surface that strongly depends on local features. In addition to this study, the Balearic Islands Coastal Observing and Forecasting System (SOCIB) has implemented a smart platform to monitor, visualize and share timely information on sub-regional MHWs, from event detection in real-time to long-term variations in response to global warming, to diverse stakeholders. Society-aligned ocean information at sub-regional scale will support the policy decision-making and the implementation of specific actions at local, national and regional scales, and thus contribute to respond to societal and worldwide environmental challenges.
... The seasonal bloom in the NW Mediterranean is triggered by deep water formation episodes that take place in the Gulf of Lion. These events are driven by evaporation caused by strong, cold and dry northerly winds (MEDOC group, 1970;Schott et al., 1996;Testor et al., 2018). Winter convection in this region is one of the major contributors to the overall primary production in the NW Mediterranean. ...
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Fish recruitment variability results from a complex mix of biological and physical processes, and their interactions, acting in the early life stages. In this study, we aim to investigate the environmental factors driving the recruitment success of blue whiting (Micromesistius poutassou) in the NW Mediterranean through reconstruction of early life history traits derived from otoliths microstructure analysis. To this purpose, the study characterizes the oceanographic conditions in winter-spring of two contrasted years, 2017 and 2018, and relates them to the onset and duration of the spawning, growth performance and condition of blue whiting recruits. Winter 2017 was mild, with a short period of cold temperatures and limited winter vertical mixing. In winter 2018, temperatures reached lower values and extended for a longer period with intense vertical mixing and dense water cascading down the continental slope at the end of February. These different conditions were mirrored in the subsequent phytoplanktonic bloom, which in 2018 occurred later and was more intense than in 2017, extending over a longer period and occupying a wider area. The reproductive period of blue whiting was linked to the duration of low winter temperatures (∼13 °C), shorter in 2017 than in 2018 (∼40 and ∼60 days respectively). While in 2017 all individuals were born in a relatively short period of time, and under similar environmental conditions, in 2018 the hatching period presented two differentiated peaks, separated by a period of relatively low hatchings at the end of February. This gap corresponded to dense water cascading and intense vertical mixing events, suggesting that these phenomena limited the survival of eggs and larvae. The few individuals hatched in this period showed the lowest growth rates (maximum values ∼1.5 mm TL day⁻¹), which would be related with the poor trophic environment during these events of intense mixing. Conversely, in both years, higher growth rates corresponded to the individuals born just before the phytoplankton bloom (attaining ∼2 mm TL day⁻¹). Recruits of 2018 showed better condition than those born the previous year which would be associated with the higher primary production detected in spring 2018. The recruitment strength in 2018, estimated from landings, was also much higher indicating that severe winter conditions translate into improved recruitment of a temperate water fish in the NW Mediterranean Sea.
... The NWMed DWF has been largely studied (Durrieu de Madron et al., 2013;Estournel et al., 2016;Houpert et al., 2016;Margirier et al., 2020;MEDOC-MEDOC Group, 1970;Testor et al., 2018) and many attempts to characterize DWF events in the NWMed using high temporal and spatial resolution numerical models have been made (Léger et al., 2016;Seyfried et al., 2017;Somot et al., 2018;Waldman et al., 2017). Most of these works are focused on the impact of atmospheric forcing and ocean preconditioning on the deep convection. ...
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Deep water formation (DWF) in the North Western Mediterranean (NWMed) is a key feature of Mediterranean overturning circulation. DWF changes under global warming may have an impact on the Mediterranean biogeochemistry and marine ecosystem. Here we analyze the deep convection in the Gulf of Lions (GoL) in a changing climate using a regional climate system model with a horizontal resolution high enough to represent DWF. We find that under the RCP8.5 scenario the NWMed DWF collapses by 2040–2050, leading to a 92% shoaling in the winter mixed layer by the end of the century. The collapse is related to a strengthening of the vertical stratification in the GoL caused by changes in properties of Modified Atlantic Water and Levantine Intermediate Water, being their relative contribution to the increase of the stratification 57.8% and 42.2%, respectively. The stratification changes also alter the Mediterranean overturning circulation and the exchange with the Atlantic.
... Furthermore, the North Western (NW) Medsea is one of the few places of the world's oceans where open ocean deep convection can occur [Marshall and Schott, 1999] in consequence of intense atmospheric forcing and oceanic preconditioning. From 2009 to 2013, deep intense mixing events occurred in this region during which the mixed layer depth reached the seafloor every winter at about 2500 m [Bosse et al., 2021;Testor et al., 2018] in the Gulf of Lions (GoL) area, whereas the offshore Ligurian area was mainly impacted by the convective events occurring in 2012 and 2013 [Margirier et al., 2020]. Due to this bottom-reaching convection, fresh and cold surface wtaers are more deeply mixed with the intermediate and deep waters, and these upward movements inject nutrients and fuel phytoplanktonic growth [Bosse et al., 2021]. ...
Thesis
La mer Méditerranée est souvent considérée comme un océan laboratoire pour comprendre les changements globaux liés à l’augmentation de CO2 atmosphérique. Ce travail, basé sur l’étude de données recueilles dans trois régions méditerranéennes, étudie les variations du CO2 océanique dans ce bassin. À l’échelle de la saison, outre les changements de température, le contenu en alcalinité influe sur le contenu en CO2 en Méditerranée orientale, tandis que les changements en carbone total sont responsables des variations dans le bassin occidental. En zone côtière urbanisée, l’émission de CO2 anthropique conditionne les échanges air-mer de CO2. Cette étude montre que l’augmentation de carbone et l’acidification à l’échelle de plusieurs années ne sont pas seulement dues à l’augmentation du CO2 atmosphérique : le contenu en alcalinité module ces tendances dans le bassin oriental, tandis que, dans le bassin occidental, ces tendances sont vraisemblablement influencées par la dynamique des courants.
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The presence of two large-scale cyclonic gyres in the Algerian Basin influences the general and eddy circulation, but their effects on water mass transfer remain poorly characterized. Our study has confirmed the presence of these gyres using the first direct current measurements of the whole water column collected during the SOMBA-GE2014 cruise, specifically designed to investigate these gyres. Using cruise sections and a climatology from 60 years of in situ measurements, we have also shown the effect of these gyres on the distribution at intermediate depth of Levantine Intermediate Water (LIW) with warmer (∼ 0.15 ∘C) and saltier (∼ 0.02) characteristics in the Algerian Basin than in the Provençal Basin. The Algerian Gyres, combined with the effect of anticyclonic Algerian Eddies, also impact horizontal density gradients with sinking of the isopycnals at the gyres' centers. Temporal cross-correlation of LIW potential temperature referenced to a signal observed southwest of Sardinia reveals a timescale of transit of 4 months to get to the center of the Algerian Basin. The LIW potential temperature and salinity trends, on average in the basin interior, are estimated to be +0.0022 ± 0.0002 ∘Cyr-1 and +0.0022 ± 0.0001 yr−1, respectively, over the 1968–2017 period and accelerating to +0.048 ± 0.003 ∘Cyr-1 and +0.0076 ± 0.0009 yr−1 over the 2013–2017 period.
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The warming of the surface ocean is expected to increase the stratification of the upper water column. This would decrease the efficiency of the wind-induced mixing, reducing the nutrient supply to the euphotic layer and the productivity of the oceans. Climatic projections show that the Mediterranean Sea will experience a strong warming and salting along the twenty first century. Nevertheless, very few works have found and quantified changes in the water column stratification of the Western Mediterranean. In this work, we obtain time series of Mixed Layer Depth (MLD) along the Spanish Mediterranean waters and the Gulf of Cádiz, using periodic CTD profiles collected under the umbrella of the Ocean Observing system of the Instituto Español de Oceanografía (IEO-CSIC). The length of the time series analyzed is variable, depending on the geographical area, but in some cases these time series extend from the beginning of the 1990s decade. Our results show that at present, no statistically significant changes can be detected. These results are confirmed by the analysis of MLD time series obtained from Argo profilers. Some of the meteorological factors that could affect the water column stratification (wind intensity and precipitation rates) did not experience significant changes for the 1990-2021 period, neither were observed long-term changes in the chlorophyll concentration. The hypothesis proposed to explain this lack of trends, is that the salinity increase of the surface waters has compensated for the warming, and consequently, the density of the upper layer of the Western Mediterranean (WMED) has remained constant. As the wind intensity has not experienced significant trends, the stratification of the Spanish Mediterranean waters and those of the Gulf of Cádiz would have not been affected. Nevertheless, we do not discard that our results are a consequence of the short length of the available time series and the large variance of the variables analyzed, evidencing the importance of the maintenance of the ocean monitoring programs.
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To improve high-resolution numerical environmental prediction, it is essential to represent ocean–atmosphere interactions properly, which is not the case in current operational regional forecasting systems used in western Europe. The objective of this paper is to present a new forecast-oriented coupled ocean–atmosphere system. This system uses the state-of-the-art numerical models AROME (cy43t2) and NEMO (v3.6) with a horizontal resolution of 2.5 km. The OASIS coupler (OASIS3MCT-4.0), implemented in the SurfEX surface scheme and in NEMO, is used to perform the communications between models. A sensitivity study of this system is carried out using 7 d simulations from 12 to 19 October 2018, characterized by extreme weather events (storms and heavy precipitation) in the area of interest. Comparisons with in situ and L3 satellite observations show that the fully coupled simulation reproduces the spatial and temporal evolution of the sea surface temperature and 10 m wind speed quantitatively well. Sensitivity analysis of ocean–atmosphere coupling shows that the use of an interactive and high-resolution sea surface temperature (SST), in contrast to actual numerical weather prediction (NWP) where SST is constant, modifies the atmospheric circulation and the location of heavy precipitation. Simulated oceanic fields show a large sensitivity to coupling when compared to the operational ocean forecast. The comparison to two distinct forced ocean simulations highlights that this sensitivity is mainly controlled by the change in the atmospheric model used to drive NEMO (AROME vs. IFS operational forecast), and less by the interactive air–sea exchanges. In particular, the oceanic boundary layer depths can vary by more than 40 % locally, between the two ocean-only experiments. This impact is amplified by the interactive coupling and is attributed to positive feedback between sea surface cooling and evaporation.
Thesis
La mer Méditerranée est caractérisée par une circulation rapide des masses d’eau, des concentrations faibles en nutriments avec un fort gradient d’oligotrophie, et une acidification plus rapide que pour l’océan global. Les Eaux Levantines Intermédiaires (LIW) reliant les deux bassins sont marquées par un minimum d’oxygène (O2). Les variabilités du contenu en O2, des nutriments et du carbone inorganique restent méconnues du fait de leur faible densité d’observation. Le développement et la validation d’une méthode neuronale CANYON-MED, spécifiquement conçue pour la Méditerranée, ont permis de dériver nutriments (nitrates, phosphates, silicates) et variables du système des carbonates (alcalinité totale, carbone total et pH) à partir de variables systématiquement mesurées (pression, température, salinité et O2, position spatio-temporelle). La dynamique du minimum d’O2 dans la LIW face à la variabilité des processus de ventilation des eaux intermédiaires en Méditerranée nord-occidentale a été étudiée sur la période 2012-2020. L’application de CANYON-MED a permis la description des tendances en nutriments et carbonates dans cette zone, face au phénomène intermittent de convection profonde. L’importance de la convection sur la ventilation des masses d’eau, et sur les tendances des nutriments et d’acidification sont mises en évidence, dans un contexte de stratification accrue par le changement climatique. Enfin, la ventilation de la LIW a été explorée dans sa zone de formation (le bassin Levantin) à l’aide de flotteurs Argo sur la période 2018-2019, nuançant l’injection d’O2 dans le patch de mélange.
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The Western MEDiterranean Sea BioGeochemical Climatology (BGC-WMED, https://doi.org/10.1594/PANGAEA.930447) (Belgacem et al., 2021) presented here is a product derived from quality-controlled in situ observations. Annual mean gridded nutrient fields for the period 1981–2017 and its sub-periods 1981–2004 and 2005–2017 on a horizontal 1/4∘ × 1/4∘ grid have been produced. The biogeochemical climatology is built on 19 depth levels and for the dissolved inorganic nutrients nitrate, phosphate and orthosilicate. To generate smooth and homogeneous interpolated fields, the method of the variational inverse model (VIM) was applied. A sensitivity analysis was carried out to assess the comparability of the data product with the observational data. The BGC-WMED was then compared to other available data products, i.e., the MedBFM biogeochemical reanalysis of the Mediterranean Sea and the World Ocean Atlas 2018 (WOA18) (its biogeochemical part). The new product reproduces common features with more detailed patterns and agrees with previous records. This suggests a good reference for the region and for the scientific community for the understanding of inorganic nutrient variability in the western Mediterranean Sea, in space and in time, but our new climatology can also be used to validate numerical simulations, making it a reference data product.
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Numerous gliders have been deployed in the Gulf of Lions (Northwestern Mediterranean Sea) and in particular during episodes of open-ocean deep convection in the winter 2012–2013. The data collected represents an unprecedented density of in-situ observations providing a first in-situ statistical and 3D characterization of the important mixing agents of the deep convection phenomenon, the so-called plumes. A methodology based on a glider-static flight model was applied to infer the oceanic vertical velocity signal from the glider navigation data. We demonstrate that, during the active phase of mixing, the gliders underwent significant oceanic vertical velocities up to 18 cm.s−1. Focusing on the data collected by two gliders during the 2012–2013 winter, 120 small scale convective downward plumes were detected with a mean radius of 350∼m and separated by about 2∼km. We estimate that the plumes cover 27% of the convection area. Gliders detected downward velocities with a magnitude larger than that of the upward ones (-6 cm.s−1 versus +2 cm.s−1 on average). Along track recordings of temperature and salinity as well as biogeochemical properties (dissolved oxygen, fluorescence, turbidity) allow a statistical characterization of the water masses' properties in the plumes' core with respect to the 'background': the average downward signal is of colder (-1.8 × 10−3°C), slightly saltier (+4.9 × 10−4 psu) and thus denser waters (+7.5 × 10−4 kg.m−3). The plunging waters are also on average more fluorescent (+2.3 × 10−2 μg.L−1). The plumes are associated with a vertical diffusion coefficient of 7.0 m2.s−1 and their vertical velocity variance scales with the ratio of the buoyancy loss over the Coriolis parameter to the power 0.86.
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Winter 2012-2013 was a particularly intense and well-observed Dense Water Formation (DWF) event in the Northwestern Mediterranean Sea. In this study, we investigate the impact of the mesoscale dynamics on DWF. We perform two perturbed initial state simulation ensembles from summer 2012 to 2013, respectively mesoscale-permitting and mesoscale-resolving, with the AGRIF refinement tool in the Mediterranean configuration NEMOMED12. The mean impact of the mesoscale on DWF occurs mainly through the high-resolution physics and not the high-resolution bathymetry. This impact is shown to be modest: the mesoscale doesn't modify the chronology of the deep convective winter nor the volume of dense waters formed. It however impacts the location of the mixed patch by reducing its extent to the west of the North Balearic Front and by increasing it along the Northern Current, in better agreement with observations. The maximum mixed patch volume is significantly reduced from 5.7 ± 0.2 to 4.2 ± 0.6 1013m3. Finally, the spring restratification volume is more realistic and enhanced from 1.4 ± 0.2 to 1.8 ± 0.2 1013m3 by the mesoscale. We also address the mesoscale impact on the ocean intrinsic variability by performing perturbed initial state ensemble simulations. The mesoscale enhances the intrinsic variability of the deep convection geography, with most of the mixed patch area impacted by intrinsic variability. The DWF volume has a low intrinsic variability but it is increased by 2-3 times with the mesoscale. We relate it to a dramatic increase of the Gulf of Lions eddy kinetic energy from 5.0 ± 0.6 to 17.3 ± 1.5cm2/s2, in remarkable agreement with observations.
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