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  <front>
    <journal-meta>
<journal-id journal-id-type="publisher">BG</journal-id>
<journal-title-group>
<journal-title>Biogeosciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">BG</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Biogeosciences</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1726-4189</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>

    <article-meta>
      <article-id pub-id-type="doi">10.5194/bg-13-1287-2016</article-id><title-group><article-title>Decline of the Black Sea oxygen inventory</article-title>
      </title-group><?xmltex \runningtitle{Decline of the Black Sea oxygen inventory}?><?xmltex \runningauthor{A. Capet et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff2">
          <name><surname>Capet</surname><given-names>Arthur</given-names></name>
          <email>arthurcapet@gmail.com</email>
        <ext-link>https://orcid.org/0000-0002-5939-3836</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Stanev</surname><given-names>Emil V.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Beckers</surname><given-names>Jean-Marie</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Murray</surname><given-names>James W.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Grégoire</surname><given-names>Marilaure</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>OGS, National Institute of Oceanography and Experimental Geophysics, Trieste, Italy</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>MAST, MARE, University of Liège, Liège, Belgium</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>HZG, Helmholtz-Zentrum Geesthacht, Hamburg, Germany</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>GHER, GeoHydrodynamics and Environment Research, University of Liège, Liège, Belgium</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>School of Oceanography, University of Washington, Seattle, WA, USA</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Arthur Capet (arthurcapet@gmail.com)</corresp></author-notes><pub-date><day>1</day><month>March</month><year>2016</year></pub-date>
      
      <volume>13</volume>
      <issue>4</issue>
      <fpage>1287</fpage><lpage>1297</lpage>
      <history>
        <date date-type="received"><day>31</day><month>August</month><year>2015</year></date>
           <date date-type="rev-request"><day>2</day><month>October</month><year>2015</year></date>
           <date date-type="rev-recd"><day>17</day><month>February</month><year>2016</year></date>
           <date date-type="accepted"><day>19</day><month>February</month><year>2016</year></date>
      </history>
      <permissions>
<license license-type="open-access">
<license-p>This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions><self-uri xlink:href="https://bg.copernicus.org/articles/.html">This article is available from https://bg.copernicus.org/articles/.html</self-uri>
<self-uri xlink:href="https://bg.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://bg.copernicus.org/articles/.pdf</self-uri>


      <abstract>
    <p>We show that from 1955 to 2015, the inventory of oxygen in the Black Sea has
decreased by 44 % and the basin-averaged oxygen penetration depth has
decreased from 140 m in 1955 to 90 m in 2015, which is the shallowest
annual value recorded during that period.</p>
    <p>The oxygenated Black Sea surface layer separates the world's largest
reservoir of toxic hydrogen sulfide from the atmosphere. The threat of
chemocline excursion events led to hot debates in the past decades arguing on
the vertical stability of the Black Sea oxic/suboxic interface. In the 1970s
and 1980s, when the Black Sea faced severe eutrophication, enhanced
respiration rates reduced the thickness of the oxygenated layer.
Re-increasing oxygen inventory in 1985–1995 supported arguments in favor of
the stability of the oxic layer. Concomitant with a reduction of nutrient
loads, it also supported the perception of a Black Sea recovering from
eutrophication. More recently, atmospheric warming was shown to reduce the
ventilation of the lower oxic layer by lowering cold intermediate layer (CIL)
formation rates.</p>
    <p>The debate on the vertical migration of the oxic interface also addressed the
natural spatial variability affecting Black Sea properties when expressed in
terms of depth. Here we show that using isopycnal coordinates does not overcome the
significant spatial variability of oxygen penetration depth. By
considering this spatial variability, the analysis of a composite historical
set of oxygen profiles evidenced a significant shoaling of the oxic layer,
and showed that the transient “recovery” of the 1990s was mainly a result
of increased CIL formation rates during that period.</p>
    <p>As both atmospheric warming and eutrophication are expected to increase in
the near future, monitoring the dynamics of the Black Sea oxic layer is
urgently required to assess the threat of further shoaling.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p>The Black Sea deep waters constitutes the world's largest reservoir of toxic
hydrogen sulfide. 100 m of ventilated surface waters are all that separate
this reservoir from the atmosphere. This situation results from the permanent
halocline <xref ref-type="bibr" rid="bib1.bibx29" id="paren.1"/> that separates the surface layer (of low salinity
due to river inflow) from the deeper layer (of high salinity due to inflowing
Mediterranean seawater), restraining ventilation to the upper layer
(Fig. <xref ref-type="fig" rid="Ch1.F1"/>).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><caption><p>Typical profiles of temperature, salinity,
Brunt–Väisälä frequency (<inline-formula><mml:math display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula>), potential density anomaly
(<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi mathvariant="italic">θ</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) and oxygen concentration in the central Black Sea (May).
Note the two peaks in the vertical stratification: the thermocline, which is
seasonal and corresponds roughly to the upper limit of the cold intermediate layer and the halocline, which is permanent, and correspond roughly to the
lower limit of the cold intermediate layer and the upper boundary of the
suboxic zone. Red dotted lines and shaded areas illustrate the diagnostic
values derived from each profile (Sect. <xref ref-type="sec" rid="Ch1.S2.SS2"/>).</p></caption>
        <?xmltex \igopts{width=469.470472pt}?><graphic xlink:href="https://bg.copernicus.org/articles/13/1287/2016/bg-13-1287-2016-f01.png"/>

      </fig>

      <p>In the lower part of the halocline, a permanent suboxic layer separates the
Black Sea surface oxygenated waters ([O<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>] <inline-formula><mml:math display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M) from the
deep sulfidic waters ([H<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>S] <inline-formula><mml:math display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M)
<xref ref-type="bibr" rid="bib1.bibx23 bib1.bibx24 bib1.bibx41" id="paren.2"/>. More precisely, <xref ref-type="bibr" rid="bib1.bibx23" id="text.3"/>
considered a threshold of 10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M of oxygen because they analyzed
high-quality oxygen data. The threshold of 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M of oxygen was
applied later to analyze historical oxygen data of lower quality. The upper
(O<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> disappearance) and lower (H<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mi>S</mml:mi></mml:mrow></mml:math></inline-formula> onset) interfaces of this suboxic
layer are controlled by different biogeochemical and physical
processes <xref ref-type="bibr" rid="bib1.bibx16 bib1.bibx36" id="paren.4"/>, and undergo uncorrelated vertical
migrations <xref ref-type="bibr" rid="bib1.bibx17" id="paren.5"/>. Sinking organic matter is mainly respirated
aerobically within the oxycline: the lower part of the oxygenated layer
where oxygen concentration decreases downwards to 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M. Increasing
flux of organic matter, induced by a period of high nutrient load from the
1970s to the late 1880s, resulted in higher oxygen consumption above the
suboxic layer and a shoaling of the upper suboxic interface
<xref ref-type="bibr" rid="bib1.bibx10 bib1.bibx17 bib1.bibx42" id="paren.6"/>.</p>
      <p>After reduction of nutrient inputs around 1990 <xref ref-type="bibr" rid="bib1.bibx19" id="paren.7"/>, the Black
Sea was described as a recovering ecosystem <xref ref-type="bibr" rid="bib1.bibx21 bib1.bibx27" id="paren.8"/>. This
perspective was supported by improved eutrophication indices in the open sea
<xref ref-type="bibr" rid="bib1.bibx15" id="paren.9"/> as well as the stabilization of the upper suboxic
interface in the 1990s <xref ref-type="bibr" rid="bib1.bibx17" id="paren.10"/>. However, the timescale of the
expected recovery (i.e., the timescale associated with the chain of
biogeochemical mechanisms relating oxycline penetration depth to riverine
nutrient loads) is not quantitatively understood. Several processes cause the
oxycline depth to respond with a time lag to the reduction of riverine
nutrient inputs. First, nutrients are mainly delivered to the northwestern
shelf, where the accumulation of organic matter in the sediments buffers the
riverine inputs, with slow diagenetic processes controlling and delaying the
nutrient outflow across the seaward boundary <xref ref-type="bibr" rid="bib1.bibx6" id="paren.11"/>. Second, the
intermediate oxidation–reduction cycling of nitrogen, sulfur, manganese,
iron and phosphorus that separates oxygen from hydrogen sulfide
<xref ref-type="bibr" rid="bib1.bibx33 bib1.bibx10 bib1.bibx16 bib1.bibx43" id="paren.12"/> can delay the
response of the lower suboxic interface to changing nutrient fluxes by
several years <xref ref-type="bibr" rid="bib1.bibx16" id="paren.13"/>.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><caption><p>Temporal distribution of the ship-based oxygen profiles merged from
the World Ocean Database, R/V <italic>Knorr</italic> 2003 and R/V <italic>Endeavor</italic>
2005 campaigns. Only the profiles containing at least five observation depths,
one observation above 30 m depth and one record with
[O<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>] <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 20  <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M were considered.</p></caption>
        <?xmltex \igopts{width=469.470472pt}?><graphic xlink:href="https://bg.copernicus.org/articles/13/1287/2016/bg-13-1287-2016-f02.png"/>

      </fig>

      <p>In addition to these biogeochemical factors, the dynamics of the upper and
lower interfaces of the suboxic layer are controlled by physical processes
<xref ref-type="bibr" rid="bib1.bibx16 bib1.bibx36" id="paren.14"/>. In the Black Sea, dense waters formed by
winter cooling and mixing <xref ref-type="bibr" rid="bib1.bibx37" id="paren.15"/> do not sink to the deepest
layer, as in the Mediterranean Sea, but accumulate on top of the permanent
halocline (Fig. <xref ref-type="fig" rid="Ch1.F1"/>). The resulting cold intermediate layer (CIL)
is a major feature of the Black Sea vertical structure. Cold intermediate
water formation and advection by the cyclonic basin-wide Rim Current
<xref ref-type="bibr" rid="bib1.bibx29 bib1.bibx5" id="paren.16"/> ventilate the oxycline and thereby influence
variability in the depth of the upper suboxic interface
<xref ref-type="bibr" rid="bib1.bibx16" id="paren.17"/>. Recently, atmospheric warming <xref ref-type="bibr" rid="bib1.bibx27" id="paren.18"/> was
shown to reduce the ventilation of the lower oxic layer
<xref ref-type="bibr" rid="bib1.bibx42 bib1.bibx30" id="paren.19"/>. At deeper levels, the dense sinking plume
formed by the Mediterranean inflow through the Bosporus, which entrains water
from the overlying CIL, injects fingers of oxygenated water directly into the
deeper part of the suboxic layer and upper sulfidic layer and thus acts to
control the depth of the lower suboxic interface
<xref ref-type="bibr" rid="bib1.bibx17 bib1.bibx18 bib1.bibx16 bib1.bibx12" id="paren.20"/>.</p>
      <p>Previous long-term analyses of the vertical migration of the suboxic
interfaces either ended (1955–1995; <xref ref-type="bibr" rid="bib1.bibx17" id="altparen.21"/>) or started
(1985–2015; <xref ref-type="bibr" rid="bib1.bibx30" id="altparen.22"/>) with the eutrophication period,
excluding the large–scale overview required to grasp the interactions of
eutrophication and climate factors. Those analyses lacked a comprehensive
consideration of the natural spatial and seasonal variability of the vertical
distribution of oxygen.</p>
      <p>In the presence of large gradients, uneven data distribution may induce
artificial signals when interannual trends are assessed from direct annual
averages. In the stratified Black Sea, properties expressed in terms of depth
coordinates (<inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">m</mml:mi></mml:math></inline-formula>) present a high spatial variability due to mesoscale
features <xref ref-type="bibr" rid="bib1.bibx14" id="paren.23"/> and to the general curvature of Black Sea
isopycnals <xref ref-type="bibr" rid="bib1.bibx29 bib1.bibx36" id="paren.24"/>. As an alternative, using density
(isopycnal levels, <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi mathvariant="italic">θ</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) as vertical coordinate is generally
considered a stable solution to assess the vertical migration of the
chemocline on a decadal scale <xref ref-type="bibr" rid="bib1.bibx41 bib1.bibx32 bib1.bibx24" id="paren.25"/>.
However, the spatial confinement of the lateral oxygen injections associated
with the Bosporus plume, as well as the spatial variability of diapycnal
ventilating processes <xref ref-type="bibr" rid="bib1.bibx44" id="paren.26"/>, imposes a horizontal structure
to the oxygen penetration depth when expressed in terms of density
<xref ref-type="bibr" rid="bib1.bibx34 bib1.bibx12" id="paren.27"/>. As this spatial gradient might scale with the
temporal variations (a range of 0.17 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">kg</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> was observed during
the Knorr 2003 campaign, <xref ref-type="bibr" rid="bib1.bibx12" id="altparen.28"/>), it has to be considered when
deriving interannual trends.</p>
      <p>The present study describes the application of the DIVA (Data-Interpolating
Variational Analysis) detrending procedure <xref ref-type="bibr" rid="bib1.bibx40 bib1.bibx7" id="paren.29"/>
to untangle the temporal and spatial variability of three indices related to
the Black Sea oxygenation status: the depth and density level of oxygen
penetration and the oxygen inventory. These values were diagnosed from a
composite historical data set of oxygen vertical profiles. We review the
evolution of those indices through the past 60 years and discuss the
respective controls of eutrophication and climate factors.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><caption><p>Distribution of the ship-based oxygen profiles
(Fig. <xref ref-type="fig" rid="Ch1.F2"/>) available for each period (black dots). The last
panel displays the trajectories of the ARGO floats. Number of profiles for
each period are given in the text. Map data:
<sup>©</sup>Google 2015.</p></caption>
        <?xmltex \igopts{width=469.470472pt}?><graphic xlink:href="https://bg.copernicus.org/articles/13/1287/2016/bg-13-1287-2016-f03.png"/>

      </fig>

</sec>
<sec id="Ch1.S2">
  <title>Material and methods</title>
<sec id="Ch1.S2.SS1">
  <title>Data</title>
      <p>We gathered a composite set of 4385 ship-based vertical profiles (oxygen,
temperature and salinity) obtained between 1955 and 2005 in the Black Sea
using CTD rosette bottles, continuous pumping profilers <xref ref-type="bibr" rid="bib1.bibx10" id="paren.30"/>
and in situ analyzers <xref ref-type="bibr" rid="bib1.bibx12" id="paren.31"/> from the World Ocean Database
(<uri>http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html</uri>), and R/V
<italic>Knorr</italic> 2003 and R/V <italic>Endeavor</italic> 2005 campaigns
(<uri>http://www.ocean.washington.edu/cruises/Knorr2003/</uri>,
<uri>http://www.ocean.washington.edu/cruises/Endeavor2005/</uri>). Only the
profiles containing at least five observation depths, one observation above
30 m depth and one record with [O<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>] <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M were retained
for analysis. The temporal and spatial distribution of the selected
ship-based profiles are displayed in Figs. <xref ref-type="fig" rid="Ch1.F2"/> and
<xref ref-type="fig" rid="Ch1.F3"/>, respectively.</p>
      <p>To complement the analysis of ship-based casts, we considered profiles
originating from 10 Argo autonomous profilers (May 2010–December 2015).
Only good quality-checked real-time data were considered <xref ref-type="bibr" rid="bib1.bibx8" id="paren.32"/>.
Two of these floats (Argo ID 7900465 and 7900466) have been presented and
discussed by <xref ref-type="bibr" rid="bib1.bibx35" id="text.33"/>, where the consistence and comparability of
Argo and historical profiles is asserted within a 1 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M error range.</p>
      <p>Several studies address the error of Argo real-time oxygen data
<xref ref-type="bibr" rid="bib1.bibx2 bib1.bibx38 bib1.bibx13" id="paren.34"><named-content content-type="pre">e.g.,</named-content></xref>. Demonstrating that the
Black Sea real-time Argo data are precisely (i.e., at fine scales) comparable
with historical Winkler data, or identifying the relevant correction, is
beyond the scope of the present study which addresses monthly to decadal timescales. Evenly distributed small-scale error (e.g., difference between
ascending and descending profiles due to sensor time response) were thus
filtered by the temporal smoothing. However, a systematic error is not
strictly excluded which could reach an underestimation of 10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M
(Virginie Thierry, IFREMER, personal communication, January 2016). Therefore,
we evaluated a “worst-case” scenario in the analysis of Argo data by
considering a systematic underestimation of oxygen concentration by
10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M.</p>
      <p>Although most of the floats drifted along the basin periphery, some were also
advected in the central part (Fig. <xref ref-type="fig" rid="Ch1.F3"/>). These trajectories
highlight the range of spatial variability for the diagnostics described in
Sect. <xref ref-type="sec" rid="Ch1.S2.SS2"/>.</p>
      <p>The investigation time frame was divided into periods according to data
availability and to dissociate known phases of eutrophication <xref ref-type="bibr" rid="bib1.bibx25 bib1.bibx19" id="paren.35"/> and CIL dynamics <xref ref-type="bibr" rid="bib1.bibx31 bib1.bibx7" id="paren.36"/> – see also
<xref ref-type="bibr" rid="bib1.bibx27" id="text.37"/> for decadal cycles in the Black Sea: 1955–1975 (1575
ship-based profiles), 1976–1985 (1350 ship-based profiles), 1986–1998 (1324
ship-based profiles) and 1999–2015 (136 ship-based profiles and 1393 Argo
profiles).</p>
</sec>
<sec id="Ch1.S2.SS2">
  <title>Profile analysis</title>
      <p>From each profile we derived (1) the depth and (2) the potential density
anomaly <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi mathvariant="italic">θ</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> where oxygen concentration went below 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M
and (3) the oxygen inventory, integrated above this limit
(Fig. <xref ref-type="fig" rid="Ch1.F1"/>). The threshold value of 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M used to define
the upper interface of the suboxic layer was suggested to compare oxygen
observations issued from sensors with different detection limits
<xref ref-type="bibr" rid="bib1.bibx17" id="paren.38"/>. To evaluate how a 10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M underestimation by
Argo profilers would affect the main conclusions, oxygen penetration depths
an density levels for Argo were also computed using a threshold of
10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M.</p>
      <p>The CIL cold content (Fig. <xref ref-type="fig" rid="Ch1.F1"/>) was diagnosed from corresponding
salinity and temperature profiles following <xref ref-type="bibr" rid="bib1.bibx7" id="text.39"/>. It
indicates on the intensity of CIL formation smoothed over 4–5 years, i.e,.
the residence time of cold intermediate waters
<xref ref-type="bibr" rid="bib1.bibx37 bib1.bibx31 bib1.bibx7" id="paren.40"/>:

                <disp-formula id="Ch1.E1" content-type="numbered"><mml:math display="block"><mml:mrow><mml:mi mathvariant="normal">CIL</mml:mi><mml:mspace linebreak="nobreak" width="0.25em"/><mml:mi mathvariant="normal">cold</mml:mi><mml:mspace width="0.25em" linebreak="nobreak"/><mml:mi mathvariant="normal">content</mml:mi><mml:mo>=</mml:mo><mml:mi>c</mml:mi><mml:mi mathvariant="italic">ρ</mml:mi><mml:munder><mml:mo movablelimits="false">∫</mml:mo><mml:mi mathvariant="normal">CIL</mml:mi></mml:munder><mml:mfenced close="]" open="["><mml:mi>T</mml:mi><mml:mo>(</mml:mo><mml:mi>z</mml:mi><mml:mo>)</mml:mo><mml:mo>-</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mtext>CIL</mml:mtext></mml:msub></mml:mfenced><mml:mtext>d</mml:mtext><mml:mi>z</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>

          where <inline-formula><mml:math display="inline"><mml:mi mathvariant="italic">ρ</mml:mi></mml:math></inline-formula> is the density and <inline-formula><mml:math display="inline"><mml:mi>c</mml:mi></mml:math></inline-formula> the heat capacity and
<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mtext>CIL</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn>8.35</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C <xref ref-type="bibr" rid="bib1.bibx35" id="paren.41"/>.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <title>DIVA analysis</title>
      <p>Climatologies for the whole period and interannual trends were identified for
the three oxygen diagnostics by applying the DIVA detrending algorithm on the
ship-based data set (see details in Appendix <xref ref-type="sec" rid="App1.Ch1.S1"/>).</p>
      <p>In short, the DIVA interpolation software
(<uri>http://modb.oce.ulg.ac.be/mediawiki/index.php/DIVA</uri>;
<xref ref-type="bibr" rid="bib1.bibx40" id="altparen.42"/>) computes a gridded climatology obtained by minimizing
a cost function which penalizes gradients and misfits with observations. The
DIVA detrending algorithm <xref ref-type="bibr" rid="bib1.bibx7" id="paren.43"/> computes trends for each
year, i.e., the average difference between data pertaining to this year and
the spatial analysis at these data locations. This procedure allows one to
account for the sampling error associated with spatial/temporal variability.</p><?xmltex \hack{\newpage}?>
</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results</title>
<sec id="Ch1.S3.SS1">
  <title>Spatial variability</title>
      <p>The spatial distribution of the oxygen penetration depth
(Fig. <xref ref-type="fig" rid="Ch1.F4"/>a) reflects the general curvature of the Black Sea
vertical structure. A range of approximately 70 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">m</mml:mi></mml:math></inline-formula> was observed
between oxygen penetration depth in the periphery (150 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">m</mml:mi></mml:math></inline-formula>) and in the
central part (80 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">m</mml:mi></mml:math></inline-formula>).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><caption><p>Annual climatologies of <bold>(a)</bold> oxygen penetration depth (where
[O<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>] <inline-formula><mml:math display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 20 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M), <bold>(b)</bold> potential anomaly at oxygen
penetration depth and <bold>(c)</bold> oxygen inventory. These spatial
climatologies were constructed from the ship-based data set (1955–2005),
accounting for the temporal variability of these diagnostics and the uneven
distribution of data (see Sect. <xref ref-type="sec" rid="Ch1.S2.SS3"/>).</p></caption>
          <?xmltex \igopts{width=184.942913pt}?><graphic xlink:href="https://bg.copernicus.org/articles/13/1287/2016/bg-13-1287-2016-f04.png"/>

        </fig>

      <p>A significant spatial variability remains when expressing oxygen penetration
in terms of potential density anomaly <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi mathvariant="italic">θ</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>
(Fig. <xref ref-type="fig" rid="Ch1.F4"/>b). While the central part bears typical values of
15.75 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">kg</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>, a deeper anomaly (in terms of density) can be seen
in the area of the Bosporus plume (16.1 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">kg</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>), which then
decreases along the southern (15.85–15.9 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">kg</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>) and eastern
periphery (15.85 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">kg</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>). These result in a range of spatial
variability of 0.35 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">kg</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5" specific-use="star"><caption><p><bold>(a)</bold> Oxygen penetration depth, <bold>(b)</bold> oxygen
penetration density levels and <bold>(c)</bold> oxygen inventory derived from
Argo profiles. The color legend gives the unique Argo identification number
of the floats. Colored lines and color-filled areas indicate smoothed time
series for each float (second-degree LOESS smoother, span <inline-formula><mml:math display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.5, 0.95
confidence intervals). The black line and gray shaded area are the smoothed
time series obtained when considering all floats (reported in
Fig. <xref ref-type="fig" rid="Ch1.F6"/>).</p></caption>
          <?xmltex \igopts{width=384.112205pt}?><graphic xlink:href="https://bg.copernicus.org/articles/13/1287/2016/bg-13-1287-2016-f05.png"/>

        </fig>

      <p>The spatial distribution of the oxygen inventory (Fig. <xref ref-type="fig" rid="Ch1.F4"/>c)
follows that of the oxygen penetration depth. The range of spatial
variability reaches 12 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">mol</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math></inline-formula> m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, i.e., between
17 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">mol</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math></inline-formula> m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the central part and
29 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">mol</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math></inline-formula> m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the periphery.</p>
      <p>The ranges of spatial variability derived from these spatial analyses agreed
with those depicted by the Argo profilers (Fig. <xref ref-type="fig" rid="Ch1.F5"/>), bearing
in mind the different timescales under consideration.</p>
</sec>
<sec id="Ch1.S3.SS2">
  <title>Temporal variability</title>
      <p>Between 1955 and 2005, the oxygen penetration depth rose by an average rate of
7.9 m per decade (Fig. <xref ref-type="fig" rid="Ch1.F6"/>a). The basin average was of 140 m in
1955 (ship-based), 100 m in 2005 (ship-based) and 90 m in 2015 (Argo).
Considering a systematic underestimation by 10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M in the Argo data
would result in an oxygen penetration depth around 95 m for 2015 (Argo).</p>
      <p>This shoaling was also observed on the potential density scale
(<inline-formula><mml:math display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.074 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">kg</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> per decade, Fig. <xref ref-type="fig" rid="Ch1.F6"/>b). The basin
average was of 16.05 kg m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in 1955 (ship-based), 15.6 kg m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>
in 2005 (ship-based) and around 15.3 kg m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in 2015 (Argo).
Considering a systematic underestimation by 10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M in the Argo data
would result in values of 15.5 m for 2015 (Argo).</p>
      <p>The oxygen inventory, integrated from the surface down to the suboxic upper
interface, decreased by 44 % during the last 60 years
(Fig. <xref ref-type="fig" rid="Ch1.F6"/>c), considering the ship-based estimate for 1955
(27 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">mol</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mi mathvariant="normal">O</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>) and the Argo estimate for 2015
(15 <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">mol</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mi mathvariant="normal">O</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>). The few ship-based profiles available after the
mid-1990s revealed the lowest oxygen inventories recorded during the time frame
covered by the present study.</p>
      <p>The temporal signals departed from these linear trends between 1988 and 1996,
during which deeper oxygen penetration (both in terms of depth and density)
and higher oxygen content were observed.</p>
</sec>
<sec id="Ch1.S3.SS3">
  <title>Oxygen inventory and CIL cold content</title>
      <p>Positive relationships between oxygen inventory and CIL cold content were
obtained for all periods (Fig. <xref ref-type="fig" rid="Ch1.F7"/>). Considering a given level of
CIL cold content, the corresponding oxygen inventory decreased significantly
from period 1955–1975 to period 1986–1998 (Fig. <xref ref-type="fig" rid="Ch1.F7"/>b).</p>
      <p>The relationship between oxygen inventory and CIL cold content for the period
1999–2015 does not differ significantly from that obtained for the period
1986–1998 (Fig. <xref ref-type="fig" rid="Ch1.F7"/>b). This comparison should be considered with
caution, however, as oxygen profiles for the period 1999–2015 originate
mainly from Argo floats whose sampling rate is much higher than ship-based
casts.</p>
      <p>High CIL cold content is much more frequent during the period 1986–1998,
while low CIL cold content is more frequent during 1999–2013.</p><?xmltex \hack{\newpage}?>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Discussion</title>
      <p>The spatial analysis of oxygen penetration depth showed that the use of
density coordinates does not eliminate the sampling error associated with
uneven spatial coverage (Fig. <xref ref-type="fig" rid="Ch1.F4"/>). Deeper oxygen
penetration (on a density scale) in the Bosporus area were expected, in relation with the
intermediate lateral injections associated with the Bosporus plume. In
addition, deeper oxygen penetration (on a density scale) in the southern and eastern
periphery suggests the occurrence of diapycnal ventilation along the steep
bathymetry <xref ref-type="bibr" rid="bib1.bibx44" id="paren.44"/>. The aggregation of the most recent
ship-based profiles in the Bosporus area and in the southeastern region
(Fig. <xref ref-type="fig" rid="Ch1.F3"/>), might have led to an overestimation of the
basin-average oxygen penetration depth in the last decade, hence to an
underestimation of the shoaling trend of the Black Sea oxic layer.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><caption><p>Trends of <bold>(a)</bold> oxygen penetration depth, <bold>(b)</bold> oxygen
penetration density level (<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi mathvariant="italic">θ</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) and <bold>(c)</bold> oxygen inventory
deduced from (dots) DIVA analysis of ship-based casts and (blue) ARGO floats.
In <bold>(a)</bold> and <bold>(b)</bold>, the diagnostics from ARGO are also shown
for the lower threshold of 10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M to acknowledge a potential bias
between Winkler and Argo data. Red lines: the linear trends assessed from the
ship-based data set are <inline-formula><mml:math display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>7.9 m decades<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>,
<inline-formula><mml:math display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.074 kg m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> decades<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> and
<inline-formula><mml:math display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.44 mol O m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> decades<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> for <bold>(a)</bold>, <bold>(b)</bold> and
<bold>(c)</bold>, respectively. Error bars on DIVA estimated trends indicate the
standard error associated with the estimation of the mean misfit for each
year (see Appendix <xref ref-type="sec" rid="App1.Ch1.S1"/>).</p></caption>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://bg.copernicus.org/articles/13/1287/2016/bg-13-1287-2016-f06.png"/>

      </fig>

      <p>Considering spatial variability revealed a clear shoaling trend for oxygen
penetration depth. This shoaling can be seen on both depth and density scales
(Fig. <xref ref-type="fig" rid="Ch1.F6"/>a, b). This confirms the hypothesis that the shoaling of
oxygen penetration depth is not due to a general shoaling of the main
halocline, but is associated with a shifted biogeochemical balance in the
oxygen budget <xref ref-type="bibr" rid="bib1.bibx10 bib1.bibx17 bib1.bibx42" id="paren.45"/>.</p>
      <p>Using <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi mathvariant="italic">θ</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> coordinates depicts clearer temporal variations
(Figs. <xref ref-type="fig" rid="Ch1.F5"/> and <xref ref-type="fig" rid="Ch1.F6"/>). The shoaling rate varies in time
and was more intense during 1970–1985 and from 1996 onwards. Argo
diagnostics using different oxygen threshold show a larger discrepancy in the
case of pycnal coordinates. The co-occurrence of density and oxygen gradients
(Fig. <xref ref-type="fig" rid="Ch1.F1"/>) results in a higher sensitivity to the sensor accuracy
for the <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi mathvariant="italic">θ</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> diagnostic for oxygen penetration. However, even a
systematic underestimation by 10 <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M of oxygen concentration by Argo
profilers does not invalidate our results.</p>
      <p>The positive correlations between CIL cold content and oxygen inventory
observed for all the periods illustrate the ventilation of intermediate
layers by CIL formation and advection (Fig. <xref ref-type="fig" rid="Ch1.F7"/>b). In the early 1990s,
the transient recovery of the three oxygenation diagnostics
(Figs. <xref ref-type="fig" rid="Ch1.F6"/>a, b, c, <xref ref-type="fig" rid="Ch1.F7"/>a) provided arguments supporting the
stability of the oxic interface <xref ref-type="bibr" rid="bib1.bibx41 bib1.bibx4" id="paren.46"/>. This
stabilization matched the convenient perception of a general recovery of the
Black Sea ecosystem after the reduction of nutrient load around 1990
<xref ref-type="bibr" rid="bib1.bibx19" id="paren.47"/>. However, Fig. <xref ref-type="fig" rid="Ch1.F7"/> indicates that the oxygenation
diagnostics obtained for the period 1986–1998 were associated with much
higher ventilation rates (i.e., higher CIL cold content) than during the
previous periods. If, in response to nutrient reduction, the biogeochemical
oxygen consumption terms had been lower during the period 1986–1998 than
previously, the increased ventilation during that period would have resulted
in higher oxygen inventories. Instead, oxygen inventories observed during
1986–1998 are lower than those observed in the previous decade for similar
levels of CIL cold content. We conclude that high CIL formation rates during
this period <xref ref-type="bibr" rid="bib1.bibx31 bib1.bibx7" id="paren.48"/> provided enough ventilation to
mask ongoing high oxygen consumption.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7" specific-use="star"><caption><p>Impact of convective ventilation on oxygen inventory. Frequency
distributions of <bold>(a)</bold> oxygen inventory and <bold>(c)</bold> cold intermediate layer (CIL) cold content diagnosed from ship-based and Argo
profiles for different periods (color legend). <bold>(b)</bold> LOESS regressions
(second degree polynomials, span <inline-formula><mml:math display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.75, <xref ref-type="bibr" rid="bib1.bibx9" id="altparen.49"/>) between oxygen
inventory and CIL cold content for the different periods (confidence interval
<inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="italic">α</mml:mi><mml:mo>=</mml:mo><mml:mn>0.99</mml:mn></mml:mrow></mml:math></inline-formula>). The positive relationships observed during each period
illustrate the ventilating action of CIL formation as a source of oxygen to
the intermediate levels. The shift of these relationships towards lower
oxygen inventories indicates shift in the oxygen budgets (higher consumption)
that are independent of the intensity of CIL formation.</p></caption>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://bg.copernicus.org/articles/13/1287/2016/bg-13-1287-2016-f07.png"/>

      </fig>

      <p>The fact that the relationship between oxygen inventories and CIL content for
the last period 1999–2015 is similar to that of 1986–1998 indicates a
stabilization in the biogeochemical oxygen consumption terms. Higher air
temperature in this last period <xref ref-type="bibr" rid="bib1.bibx26 bib1.bibx27 bib1.bibx30" id="paren.50"/>, by
limiting winter convective ventilation events <xref ref-type="bibr" rid="bib1.bibx7" id="paren.51"/> led to
the lowest oxygen inventories ever recorded for the Black Sea
(Fig. <xref ref-type="fig" rid="Ch1.F6"/>c).</p>
      <p>Forecasted global warming, without excluding transient high ventilation
periods, will limit CIL water formation <xref ref-type="bibr" rid="bib1.bibx7" id="paren.52"/> and reduce the
oxygenation of the Black Sea intermediate layers. At the same time,
uncertainties remain regarding the capacity of re-flourishing economies of
the lower Danube watershed to recover their productivity in a more
sustainable, less polluting form. Economic development in the Danube Basin
could reverse the improving situation of eutrophication if nutrients are not
managed properly <xref ref-type="bibr" rid="bib1.bibx19" id="paren.53"/>. Under these conditions, there is no
reason to expect that the oxycline shoaling observed over the past 60 years
will stabilize.</p>
      <p>There are reasons to worry about a rising oxycline in the Black Sea. First,
biological activity is distributed vertically on the whole oxygenated layer,
as indicated by zooplankton dial migration <xref ref-type="bibr" rid="bib1.bibx28" id="paren.54"/>. The
reduction of the oxygenated volume described in this study could therefore
have impacted on Black Sea living stocks by reducing carrying capacity and
increasing predation encounter rates. It would be timely to estimate now
the impact that a further shoaling of the oxic interfaces would bear on the
Black Sea resources for the fishing industry.</p>
      <p>Second, under present conditions, a massive atmospheric release of hydrogen
sulfide caused by a sudden outcropping of anoxic waters remains unlikely,
due to the stability of the Black Sea pycnal structure. Such outcropping
event of sulfidic waters would have dramatic ecological and economical
consequences <xref ref-type="bibr" rid="bib1.bibx22" id="paren.55"/>. On  27 September 2005, an anomalous
quasi-tropical cyclone was observed over the western Black Sea that led, in a
few days, to the outcropping of waters initially located at 30 m depth
<xref ref-type="bibr" rid="bib1.bibx11" id="paren.56"/>. Two years earlier, sulfide was measured in the same area
(western central gyre) at around 80 m <xref ref-type="bibr" rid="bib1.bibx12" id="paren.57"/>. Because global
warming is expected to increase the occurrence of extreme meteorological
events <xref ref-type="bibr" rid="bib1.bibx1" id="paren.58"/>, every meter of oxycline shoaling would bring the
Black Sea chemocline excursion events closer to the realm of possibility.</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Conclusions</title>
      <p>The present study evidenced the decline of the Black Sea oxygen inventory
during the second half of the 20th century and first decade of the 21st, and
highlighted the threat that further atmospheric warming casts upon the
vertical stability of the Black Sea oxygenated layer.</p>
      <p>Further works are urgently required to assess how actual nutrient emission
policies adequately prevent, in the context of forecasted warming, the
ecological and economical damages that would arise from a further shoaling of
the oxic interface.</p>
      <p>Spatially resolved biogeochemical models are needed to integrate explicitly
the interacting processes affecting the Black Sea oxycline.</p>
      <p><?xmltex \hack{\newpage}?>It is also essential (1) to determine to which extent the shoaling of the
oxygen penetration depth entrains a shoaling of the sulfidic
onset depth, (2) to set up a continuous monitoring
of the Black Sea oxygen inventory and the intensity of winter convective
ventilation (through CIL cold content), and (3) to clarify and quantify the
interplay of diapycnal and isopycnal ventilation mechanisms and, in
particular, the role played by the peripheral permanent/semi-permanent
mesoscale structures and how this relates to the intensity of the Rim Current
<xref ref-type="bibr" rid="bib1.bibx36 bib1.bibx20" id="paren.59"/>. We propose that these objectives might be
answered by maintaining in the Black Sea a minimum population of both moored
and drifting autonomous profilers equipped with oxygen and sulfidic sensors.</p><?xmltex \hack{\clearpage}?>
</sec>

      
      </body>
    <back><app-group>

<app id="App1.Ch1.S1">
  <title>The DIVA detrending algorithm</title>
      <p>DIVA (Data-Interpolating Variational Analysis) is a method for spatial
interpolation. Its principle is to construct an analyzed field <inline-formula><mml:math display="inline"><mml:mi mathvariant="italic">φ</mml:mi></mml:math></inline-formula> that
satisfies a set of constraints expressed in the form of a cost function over
a spatial domain <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">Ω</mml:mi></mml:math></inline-formula>. The cost function is made up of (1) an
<italic>observation constraint</italic>, which penalizes the misfit between data and
analysis, and (2) a <italic>smoothness constraint</italic>, which penalizes the
irregularity of the analyzed field (gradients, Laplacian, etc.).</p>
      <p>Let us assume that we work with data anomalies, i.e., a reference (or
background) field is subtracted from the data points prior the analysis. For
<inline-formula><mml:math display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula> data anomalies <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> at locations <inline-formula><mml:math display="inline"><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, the cost function reads,
in Cartesian coordinates,

              <disp-formula specific-use="align" content-type="numbered"><mml:math display="block"><mml:mtable displaystyle="true"><mml:mtr><mml:mtd><mml:mrow><mml:mi>J</mml:mi><mml:mo>[</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>=</mml:mo><mml:munder><mml:mo movablelimits="false">∫</mml:mo><mml:mi mathvariant="normal">Ω</mml:mi></mml:munder><mml:mfenced close=")" open="("><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>:</mml:mo><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant="italic">α</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>⋅</mml:mo><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant="italic">α</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:msub><mml:msup><mml:mi mathvariant="italic">φ</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mfenced><mml:mi mathvariant="normal">d</mml:mi><mml:mi mathvariant="normal">Ω</mml:mi></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd/><mml:mtd><mml:mrow><mml:mo>+</mml:mo><mml:munderover><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:munderover><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msup><mml:mfenced close="]" open="["><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:mtd></mml:mtr><mml:mlabeledtr id="App1.Ch1.E1"><mml:mtd/><mml:mtd/><mml:mtd><mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>J</mml:mi><mml:mi mathvariant="normal">smooth</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>]</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>J</mml:mi><mml:mi mathvariant="normal">obs</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>]</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mlabeledtr></mml:mtable></mml:math></disp-formula>

          where <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">α</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">α</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> are coefficients related to
characteristics of the data set. <inline-formula><mml:math display="inline"><mml:mi mathvariant="bold">∇</mml:mi></mml:math></inline-formula> is the horizontal
gradient operator and <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>:</mml:mo><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="bold">∇</mml:mi><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mo>∑</mml:mo><mml:mi>i</mml:mi></mml:msub><mml:msub><mml:mo>∑</mml:mo><mml:mi>j</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:msup><mml:mo>∂</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>/</mml:mo><mml:mo>∂</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>∂</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>)</mml:mo><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mo>(</mml:mo><mml:msup><mml:mo>∂</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>/</mml:mo><mml:mo>∂</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>∂</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, the generalization of the scalar product of two
vectors.</p>
      <p>The first term of Eq. (<xref ref-type="disp-formula" rid="App1.Ch1.E1"/>) measures the spatial variability
(curvature, gradient and value) of the analyzed field and is identified as
the smoothness constraint. The second term is a weighted sum of data-analysis
misfits and is identified as the observation constraint: it tends to pull the
analyzed field towards the observations. The coefficients of
Eq. (<xref ref-type="disp-formula" rid="App1.Ch1.E1"/>) can be determined from  (1) the relative weights <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>
attributed to each observation <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, (2) the correlation length <inline-formula><mml:math display="inline"><mml:mi>L</mml:mi></mml:math></inline-formula> and
(3) the signal-to-noise ratio <inline-formula><mml:math display="inline"><mml:mi mathvariant="italic">λ</mml:mi></mml:math></inline-formula> <xref ref-type="bibr" rid="bib1.bibx40" id="paren.60"/>. The analyses
presented in this study were achieved with equal weights <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>w</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>,
<inline-formula><mml:math display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mn>0.8</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="italic">λ</mml:mi><mml:mo>=</mml:mo><mml:mn>0.5</mml:mn></mml:mrow></mml:math></inline-formula>. The minimization of Eq. (<xref ref-type="disp-formula" rid="App1.Ch1.E1"/>)
is solved over <inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">Ω</mml:mi></mml:math></inline-formula> with a finite-element technique <xref ref-type="bibr" rid="bib1.bibx3" id="paren.61"/>
which excludes data influence across land points <xref ref-type="bibr" rid="bib1.bibx39" id="paren.62"/>.</p>
      <p><?xmltex \hack{\newpage}?>The detrending algorithm, presented in <xref ref-type="bibr" rid="bib1.bibx7" id="text.63"/> with synthetic
and real case studies, proceeds as follows. Input data can be classified
amongst the different classes <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (e.g., 1990, 1991, …) of a given
group <inline-formula><mml:math display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> (e.g., the year). The observation constraint of the functional
Eq. (<xref ref-type="disp-formula" rid="App1.Ch1.E1"/>) can then be rewritten by including an unknown trend
value for each class (<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>, …):

              <disp-formula specific-use="align" content-type="numbered"><mml:math display="block"><mml:mtable displaystyle="true"><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>J</mml:mi><mml:mi mathvariant="normal">obs</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>=</mml:mo><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:mrow></mml:munder><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msup><mml:mfenced open="[" close="]"><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:mtd></mml:mtr><mml:mlabeledtr id="App1.Ch1.E2"><mml:mtd/><mml:mtd/><mml:mtd><mml:mrow><mml:mo>+</mml:mo><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:munder><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msup><mml:mfenced open="[" close="]"><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:mi mathvariant="normal">⋯</mml:mi><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mlabeledtr></mml:mtable></mml:math></disp-formula>

          If the function <inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> were known, minimization with respect to each
of the unknowns <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> would yield
          <disp-formula id="App1.Ch1.E3" content-type="numbered"><mml:math display="block"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mfenced close="]" open="["><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:mi mathvariant="italic">φ</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mfenced></mml:mrow><mml:mrow><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula>
        and similarly for the other classes: the trend for each class is the weighted
misfit of the class with respect to the overall analysis.</p>
      <p>Using an analysis without detrending as a first guess for <inline-formula><mml:math display="inline"><mml:mi mathvariant="italic">φ</mml:mi></mml:math></inline-formula>, trends
are computed for each classes in each group and subtracted from the original
data. Following this, a new analysis is performed, the trends are
recalculated, and the iterations continue until a specified convergence
criterion is fulfilled. The procedure can be generalized with several groups
of classes. The present study considered years and months.</p>
      <p>The DIVA software and up-to-date related information can be found on
<uri>http://modb.oce.ulg.ac.be/mediawiki/index.php/DIVA</uri>.</p><?xmltex \hack{\clearpage}?>
</app>
  </app-group><ack><title>Acknowledgements</title><p>This study was achieved in the context of the PERSEUS project, funded by the
EU under FP7 Theme “Oceans of Tomorrow” OCEAN.2011-3 Grant Agreement
No. 287600. A. Capet is currently cofunded by the European Union under
FP7-People-Co-funding of Regional, National and International Programmes, GA
n. 600407 and the Italian Ministry of University and Research and National
Research Council (Bandiera project RITMARE). E. V. Stanev acknowledges
support from the EC project E-AIMS (grant 312642). Argo data were collected,
checked and made freely available by the International Argo Program, part of
the Global Ocean Observing System, and the national programs that contribute
to it (<uri>http://www.argo.ucsd.edu</uri>,
<uri>http://argo.jcommops.org</uri>). This is MARE publication number 323.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?> Edited by:
L. Stramma</p></ack><ref-list>
    <title>References</title>

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    <!--<article-title-html>Decline of the Black Sea oxygen inventory</article-title-html>
<abstract-html><p class="p">We show that from 1955 to 2015, the inventory of oxygen in the Black Sea has
decreased by 44 % and the basin-averaged oxygen penetration depth has
decreased from 140 m in 1955 to 90 m in 2015, which is the shallowest
annual value recorded during that period.</p><p class="p">The oxygenated Black Sea surface layer separates the world's largest
reservoir of toxic hydrogen sulfide from the atmosphere. The threat of
chemocline excursion events led to hot debates in the past decades arguing on
the vertical stability of the Black Sea oxic/suboxic interface. In the 1970s
and 1980s, when the Black Sea faced severe eutrophication, enhanced
respiration rates reduced the thickness of the oxygenated layer.
Re-increasing oxygen inventory in 1985–1995 supported arguments in favor of
the stability of the oxic layer. Concomitant with a reduction of nutrient
loads, it also supported the perception of a Black Sea recovering from
eutrophication. More recently, atmospheric warming was shown to reduce the
ventilation of the lower oxic layer by lowering cold intermediate layer (CIL)
formation rates.</p><p class="p">The debate on the vertical migration of the oxic interface also addressed the
natural spatial variability affecting Black Sea properties when expressed in
terms of depth. Here we show that using isopycnal coordinates does not overcome the
significant spatial variability of oxygen penetration depth. By
considering this spatial variability, the analysis of a composite historical
set of oxygen profiles evidenced a significant shoaling of the oxic layer,
and showed that the transient “recovery” of the 1990s was mainly a result
of increased CIL formation rates during that period.</p><p class="p">As both atmospheric warming and eutrophication are expected to increase in
the near future, monitoring the dynamics of the Black Sea oxic layer is
urgently required to assess the threat of further shoaling.</p></abstract-html>
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