<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing with OASIS Tables v3.0 20080202//EN" "journalpub-oasis3.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:oasis="http://docs.oasis-open.org/ns/oasis-exchange/table" xml:lang="en" dtd-version="3.0">
  <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-17-6507-2020</article-id><title-group><article-title>A new intermittent regime of convective ventilation threatens the Black Sea oxygenation status</article-title><alt-title>Intermittent Black Sea ventilation</alt-title>
      </title-group><?xmltex \runningtitle{Intermittent Black Sea ventilation}?><?xmltex \runningauthor{A.~Capet et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name><surname>Capet</surname><given-names>Arthur</given-names></name>
          <email>acapet@uliege.be</email>
        <ext-link>https://orcid.org/0000-0002-5939-3836</ext-link></contrib>
        <contrib contrib-type="author" corresp="no">
          <name><surname>Vandenbulcke</surname><given-names>Luc</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-9870-6199</ext-link></contrib>
        <contrib contrib-type="author" corresp="no">
          <name><surname>Grégoire</surname><given-names>Marilaure</given-names></name>
          
        </contrib>
        <aff id="aff1"><institution>MAST, FOCUS, University of Liège, Liège, Belgium</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Arthur Capet (acapet@uliege.be)</corresp></author-notes><pub-date><day>23</day><month>December</month><year>2020</year></pub-date>
      
      <volume>17</volume>
      <issue>24</issue>
      <fpage>6507</fpage><lpage>6525</lpage>
      <history>
        <date date-type="received"><day>10</day><month>March</month><year>2020</year></date>
           <date date-type="rev-request"><day>30</day><month>March</month><year>2020</year></date>
           <date date-type="rev-recd"><day>13</day><month>November</month><year>2020</year></date>
           <date date-type="accepted"><day>16</day><month>November</month><year>2020</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2020 Arthur Capet et al.</copyright-statement>
        <copyright-year>2020</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020.html">This article is available from https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020.html</self-uri><self-uri xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020.pdf">The full text article is available as a PDF file from https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020.pdf</self-uri>
      <abstract><title>Abstract</title>
    <p id="d1e95">The Black Sea is entirely anoxic, except for a thin (<inline-formula><mml:math id="M1" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 100 m) ventilated surface layer.
Since 1955, the oxygen content of this upper layer has decreased by 44 %.
The reasons hypothesized for this decrease are, first, a period of eutrophication from the mid-1970s to the early 1990s and, second, a reduction in the ventilation processes, suspected for recent years (post-2005).
Here, we show that the Black Sea convective ventilation regime has been drastically altered by atmospheric warming during the last decade.
Since 2009, the prevailing regime has been below the range of variability recorded since 1955 and has been characterized by consecutive years during which the usual partial renewal of intermediate water has not occurred.
Oxygen records from the last decade are used to detail the  relationship between cold-water formation events and oxygenation at different density levels, to highlight the role of convective ventilation in the oxygen budget of the intermediate layers and to emphasize the impact that a persistence in the reduced ventilation regime would bear on the oxygenation structure of the Black Sea and on its biogeochemical balance.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e114">By reducing water density and increasing vertical stratification, global warming is expected to impede ventilation mechanisms in the world ocean and regional seas with potential consequences for the oxygenation of the subsurface layer <xref ref-type="bibr" rid="bib1.bibx7 bib1.bibx26 bib1.bibx11" id="paren.1"/>.
On a global scale, the reduction in ventilation processes constitutes a larger contribution to marine deoxygenation than the warming-induced reduction in oxygen solubility <xref ref-type="bibr" rid="bib1.bibx8" id="paren.2"/>.
While the reduction in ventilation mechanisms is often evidenced, it remains challenging to determine whether such changes are the signal of natural variability or rather bear witness to a significant regime change attributed to global warming <xref ref-type="bibr" rid="bib1.bibx32" id="paren.3"/>.</p>
      <p id="d1e126">The Black Sea provides a miniature global ocean framework where processes of global interest occur at a scale more amenable to investigation.
Its deep basin is permanently stratified, and the ventilation of the subsurface layer relies in substantial parts on the convective transport of cold, oxygen-rich water formed each winter at the surface.
Between 1955 and 2015, the Black Sea oxygen inventory declined by 40 % <xref ref-type="bibr" rid="bib1.bibx15" id="paren.4"/>, which echoes the significant deoxygenation trend that affected the world ocean over a similar period <xref ref-type="bibr" rid="bib1.bibx47" id="paren.5"/>.</p>
      <p id="d1e135">The permanent stratification of the Black Sea results from two external inflows <xref ref-type="bibr" rid="bib1.bibx43" id="paren.6"/>.
The saline Mediterranean inflow enters the Black Sea by the lower part of the Bosporus Strait, the sole and narrow opening of the Black Sea towards the global ocean.
The greatest part of the terrestrial freshwater inflow enters the Black Sea on its northwestern shelf.
The contrast in density (salinity) between these two inflows maintains a permanent stratification in the open basin (halocline) that prevents ventilation of the deep layers.
This lack of ventilation induces the permanent anoxic conditions that characterize 90 % of the Black Sea waters. Between the oxic and anoxic (euxinic) layers, a suboxic zone, where both dissolved oxygen and hydrogen sulfide are below reliable detection limits <xref ref-type="bibr" rid="bib1.bibx37" id="paren.7"/>, is maintained by biogeochemical processes <xref ref-type="bibr" rid="bib1.bibx53" id="paren.8"/>.</p>
      <?pagebreak page6508?><p id="d1e147">Just above the main halocline, the ventilation of the Black Sea subsurface waters (<inline-formula><mml:math id="M2" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 50–100 m), is ensured by convective circulation.
It proceeds from the sinking of surface waters, made colder and denser by loss of heat towards the atmosphere in wintertime <xref ref-type="bibr" rid="bib1.bibx24" id="paren.9"/>.
A similar ventilation process is observed, for instance, in the Mediterranean Gulf of Lion <xref ref-type="bibr" rid="bib1.bibx34 bib1.bibx17 bib1.bibx56" id="paren.10"><named-content content-type="pre">e.g.,</named-content></xref>.
In the Black Sea, however, the dense oxygenated waters never reach the deepest parts, as their sinking is restrained at an intermediate depth by the permanent halocline.
The accumulation of cold waters at an intermediate depth forms the so-called Cold Intermediate Layer (CIL).
The process of CIL formation thus provides an annual ventilating mechanism that structures the vertical distribution of oxygen <xref ref-type="bibr" rid="bib1.bibx28 bib1.bibx20 bib1.bibx15" id="paren.11"/> and, by extension, that of nutrients <xref ref-type="bibr" rid="bib1.bibx28 bib1.bibx44" id="paren.12"/> and living components of the ecosystem <xref ref-type="bibr" rid="bib1.bibx46" id="paren.13"/>.</p>
      <p id="d1e176">The semi-enclosed character of the Black Sea, combined with the fact that ventilation is limited to the upper <inline-formula><mml:math id="M3" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 100 m, makes it highly sensitive to variations in external forcing.
In particular, variations in atmospheric conditions (e.g., air temperature, wind curl) result in pronounced and relatively fast inter-annual alterations of the Black Sea physical structure <xref ref-type="bibr" rid="bib1.bibx41 bib1.bibx13 bib1.bibx30" id="paren.14"/>.</p>
      <p id="d1e189">While several studies have evidenced a warming trend in the Black Sea surface temperature <xref ref-type="bibr" rid="bib1.bibx4 bib1.bibx59" id="paren.15"/>, <xref ref-type="bibr" rid="bib1.bibx35" id="text.16"/> showed that the Black Sea intermediate waters present an even stronger warming trend.
This difference between the surface and subsurface temperature trends can be explained by the fact that the CIL dynamics buffers the atmospheric warming trends and minimizes its signature in sea surface temperature <xref ref-type="bibr" rid="bib1.bibx39" id="paren.17"/>.</p>
      <p id="d1e201">The inter-annual variability in CIL formation can be explained for the most part on the basis of winter air temperature anomalies <xref ref-type="bibr" rid="bib1.bibx40 bib1.bibx14" id="paren.18"/>,
although intensity of the basin-wide cyclonic circulation <xref ref-type="bibr" rid="bib1.bibx55 bib1.bibx13 bib1.bibx29" id="paren.19"/>,
the freshwater budget <xref ref-type="bibr" rid="bib1.bibx5" id="paren.20"/> and the intensity of short-term meso-scale intrusions also play a role <xref ref-type="bibr" rid="bib1.bibx20 bib1.bibx42 bib1.bibx2" id="paren.21"/>.
An extensive description of the CIL dynamics, detailing the contributions of and variability in the mechanisms mentioned above was recently provided by <xref ref-type="bibr" rid="bib1.bibx36" id="text.22"/>.
One aspect is particularly relevant to our study:
in wintertime, the deepening of the mixed layer and the uplifting of isopycnals in the basin center (as the cyclonic circulation intensifies) expose subsurface waters to atmospheric cooling.
If a well-formed CIL was present during the previous year, subsurface waters exposed to atmospheric cooling will already be cold, which increases the amount of newly formed CIL waters <xref ref-type="bibr" rid="bib1.bibx50" id="paren.23"/>.
Due to this positive feedback and to the accumulation of CIL waters formed during successive years, the inter-annual CIL dynamics is better described when winter air temperature anomalies are accumulated over 3 to 4 years, rather than considered on a year-to-year basis <xref ref-type="bibr" rid="bib1.bibx14" id="paren.24"/>, which is in agreement with the 5 years upper estimate provided by <xref ref-type="bibr" rid="bib1.bibx31" id="text.25"/> for the residence time within the CIL layer.</p>
      <p id="d1e229">Given this non-linear context, there are reasons to suspect that global warming, by increasing the average air temperature around which annual fluctuations occur,
may induce a persistent shift in the regime of the Black Sea subsurface ventilation.
Indeed, <xref ref-type="bibr" rid="bib1.bibx54" id="text.26"/> used Argo float data (2005–2018) to highlight a recent constriction of the CIL layer, following a trend leading to conditions where the CIL, as a layer colder than the underlying waters, would no longer exist.
The authors further indicate implications for the Black Sea thermo-haline properties, as this recent weakening of the CIL layer goes hand in hand with increasing trends in surface and subsurface salinity, indicative of diapycnal mixing at the basis of the former CIL layer.</p>
      <p id="d1e235">Here, we combine different data sources to analyze the variability in the Black Sea intermediate-layer ventilation over the last 65 years and, in particular, investigate the existence of a statistically significant shift in the CIL formation regime, in regard to the variability observed over this period.
The hypothesis of a significant regime shift is tested against the more traditional linear and periodic interpretation of the observed trends <xref ref-type="bibr" rid="bib1.bibx5" id="paren.27"><named-content content-type="pre">e.g.,</named-content></xref>, as the consequence for Black Sea ventilation and the future of the Black Sea oxygenation status in particular are drastically different.</p>
      <p id="d1e243">Indeed, <xref ref-type="bibr" rid="bib1.bibx28" id="text.28"/> evidenced a clear relationship between oxygen conditions in the lower part of the CIL layer and the temperature in that layer which is directly related to inter-annual variations in the CIL formation intensity.
This relationship explains a large part of the inter-annual fluctuations in oxygen concentration in that layer, which occur at a timescale of a few years.
Those fluctuations are superimposed on the larger-scale change in oxygenation state that is attributed to an increase in the primary production induced by the eutrophication phase of the late 1970s.</p>
      <p id="d1e250">Our analysis thus aims to expand on these investigations and in particular to focus on the annual convective ventilation as a component of the complex Black Sea deoxygenation dynamics <xref ref-type="bibr" rid="bib1.bibx28" id="paren.29"/>, in the context of the recent warming trend affecting the Black Sea <xref ref-type="bibr" rid="bib1.bibx36" id="paren.30"/>.</p>
      <p id="d1e259">Section <xref ref-type="sec" rid="Ch1.S2"/> details the datasets considered to characterize the Black Sea CIL and oxygenation dynamics and the method of regime shift analysis.
In Sect. <xref ref-type="sec" rid="Ch1.S3"/>, we analyze the long-term CIL dynamics through the lens of regime shift analysis.
In Sect. <xref ref-type="sec" rid="Ch1.S4"/>, we use outputs from a three-dimensional hydrodynamic model and recent Argo records to relate CIL formation rates to changes in the Black Sea oxygenation conditions.
In Sect. <xref ref-type="sec" rid="Ch1.S5"/>, we discuss those results in the frame of larger timescales, while we conclude in Sect. <xref ref-type="sec" rid="Ch1.S6"/>.</p>
</sec>
<?pagebreak page6509?><sec id="Ch1.S2">
  <label>2</label><title>Material and methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>The cold-intermediate-layer cold content</title>
      <p id="d1e287">While annual CIL formation rates are difficult to assess directly from observations, the status of the CIL can be quantified locally on the basis of vertical profiles of temperature and salinity.
This simple indicator, based on routinely monitored variables, provides a suitable metric to combine various sources of data while summarizing an essential aspect of the thermo-haline conditions.
The CIL cold content <inline-formula><mml:math id="M4" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> is defined as the heat deficit within the CIL, integrated along the vertical:
            <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M5" display="block"><mml:mrow><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:munder><mml:mo movablelimits="false">∫</mml:mo><mml:mtext>CIL</mml:mtext></mml:munder><mml:mi mathvariant="italic">ρ</mml:mi><mml:mo>(</mml:mo><mml:mi>z</mml:mi><mml:mo>)</mml:mo><mml:mfenced open="[" close="]"><mml:mrow><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:mrow></mml:mfenced><mml:mspace linebreak="nobreak" width="0.25em"/><mml:mi mathvariant="normal">d</mml:mi><mml:mi>z</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M6" display="inline"><mml:mi>z</mml:mi></mml:math></inline-formula> is depth; <inline-formula><mml:math id="M7" display="inline"><mml:mi mathvariant="italic">ρ</mml:mi></mml:math></inline-formula>, the in situ density; <inline-formula><mml:math id="M8" display="inline"><mml:mrow><mml:msub><mml:mi>c</mml:mi><mml:mtext>p</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula>, is the heat capacity of seawater; and <inline-formula><mml:math id="M9" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mtext>CIL</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">8.35</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C is the temperature threshold which, together with a density criterion <inline-formula><mml:math id="M11" display="inline"><mml:mrow><mml:mi mathvariant="italic">ρ</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">1014.5</mml:mn></mml:mrow></mml:math></inline-formula> kg m<inline-formula><mml:math id="M12" 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>, defines the CIL layer over which the integration is performed <xref ref-type="bibr" rid="bib1.bibx50 bib1.bibx52 bib1.bibx14" id="paren.31"/>.
Although the use of a given temperature threshold to define the occurrence of convective mixing is subject to discussion,
the existence of a fixed temperature threshold to characterize the CIL as a distinct water mass and in particular to identify its lower boundary is evident given the fixed value of <inline-formula><mml:math id="M13" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">9</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M14" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C that characterizes the underlying deep waters <xref ref-type="bibr" rid="bib1.bibx54" id="paren.32"/>.
The above definition has been chosen for consistency with the previous literature.</p>
      <p id="d1e452"><inline-formula><mml:math id="M15" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> is expressed in units of J m<inline-formula><mml:math id="M16" 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> and provides a vertically integrated diagnostic which is more informative than, for instance, the temperature at a fixed depth or the depth of a given isothermal surface.
Although <inline-formula><mml:math id="M17" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> is a deficit, we inverted the sign of <inline-formula><mml:math id="M18" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> in comparison with the previous literature <xref ref-type="bibr" rid="bib1.bibx50 bib1.bibx45 bib1.bibx14" id="paren.33"/> for the convenience of working with a positive quantity.
Large <inline-formula><mml:math id="M19" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> values thus correspond to large heat deficit in the CIL, i.e., to low temperature in a well-formed CIL layer, which is characteristic of cold years.
A decrease in <inline-formula><mml:math id="M20" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> corresponds to a weakening of cold-water formation (typically for warm years), an increase in the intermediate-water temperature and/or a decrease in the vertical extent of the CIL.</p>
      <p id="d1e505"><inline-formula><mml:math id="M21" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> has been estimated for each year over the 1955–2019 period using four data sources summarized in Table <xref ref-type="table" rid="Ch1.T1"/>.
These sources include in situ historical (ship casts) and modern (Argo) observations, as well as empirical and mechanistic modeling (Fig. <xref ref-type="fig" rid="Ch1.F1"/>).
Annual and spatial average values for the deep sea (depth <inline-formula><mml:math id="M22" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> m) were derived from each dataset, while considering the errors induced by uneven sampling in the context of pronounced seasonal and spatial variability.
Each data source has particular advantages and drawbacks and involves specific data processing to reach estimates of annual and spatial <inline-formula><mml:math id="M23" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> averages as described below. All processed annual time series are made available in netCDF format in a public repository (see “Data availability”).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e539"> Overview of the four datasets used to characterize the CIL inter-annual variability. Details are provided for each dataset in Sect. <xref ref-type="sec" rid="Ch1.S2.SS1"/>.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="4">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="2cm"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="4.5cm"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="5.5cm"/>
     <oasis:colspec colnum="4" colname="col4" align="justify" colwidth="3.5cm"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Dataset</oasis:entry>
         <oasis:entry colname="col2">Rationale</oasis:entry>
         <oasis:entry colname="col3">Advantages (<inline-formula><mml:math id="M24" display="inline"><mml:mo lspace="0mm">+</mml:mo></mml:math></inline-formula>) &amp; drawbacks (<inline-formula><mml:math id="M25" display="inline"><mml:mo lspace="0mm">-</mml:mo></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col4">References</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">(period)</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Ship casts <?xmltex \hack{\hfill\break}?>(1956–2011)</oasis:entry>
         <oasis:entry colname="col2">In situ profiles analyzed with the <?xmltex \hack{\hfill\break}?>DIVA detrending methodology to<?xmltex \hack{\hfill\break}?>disentangle spatial and temporal <?xmltex \hack{\hfill\break}?>variability</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M26" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> Large time cover<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M27" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> Direct observation<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M28" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> Uneven spatial and seasonal sampling<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M29" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> Annual gaps</oasis:entry>
         <oasis:entry colname="col4"><xref ref-type="bibr" rid="bib1.bibx9" id="text.34"/><?xmltex \hack{\hfill\break}?> <xref ref-type="bibr" rid="bib1.bibx14" id="text.35"/></oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Atmospheric <?xmltex \hack{\hfill\break}?>predictors <?xmltex \hack{\hfill\break}?>(1956–2012)</oasis:entry>
         <oasis:entry colname="col2">Empirical combination of atmospheric descriptors (winter air temperature anomalies) calibrated to <?xmltex \hack{\hfill\break}?>reproduce the above time series</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M30" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> Full time cover<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M31" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> Not observation<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M32" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> Validity of statistical model not <?xmltex \hack{\hfill\break}?>guaranteed outside its range of calibration</oasis:entry>
         <oasis:entry colname="col4"><xref ref-type="bibr" rid="bib1.bibx18" id="text.36"/><?xmltex \hack{\hfill\break}?> <xref ref-type="bibr" rid="bib1.bibx14" id="text.37"/></oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">GHER3D <?xmltex \hack{\hfill\break}?>(1981–2017)</oasis:entry>
         <oasis:entry colname="col2">Three-dimensional hydrodynamic<?xmltex \hack{\hfill\break}?>model (GHER), unconstrained <?xmltex \hack{\hfill\break}?>simulation (no data assimilation),<?xmltex \hack{\hfill\break}?>5 km resolution, ERA-Interim <?xmltex \hack{\hfill\break}?>atmospheric forcing</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M33" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> Synopticity<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M34" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> Underlying mechanistic understanding<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M35" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> Not observation</oasis:entry>
         <oasis:entry colname="col4"><xref ref-type="bibr" rid="bib1.bibx49" id="text.38"/><?xmltex \hack{\hfill\break}?> <xref ref-type="bibr" rid="bib1.bibx58" id="text.39"/><?xmltex \hack{\hfill\break}?> <xref ref-type="bibr" rid="bib1.bibx13" id="text.40"/></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Argo  <?xmltex \hack{\hfill\break}?>(2005–2019)</oasis:entry>
         <oasis:entry colname="col2">Drifting autonomous profilers, <?xmltex \hack{\hfill\break}?>average of synchronous profiles</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M36" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> Direct observation<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M37" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> Intra-annual resolution<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M38" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> Uneven spatial sampling<?xmltex \hack{\hfill\break}?> <inline-formula><mml:math id="M39" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> Recent years only</oasis:entry>
         <oasis:entry colname="col4"><xref ref-type="bibr" rid="bib1.bibx51" id="text.41"/><?xmltex \hack{\hfill\break}?> <xref ref-type="bibr" rid="bib1.bibx2" id="text.42"/><?xmltex \hack{\hfill\break}?> <xref ref-type="bibr" rid="bib1.bibx54" id="text.43"/></oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e839"><list list-type="bullet">
            <list-item>

      <p id="d1e844"><italic>In situ ship-cast profiles</italic>. The advantage of ship-based profiles is their extended temporal coverage.
The drawbacks are the difficulty to untangle spatial and temporal variability (as for any non-synoptic data source), the uneven sampling effort, and the low data availability posterior to 2000.
The <inline-formula><mml:math id="M40" display="inline"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mtext>Ships</mml:mtext></mml:msup></mml:mrow></mml:math></inline-formula> time series was provided by the application of the DIVA detrending methodology on ship-cast profiles extracted from the World Ocean Database <xref ref-type="bibr" rid="bib1.bibx9" id="paren.44"/> in the box 40–47<inline-formula><mml:math id="M41" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>30<inline-formula><mml:math id="M42" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula> N, 27–42<inline-formula><mml:math id="M43" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E for the period 1955–2011.
DIVA is sophisticated data interpolation software <xref ref-type="bibr" rid="bib1.bibx57" id="paren.45"/> based on a variational approach. The detrending methodology <xref ref-type="bibr" rid="bib1.bibx14" id="paren.46"/> provided inter-annual trends, here representative of the central basin, cleared from the errors induced by the combination of uneven sampling and pronounced variability along the seasonal and spatial dimensions.
We redirect the reader to <xref ref-type="bibr" rid="bib1.bibx14" id="text.47"/> for further details on data sources, data distribution and methodology.</p>
            </list-item>
            <list-item>

      <p id="d1e903"><italic>Atmospheric predictors</italic>.
The statistical model considered here consists of a lagged regression model based on winter air temperature anomalies, i.e., using the form
<inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup><mml:mo>=</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mtext>ATW</mml:mtext><mml:mi>i</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mtext>ATW</mml:mtext><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mtext>ATW</mml:mtext><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn mathvariant="normal">4</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mtext>ATW</mml:mtext><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>,
where <inline-formula><mml:math id="M45" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> is a year index and ATW<inline-formula><mml:math id="M46" display="inline"><mml:msub><mml:mi/><mml:mi>i</mml:mi></mml:msub></mml:math></inline-formula> stands for the anomaly of the preceding winter air temperature (December–March).</p>

      <p id="d1e1016">This model was obtained by a stepwise selection among potential descriptor variables (including summer and winter air temperature, winds, and freshwater discharge), in order to reproduce the inter-annual variability in <inline-formula><mml:math id="M47" display="inline"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mtext>Ships</mml:mtext></mml:msup></mml:mrow></mml:math></inline-formula> <xref ref-type="bibr" rid="bib1.bibx14" id="paren.48"/> and proposed as an alternative to the winter severity index defined by <xref ref-type="bibr" rid="bib1.bibx48" id="text.49"/>.
<inline-formula><mml:math id="M48" display="inline"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msup></mml:mrow></mml:math></inline-formula> is thus naturally representative of the same quantity, i.e.,  annually and spatially averaged <inline-formula><mml:math id="M49" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>.
The advantage of this approach is the opportunity to fill the gaps between observations in past years, using atmospheric reanalysis of 2 m air temperature provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for the period 1980–2013.
Its drawbacks lie in its empirical nature and indirect relationship to observable sea conditions.
<inline-formula><mml:math id="M50" display="inline"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msup></mml:mrow></mml:math></inline-formula> was only extracted for the years covered in <xref ref-type="bibr" rid="bib1.bibx14" id="text.50"/>, considering that the potential non-linearity in the air temperature–<inline-formula><mml:math id="M51" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> relationship may be exacerbated for the low <inline-formula><mml:math id="M52" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> values typical of recent years.</p>
            </list-item>
            <list-item>

      <p id="d1e1086"><italic>Three-dimensional (3D) hydrodynamic model</italic>.
The Black Sea implementation of the 3D hydrodynamic model GHER has been used in several studies <xref ref-type="bibr" rid="bib1.bibx22 bib1.bibx49 bib1.bibx58" id="paren.51"/>.
In particular, <xref ref-type="bibr" rid="bib1.bibx13" id="text.52"/> present the model setup used in this study and analyze the simulated CIL dynamics.
This simulation, extending over the period 1981–2017, has been produced without any form<?pagebreak page6510?> of data assimilation, on the basis of the ERA-Interim set of atmospheric forcing provided by the ECMWF data center <xref ref-type="bibr" rid="bib1.bibx18" id="paren.53"/>. Aggregated weekly outputs of the GHER3D model are made available on a public repository (see “Data availability”).
<inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mtext>Model3D</mml:mtext></mml:msup></mml:mrow></mml:math></inline-formula> was derived from synoptic weekly model outputs and averaged for each  year and spatially over the deep basin (depth <inline-formula><mml:math id="M54" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> m).
The advantages of this approach are the synoptic coverage in time and space and the mechanistic nature of the model, which implies a reproducible understanding of the process of CIL formation.
A drawback lies in the numerical and conceptual error that might affect unconstrained model outputs.</p>
            </list-item>
            <list-item>

      <p id="d1e1124"><italic>Argo profilers</italic>.  The advantages of autonomous Argo profilers are a high temporal resolution and the continuous coverage of recent years, which offer unprecedented means to explore the CIL dynamics at fine spatial and temporal scales <xref ref-type="bibr" rid="bib1.bibx2 bib1.bibx54" id="paren.54"/>.
The drawbacks are the mingled spatial and temporal variability inherent to Argo data, the incomplete spatial coverage, and the lack of data prior to 2005.
This dataset was collected and made freely available by the Coriolis project and programs that contribute to it (<uri>http://www.coriolis.eu.org</uri>, last access: 3 March 2020).
The request criteria used were as follows: bounding box – 40–47<inline-formula><mml:math id="M55" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 27–42<inline-formula><mml:math id="M56" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E; period (DD/MM/YYYY) – between “01/01/2005” and “31/12/2019”; data type(s) – “Argo profiles”, “Argo trajectory”; required physical parameters – “sea temperature” or “practical salinity”; quality – good.
On average, this set includes about 9 floats per year, with a minimum of 2 floats for 2005 and more than 12 floats from 2013 to 2019.
<inline-formula><mml:math id="M57" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> values were derived from individual Argo profiles (Fig. <xref ref-type="fig" rid="Ch1.F1"/>).
All available profiles in a given year were averaged to produce the annual Argo time series <inline-formula><mml:math id="M58" display="inline"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mtext>Argo</mml:mtext></mml:msup></mml:mrow></mml:math></inline-formula>.
Although homogeneous seasonal sampling can be assumed, we note that the uneven spatial coverage of Argo profiles might induce a bias in the inferred trends.
This potential bias stems from the horizontal gradient in <inline-formula><mml:math id="M59" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>, that is structured radially from the central (lower <inline-formula><mml:math id="M60" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>) to the peripheral (higher <inline-formula><mml:math id="M61" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>) regions of the Black Sea <xref ref-type="bibr" rid="bib1.bibx50 bib1.bibx14" id="paren.55"/>.
As Argo samplings are generally more abundant in the peripheral regions, i.e., outside of the divergent cyclonic gyres, this suggests that <inline-formula><mml:math id="M62" display="inline"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mtext>Argo</mml:mtext></mml:msup></mml:mrow></mml:math></inline-formula> might be slightly biased towards high values.</p>
            </list-item>
          </list>With the exception of the pair <inline-formula><mml:math id="M63" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> and  <inline-formula><mml:math id="M64" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>, all datasets either are strictly independent or can be considered as such (see Table <xref ref-type="table" rid="App1.Ch1.S1.T2"/>).
A composite time series was constructed as the weighted average of the four time series, restricted to available sources for years during which all sources were not available:
            <disp-formula id="Ch1.E2" content-type="numbered"><label>2</label><mml:math id="M65" display="block"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mo>∑</mml:mo><mml:mi>j</mml:mi></mml:msub><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:msubsup><mml:mo>×</mml:mo><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:msubsup></mml:mrow><mml:mrow><mml:msub><mml:mo>∑</mml:mo><mml:mi>j</mml:mi></mml:msub><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:msubsup></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M66" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> is an annual index, <inline-formula><mml:math id="M67" display="inline"><mml:mi>j</mml:mi></mml:math></inline-formula> stands for a source index (<inline-formula><mml:math id="M68" display="inline"><mml:mrow><mml:mi>j</mml:mi><mml:mo>∈</mml:mo><mml:mfenced open="{" close="}"><mml:mtext>Model3D, Atmos, Argo, Ships</mml:mtext></mml:mfenced></mml:mrow></mml:math></inline-formula>).
In order to emphasize the value of direct observations, the weights <inline-formula><mml:math id="M69" display="inline"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mtext>Argo</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M70" display="inline"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>) equal 1 if <inline-formula><mml:math id="M71" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Argo</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M72" display="inline"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>) is defined (i.e., the time series covers the year <inline-formula><mml:math id="M73" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula>) and is 0 otherwise, while <inline-formula><mml:math id="M74" display="inline"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mtext>Model3D</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M75" display="inline"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>) equals 0.5 if <inline-formula><mml:math id="M76" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Model3D</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M77" display="inline"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>) is defined and is 0 otherwise.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><label>Figure 1</label><caption><p id="d1e1435"> Time series of the Black Sea CIL cold content (<inline-formula><mml:math id="M78" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>) originating from various data sources (Table <xref ref-type="table" rid="Ch1.T1"/>), displayed at original temporal resolution:
(black dots) inter-annual trend derived from ship casts; (gray-shaded area) confidence bounds (<inline-formula><mml:math id="M79" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.01</mml:mn></mml:mrow></mml:math></inline-formula>) of the statistical model based on winter air temperature anomalies; (thick dark red line) GHER3D model; (thin colored lines) individual Argo floats.
<bold>(a)</bold> Complete period of analysis, <bold>(b)</bold> focus on recent years.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f01.png"/>

        </fig>

      <?pagebreak page6511?><p id="d1e1471">The composite time series was then used as a synoptic metric for the inter-annual variability in the convective ventilation of the Black Sea intermediate layers.
The consistency of the different CIL cold-content data sources is demonstrated by the high correlations obtained between the annual time series (from 0.91 to 0.98; see detailed comparative statistics in Appendix <xref ref-type="sec" rid="App1.Ch1.S1"/>).
Despite the small number of overlapping years between certain series (e.g., 7 years between <inline-formula><mml:math id="M80" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ship</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M81" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Argo</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>; see Fig. <xref ref-type="fig" rid="App1.Ch1.S1.F8"/>), all correlations are significant (<inline-formula><mml:math id="M82" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>).
The close correspondence between independent time series, issued respectively from strictly observational and purely mechanistic modeling approaches provides a high confidence in their accuracy and ensures the robustness of the forthcoming analysis.</p>
      <p id="d1e1516">More precisely, the standard deviations estimated from the different series are similar (<inline-formula><mml:math id="M83" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">100</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M84" 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>), despite their distinct temporal coverage.
The root-mean-square errors that characterize the disagreement between the different data sources remain below this temporal standard deviation (in all but one case; see Appendix <xref ref-type="sec" rid="App1.Ch1.S1"/> for details).
This justifies merging the different sources into a unique composite time series, enabling a robust long-term analysis of the variability in the Black Sea CIL formation.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Regime shift analysis and descriptive model selection</title>
      <p id="d1e1551">The inter-annual variability in the Black Sea CIL formation is analyzed in the framework of regime shift analysis.
The natural first step towards identification of a regime shift in a time series is the identification of change points <xref ref-type="bibr" rid="bib1.bibx3" id="paren.56"/>.</p>
      <p id="d1e1557">The rationale behind change point models is to identify periods over which a time series depicts statistically distinct regimes.
In its simplest form, a change point model will aim to identify distinct regimes that differ in terms of their mean, i.e., during which fluctuations take place around distinct averages.
Note that other types of change point analyses can be done, which would consider other metrics (variance, autocorrelation, skewness) instead of the mean to break up the series. For the sake of simplicity, only the first moment (mean) is considered in this study.</p>
      <p id="d1e1560">The change point model used for this regime shift analysis has been derived and verified (Appendix <xref ref-type="sec" rid="App1.Ch1.S2"/>) following the methodology described in the documentation of the R package <monospace>strucchange</monospace> <xref ref-type="bibr" rid="bib1.bibx60" id="paren.57"/>.
The procedure includes the following steps.</p>
      <?pagebreak page6512?><p id="d1e1571">First, the presence of at least one significant change point in the time series was tested against the null hypothesis that considers annual fluctuations around a fixed average value for the entire time series.
To this aim, the <monospace>strucchange</monospace> package provides different methods based on the generalized fluctuation test framework as well as from the <inline-formula><mml:math id="M85" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula>-test (Chow test) framework.</p>
      <p id="d1e1585">Second, the locations of the most likely change points in the time series were identified.
Assuming that <inline-formula><mml:math id="M86" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula> change points separates <inline-formula><mml:math id="M87" display="inline"><mml:mrow><mml:mi>N</mml:mi><mml:mo>+</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> periods, this step thus consists in identifying the locations of the change points and the mean value specific to each period.
This identification proceeds from an optimization procedure aiming to minimize the residual sum of squares (RSS) between the time series and the change point model (i.e., constant mean value for each specific period).</p>
      <p id="d1e1607">Five change point models were derived for the composite time series, considering from one (<inline-formula><mml:math id="M88" display="inline"><mml:mrow><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>) to five (<inline-formula><mml:math id="M89" display="inline"><mml:mrow><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula>) change points.
The final step consists in selecting, among those five models, the one that “best” describes the time series,
obviously considering additional change points can only reduce the RSS.
This is generally true for any descriptive model and has led to the definition of the Akaike information criterion (AIC) for model selection.
Basically, the AIC considers the RSS of each model but includes a penalty for the number of parameters <xref ref-type="bibr" rid="bib1.bibx1" id="paren.58"/>, such that if two models bear the same RSS, the one involving fewer parameters will be favored. Note that in our case, the parameters identified for change point models include both the locations of change points and the specific mean for each period.
The model with the smallest AIC should be favored for interpretation.
In Sect. <xref ref-type="sec" rid="Ch1.S3.SS1"/>, the AIC is also used to compare the regime shift models to  linear and periodic models of <inline-formula><mml:math id="M90" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>.</p>
      <p id="d1e1650">More technical details and verification of underlying assumptions are given in Appendix <xref ref-type="sec" rid="App1.Ch1.S2"/>.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Oxygen</title>
      <p id="d1e1663">Biogeochemical Argo (BGC-Argo) oxygen observations were obtained from the Coriolis data center for a period extending from 1 January 2010 to 1 January 2020.
Only descending Argo profiles were considered, to minimize discrepancies associated with oxygen sensor response time <xref ref-type="bibr" rid="bib1.bibx6" id="paren.59"/>.
To minimize the impact of spatial variability, oxygen saturation was considered using a potential-density anomaly (<inline-formula><mml:math id="M91" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula>) vertical scale, and the year 2010 was discarded for lack of observations.
While both oxygen concentration (<inline-formula><mml:math id="M92" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>M) and oxygen saturation (%) were considered in our first analyses,
the narrow range of thermo-haline variability in the layers of interest results in very small variations in the oxygen solubility.
As a consequence, considering one or the other of these two variables led to very similar results, and we opted for oxygen saturation in the following.</p>
      <p id="d1e1684">Figure <xref ref-type="fig" rid="Ch1.F2"/> indicates that the use of <inline-formula><mml:math id="M93" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> vertical coordinates minimizes the range of spatial variability (see years 2014–2018, when more Argo floats were operating) and justifies the use of monthly medians as an integrated indicator of the basin-wide oxygenation status at different layers.
For deeper density layers (Fig. <xref ref-type="fig" rid="Ch1.F2"/>c), a larger interquartile range is induced by Argo floats profiling in the vicinity of the Bosporus-influenced area, as plumes of Bosporus ventilation introduce a larger horizontal variability in oxygen saturation.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>The Black Sea cold-intermediate-layer dynamics over 1955–2019</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Descriptive models</title>
      <p id="d1e1714">The composite time series <inline-formula><mml:math id="M94" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is depicted in Fig. <xref ref-type="fig" rid="Ch1.F3"/>, along with individual components.</p>
      <p id="d1e1730">The poor statistics associated with a linear-model description of <inline-formula><mml:math id="M95" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, in the form <inline-formula><mml:math id="M96" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:msub><mml:mi>l</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:mi>i</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>l</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M97" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> stands for an annual index; adjusted <inline-formula><mml:math id="M98" display="inline"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>; AIC <inline-formula><mml:math id="M99" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 794, with <inline-formula><mml:math id="M100" display="inline"><mml:mrow><mml:msub><mml:mi>l</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.59</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">0.78</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M101" 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> yr<inline-formula><mml:math id="M102" 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>), cause the perception of a linear trend extending over the entire period to be dismissed.
Using the periodic model, <inline-formula><mml:math id="M103" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mi>sin⁡</mml:mi><mml:mfenced close=")" open="("><mml:mrow><mml:mstyle displaystyle="false"><mml:mfrac style="text"><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mi mathvariant="italic">π</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>×</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math></inline-formula>, gives a better representation of the cold-content inter-annual variability (AIC <inline-formula><mml:math id="M104" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 763), and provides broad characteristics of <inline-formula><mml:math id="M105" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>:
the mean value, <inline-formula><mml:math id="M106" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">222</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">12</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M107" 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>;
the amplitude of inter-annual variability, <inline-formula><mml:math id="M108" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">114</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">16</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M109" 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>; and
the periodicity of pseudo-oscillations, <inline-formula><mml:math id="M110" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">43.04</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">0.02</mml:mn></mml:mrow></mml:math></inline-formula> years.</p>
      <p id="d1e1994">A combination of linear and periodic models, with the form
<inline-formula><mml:math id="M111" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:mi>l</mml:mi><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mi>l</mml:mi><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mi>sin⁡</mml:mi><mml:mo>(</mml:mo><mml:mstyle displaystyle="false"><mml:mfrac style="text"><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mi mathvariant="italic">π</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>×</mml:mo><mml:mi>i</mml:mi><mml:mo>+</mml:mo><mml:mi>l</mml:mi><mml:msub><mml:mi>p</mml:mi><mml:mn mathvariant="normal">4</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:mi>i</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, slightly improves the descriptive statistics (AIC <inline-formula><mml:math id="M112" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 758.5).
However, all of the above descriptive  models overestimate <inline-formula><mml:math id="M113" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> in recent years, as the composite time series <inline-formula><mml:math id="M114" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> shows a departure from its usual range of variability during the last decade.
This is evidenced by ranking the 65 years of <inline-formula><mml:math id="M115" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> on the basis of their cold content.
It is remarkable that, of the 10 years with the least cold content, 8 occurred after 2010.</p>
      <p id="d1e2101">Each of the change point models appears to be statistically more informative, sensu AIC, than a linear or periodic interpretation of the time series.
In particular the four-segment model (i.e., three change points, AIC <inline-formula><mml:math id="M116" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 752) should be favored for interpretation.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><label>Figure 2</label><caption><p id="d1e2114"> Oxygen saturation levels derived from individual BGC-Argo profiles at <inline-formula><mml:math id="M117" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> values of <bold>(a)</bold> 14.5, <bold>(b)</bold> 15.0 and <bold>(c)</bold> 15.5 kg m<inline-formula><mml:math id="M118" 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>.
Colored points correspond to different Argo floats.
The blue line represents monthly medians, while the shaded area covers monthly interquartile ranges.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f02.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Regime shifts in the cold-intermediate-layer cold content</title>
      <p id="d1e2159">The evolution of <inline-formula><mml:math id="M119" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> over 1955–2019 is thus best described by discriminating four periods (P1–P4, Fig. <xref ref-type="fig" rid="Ch1.F3"/>), objectively identified through regime shift analysis.</p>
      <p id="d1e2175">A “standard regime” is identified that is consistent for periods P1 (1955–1984) and P3 (1999–2008), which gives averages <inline-formula><mml:math id="M120" display="inline"><mml:mrow><mml:mo>〈</mml:mo><mml:mi>C</mml:mi><mml:msub><mml:mo>〉</mml:mo><mml:mtext>P1</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">191</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">15</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M121" 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> and <inline-formula><mml:math id="M122" display="inline"><mml:mrow><mml:mo>〈</mml:mo><mml:mi>C</mml:mi><mml:msub><mml:mo>〉</mml:mo><mml:mtext>P3</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">183</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">29</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M123" 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>, respectively.
This regime is also consistent with the average <inline-formula><mml:math id="M124" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> obtained without considering any change points, <inline-formula><mml:math id="M125" display="inline"><mml:mrow><mml:mo>〈</mml:mo><mml:mi>C</mml:mi><mml:mo>〉</mml:mo><mml:mo>=</mml:mo><mml:mn mathvariant="normal">201</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">15</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M126" 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> (Fig. <xref ref-type="fig" rid="Ch1.F3"/>).</p>
      <p id="d1e2290">Departing from this routine, a cold period (P2)  is visible from 1985 to 1998, during which <inline-formula><mml:math id="M127" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> fluctuates around a larger average value, <inline-formula><mml:math id="M128" display="inline"><mml:mrow><mml:mo>〈</mml:mo><mml:mi>C</mml:mi><mml:msub><mml:mo>〉</mml:mo><mml:mtext>P2</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">345</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">26</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M129" 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>.
This cold period has been described in numerous studies <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx41" id="paren.60"><named-content content-type="pre">e.g.,</named-content></xref> and is attributed to strong and persistent anomalies in atmospheric teleconnection<?pagebreak page6513?> patterns (East Atlantic–West Russia and North Atlantic oscillations; <xref ref-type="bibr" rid="bib1.bibx25 bib1.bibx13" id="altparen.61"/>). Noteworthy is that similar cold periods were identified earlier in the 20th century (late 1920s–early 1930s and early 1950s; <xref ref-type="bibr" rid="bib1.bibx24" id="altparen.62"/>).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><label>Figure 3</label><caption><p id="d1e2350">  Multi-decadal variability in the Black Sea CIL cold content and distinct periods identified by the regime shift analysis (P1–P4). Confidence intervals for mean <inline-formula><mml:math id="M130" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> values are indicated by the orange-shaded area for each period and by the gray-shaded area for the null hypothesis (i.e., considering no regime shifts). Confidence intervals for the time limits of each period are indicated with red error bars.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f03.png"/>

        </fig>

      <p id="d1e2366">From 2009 to 2019, a warmer period (P4) is identified during which <inline-formula><mml:math id="M131" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> varies around a lower average <inline-formula><mml:math id="M132" display="inline"><mml:mrow><mml:mo>〈</mml:mo><mml:mi>C</mml:mi><mml:msub><mml:mo>〉</mml:mo><mml:mtext>P4</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">60</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">28</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M133" 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>.
The regime shift analysis thus evidences a general weakening of the cold-water formation and associated ventilation that has prevailed in the Black Sea for about 10 years.
Warm years and low cold content were also observed during the years 1961 and 1963, but those were not identified as part of a statistically distinct “warm” regime and should be considered strong fluctuations within P1.
The regime shift analysis thus indicates that the current restricted ventilation conditions have no precedent in modern history.</p>
</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Cold-intermediate-layer formation as an oxygenation process</title>
      <p id="d1e2420">The intra-annual resolution provided by the 3D model and Argo time series (Fig. <xref ref-type="fig" rid="Ch1.F1"/>) suggests that the partial CIL renewal, which was taking place systematically each year before 2009, has now become occasional.
Here we focus on period P4, better detailed in our datasets, to characterize the CIL formation as a basin-wide ventilation process and its relationship with changes in oxygen saturation at different <inline-formula><mml:math id="M134" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> levels.</p>
      <p id="d1e2432">Basin-wide CIL formation and destruction rates were computed from the synoptic 3D model outputs,
as differences between weekly <inline-formula><mml:math id="M135" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> values (Fig. <xref ref-type="fig" rid="Ch1.F4"/>).
The seasonal sequence depicts CIL formation peaks from late December to March,
typically reaching <inline-formula><mml:math id="M136" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> formation rates of 5, 10 and 1 MJ m<inline-formula><mml:math id="M137" 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> d<inline-formula><mml:math id="M138" 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 the period P1–P3, P2 and P4, respectively (Fig. <xref ref-type="fig" rid="Ch1.F4"/>a–c).
The CIL cold content is then eroded at different rates before, during and at the end of the thermocline season, with a damped erosion rate through the thermocline season between 0 and about 1 MJ m<inline-formula><mml:math id="M139" 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> d<inline-formula><mml:math id="M140" 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>.
CIL formation processes have been described extensively in the past <xref ref-type="bibr" rid="bib1.bibx2 bib1.bibx36" id="paren.63"><named-content content-type="pre">e.g.,</named-content></xref>, in more detail than is allowed by the integrated perspective adopted here.
This integrated point of view, however, serves to point out the striking quasi-absence of CIL formation peaks for the years 2001, 2007, 2009, 2010, 2013 and 2014 (Fig. <xref ref-type="fig" rid="Ch1.F4"/>d).
In fact, during the period of Argo oxygen sampling (2011–2020), only 2012 and 2017 depict important CIL formation events, while minor CIL formation events are shown for 2015 and 2016.</p>
      <p id="d1e2509">Oxygen saturation in this period varies in concordance with CIL formation up to <inline-formula><mml:math id="M141" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> layers of about 16.0 kg m<inline-formula><mml:math id="M142" 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> (Fig. <xref ref-type="fig" rid="Ch1.F5"/>).
Large increases can be observed from December to March in the years 2012, 2015, 2016 and 2017 when CIL formation is significant, which denotes the impact of convective ventilation.
The narrow interquartile ranges depicted in Fig. <xref ref-type="fig" rid="Ch1.F5"/> denote the efficiency of the isopycnal diffusion of oxygen: the amount of oxygen imported with the newly formed CIL waters is distributed horizontally and contributes to increasing the average oxygen saturation of a given <inline-formula><mml:math id="M143" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> layer.</p>
      <?pagebreak page6514?><p id="d1e2542">While major CIL formation events in 2012 and 2017 induced a significant increase in oxygen saturation through the whole oxygenated water column, the minor events in 2015 and 2016 seem to have had a limited penetration depth.
For instance, oxygen saturation at
14.6 kg m<inline-formula><mml:math id="M144" 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> only stagnates during 2015 and 2016 as compared to 2014, while oxygen saturation at
15.1 kg m<inline-formula><mml:math id="M145" 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> keeps decreasing during these 2 years, indicating that the amount of oxygen brought to this layer during minor ventilation events is not sufficient to counterbalance the biogeochemical oxygen consumption terms (i.e., respiration and oxidation of reduced substances diffusing upwards).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><?xmltex \currentcnt{4}?><label>Figure 4</label><caption><p id="d1e2572">
Weekly averaged basin-wide CIL cold-content formation and destruction rates (<inline-formula><mml:math id="M146" display="inline"><mml:mrow><mml:mi mathvariant="normal">d</mml:mi><mml:mi>C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:math></inline-formula>), obtained as differences between the weekly integrated CIL cold content provided by the GHER3D model:
<bold>(a–c)</bold> in a seasonal frame with weekly medians (line) and interquartile range (shaded area), merging years from the periods P1 and P3 (considered together), P2, and P4; <bold>(d)</bold> on an inter-annual scale, with a 3-week running average (blue line). Vertical dashed lines separate the four periods identified by the regime shift model.
The red lines delineate the thresholds of <inline-formula><mml:math id="M147" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> MJ m<inline-formula><mml:math id="M148" 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> d<inline-formula><mml:math id="M149" 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>, corresponding to the lower bound of the CIL erosion rate during summers.</p></caption>
        <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f04.png"/>

      </fig>

      <p id="d1e2638">Following our attempt to summarize large datasets and to characterize a basin-scale annual oxygenation rate, we computed for each layer an annual oxygenation index as the difference between the median oxygen saturation in November between successive years. The rationale behind this approach is that CIL formation typically extends from December to March (Fig. <xref ref-type="fig" rid="Ch1.F4"/>a–c).</p>
      <p id="d1e2643">In order to obtain a general indication of the (pycnal) penetration depth of the convective ventilation associated with CIL formation,
we assessed the Pearson correlation coefficient between this annual oxygenation index and a first-order assessment of annual CIL formation, obtained as the annual difference in the <inline-formula><mml:math id="M150" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> composite time series.
The correlation between oxygenation and CIL formation is high near the surface and decreases continuously as deeper pycnal levels are considered.
Those correlations remain significant (<inline-formula><mml:math id="M151" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.1</mml:mn></mml:mrow></mml:math></inline-formula>) until <inline-formula><mml:math id="M152" display="inline"><mml:mrow><mml:mi mathvariant="italic">σ</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">15.4</mml:mn></mml:mrow></mml:math></inline-formula> kg m<inline-formula><mml:math id="M153" 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> (Fig. <xref ref-type="fig" rid="Ch1.F6"/>).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5" specific-use="star"><?xmltex \currentcnt{5}?><label>Figure 5</label><caption><p id="d1e2693">
Monthly medians of oxygen saturation at different <inline-formula><mml:math id="M154" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> layers. Shaded areas indicate the monthly interquartile range (Fig. <xref ref-type="fig" rid="Ch1.F2"/>).</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f05.png"/>

      </fig>

</sec>
<sec id="Ch1.S5">
  <label>5</label><title>Discussion</title>
      <p id="d1e2720">The regime shift paradigm describes an abrupt and significant change in the observable outcome of a non-linear system, as could result from a threshold in this system response to external forcing.
In contrast, a periodic model supposes either a linear response to periodically varying external forcing or an oscillation resulting from internal dynamics.
It is our hypothesis, supported by the quantitative analysis presented in this study, that the regime shift model should be favored for interpreting the recent evolution of the Black Sea CIL dynamics.
The prerequisite for the regime shift analysis was first to issue an unified, synoptic metric to characterize inter-annual variations in the CIL content.
The consistency between the different data sources used to construct this metric demonstrates the robustness of this metric.
To our knowledge, no multi-source comparison has been previously achieved over such an extended period.
Note that some dependencies exist among certain sources, as discussed explicitly in Table <xref ref-type="table" rid="App1.Ch1.S1.T2"/>.</p>
      <p id="d1e2725">Although we acknowledge that the statistical advantage (AIC) of the regime shift description is subtle, we consider that it deserves further consideration as this difference in interpretation is fundamental in regards to the expected consequences on the Black Sea oxygenation status and in particular the threat to Black Sea marine populations, whose ecological adaptation (and rate of exploitation) has been built upon a ventilation regime and consequent biogeochemical balance that may no longer prevail.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6"><?xmltex \currentcnt{6}?><label>Figure 6</label><caption><p id="d1e2730">
Pearson correlation coefficient between basin-wide annual oxygenation and CIL formation estimates for different <inline-formula><mml:math id="M155" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> layers.
Color of the points relates to the order of magnitude of associated <inline-formula><mml:math id="M156" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> values.</p></caption>
        <?xmltex \igopts{width=170.716535pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f06.png"/>

      </fig>

      <p id="d1e2754">Indeed, it appears that the intermittency of significant CIL formation events characterizes the new ventilation regime: the ventilation of the Black Sea intermediate layers does not occur each year anymore but is occasionally absent for 1 or 2 consecutive years.
Moreover, major CIL formation events, which bear the potential for deeper oxygenation, appear significantly less frequently.</p>
      <p id="d1e2757">The extent to which the current regime differs from the previous ventilation regimes is clearly illustrated on a <inline-formula><mml:math id="M157" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M158" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> diagram:
in situ measurements from period P4 are commonly found in a range of the <inline-formula><mml:math id="M159" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M160" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> diagram (<inline-formula><mml:math id="M161" display="inline"><mml:mrow><mml:mi>T</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">8.35</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M162" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and <inline-formula><mml:math id="M163" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> within 14.5–15 kg m<inline-formula><mml:math id="M164" 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>) that was extremely rare in previous periods (see density contours on Fig. <xref ref-type="fig" rid="Ch1.F7"/>a–d;<?pagebreak page6515?> two-dimensional density estimates were obtained with the R function <monospace>MASS:kde2d</monospace>).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7" specific-use="star"><?xmltex \currentcnt{7}?><label>Figure 7</label><caption><p id="d1e2836">
<bold>(a–d)</bold> Potential temperature versus salinity (<inline-formula><mml:math id="M165" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M166" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> diagram) for bottle, CTD and Argo in situ data available from the World Ocean Database for the period 1955–2020 <xref ref-type="bibr" rid="bib1.bibx10" id="paren.64"/>. Data from periods P1, P2, P3 and P4 are shown in <bold>(a)</bold>–<bold>(d)</bold>, respectively. Black contours delineate 90 %, 75 % and 50 % of the observations for each period.
<bold>(d)</bold> Historical oxygen records collected from the World Ocean Database for the period 1955–2020 <xref ref-type="bibr" rid="bib1.bibx10" id="paren.65"/>,  averaged for hexagonal bins of the <inline-formula><mml:math id="M167" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M168" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> diagram.
The 75 % contour of P2 (yellow) and P4 (red) are repeated to highlight the difference in oxygenation state at a given density between the two corresponding contrasted CIL regimes.
For all panels, the isopycnal <inline-formula><mml:math id="M169" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> layers are indicated by  curved gray lines. The dashed black line locates the <inline-formula><mml:math id="M170" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mtext>CIL</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">8.35</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M171" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C criterion used to identify CIL waters.</p></caption>
        <?xmltex \igopts{width=426.791339pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f07.png"/>

      </fig>

      <p id="d1e2924">As indicated by <xref ref-type="bibr" rid="bib1.bibx54" id="text.66"/>, this trend may lead to the disappearance of a characteristic layer of the Black Sea,
which constituted a major component of its thermo-haline structure and constrained the exchanges between surface, subsurface and intermediate layers.
In particular, the authors highlight surface and subsurface salinity trends that indicate recent occurrences of diapycnal mixing at the lower base of the intermediate layer, where waters are characterized by a strong reduction potential due to the presence of reduced iron and manganese species, ammonium, and finally hydrogen sulfide <xref ref-type="bibr" rid="bib1.bibx44" id="paren.67"/>.</p>
      <p id="d1e2933">On a decadal timescale, the average oxygen signature of a given isopycnal layer within the CIL depends on the frequency of CIL formation events of sufficient intensity (Sect. <xref ref-type="sec" rid="Ch1.S4"/>), which is in line with the ventilation dynamics described by <xref ref-type="bibr" rid="bib1.bibx24" id="text.68"/> for the upper pycnocline.
Although inter-annual fluctuations in the CIL formation rate still occur, the regime shift analysis specifically describes a reduction in the average CIL cold content, which appears to be associated with a reduction in the frequency of significant ventilation events (Fig. <xref ref-type="fig" rid="Ch1.F4"/>) and therefore a potential decrease in the oxygen saturation signature in the lower part of the CIL.</p>
      <p id="d1e2944">Importantly, this reduction may also affect deeper layers of the Black Sea.
Indeed, the mid-pycnocline (<inline-formula><mml:math id="M172" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> between about 14.6 and 16 kg m<inline-formula><mml:math id="M173" 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>) is formed by the two end-member mixing lines
between the CIL layer and the Bosporus inflow <xref ref-type="bibr" rid="bib1.bibx38 bib1.bibx24" id="paren.69"/>, which proceeds from the entrainment of CIL waters by the Bosporus inflow and subsequent lateral ventilation <xref ref-type="bibr" rid="bib1.bibx12" id="paren.70"/>.
Considering the characteristic residence time for the upper (about 5 years; <xref ref-type="bibr" rid="bib1.bibx31" id="altparen.71"/>) and intermediate (9–15 years; <xref ref-type="bibr" rid="bib1.bibx24" id="altparen.72"/>) pycnocline, it is appropriate to consider such temporal averages to characterize the oxygen signature of the CIL member composing the mixture of pycnocline waters.</p>
      <p id="d1e2979">Displaying historical oxygen saturation data (1955–2020) on the <inline-formula><mml:math id="M174" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M175" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> diagram (Fig. <xref ref-type="fig" rid="Ch1.F7"/>e) indeed shows generally deeper<?pagebreak page6516?> oxygenation during high-CIL regimes than during low-CIL regimes.
For instance, oxygen saturation in the density range <inline-formula><mml:math id="M176" display="inline"><mml:mrow><mml:mi mathvariant="italic">σ</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">14.8</mml:mn></mml:mrow></mml:math></inline-formula>–15.2 kg m<inline-formula><mml:math id="M177" 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> lies in the range of 30 %–70 % in the <inline-formula><mml:math id="M178" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M179" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> region that is characteristic of P2, while it only reaches 10 %–50 % in the <inline-formula><mml:math id="M180" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M181" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> region characteristic of P4.
This indicates that the analysis linking oxygenation and CIL formation for the recent period (Sect. <xref ref-type="sec" rid="Ch1.S4"/>) can be extended to larger timescales by considering changes in the frequency of significant CIL formation events.
Thus, the depth up to which the reduction in CIL formation may impact on the biogeochemical balance of the Black Sea (by affecting the oxygenation level) will depend on the period over which the current ventilation regime will continue.</p>
      <p id="d1e3053">Indeed, it is important to highlight that the current regime may not necessarily be a new steady regime – even though it has been identified as such by the change point analysis – but that it could be part of a transient downwards trend that started in the mid-1990s.
The reason why it appeared important to us to oppose non-linear regime dynamics with smooth linear and sine trend models is that the recent CIL dynamics, when depicted by the regime change approach, is not slow-trending but suggests a step change towards a new phenology for the intermediate Black Sea.
Only future observations may now confirm or invalidate the relevance of the proposed regime shift paradigm.</p>
      <p id="d1e3056">Beyond the changes in convective ventilation highlighted above, it thus appears as a lead priority to assess the biogeochemical consequences of this new thermo-haline dynamics of the Black Sea.
In particular, the influence of CIL formation on the biogeochemical components of the oxygen budget should be addressed in more detail, asking for instance how the presence or absence of CIL formation influences planktonic growth, trophic interactions and organic-matter respiration rates.
We voluntarily adopted here a wide integrative point of view so as to highlight the large-scale relevance of the identified regime shift to Black Sea oxygenation.
However, we still consider that a detailed assessment of all components of the oxygen budget, i.e., ventilating processes and biogeochemical terms, is required in order to infer the future evolution of the Black Sea oxygenation status <xref ref-type="bibr" rid="bib1.bibx21" id="paren.73"><named-content content-type="pre">e.g.,</named-content></xref>.</p>
      <?pagebreak page6517?><p id="d1e3064">Although there are clear indications of a long-term warming trend in the Black Sea <xref ref-type="bibr" rid="bib1.bibx4" id="paren.74"/>, it remains a delicate task to strictly dissociate the contributions of global warming from those of regional atmospheric oscillations <xref ref-type="bibr" rid="bib1.bibx25 bib1.bibx13" id="paren.75"/>.
One such assessment in the neighboring Mediterranean Sea <xref ref-type="bibr" rid="bib1.bibx33" id="paren.76"/> concluded that global warming trend and regional oscillation contributed 42 % and 58 % of the recent regional sea surface temperature trend (1985–2009),  respectively.
While a corresponding assessment will have to be routinely reevaluated for the Black Sea as the time series expands,
it may conservatively be considered that global warming has made a significant contribution to warming winters in the Black Sea.
This contribution is expected to increase in the next decades <xref ref-type="bibr" rid="bib1.bibx27" id="paren.77"/>.</p>
</sec>
<sec id="Ch1.S6" sec-type="conclusions">
  <label>6</label><title>Conclusions</title>
      <p id="d1e3087">We have analyzed the variability in the Black Sea CIL formation over the last 65 years and investigated the existence of regime shifts in this dynamics.
For this purpose, we have produced a composite time series of the CIL cold content (<inline-formula><mml:math id="M182" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>) that is considered a proxy for the intensity of the convective ventilation resulting from the formation of dense oxygenated waters.
This composite time series is built from four different data products issued from observations and modeling so as to optimize its temporal extent in regards to preceding studies <xref ref-type="bibr" rid="bib1.bibx41" id="paren.78"><named-content content-type="pre">e.g.,</named-content></xref>.
The consistency between those products and in particular the close correspondence between observational and mechanistic predictive time series supports the reliability of the composite series and its adequacy to describe the evolution of the Black Sea subsurface convective ventilation during the last 65 years.</p>
      <p id="d1e3102">The composite time series was analyzed to detect different regimes, corresponding to periods characterized by significantly distinct averages.
We identified three main regimes that have existed over last 65 years:
(1) a standard regime prevailing during 1955–1984 and 1999–2008 that is consistent with the full-period average <inline-formula><mml:math id="M183" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>,
(2) a cold regime (high <inline-formula><mml:math id="M184" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>, 1985–1998) which has been previously documented (see references in Sect. 3), and
(3) a warm regime (low <inline-formula><mml:math id="M185" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula>, 2009–2019) which is characterized by the intermittency of the annual partial CIL renewal.
Statistical considerations indicate that the abrupt shift can not adequately be described by a combination of long-term linear and periodic trends. However, monitoring the future evolution of the CIL is necessary to confirm that this abrupt-change description should be favored over that of a transient dynamics.</p>
      <?pagebreak page6518?><p id="d1e3126">The synoptic CIL formation rates provided by the 3D hydrodynamic model and the detailed description of oxygenation conditions provided by BGC-Argo floats allowed us to detail the role of CIL formation in oxygenating the upper part of the Black Sea intermediate layers through convective ventilation (i.e., <inline-formula><mml:math id="M186" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> of between 14.4 and 15.4 kg m<inline-formula><mml:math id="M187" 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>).
Given that cold winter air temperature is the leading driver of CIL formation <xref ref-type="bibr" rid="bib1.bibx40 bib1.bibx24 bib1.bibx14" id="paren.79"/>;
given that CIL formation constitutes a dominant ventilation mechanism for the Black Sea intermediate layer;
and assuming that oxygen conditions constitute an environmental structuring factor affecting the ecosystem organization, its vigor and its resilience <xref ref-type="bibr" rid="bib1.bibx11" id="paren.80"/>,
this shift in the Black Sea ventilation regime may be associated with global warming and is expected to affect its biogeochemical balance and to threaten marine populations adapted to previously prevailing ventilation regimes.</p>
      <p id="d1e3154">To understand how global warming impacts marine deoxygenation dynamics is a worldwide concern.
The relatively fast and clear response that stems from the specific Black Sea geomorphology makes it a natural laboratory to study this dependency and related phenomena, although the specificity of this morphology also limits the direct transposition of Black Sea results to the global ocean.
Here, we showed that non-linear dynamics and feedbacks in ventilation mechanisms resulted in a significant shift in the average ventilation regime, in response to rising air temperature.
Since the temporal extent of low-oxygen conditions is critical for ecosystems, we stress the importance of assessing the potential for similar ventilation regime shifts in other oxygen-deficient basins.</p><?xmltex \hack{\clearpage}?>
</sec>

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

<?pagebreak page6519?><app id="App1.Ch1.S1">
  <?xmltex \currentcnt{A}?><label>Appendix A</label><?xmltex \opttitle{Comparison of the $C$ time series issued from different data sources}?><title>Comparison of the <inline-formula><mml:math id="M188" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> time series issued from different data sources</title>

      <?xmltex \floatpos{h!}?><fig id="App1.Ch1.S1.F8" specific-use="star"><?xmltex \currentcnt{A1}?><label>Figure A1</label><caption><p id="d1e3179"> Statistics of comparison between the different data sources: Pearson correlation coefficient (<inline-formula><mml:math id="M189" display="inline"><mml:mi>r</mml:mi></mml:math></inline-formula>), root-mean-square deviation (RMS), average bias (bias); number of overlapping years between time series (<inline-formula><mml:math id="M190" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula>) and, on diagonal elements, the standard deviation of each time series (SD).</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://bg.copernicus.org/articles/17/6507/2020/bg-17-6507-2020-f08.png"/>

      </fig>

      <p id="d1e3202">The <inline-formula><mml:math id="M191" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> time series are denoted <inline-formula><mml:math id="M192" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> for source <inline-formula><mml:math id="M193" display="inline"><mml:mi>m</mml:mi></mml:math></inline-formula> (<inline-formula><mml:math id="M194" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> is the year index).
Each pair of time series (<inline-formula><mml:math id="M195" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M196" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>) are compared over the years <inline-formula><mml:math id="M197" display="inline"><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> for which <inline-formula><mml:math id="M198" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M199" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> are both defined.
The following statistics are given for each pair of data sources in Fig. <xref ref-type="fig" rid="App1.Ch1.S1.F8"/>:
<list list-type="bullet"><list-item>
      <p id="d1e3316"><inline-formula><mml:math id="M200" display="inline"><mml:mrow><mml:msup><mml:mi>N</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>, the number of elements in <inline-formula><mml:math id="M201" display="inline"><mml:mrow><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>;</p></list-item><list-item>
      <p id="d1e3351">Pearson correlation coefficient,<disp-formula id="App1.Ch1.S1.E3" content-type="numbered"><label>A1</label><mml:math id="M202" display="block"><mml:mrow><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:munder><mml:mfenced open="(" close=")"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:mover accent="true"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mi>m</mml:mi></mml:msup></mml:mrow><mml:mo mathvariant="normal">‾</mml:mo></mml:mover></mml:mrow></mml:mfenced><mml:mfenced open="(" close=")"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:mover accent="true"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mi>n</mml:mi></mml:msup></mml:mrow><mml:mo mathvariant="normal">‾</mml:mo></mml:mover></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:msqrt><mml:mrow><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:munder><mml:msup><mml:mfenced close=")" open="("><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:mover accent="true"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mi>m</mml:mi></mml:msup></mml:mrow><mml:mo mathvariant="normal">‾</mml:mo></mml:mover></mml:mrow></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:msqrt><mml:msqrt><mml:mrow><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:munder><mml:msup><mml:mfenced open="(" close=")"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:mover accent="true"><mml:mrow><mml:msup><mml:mi>C</mml:mi><mml:mi>n</mml:mi></mml:msup></mml:mrow><mml:mo mathvariant="normal">‾</mml:mo></mml:mover></mml:mrow></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:msqrt></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>;</mml:mo></mml:mrow></mml:math></disp-formula></p></list-item><list-item>
      <p id="d1e3510">the root-mean-square deviation between time series,<disp-formula id="App1.Ch1.S1.E4" content-type="numbered"><label>A2</label><mml:math id="M203" display="block"><mml:mrow><mml:msqrt><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:munder><mml:msup><mml:mfenced close=")" open="("><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:msup><mml:mi>N</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mstyle></mml:msqrt><mml:mo>;</mml:mo></mml:mrow></mml:math></disp-formula></p></list-item><list-item>
      <p id="d1e3575">the average bias,<disp-formula id="App1.Ch1.S1.E5" content-type="numbered"><label>A3</label><mml:math id="M204" display="block"><mml:mrow><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:munder><mml:mfenced open="(" close=")"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:msup><mml:mi>N</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>;</mml:mo></mml:mrow></mml:math></disp-formula></p></list-item><list-item>
      <p id="d1e3636">the percentage bias,<disp-formula id="App1.Ch1.S1.E6" content-type="numbered"><label>A4</label><mml:math id="M205" display="block"><mml:mrow><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:munder><mml:mfenced open="(" close=")"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:munder><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mi>I</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:munder><mml:mstyle displaystyle="false"><mml:mfrac style="text"><mml:mrow><mml:mfenced close=")" open="("><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:mfenced></mml:mrow><mml:mn mathvariant="normal">2</mml:mn></mml:mfrac></mml:mstyle></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>×</mml:mo><mml:mn mathvariant="normal">100</mml:mn><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula></p></list-item></list></p>
      <p id="d1e3731">For a better appreciation of variation scales, the temporal standard deviation is also shown for each data source.</p>
      <p id="d1e3735">The last value of the atmospheric predictor time series (<inline-formula><mml:math id="M206" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mn mathvariant="normal">2013</mml:mn><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>; Fig. <xref ref-type="fig" rid="Ch1.F1"/>) was not considered in the composite time series, as it was based on the two, rather than three, predictor values available at the time of publication (hence the larger associated uncertainty).
It is remarkable, however, that the published prognostic values for 2012 and 2013 match with independent Argo estimates <xref ref-type="bibr" rid="bib1.bibx14" id="paren.81"/>.</p>
      <p id="d1e3756">Finally, Table <xref ref-type="table" rid="App1.Ch1.S1.T2"/> provides specific comments on the dependence relationship between the different time series presented above. Only <inline-formula><mml:math id="M207" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> and  <inline-formula><mml:math id="M208" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> can be considered strictly dependent. <inline-formula><mml:math id="M209" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Model3D</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> is influenced by the same datasets that were used to build <inline-formula><mml:math id="M210" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> and  <inline-formula><mml:math id="M211" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> but through drastically different processing pathways and can thus be considered practically independent.</p>

<?xmltex \floatpos{t}?><table-wrap id="App1.Ch1.S1.T2" specific-use="star"><?xmltex \currentcnt{A1}?><label>Table A1</label><caption><p id="d1e3830">Dependence relationships between the different datasets.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="4">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="1cm"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="5.5cm"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="7.5cm"/>
     <oasis:colspec colnum="4" colname="col4" align="justify" colwidth="1.3cm"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Ship casts</oasis:entry>
         <oasis:entry colname="col3">Model3D</oasis:entry>
         <oasis:entry colname="col4">Argo</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Atmos</oasis:entry>
         <oasis:entry colname="col2">The statistical model providing <inline-formula><mml:math id="M212" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> is built on the basis of <inline-formula><mml:math id="M213" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>. So, even if <inline-formula><mml:math id="M214" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> is more homogeneous and complete than <inline-formula><mml:math id="M215" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>, it can not be considered independent.</oasis:entry>
         <oasis:entry colname="col3">Atmospheric conditions used to build <inline-formula><mml:math id="M216" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Atmos</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>  are issued from the same datasets (ECMWF) that were used to force the 3D model. So, formally, both approaches are influenced by a common dataset but through drastically different processing pathways. We consider no direct dependency in this case.</oasis:entry>
         <oasis:entry colname="col4">Strictly independent</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Ship <?xmltex \hack{\hfill\break}?>casts</oasis:entry>
         <oasis:entry colname="col2">–</oasis:entry>
         <oasis:entry colname="col3">The 3D model simulations involve no data assimilation. The model has been calibrated by testing different parameterizations of the atmospheric fluxes' bulk formulations, using <inline-formula><mml:math id="M217" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M218" display="inline"><mml:mi>S</mml:mi></mml:math></inline-formula> in situ data from the same set that has been used to build <inline-formula><mml:math id="M219" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula>. However, this calibration was not based on <inline-formula><mml:math id="M220" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> itself. Also, the selected parameterization remains fixed for the whole simulation time. So, although both time series are influenced by a common dataset, we consider there to be no direct dependency.</oasis:entry>
         <oasis:entry colname="col4">Strictly independent</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Model3D</oasis:entry>
         <oasis:entry colname="col2">–</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
         <oasis:entry colname="col4">Strictly independent</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

<?xmltex \hack{\clearpage}?><?xmltex \floatpos{t}?><table-wrap id="App1.Ch1.S1.T3"><?xmltex \currentcnt{A2}?><label>Table A2</label><caption><p id="d1e4023"> Tests for the presence of significant structural changes in <inline-formula><mml:math id="M221" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>. The <inline-formula><mml:math id="M222" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> value indicates the probability that the null hypothesis (i.e., there are no significant change points)
should be maintained. All tests, except that highlighted in bold, indicate the presence of significant structural changes in <inline-formula><mml:math id="M223" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> with a confidence level higher than 95 %.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Approach</oasis:entry>
         <oasis:entry colname="col2">Test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M224" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> value</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M225" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula> statistics</oasis:entry>
         <oasis:entry colname="col2">supF test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M226" display="inline"><mml:mrow><mml:mn mathvariant="normal">1.7</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">aveF test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M227" display="inline"><mml:mrow><mml:mn mathvariant="normal">5.3</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">expF test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M228" display="inline"><mml:mrow><mml:mn mathvariant="normal">1.7</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">8</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Fluctuations</oasis:entry>
         <oasis:entry colname="col2">OLS-based CUSUM test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M229" display="inline"><mml:mrow><mml:mn mathvariant="normal">1.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Recursive CUSUM test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M230" display="inline"><mml:mrow><mml:mtext mathvariant="bold">3.6</mml:mtext><mml:mo mathvariant="bold">×</mml:mo><mml:msup><mml:mtext mathvariant="bold">10</mml:mtext><mml:mrow><mml:mo>-</mml:mo><mml:mtext mathvariant="bold">1</mml:mtext></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">OLS-based MOSUM test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M231" display="inline"><mml:mrow><mml:mn mathvariant="normal">1.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Recursive MOSUM test</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M232" display="inline"><mml:mrow><mml:mn mathvariant="normal">1.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

<?xmltex \floatpos{t}?><table-wrap id="App1.Ch1.S1.T4"><?xmltex \currentcnt{A3}?><label>Table A3</label><caption><p id="d1e4292"> Correlations between (second column) time-lagged replicates of the original <inline-formula><mml:math id="M233" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and (third column) time-lagged replicates of the residuals of the four-segment model.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Lag</oasis:entry>
         <oasis:entry colname="col2">Original</oasis:entry>
         <oasis:entry colname="col3">Residuals</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">0</oasis:entry>
         <oasis:entry colname="col2">1.0</oasis:entry>
         <oasis:entry colname="col3">1.0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">1</oasis:entry>
         <oasis:entry colname="col2">0.57</oasis:entry>
         <oasis:entry colname="col3">0.13</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2</oasis:entry>
         <oasis:entry colname="col2">0.38</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M234" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.22</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">3</oasis:entry>
         <oasis:entry colname="col2">0.32</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M235" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.11</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">4</oasis:entry>
         <oasis:entry colname="col2">0.33</oasis:entry>
         <oasis:entry colname="col3">0.03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">5</oasis:entry>
         <oasis:entry colname="col2">0.26</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M236" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

<?xmltex \hack{\clearpage}?>
</app>

<?pagebreak page6522?><app id="App1.Ch1.S2">
  <?xmltex \currentcnt{B}?><label>Appendix B</label><title>Regime shift analysis</title>
      <p id="d1e4442">The basic change point problem that is considered in this study can be expressed as follows: to identify the change point <inline-formula><mml:math id="M237" display="inline"><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:math></inline-formula> in a sequence <inline-formula><mml:math id="M238" display="inline"><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> of independent random variables with constant variance, such that the expectation of <inline-formula><mml:math id="M239" display="inline"><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is <inline-formula><mml:math id="M240" display="inline"><mml:mi mathvariant="italic">μ</mml:mi></mml:math></inline-formula> for <inline-formula><mml:math id="M241" display="inline"><mml:mrow><mml:mi>i</mml:mi><mml:mo>&lt;</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M242" display="inline"><mml:mrow><mml:mi mathvariant="italic">μ</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> otherwise.
Obviously, this problem can be generalized for several change points.
The procedure for change point detection is stepwise and has been achieved following the methodology described in the documentation of the R package <monospace>strucchange</monospace> <xref ref-type="bibr" rid="bib1.bibx60" id="paren.82"/>.</p>
      <p id="d1e4522">First, the existence of at least one significant change point had to be tested. The package
<monospace>strucchange</monospace> provides seven statistical tests to compute the <inline-formula><mml:math id="M243" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> value at which the null hypothesis of no change points can be rejected.
The presence of change points can be tested on the basis of <inline-formula><mml:math id="M244" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula>-statistic tests or generalized fluctuation tests <xref ref-type="bibr" rid="bib1.bibx60" id="paren.83"><named-content content-type="post">and
references therein</named-content></xref>.
Table <xref ref-type="table" rid="App1.Ch1.S1.T3"/> provides the significance level at which the null hypothesis can be rejected for each test implemented in the <monospace>strucchange</monospace> R package.
Among the seven tests considered to assess the presence of at least one significant change point in <inline-formula><mml:math id="M245" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, six reject the null hypothesis with a significance level <inline-formula><mml:math id="M246" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>.</p>
      <p id="d1e4576">Second, the locations of the <inline-formula><mml:math id="M247" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula> most likely change points were identified.
In this study, we considered from one to five change points.
The change point locations can be estimated by finding the index values <inline-formula><mml:math id="M248" display="inline"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mo>[</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mi mathvariant="normal">…</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mi>N</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow></mml:math></inline-formula> that maximize a likelihood ratio, defined as the ratio of the residual sum of squares for the alternative hypothesis (i.e., change points at <inline-formula><mml:math id="M249" display="inline"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> with <inline-formula><mml:math id="M250" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo>≠</mml:mo><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula>) to that of the null hypothesis (i.e., no change point, <inline-formula><mml:math id="M251" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula>).</p>
      <p id="d1e4672">Formally, the methodology to identify and date structural change is designed for normal random variables,
two conditions which can not be guaranteed for environmental time series such as those considered here.
We detail here why (1) the departure of <inline-formula><mml:math id="M252" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> distribution from a Gaussian distribution,
(2) the autocorrelation in the composite time series <inline-formula><mml:math id="M253" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and
(3) the biases between source-specific components of  <inline-formula><mml:math id="M254" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>
do not affect the conclusions drawn above.</p>
<sec id="App1.Ch1.S2.SS1">
  <label>B1</label><title>Normality</title>
      <p id="d1e4712">Skewness in the distribution of <inline-formula><mml:math id="M255" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> values and its departure from normality is visible at low <inline-formula><mml:math id="M256" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> values (not shown), as expected for physical reasons: <inline-formula><mml:math id="M257" display="inline"><mml:mi>C</mml:mi></mml:math></inline-formula> is a vertically integrated property, naturally bounded by zero.
However, the Shapiro–Wilk test that measures the correlation between the quantiles of <inline-formula><mml:math id="M258" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and those of a normal distribution indicates no significant departure from normality:  <inline-formula><mml:math id="M259" display="inline"><mml:mrow><mml:mi>W</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.975</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M260" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.23</mml:mn></mml:mrow></mml:math></inline-formula>.
For completeness, a Box–Cox transformation (<inline-formula><mml:math id="M261" display="inline"><mml:mrow><mml:mi mathvariant="italic">λ</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.7</mml:mn></mml:mrow></mml:math></inline-formula>) of the original data has been tested which slightly enhances the Shapiro–Wilk test
(<inline-formula><mml:math id="M262" display="inline"><mml:mrow><mml:mi>W</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.98</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M263" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.39</mml:mn></mml:mrow></mml:math></inline-formula>) but brings no sensible alteration in the conclusions of the structural-change analysis.</p>
</sec>
<sec id="App1.Ch1.S2.SS2">
  <label>B2</label><title>Autocorrelation</title>
      <p id="d1e4816">Similarly, <inline-formula><mml:math id="M264" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> can not be considered a random variable.
In particular, we introduced in Sect. <xref ref-type="sec" rid="Ch1.S1"/> the CIL preconditioning and partial renewal mechanisms,
both physical reasons for which autocorrelation may be expected in <inline-formula><mml:math id="M265" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>.
Indeed, correlations between the original and <inline-formula><mml:math id="M266" display="inline"><mml:mi>k</mml:mi></mml:math></inline-formula>-lagged time series are, at first glance, significant up to <inline-formula><mml:math id="M267" display="inline"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> (Table <xref ref-type="table" rid="App1.Ch1.S1.T4"/>; the confidence interval above which autocorrelation can be considered to be significant is given by <inline-formula><mml:math id="M268" display="inline"><mml:mrow><mml:mn mathvariant="normal">1.96</mml:mn><mml:mo>/</mml:mo><mml:msqrt><mml:mi>N</mml:mi></mml:msqrt><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.25</mml:mn></mml:mrow></mml:math></inline-formula>).
However, it should be considered that the regime shift evidenced in this study may itself induce apparent autocorrelation statistics.
To evidence that this is indeed the case encountered here, the correlations between the original and lagged time series of the residuals stemming from the four-segment change point model are indicated in Table <xref ref-type="table" rid="App1.Ch1.S1.T4"/>.
The fact that no significant autocorrelation persists when change points are considered indicates that the non-randomness of <inline-formula><mml:math id="M269" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> does not jeopardize conclusions drawn from the application of the structural-change methodology.</p>
</sec>
<sec id="App1.Ch1.S2.SS3">
  <label>B3</label><title>Biases between components of the composite time series</title>
      <p id="d1e4903">Given that biases exist between different data sources, it might be argued that the composite time series is skewed by the uneven temporal coverage of the different sources.
For instance, if a strongly biased source were to solely cover a given period, the composite series would be biased over that period.
To ensure that this issue does not affect the presented conclusions, <inline-formula><mml:math id="M270" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>unbiased</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> was constructed as
<inline-formula><mml:math id="M271" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> but removing from each component <inline-formula><mml:math id="M272" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>, the bias identified with the longest <inline-formula><mml:math id="M273" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>Ships</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> time series (which series is
used for reference does not impact on structural-change conclusions).
When <inline-formula><mml:math id="M274" display="inline"><mml:mrow><mml:msubsup><mml:mi>C</mml:mi><mml:mi>i</mml:mi><mml:mtext>unbiased</mml:mtext></mml:msubsup></mml:mrow></mml:math></inline-formula> is considered instead of <inline-formula><mml:math id="M275" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, similar results are obtained in terms of change point model significance and positions of the change points.</p><?xmltex \hack{\clearpage}?>
</sec>
</app>
  </app-group><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e4987">The data used are listed in Table <xref ref-type="table" rid="Ch1.T1"/>.
Argo data were collected and made freely available by the Coriolis project (<uri>http://www.coriolis.eu.org/</uri>, last access: 3 March 2020, <xref ref-type="bibr" rid="bib1.bibx23" id="altparen.84"/>) and programs that contribute to it.
Era-Interim atmospheric conditions were obtained from the ECMWF interface (<uri>http://apps.ecmwf.int/datasets/</uri>, last access: 1 August 2019, <xref ref-type="bibr" rid="bib1.bibx19" id="altparen.85"/>).
Aggregated weekly outputs of the GHER3D model, as well as processed annual time series from the different sources, are publicly available in a Zenodo repository: <uri>https://doi.org/10.5281/zenodo.3691960</uri>, last access: 28 February 2020, <xref ref-type="bibr" rid="bib1.bibx16" id="altparen.86"/>.</p>
  </notes><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e5014">AC processed the data, set the regime shift methodology, achieved the analyses, issued the visualizations, and wrote the initial version and revisions of the manuscript.
All authors contributed to defining the general methodology, to discussing the results and to revising the final manuscript.
MG supervised the research.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e5020">The authors declare that they have no conflict of interest.</p>
  </notes><notes notes-type="sistatement"><title>Special issue statement</title>

      <p id="d1e5026">This article is part of the special issue “Ocean deoxygenation: drivers and consequences – past, present and future (BG/CP/OS inter-journal SI)”. It is a result of the International Conference on Ocean Deoxygenation, Kiel, Germany, 3–7 September 2018.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e5032">This study was funded by the Fonds de la Recherche Scientifique (FNRS) and convention BENTHOX (PDR T.1009.15) and directly benefited from the resources made available within the PERSEUS project, funded by the European Commission, grant agreement 287600.
Luc Vandenbulcke is funded by the EU Copernicus Marine Environment Service program (contract BS-MFC).
Arthur Capet and Marilaure Grégoire are a postdoctoral fellow and research director, respectively, at the FNRS.
Computational resources have been provided by the supercomputing facilities of the Consortium des Équipements de Calcul Intensif en Federation Wallonie Bruxelles (CÉCI) funded by the Fond de la Recherche Scientifique de Belgique (FRS-FNRS).
Finally, this study was substantially enhanced thanks to the patient revisions proposed by Fabian Große, James W. Murray, Michael Dowd and an anonymous reviewer, to whom we hereby express our deepest gratitude.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e5037">This research has been supported by FRS-FNRS.</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e5044">This paper was edited by Katja Fennel and reviewed by James W. Murray, Fabian Große, Michael Dowd and one anonymous referee.</p>
  </notes><ref-list>
    <title>References</title>

      <ref id="bib1.bibx1"><label>Akaike(1974)</label><?label AKAIKE1974?><mixed-citation>
Akaike, H.: A new look at the statistical model identification, IEEE
T. Automat. Contr., 19, 716–723, 1974.</mixed-citation></ref>
      <ref id="bib1.bibx2"><label>Akpinar et al.(2017)</label><?label AKPINAR2017?><mixed-citation>
Akpinar, A., Fach, B. A., and Oguz, T.: Observing the subsurface thermal
signature of the Black Sea cold intermediate layer with Argo profiling
floats, Deep-Sea Res. Pt. I, 124, 140–152, 2017.</mixed-citation></ref>
      <ref id="bib1.bibx3"><label>Andersen et al.(2009)Andersen, Carstensen, Hernandez-Garcia, and
Duarte</label><?label ANDERSEN2009?><mixed-citation>
Andersen, T., Carstensen, J., Hernandez-Garcia, E., and Duarte, C. M.:
Ecological thresholds and regime shifts: approaches to identification, Trends
Ecol. Evol., 24, 49–57, 2009.</mixed-citation></ref>
      <ref id="bib1.bibx4"><label>Belkin(2009)</label><?label BELKIN2009?><mixed-citation>
Belkin, I. M.: Rapid warming of large marine ecosystems, Progr. Oceanogr., 81,
207–213, 2009.</mixed-citation></ref>
      <ref id="bib1.bibx5"><label>Belokopytov(2011)</label><?label BELOKOPYTOV2011?><mixed-citation>
Belokopytov, V.: Interannual variations of the renewal of waters of the cold
intermediate layer in the Black Sea for the last decades, Phys. Oceanogr.,
20, 347–355, 2011.</mixed-citation></ref>
      <ref id="bib1.bibx6"><label>Bittig et al.(2014)</label><?label Bittig2014-hx?><mixed-citation>Bittig, H. C., Fiedler, B., Scholz, R., Krahmann, G., and Körtzinger, A.:
Time response of oxygen optodes on profiling platforms and its dependence on
flow speed and temperature, Limnol. Oceanogr., 12, 617–636,
<ext-link xlink:href="https://doi.org/10.4319/lom.2014.12.617" ext-link-type="DOI">10.4319/lom.2014.12.617</ext-link>, 2014.</mixed-citation></ref>
      <ref id="bib1.bibx7"><label>Bopp et al.(2002)</label><?label BOPP2002?><mixed-citation>Bopp, L., Le Quéré, C., Heimann, M., Manning, A. C., and Monfray, P.:
Climate-induced oceanic oxygen fluxes: Implications for the contemporary
carbon budget, Global Biogeochem. Cy., 16, <ext-link xlink:href="https://doi.org/10.1029/2001gb001445" ext-link-type="DOI">10.1029/2001gb001445</ext-link>, 2002.</mixed-citation></ref>
      <ref id="bib1.bibx8"><label>Bopp et al.(2013)</label><?label BOPP2013?><mixed-citation>
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013.</mixed-citation></ref>
      <ref id="bib1.bibx9"><label>Boyer et al.(2009)</label><?label BOYER2009?><mixed-citation>
Boyer, T., Antonov, J., Garcia, H., Johnson, D., Locarnini, R., Mishonov, A.,
Pitcher, M., Baranova, O., and Smolyar, I.: Chapter 1: Introduction, World
ocean database, p. 216, 2009.</mixed-citation></ref>
      <ref id="bib1.bibx10"><label>Boyer et al.(2018)</label><?label BOYER2013?><mixed-citation>
Boyer, T. P., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., Locarnini, R. A., Mishonov, A. V., Paver, C. R., Reagan, J. R., Seidov, D., Smolyar, I. V., Weathers, K., and Zweng, M. M.: World Ocean Database 2018, A. V. Mishonov, Technical Ed., NOAA Atlas NESDIS 87, 2018.</mixed-citation></ref>
      <ref id="bib1.bibx11"><label>Breitburg et al.(2018)</label><?label BREITBURG2018?><mixed-citation>Breitburg, D., Levin, L. A., Oschlies, A., Grégoire, M., Chavez, F. P., Conley, D. J., Garçon, V., Gilbert, D., Gutiérrez, D., Isensee, K., Jacinto, G. S., Limburg, K. E., Montes, I., Naqvi, S. W. A., Pitcher, G. C., Rabalais, N. N., Roman, M. R., Rose, K. A., Seibel, B. A., Telszewski, M., Yasuhara, M., and Zhang, J.: Declining oxygen in the global ocean and coastal waters, Science,
359, eaam7240, <ext-link xlink:href="https://doi.org/10.1126/science.aam7240" ext-link-type="DOI">10.1126/science.aam7240</ext-link>, 2018.</mixed-citation></ref>
      <ref id="bib1.bibx12"><label>Buesseler et al.(1991)</label><?label BUESSELER1991?><mixed-citation>Buesseler, K. O., Livingston, H. D., and Casso, S. A.: Mixing between oxic and
anoxic waters of the Black Sea as traced by Chernobyl cesium isotopes, Deep-Sea Res., 38, 725–745, <ext-link xlink:href="https://doi.org/10.1016/S0198-0149(10)80006-8" ext-link-type="DOI">10.1016/S0198-0149(10)80006-8</ext-link>, 1991.</mixed-citation></ref>
      <ref id="bib1.bibx13"><label>Capet et al.(2012)</label><?label CAPET2012?><mixed-citation>Capet, A., Barth, A., Beckers, J.-M., and Grégoire, M.: Interannual
variability of Black Sea's hydrodynamics and connection to atmospheric
patterns, Deep-Sea Res. Pt. II, 77, 128–142,
<ext-link xlink:href="https://doi.org/10.1016/j.dsr2.2012.04.010" ext-link-type="DOI">10.1016/j.dsr2.2012.04.010</ext-link>,
2012.</mixed-citation></ref>
      <ref id="bib1.bibx14"><label>Capet et al.(2014)</label><?label CAPET2014?><mixed-citation>Capet, A., Troupin, C., Carstensen, J., Grégoire, M., and Beckers, J.-M.:
Untangling spatial and temporal trends in the variability of the Black Sea
Cold Intermediate Layer and mixed Layer Depth using the DIVA detrending
procedure, Ocean Dynam., 64, 315–324, 2014.
 </mixed-citation></ref><?xmltex \hack{\newpage}?>
      <ref id="bib1.bibx15"><label>Capet et al.(2016)</label><?label CAPET2016?><mixed-citation>
Capet, A., Stanev, E. V., Beckers, J.-M., Murray, J. W., and Grégoire, M.: Decline of the Black Sea oxygen inventory, Biogeosciences, 13, 1287–1297, https://doi.org/10.5194/bg-13-1287-2016, 2016.</mixed-citation></ref>
      <ref id="bib1.bibx16"><label>Capet et al.(2020)</label><?label Capet2020?><mixed-citation>Capet, A., Vandenbulcke, L., and Grégoire, M.: Black Sea cold intermediate layer cold content from in-situ and modelling sources (1955-2019),  [Data set],  available at: <uri>https://doi.org/10.5281/zenodo.3691960</uri>, last access: 28 February 2020.</mixed-citation></ref>
      <ref id="bib1.bibx17"><label>Coppola et al.(2017)</label><?label COPPOLA2017?><mixed-citation>
Coppola, L., Prieur, L., Taupier-Letage, I., Estournel, C., Testor, P.,
Lefèvre, D., Belamari, S., LeReste, S., and Taillandier, V.: Observation
of oxygen ventilation into deep waters through targeted deployment of
multiple Argo-O2 floats in the north-western Mediterranean Sea in 2013, J.
Geophys. Res.-Oceans, 122, 6325–6341, 2017.</mixed-citation></ref>
      <ref id="bib1.bibx18"><label>Dee et al.(2011)</label><?label ERAINTERIM?><mixed-citation>
Dee, D. P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S.,
Andrae, U., Balmaseda, M., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M.,  Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and             Vitart, F.: The ERA-Interim
reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.</mixed-citation></ref>
      <ref id="bib1.bibx19"><label>European Centre for Medium-Range Weather Forecasts(2019)</label><?label EuropeanCentre2019?><mixed-citation>European Centre for Medium-Range Weather Forecasts: ECMWF Public Datasets, available at:  <uri>https://apps.ecmwf.int/datasets/</uri>, last access: 1 August 2019.</mixed-citation></ref>
      <ref id="bib1.bibx20"><label>Gregg and Yakushev(2005)</label><?label GREGG2005?><mixed-citation>Gregg, M. and Yakushev, E.: Surface ventilation of the Black Sea's cold
intermediate layer in the middle of the western gyre, Geophys. Res. Lett.,
32, 3, <ext-link xlink:href="https://doi.org/10.1029/2004gl021580" ext-link-type="DOI">10.1029/2004gl021580</ext-link>, 2005.</mixed-citation></ref>
      <ref id="bib1.bibx21"><?xmltex \def\ref@label{{Gr{\'{e}}goire and Lacroix(2001)}}?><label>Grégoire and Lacroix(2001)</label><?label GREGOIRE2001?><mixed-citation>Grégoire, M. and Lacroix, G.: Study of the oxygen budget of the Black Sea
waters using a 3D coupled hydrodynamical–biogeochemical model, J. Mar.
Syst., 31, 175–202, <ext-link xlink:href="https://doi.org/10.1016/S0924-7963(01)00052-5" ext-link-type="DOI">10.1016/S0924-7963(01)00052-5</ext-link>, 2001.</mixed-citation></ref>
      <ref id="bib1.bibx22"><?xmltex \def\ref@label{{Gr{\'{e}}goire et~al.(1998)}}?><label>Grégoire et al.(1998)</label><?label GREGOIRE1998?><mixed-citation>Grégoire, M., Beckers, J. M., Nihoul, J. C. J., and Stanev, E.:
Reconnaissance of the main Black Sea's ecohydrodynamics by means of a 3D
interdisciplinary model, J. Mar. Syst., 16, 85–105,
<ext-link xlink:href="https://doi.org/10.1016/S0924-7963(97)00101-2" ext-link-type="DOI">10.1016/S0924-7963(97)00101-2</ext-link>, 1998.</mixed-citation></ref>
      <ref id="bib1.bibx23"><label>IFREMER(2020)</label><?label IFREMER2020?><mixed-citation>IFREMER: Coriolis: In situ data for operational oceanography, available at:  <uri>http://www.coriolis.eu.org/</uri>, last access: 3 March 2020.</mixed-citation></ref>
      <ref id="bib1.bibx24"><label>Ivanov et al.(2000)</label><?label IVANOV2000?><mixed-citation>
Ivanov, L., Belokopytov, V., Ozsoy, E., and Samodurov, A.: Ventilation of the
Black Sea pycnocline on seasonal and interannual time scales, Mediterr. Mar.
Sci., 1, 61–74, 2000.</mixed-citation></ref>
      <ref id="bib1.bibx25"><label>Kazmin and Zatsepin(2007)</label><?label KAZMIN2007?><mixed-citation>
Kazmin, A. S. and Zatsepin, A. G.: Long-term variability of surface temperature
in the Black Sea, and its connection with the large-scale atmospheric
forcing, J. Mar. Syst., 68, 293–301, 2007.</mixed-citation></ref>
      <ref id="bib1.bibx26"><label>Keeling et al.(2010)</label><?label KEELING2010?><mixed-citation>Keeling, R. F., Körtzinger, A., and Gruber, N.: Ocean deoxygenation in a
warming world, Annu. Rev. Mar. Sci, 2, 199–229,
<ext-link xlink:href="https://doi.org/10.1146/annurev.marine.010908.163855" ext-link-type="DOI">10.1146/annurev.marine.010908.163855</ext-link>, 2010.</mixed-citation></ref>
      <ref id="bib1.bibx27"><label>Kirtman et al.(2014)</label><?label KIRTMAN2014?><mixed-citation>
Kirtman, B., Power, S. B., Adedoyin, J. A., Boer, G. J., Camilloni, I.,
Doblas-Reyes, F. J., Fiore, A. M., Kimoto, M., Meehl, G. A., Prather, M.,
Sarr, A., Schar, C., Sutton, R., van Oldenborgh, G. J., Vecchi, G., and Wang,
H. J.: Near-term climate change: projections and predictability, in: Climate
change 2013: the physical science basis: contribution to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited by:
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M. M. B., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge
University Press, Cambridge, 76 pp., 2014.</mixed-citation></ref>
      <ref id="bib1.bibx28"><label>Konovalov and Murray(2001)</label><?label KONOVALOV2001?><mixed-citation>
Konovalov, S. K. and Murray, J. W.: Variations in the chemistry of the Black
Sea on a time scale of decades (1960–1995), J. Mar. Syst., 31,
217–243, 2001.</mixed-citation></ref>
      <ref id="bib1.bibx29"><label>Korotaev et al.(2014)</label><?label KOROTAEV2014?><mixed-citation>
Korotaev, G., Knysh, V., and Kubryakov, A.: Study of formation process of cold
intermediate layer based on reanalysis of Black Sea hydrophysical fields for
1971–1993, Izv. Atmos. Ocean. Phys., 50, 35–48, 2014.</mixed-citation></ref>
      <ref id="bib1.bibx30"><label>Kubryakov et al.(2016)</label><?label KUBRYAKOV2016?><mixed-citation>
Kubryakov, A. A., Stanichny, S. V., Zatsepin, A. G., and Kremenetskiy, V. V.:
Long-term variations of the Black Sea dynamics and their impact on the marine
ecosystem, J. Mar. Syst., 163, 80–94, 2016.</mixed-citation></ref>
      <ref id="bib1.bibx31"><label>Lee et al.(2002)</label><?label LEE2002?><mixed-citation>Lee, B.-S., Bullister, J. L., Murray, J. W., and Sonnerup, R. E.: Anthropogenic
chlorofluorocarbons in the Black Sea and the Sea of Marmara, Deep-Sea Res.
Pt. I, 49, 895–913, <ext-link xlink:href="https://doi.org/10.1016/S0967-0637(02)00005-5" ext-link-type="DOI">10.1016/S0967-0637(02)00005-5</ext-link>, 2002.</mixed-citation></ref>
      <ref id="bib1.bibx32"><label>Long et al.(2016)</label><?label LONG2016?><mixed-citation>
Long, M. C., Deutsch, C., and Ito, T.: Finding forced trends in oceanic oxygen,
Global Biogeochem. Cy., 30, 381–397, 2016.</mixed-citation></ref>
      <ref id="bib1.bibx33"><label>Macias et al.(2013)Macias, Garcia-Gorriz, and Stips</label><?label MACIAS2013?><mixed-citation>Macias, D., Garcia-Gorriz, E., and Stips, A.: Understanding the causes of
recent warming of mediterranean waters. How much could be attributed to
climate change?, PLoS One, 8, e81591, <ext-link xlink:href="https://doi.org/10.1371/journal.pone.0081591" ext-link-type="DOI">10.1371/journal.pone.0081591</ext-link>, 2013.</mixed-citation></ref>
      <ref id="bib1.bibx34"><label>MEDOC group et al.(1970)</label><?label MEDOC1970?><mixed-citation>
MEDOC group: Observation of formation of deep water in the
Mediterranean Sea, 1969, Nature, 227, 1037–1040, 1970.</mixed-citation></ref>
      <ref id="bib1.bibx35"><label>Miladinova et al.(2017)</label><?label MACIAS2016?><mixed-citation>Miladinova, S., Stips, A., Garcia-Gorriz, E., and Macias Moy, D.: Black Sea
thermohaline properties: Long-term trends and variations, J. Geophys.
Res.-Oceans, 122, 5624–5644, <ext-link xlink:href="https://doi.org/10.1002/2016JC012644" ext-link-type="DOI">10.1002/2016JC012644</ext-link>, 2017.</mixed-citation></ref>
      <ref id="bib1.bibx36"><label>Miladinova et al.(2018)Miladinova, Stips, Garcia-Gorriz, and
Moy</label><?label MILADINOVA2018?><mixed-citation>
Miladinova, S., Stips, A., Garcia-Gorriz, E., and Moy, D. M.: Formation and
changes of the Black Sea cold intermediate layer, Progr. Oceanogr., 167,
11–23, 2018.</mixed-citation></ref>
      <ref id="bib1.bibx37"><label>Murray et al.(1989)Murray, Jannasch, Honjo, Anderson, Reeburgh, Top,
Friederich, Codispoti, and Izdar</label><?label MURRAY1989?><mixed-citation>Murray, J. W., Jannasch, H. W., Honjo, S., Anderson, R. F., Reeburgh, W. S.,
Top, Z., Friederich, G. E., Codispoti, L. A., and Izdar, E.: Unexpected
changes in the oxic/anoxic interface in the Black Sea, Nature, 338, 411–413,
<ext-link xlink:href="https://doi.org/10.1038/338411a0" ext-link-type="DOI">10.1038/338411a0</ext-link>, 1989.</mixed-citation></ref>
      <ref id="bib1.bibx38"><label>Murray et al.(1991)</label><?label MURRAY1991?><mixed-citation>Murray, J. W., Top, Z., and Özsoy, E.: Hydrographic properties and
ventilation of the Black Sea, Deep-Sea Res., 38, 663–689,
<ext-link xlink:href="https://doi.org/10.1016/S0198-0149(10)80003-2" ext-link-type="DOI">10.1016/S0198-0149(10)80003-2</ext-link>, 1991.</mixed-citation></ref>
      <ref id="bib1.bibx39"><label>Nardelli et al.(2010)</label><?label NARDELLI2010?><mixed-citation>
Nardelli, B. B., Colella, S., Santoleri, R., Guarracino, M., and Kholod, A.: A
re-analysis of Black Sea surface temperature, J. Mar. Syst., 79, 50–64,
2010.</mixed-citation></ref>
      <ref id="bib1.bibx40"><label>Oguz and Besiktepe(1999)</label><?label OGUZ1999?><mixed-citation>
Oguz, T. and Besiktepe, S.: Observations on the Rim Current structure, CIW
formation and transport in the western Black Sea, Deep-Sea Res. Pt. I, 46,
1733–1753, 1999.</mixed-citation></ref>
      <ref id="bib1.bibx41"><label>Oguz et al.(2006)</label><?label OGUZ2006?><mixed-citation>Oguz, T., Dippner, J. W., and Kaymaz, Z.: Climatic regulation of the Black
Sea hydro-meteorological and ecological properties at
interannual-to-decadal time scales, J. Mar. Syst., 60, 235–254,
<ext-link xlink:href="https://doi.org/10.1016/j.jmarsys.2005.11.011" ext-link-type="DOI">10.1016/j.jmarsys.2005.11.011</ext-link>, 2006.</mixed-citation></ref>
      <ref id="bib1.bibx42"><label>Ostrovskii and Zatsepin(2016)</label><?label OSTROVSKII2016?><mixed-citation>
Ostrovskii, A. G. and Zatsepin, A. G.: Intense ventilation of the Black Sea
pycnocline due to vertical turbulent exchange in the Rim Current area, Deep
Sea Res. Pt. I, 116, 1–13, 2016.</mixed-citation></ref>
      <ref id="bib1.bibx43"><?xmltex \def\ref@label{{\"{O}szoy and \"{U}nl\"{u}ata(1997)}}?><label>Öszoy and Ünlüata(1997)</label><?label OSZOY1997?><mixed-citation>
Öszoy, E. and Ünlüata, U.: Oceanography of the Black Sea: a
review of some recent results., Earth-Sci. Rev., 42, 231–272, 1997.</mixed-citation></ref>
      <ref id="bib1.bibx44"><label>Pakhomova et al.(2014)</label><?label PAKHOMOVA2014?><mixed-citation>
Pakhomova, S., Vinogradova, E., Yakushev, E., Zatsepin, A., Shtereva, G.,
Chasovnikov, V., and Podymov, O.: Interannual variability of the Black Sea
proper oxygen and nutr<?pagebreak page6525?>ients regime: the role of climatic and anthropogenic
forcing, Estuar. Coast. Shelf S., 140, 134–145, 2014.</mixed-citation></ref>
      <ref id="bib1.bibx45"><label>Piotukh et al.(2011)</label><?label PIOTUKH2011?><mixed-citation>Piotukh, V., Zatsepin, A., Kazmin, A., and Yakubenko, V.: Impact of the Winter
Cooling on the Variability of the Thermohaline Characteristics of the Active
Layer in the Black Sea, Oceanology, 51, 221–230,  <ext-link xlink:href="https://doi.org/10.1134/s0001437011020123" ext-link-type="DOI">10.1134/s0001437011020123</ext-link>,  2011.</mixed-citation></ref>
      <ref id="bib1.bibx46"><?xmltex \def\ref@label{{Sak{\i}nan and G{\"{u}}c{\"{u}}(2017)}}?><label>Sakınan and Gücü(2017)</label><?label SAKINAN2017?><mixed-citation>
Sakınan, S. and Gücü, A. C.: Spatial distribution of the Black Sea
copepod, Calanus euxinus, estimated using multi-frequency acoustic
backscatter, ICES J. Mar. Sci., 74, 832–846, 2017.</mixed-citation></ref>
      <ref id="bib1.bibx47"><label>Schmidtko et al.(2017)</label><?label SCHMIDTKO2017?><mixed-citation>Schmidtko, S., Stramma, L., and Visbeck, M.: Decline in global oceanic oxygen
content during the past five decades, Nature, 542, 335, <ext-link xlink:href="https://doi.org/10.1038/nature21399" ext-link-type="DOI">10.1038/nature21399</ext-link>, 2017.</mixed-citation></ref>
      <ref id="bib1.bibx48"><label>Simonov and Altman(1991)</label><?label ALTMAN1991?><mixed-citation>
Simonov, A. and Altman, E.: Hydrometeorology and Hydrochemistry of the USSR
Seas, The Black Sea, 4, 430 pp., 1991.</mixed-citation></ref>
      <ref id="bib1.bibx49"><label>Stanev and Beckers(1999)</label><?label STANEV1999?><mixed-citation>
Stanev, E. and Beckers, J.-M.: Numerical simulations of seasonal and
interannual variability of the Black Sea thermohaline circulation, J.
Mar. Syst., 22, 241–267, 1999.</mixed-citation></ref>
      <ref id="bib1.bibx50"><label>Stanev et al.(2003)</label><?label STANEV2003?><mixed-citation>Stanev, E., Bowman, M., Peneva, E., and Staneva, J.: Control of Black Sea
intermediate water mass formation by dynamics and topography: Comparison of
numerical simulations, surveys and satellite data, J. Mar. Res., 61, 59–99,
<ext-link xlink:href="https://doi.org/10.1357/002224003321586417" ext-link-type="DOI">10.1357/002224003321586417</ext-link>, 2003.</mixed-citation></ref>
      <ref id="bib1.bibx51"><label>Stanev et al.(2013)</label><?label STANEV2013?><mixed-citation>
Stanev, E., He, Y., Grayek, S., and Boetius, A.: Oxygen dynamics in the Black
Sea as seen by Argo profiling floats, Geophys. Res. Lett., 40, 3085–3090,
2013.</mixed-citation></ref>
      <ref id="bib1.bibx52"><label>Stanev et al.(2014)</label><?label STANEV2014?><mixed-citation>
Stanev, E. V., He, Y., Staneva, J., and Yakushev, E.: Mixing in the Black Sea detected from the temporal and spatial variability of oxygen and sulfide – Argo float observations and numerical modelling, Biogeosciences, 11, 5707–5732, https://doi.org/10.5194/bg-11-5707-2014, 2014.</mixed-citation></ref>
      <ref id="bib1.bibx53"><label>Stanev et al.(2018)</label><?label STANEV2018?><mixed-citation>
Stanev, E. V., Poulain, P.-M., Grayek, S., Johnson, K. S., Claustre, H., and
Murray, J. W.: Understanding the Dynamics of the Oxic-Anoxic Interface in the
Black Sea, Geophys. Res. Lett., 45, 864–871, 2018.</mixed-citation></ref>
      <ref id="bib1.bibx54"><label>Stanev et al.(2019)</label><?label STANEV2019?><mixed-citation>Stanev, E. V., Peneva, E., and Chtirkova, B.: Climate Change and Regional Ocean
Water Mass Disappearance: Case of the Black Sea, J. Geophys. Res.-Oceans,
124, 140, <ext-link xlink:href="https://doi.org/10.1029/2019JC015076" ext-link-type="DOI">10.1029/2019JC015076</ext-link>, 2019.</mixed-citation></ref>
      <ref id="bib1.bibx55"><label>Staneva and Stanev(1997)</label><?label STANEVA1997?><mixed-citation>Staneva, J. and Stanev, E.: Cold intermediate water formation in the Black Sea.
Analysis on numerical model simulations, in: Sensitivity to Change: Black
Sea, Baltic Sea and North Sea, Springer, 375–393, 1997.
 </mixed-citation></ref><?xmltex \hack{\newpage}?>
      <ref id="bib1.bibx56"><label>Testor et al.(2017)</label><?label TESTOR2017?><mixed-citation>Testor, P., Bosse, A., Houpert, L., Margirier, F., Mortier, L., Legoff, H.,
Dausse, D., Labaste, M., Karstensen, J., Hayes, D., et al.: Multiscale
Observations of Deep Convection in the Northwestern Mediterranean Sea During
Winter 2012–2013 Using Multiple Platforms, J. Geophys. Res.-Oceans, 123, 1745–1776, <ext-link xlink:href="https://doi.org/10.1002/2016jc012671" ext-link-type="DOI">10.1002/2016jc012671</ext-link>,  2017.</mixed-citation></ref>
      <ref id="bib1.bibx57"><label>Troupin et al.(2012)</label><?label TROUPIN2012?><mixed-citation>
Troupin, C., Barth, A., Sirjacobs, D., Ouberdous, M., Brankart, J.-M.,
Brasseur, P., Rixen, M., Alvera-Azcárate, A., Belounis, M., Capet, A.,
Lenartz, F., Toussaint, M.-E., and Beckers, J.-M.: Generation of analysis and consistent error fields using the Data
Interpolating Variational Analysis (DIVA), Ocean Model., 52, 90–101, 2012.</mixed-citation></ref>
      <ref id="bib1.bibx58"><label>Vandenbulcke et al.(2010)</label><?label VANDENBULCKE2010?><mixed-citation>
Vandenbulcke, L., Capet, A., Beckers, J.-M., Grégoire, M., and Besiktepe,
S.: Onboard implementation of the GHER model for the Black Sea, with SST and
CTD data assimilation, J. Oper. Oceanogr., 3, 47–54, 2010.</mixed-citation></ref>
      <ref id="bib1.bibx59"><label>von Schuckmann et al.(2018)</label><?label OSR2?><mixed-citation>von Schuckmann, K., Traon, P.-Y. L., Aaboe, S., Fanjul, E. A., Autret, E.,
Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F.,
Bourdallé-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., Breivik,
L.-A., Brewin, R. J., Capet, A., Ceschin, A., Ciliberti, S., Cossarini, G.,
de Alfonso, M., de Pascual Collar, A., de Kloe, J., Deshayes, J., Desportes,
C., Drévillon, M., Drillet, Y., Droghei, R., Dubois, C., Embury, O.,
Etienne, H., Fratianni, C., Lafuente, J. G., Sotillo, M. G., Garric, G.,
Gasparin, F., Gerin, R., Good, S., Gourrion, J., Grégoire, M., Greiner, E.,
Guinehut, S., Gutknecht, E., Hernandez, F., Hernandez, O., Høyer, J.,
Jackson, L., Jandt, S., Josey, S., Juza, M., Kennedy, J., Kokkini, Z.,
Korres, G., Kõuts, M., Lagemaa, P., Lavergne, T., Cann, B. L., Legeais,
J.-F., Lemieux-Dudon, B., Levier, B., Lien, V., Maljutenko, I., Manzano, F.,
Marcos, M., Marinova, V., Masina, S., Mauri, E., Mayer, M., Melet, A.,
Mélin, F., Meyssignac, B., Monier, M., Müller, M., Mulet, S., Naranjo, C.,
Notarstefano, G., Paulmier, A., Gomez, B. P., Gonzalez, I. P., Peneva, E.,
Perruche, C., Peterson, K. A., Pinardi, N., Pisano, A., Pardo, S., Poulain,
P.-M., Raj, R. P., Raudsepp, U., Ravdas, M., Reid, R., Rio, M.-H., Salon, S.,
Samuelsen, A., Sammartino, M., Sammartino, S., Sandø, A. B., Santoleri, R.,
Sathyendranath, S., She, J., Simoncelli, S., Solidoro, C., Stoffelen, A.,
Storto, A., Szerkely, T., Tamm, S., Tietsche, S., Tinker, J., Tintore, J.,
Trindade, A., van Zanten, D., Verhoef, A., Vandenbulcke, L., Verbrugge, N.,
Viktorsson, L., Wakelin, S. L., Zacharioudaki, A., and Zuo, H.: Copernicus
Marine Service Ocean State Report, J. Oper. Oceanogr., 11, 1–142,
<ext-link xlink:href="https://doi.org/10.1080/1755876X.2018.1489208" ext-link-type="DOI">10.1080/1755876X.2018.1489208</ext-link>, 2018.</mixed-citation></ref>
      <ref id="bib1.bibx60"><label>Zeileis et al.(2003)</label><?label ZEILEIS2003?><mixed-citation>
Zeileis, A., Kleiber, C., Krämer, W., and Hornik, K.: Testing and dating of
structural changes in practice, Comput. Stat. Data An., 44, 109–123, 2003.</mixed-citation></ref>

  </ref-list></back>
    <!--<article-title-html>A new intermittent regime of convective ventilation threatens the Black Sea oxygenation status</article-title-html>
<abstract-html><p>The Black Sea is entirely anoxic, except for a thin ( ∼ &thinsp;100&thinsp;m) ventilated surface layer.
Since 1955, the oxygen content of this upper layer has decreased by 44&thinsp;%.
The reasons hypothesized for this decrease are, first, a period of eutrophication from the mid-1970s to the early 1990s and, second, a reduction in the ventilation processes, suspected for recent years (post-2005).
Here, we show that the Black Sea convective ventilation regime has been drastically altered by atmospheric warming during the last decade.
Since 2009, the prevailing regime has been below the range of variability recorded since 1955 and has been characterized by consecutive years during which the usual partial renewal of intermediate water has not occurred.
Oxygen records from the last decade are used to detail the  relationship between cold-water formation events and oxygenation at different density levels, to highlight the role of convective ventilation in the oxygen budget of the intermediate layers and to emphasize the impact that a persistence in the reduced ventilation regime would bear on the oxygenation structure of the Black Sea and on its biogeochemical balance.</p></abstract-html>
<ref-html id="bib1.bib1"><label>Akaike(1974)</label><mixed-citation>
Akaike, H.: A new look at the statistical model identification, IEEE
T. Automat. Contr., 19, 716–723, 1974.
</mixed-citation></ref-html>
<ref-html id="bib1.bib2"><label>Akpinar et al.(2017)</label><mixed-citation>
Akpinar, A., Fach, B. A., and Oguz, T.: Observing the subsurface thermal
signature of the Black Sea cold intermediate layer with Argo profiling
floats, Deep-Sea Res. Pt. I, 124, 140–152, 2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib3"><label>Andersen et al.(2009)Andersen, Carstensen, Hernandez-Garcia, and
Duarte</label><mixed-citation>
Andersen, T., Carstensen, J., Hernandez-Garcia, E., and Duarte, C. M.:
Ecological thresholds and regime shifts: approaches to identification, Trends
Ecol. Evol., 24, 49–57, 2009.
</mixed-citation></ref-html>
<ref-html id="bib1.bib4"><label>Belkin(2009)</label><mixed-citation>
Belkin, I. M.: Rapid warming of large marine ecosystems, Progr. Oceanogr., 81,
207–213, 2009.
</mixed-citation></ref-html>
<ref-html id="bib1.bib5"><label>Belokopytov(2011)</label><mixed-citation>
Belokopytov, V.: Interannual variations of the renewal of waters of the cold
intermediate layer in the Black Sea for the last decades, Phys. Oceanogr.,
20, 347–355, 2011.
</mixed-citation></ref-html>
<ref-html id="bib1.bib6"><label>Bittig et al.(2014)</label><mixed-citation>
Bittig, H. C., Fiedler, B., Scholz, R., Krahmann, G., and Körtzinger, A.:
Time response of oxygen optodes on profiling platforms and its dependence on
flow speed and temperature, Limnol. Oceanogr., 12, 617–636,
<a href="https://doi.org/10.4319/lom.2014.12.617" target="_blank">https://doi.org/10.4319/lom.2014.12.617</a>, 2014.
</mixed-citation></ref-html>
<ref-html id="bib1.bib7"><label>Bopp et al.(2002)</label><mixed-citation>
Bopp, L., Le Quéré, C., Heimann, M., Manning, A. C., and Monfray, P.:
Climate-induced oceanic oxygen fluxes: Implications for the contemporary
carbon budget, Global Biogeochem. Cy., 16, <a href="https://doi.org/10.1029/2001gb001445" target="_blank">https://doi.org/10.1029/2001gb001445</a>, 2002.
</mixed-citation></ref-html>
<ref-html id="bib1.bib8"><label>Bopp et al.(2013)</label><mixed-citation>
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013.
</mixed-citation></ref-html>
<ref-html id="bib1.bib9"><label>Boyer et al.(2009)</label><mixed-citation>
Boyer, T., Antonov, J., Garcia, H., Johnson, D., Locarnini, R., Mishonov, A.,
Pitcher, M., Baranova, O., and Smolyar, I.: Chapter 1: Introduction, World
ocean database, p. 216, 2009.
</mixed-citation></ref-html>
<ref-html id="bib1.bib10"><label>Boyer et al.(2018)</label><mixed-citation>
Boyer, T. P., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., Locarnini, R. A., Mishonov, A. V., Paver, C. R., Reagan, J. R., Seidov, D., Smolyar, I. V., Weathers, K., and Zweng, M. M.: World Ocean Database 2018, A. V. Mishonov, Technical Ed., NOAA Atlas NESDIS 87, 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib11"><label>Breitburg et al.(2018)</label><mixed-citation>
Breitburg, D., Levin, L. A., Oschlies, A., Grégoire, M., Chavez, F. P., Conley, D. J., Garçon, V., Gilbert, D., Gutiérrez, D., Isensee, K., Jacinto, G. S., Limburg, K. E., Montes, I., Naqvi, S. W. A., Pitcher, G. C., Rabalais, N. N., Roman, M. R., Rose, K. A., Seibel, B. A., Telszewski, M., Yasuhara, M., and Zhang, J.: Declining oxygen in the global ocean and coastal waters, Science,
359, eaam7240, <a href="https://doi.org/10.1126/science.aam7240" target="_blank">https://doi.org/10.1126/science.aam7240</a>, 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib12"><label>Buesseler et al.(1991)</label><mixed-citation>
Buesseler, K. O., Livingston, H. D., and Casso, S. A.: Mixing between oxic and
anoxic waters of the Black Sea as traced by Chernobyl cesium isotopes, Deep-Sea Res., 38, 725–745, <a href="https://doi.org/10.1016/S0198-0149(10)80006-8" target="_blank">https://doi.org/10.1016/S0198-0149(10)80006-8</a>, 1991.
</mixed-citation></ref-html>
<ref-html id="bib1.bib13"><label>Capet et al.(2012)</label><mixed-citation>
Capet, A., Barth, A., Beckers, J.-M., and Grégoire, M.: Interannual
variability of Black Sea's hydrodynamics and connection to atmospheric
patterns, Deep-Sea Res. Pt. II, 77, 128–142,
<a href="https://doi.org/10.1016/j.dsr2.2012.04.010" target="_blank">https://doi.org/10.1016/j.dsr2.2012.04.010</a>,
2012.
</mixed-citation></ref-html>
<ref-html id="bib1.bib14"><label>Capet et al.(2014)</label><mixed-citation>
Capet, A., Troupin, C., Carstensen, J., Grégoire, M., and Beckers, J.-M.:
Untangling spatial and temporal trends in the variability of the Black Sea
Cold Intermediate Layer and mixed Layer Depth using the DIVA detrending
procedure, Ocean Dynam., 64, 315–324, 2014.

</mixed-citation></ref-html>
<ref-html id="bib1.bib15"><label>Capet et al.(2016)</label><mixed-citation>
Capet, A., Stanev, E. V., Beckers, J.-M., Murray, J. W., and Grégoire, M.: Decline of the Black Sea oxygen inventory, Biogeosciences, 13, 1287–1297, https://doi.org/10.5194/bg-13-1287-2016, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib16"><label>Capet et al.(2020)</label><mixed-citation>
Capet, A., Vandenbulcke, L., and Grégoire, M.: Black Sea cold intermediate layer cold content from in-situ and modelling sources (1955-2019),  [Data set],  available at: <a href="https://doi.org/10.5281/zenodo.3691960" target="_blank"/>, last access: 28 February 2020.
</mixed-citation></ref-html>
<ref-html id="bib1.bib17"><label>Coppola et al.(2017)</label><mixed-citation>
Coppola, L., Prieur, L., Taupier-Letage, I., Estournel, C., Testor, P.,
Lefèvre, D., Belamari, S., LeReste, S., and Taillandier, V.: Observation
of oxygen ventilation into deep waters through targeted deployment of
multiple Argo-O2 floats in the north-western Mediterranean Sea in 2013, J.
Geophys. Res.-Oceans, 122, 6325–6341, 2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib18"><label>Dee et al.(2011)</label><mixed-citation>
Dee, D. P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S.,
Andrae, U., Balmaseda, M., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M.,  Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and             Vitart, F.: The ERA-Interim
reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
</mixed-citation></ref-html>
<ref-html id="bib1.bib19"><label>European Centre for Medium-Range Weather Forecasts(2019)</label><mixed-citation>
European Centre for Medium-Range Weather Forecasts: ECMWF Public Datasets, available at:  <a href="https://apps.ecmwf.int/datasets/" target="_blank"/>, last access: 1 August 2019.
</mixed-citation></ref-html>
<ref-html id="bib1.bib20"><label>Gregg and Yakushev(2005)</label><mixed-citation>
Gregg, M. and Yakushev, E.: Surface ventilation of the Black Sea's cold
intermediate layer in the middle of the western gyre, Geophys. Res. Lett.,
32, 3, <a href="https://doi.org/10.1029/2004gl021580" target="_blank">https://doi.org/10.1029/2004gl021580</a>, 2005.
</mixed-citation></ref-html>
<ref-html id="bib1.bib21"><label>Grégoire and Lacroix(2001)</label><mixed-citation>
Grégoire, M. and Lacroix, G.: Study of the oxygen budget of the Black Sea
waters using a 3D coupled hydrodynamical–biogeochemical model, J. Mar.
Syst., 31, 175–202, <a href="https://doi.org/10.1016/S0924-7963(01)00052-5" target="_blank">https://doi.org/10.1016/S0924-7963(01)00052-5</a>, 2001.
</mixed-citation></ref-html>
<ref-html id="bib1.bib22"><label>Grégoire et al.(1998)</label><mixed-citation>
Grégoire, M., Beckers, J. M., Nihoul, J. C. J., and Stanev, E.:
Reconnaissance of the main Black Sea's ecohydrodynamics by means of a 3D
interdisciplinary model, J. Mar. Syst., 16, 85–105,
<a href="https://doi.org/10.1016/S0924-7963(97)00101-2" target="_blank">https://doi.org/10.1016/S0924-7963(97)00101-2</a>, 1998.
</mixed-citation></ref-html>
<ref-html id="bib1.bib23"><label>IFREMER(2020)</label><mixed-citation>
IFREMER: Coriolis: In situ data for operational oceanography, available at:  <a href="http://www.coriolis.eu.org/" target="_blank"/>, last access: 3 March 2020.
</mixed-citation></ref-html>
<ref-html id="bib1.bib24"><label>Ivanov et al.(2000)</label><mixed-citation>
Ivanov, L., Belokopytov, V., Ozsoy, E., and Samodurov, A.: Ventilation of the
Black Sea pycnocline on seasonal and interannual time scales, Mediterr. Mar.
Sci., 1, 61–74, 2000.
</mixed-citation></ref-html>
<ref-html id="bib1.bib25"><label>Kazmin and Zatsepin(2007)</label><mixed-citation>
Kazmin, A. S. and Zatsepin, A. G.: Long-term variability of surface temperature
in the Black Sea, and its connection with the large-scale atmospheric
forcing, J. Mar. Syst., 68, 293–301, 2007.
</mixed-citation></ref-html>
<ref-html id="bib1.bib26"><label>Keeling et al.(2010)</label><mixed-citation>
Keeling, R. F., Körtzinger, A., and Gruber, N.: Ocean deoxygenation in a
warming world, Annu. Rev. Mar. Sci, 2, 199–229,
<a href="https://doi.org/10.1146/annurev.marine.010908.163855" target="_blank">https://doi.org/10.1146/annurev.marine.010908.163855</a>, 2010.
</mixed-citation></ref-html>
<ref-html id="bib1.bib27"><label>Kirtman et al.(2014)</label><mixed-citation>
Kirtman, B., Power, S. B., Adedoyin, J. A., Boer, G. J., Camilloni, I.,
Doblas-Reyes, F. J., Fiore, A. M., Kimoto, M., Meehl, G. A., Prather, M.,
Sarr, A., Schar, C., Sutton, R., van Oldenborgh, G. J., Vecchi, G., and Wang,
H. J.: Near-term climate change: projections and predictability, in: Climate
change 2013: the physical science basis: contribution to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited by:
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M. M. B., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge
University Press, Cambridge, 76 pp., 2014.
</mixed-citation></ref-html>
<ref-html id="bib1.bib28"><label>Konovalov and Murray(2001)</label><mixed-citation>
Konovalov, S. K. and Murray, J. W.: Variations in the chemistry of the Black
Sea on a time scale of decades (1960–1995), J. Mar. Syst., 31,
217–243, 2001.
</mixed-citation></ref-html>
<ref-html id="bib1.bib29"><label>Korotaev et al.(2014)</label><mixed-citation>
Korotaev, G., Knysh, V., and Kubryakov, A.: Study of formation process of cold
intermediate layer based on reanalysis of Black Sea hydrophysical fields for
1971–1993, Izv. Atmos. Ocean. Phys., 50, 35–48, 2014.
</mixed-citation></ref-html>
<ref-html id="bib1.bib30"><label>Kubryakov et al.(2016)</label><mixed-citation>
Kubryakov, A. A., Stanichny, S. V., Zatsepin, A. G., and Kremenetskiy, V. V.:
Long-term variations of the Black Sea dynamics and their impact on the marine
ecosystem, J. Mar. Syst., 163, 80–94, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib31"><label>Lee et al.(2002)</label><mixed-citation>
Lee, B.-S., Bullister, J. L., Murray, J. W., and Sonnerup, R. E.: Anthropogenic
chlorofluorocarbons in the Black Sea and the Sea of Marmara, Deep-Sea Res.
Pt. I, 49, 895–913, <a href="https://doi.org/10.1016/S0967-0637(02)00005-5" target="_blank">https://doi.org/10.1016/S0967-0637(02)00005-5</a>, 2002.
</mixed-citation></ref-html>
<ref-html id="bib1.bib32"><label>Long et al.(2016)</label><mixed-citation>
Long, M. C., Deutsch, C., and Ito, T.: Finding forced trends in oceanic oxygen,
Global Biogeochem. Cy., 30, 381–397, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib33"><label>Macias et al.(2013)Macias, Garcia-Gorriz, and Stips</label><mixed-citation>
Macias, D., Garcia-Gorriz, E., and Stips, A.: Understanding the causes of
recent warming of mediterranean waters. How much could be attributed to
climate change?, PLoS One, 8, e81591, <a href="https://doi.org/10.1371/journal.pone.0081591" target="_blank">https://doi.org/10.1371/journal.pone.0081591</a>, 2013.
</mixed-citation></ref-html>
<ref-html id="bib1.bib34"><label>MEDOC group et al.(1970)</label><mixed-citation>
MEDOC group: Observation of formation of deep water in the
Mediterranean Sea, 1969, Nature, 227, 1037–1040, 1970.
</mixed-citation></ref-html>
<ref-html id="bib1.bib35"><label>Miladinova et al.(2017)</label><mixed-citation>
Miladinova, S., Stips, A., Garcia-Gorriz, E., and Macias Moy, D.: Black Sea
thermohaline properties: Long-term trends and variations, J. Geophys.
Res.-Oceans, 122, 5624–5644, <a href="https://doi.org/10.1002/2016JC012644" target="_blank">https://doi.org/10.1002/2016JC012644</a>, 2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib36"><label>Miladinova et al.(2018)Miladinova, Stips, Garcia-Gorriz, and
Moy</label><mixed-citation>
Miladinova, S., Stips, A., Garcia-Gorriz, E., and Moy, D. M.: Formation and
changes of the Black Sea cold intermediate layer, Progr. Oceanogr., 167,
11–23, 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib37"><label>Murray et al.(1989)Murray, Jannasch, Honjo, Anderson, Reeburgh, Top,
Friederich, Codispoti, and Izdar</label><mixed-citation>
Murray, J. W., Jannasch, H. W., Honjo, S., Anderson, R. F., Reeburgh, W. S.,
Top, Z., Friederich, G. E., Codispoti, L. A., and Izdar, E.: Unexpected
changes in the oxic/anoxic interface in the Black Sea, Nature, 338, 411–413,
<a href="https://doi.org/10.1038/338411a0" target="_blank">https://doi.org/10.1038/338411a0</a>, 1989.
</mixed-citation></ref-html>
<ref-html id="bib1.bib38"><label>Murray et al.(1991)</label><mixed-citation>
Murray, J. W., Top, Z., and Özsoy, E.: Hydrographic properties and
ventilation of the Black Sea, Deep-Sea Res., 38, 663–689,
<a href="https://doi.org/10.1016/S0198-0149(10)80003-2" target="_blank">https://doi.org/10.1016/S0198-0149(10)80003-2</a>, 1991.
</mixed-citation></ref-html>
<ref-html id="bib1.bib39"><label>Nardelli et al.(2010)</label><mixed-citation>
Nardelli, B. B., Colella, S., Santoleri, R., Guarracino, M., and Kholod, A.: A
re-analysis of Black Sea surface temperature, J. Mar. Syst., 79, 50–64,
2010.
</mixed-citation></ref-html>
<ref-html id="bib1.bib40"><label>Oguz and Besiktepe(1999)</label><mixed-citation>
Oguz, T. and Besiktepe, S.: Observations on the Rim Current structure, CIW
formation and transport in the western Black Sea, Deep-Sea Res. Pt. I, 46,
1733–1753, 1999.
</mixed-citation></ref-html>
<ref-html id="bib1.bib41"><label>Oguz et al.(2006)</label><mixed-citation>
Oguz, T., Dippner, J. W., and Kaymaz, Z.: Climatic regulation of the Black
Sea hydro-meteorological and ecological properties at
interannual-to-decadal time scales, J. Mar. Syst., 60, 235–254,
<a href="https://doi.org/10.1016/j.jmarsys.2005.11.011" target="_blank">https://doi.org/10.1016/j.jmarsys.2005.11.011</a>, 2006.
</mixed-citation></ref-html>
<ref-html id="bib1.bib42"><label>Ostrovskii and Zatsepin(2016)</label><mixed-citation>
Ostrovskii, A. G. and Zatsepin, A. G.: Intense ventilation of the Black Sea
pycnocline due to vertical turbulent exchange in the Rim Current area, Deep
Sea Res. Pt. I, 116, 1–13, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib43"><label>Öszoy and Ünlüata(1997)</label><mixed-citation>
Öszoy, E. and Ünlüata, U.: Oceanography of the Black Sea: a
review of some recent results., Earth-Sci. Rev., 42, 231–272, 1997.
</mixed-citation></ref-html>
<ref-html id="bib1.bib44"><label>Pakhomova et al.(2014)</label><mixed-citation>
Pakhomova, S., Vinogradova, E., Yakushev, E., Zatsepin, A., Shtereva, G.,
Chasovnikov, V., and Podymov, O.: Interannual variability of the Black Sea
proper oxygen and nutrients regime: the role of climatic and anthropogenic
forcing, Estuar. Coast. Shelf S., 140, 134–145, 2014.
</mixed-citation></ref-html>
<ref-html id="bib1.bib45"><label>Piotukh et al.(2011)</label><mixed-citation>
Piotukh, V., Zatsepin, A., Kazmin, A., and Yakubenko, V.: Impact of the Winter
Cooling on the Variability of the Thermohaline Characteristics of the Active
Layer in the Black Sea, Oceanology, 51, 221–230,  <a href="https://doi.org/10.1134/s0001437011020123" target="_blank">https://doi.org/10.1134/s0001437011020123</a>,  2011.
</mixed-citation></ref-html>
<ref-html id="bib1.bib46"><label>Sakınan and Gücü(2017)</label><mixed-citation>
Sakınan, S. and Gücü, A. C.: Spatial distribution of the Black Sea
copepod, Calanus euxinus, estimated using multi-frequency acoustic
backscatter, ICES J. Mar. Sci., 74, 832–846, 2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib47"><label>Schmidtko et al.(2017)</label><mixed-citation>
Schmidtko, S., Stramma, L., and Visbeck, M.: Decline in global oceanic oxygen
content during the past five decades, Nature, 542, 335, <a href="https://doi.org/10.1038/nature21399" target="_blank">https://doi.org/10.1038/nature21399</a>, 2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib48"><label>Simonov and Altman(1991)</label><mixed-citation>
Simonov, A. and Altman, E.: Hydrometeorology and Hydrochemistry of the USSR
Seas, The Black Sea, 4, 430 pp., 1991.
</mixed-citation></ref-html>
<ref-html id="bib1.bib49"><label>Stanev and Beckers(1999)</label><mixed-citation>
Stanev, E. and Beckers, J.-M.: Numerical simulations of seasonal and
interannual variability of the Black Sea thermohaline circulation, J.
Mar. Syst., 22, 241–267, 1999.
</mixed-citation></ref-html>
<ref-html id="bib1.bib50"><label>Stanev et al.(2003)</label><mixed-citation>
Stanev, E., Bowman, M., Peneva, E., and Staneva, J.: Control of Black Sea
intermediate water mass formation by dynamics and topography: Comparison of
numerical simulations, surveys and satellite data, J. Mar. Res., 61, 59–99,
<a href="https://doi.org/10.1357/002224003321586417" target="_blank">https://doi.org/10.1357/002224003321586417</a>, 2003.
</mixed-citation></ref-html>
<ref-html id="bib1.bib51"><label>Stanev et al.(2013)</label><mixed-citation>
Stanev, E., He, Y., Grayek, S., and Boetius, A.: Oxygen dynamics in the Black
Sea as seen by Argo profiling floats, Geophys. Res. Lett., 40, 3085–3090,
2013.
</mixed-citation></ref-html>
<ref-html id="bib1.bib52"><label>Stanev et al.(2014)</label><mixed-citation>
Stanev, E. V., He, Y., Staneva, J., and Yakushev, E.: Mixing in the Black Sea detected from the temporal and spatial variability of oxygen and sulfide – Argo float observations and numerical modelling, Biogeosciences, 11, 5707–5732, https://doi.org/10.5194/bg-11-5707-2014, 2014.
</mixed-citation></ref-html>
<ref-html id="bib1.bib53"><label>Stanev et al.(2018)</label><mixed-citation>
Stanev, E. V., Poulain, P.-M., Grayek, S., Johnson, K. S., Claustre, H., and
Murray, J. W.: Understanding the Dynamics of the Oxic-Anoxic Interface in the
Black Sea, Geophys. Res. Lett., 45, 864–871, 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib54"><label>Stanev et al.(2019)</label><mixed-citation>
Stanev, E. V., Peneva, E., and Chtirkova, B.: Climate Change and Regional Ocean
Water Mass Disappearance: Case of the Black Sea, J. Geophys. Res.-Oceans,
124, 140, <a href="https://doi.org/10.1029/2019JC015076" target="_blank">https://doi.org/10.1029/2019JC015076</a>, 2019.
</mixed-citation></ref-html>
<ref-html id="bib1.bib55"><label>Staneva and Stanev(1997)</label><mixed-citation>
Staneva, J. and Stanev, E.: Cold intermediate water formation in the Black Sea.
Analysis on numerical model simulations, in: Sensitivity to Change: Black
Sea, Baltic Sea and North Sea, Springer, 375–393, 1997.

</mixed-citation></ref-html>
<ref-html id="bib1.bib56"><label>Testor et al.(2017)</label><mixed-citation>
Testor, P., Bosse, A., Houpert, L., Margirier, F., Mortier, L., Legoff, H.,
Dausse, D., Labaste, M., Karstensen, J., Hayes, D., et al.: Multiscale
Observations of Deep Convection in the Northwestern Mediterranean Sea During
Winter 2012–2013 Using Multiple Platforms, J. Geophys. Res.-Oceans, 123, 1745–1776, <a href="https://doi.org/10.1002/2016jc012671" target="_blank">https://doi.org/10.1002/2016jc012671</a>,  2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib57"><label>Troupin et al.(2012)</label><mixed-citation>
Troupin, C., Barth, A., Sirjacobs, D., Ouberdous, M., Brankart, J.-M.,
Brasseur, P., Rixen, M., Alvera-Azcárate, A., Belounis, M., Capet, A.,
Lenartz, F., Toussaint, M.-E., and Beckers, J.-M.: Generation of analysis and consistent error fields using the Data
Interpolating Variational Analysis (DIVA), Ocean Model., 52, 90–101, 2012.
</mixed-citation></ref-html>
<ref-html id="bib1.bib58"><label>Vandenbulcke et al.(2010)</label><mixed-citation>
Vandenbulcke, L., Capet, A., Beckers, J.-M., Grégoire, M., and Besiktepe,
S.: Onboard implementation of the GHER model for the Black Sea, with SST and
CTD data assimilation, J. Oper. Oceanogr., 3, 47–54, 2010.
</mixed-citation></ref-html>
<ref-html id="bib1.bib59"><label>von Schuckmann et al.(2018)</label><mixed-citation>
von Schuckmann, K., Traon, P.-Y. L., Aaboe, S., Fanjul, E. A., Autret, E.,
Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F.,
Bourdallé-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., Breivik,
L.-A., Brewin, R. J., Capet, A., Ceschin, A., Ciliberti, S., Cossarini, G.,
de Alfonso, M., de Pascual Collar, A., de Kloe, J., Deshayes, J., Desportes,
C., Drévillon, M., Drillet, Y., Droghei, R., Dubois, C., Embury, O.,
Etienne, H., Fratianni, C., Lafuente, J. G., Sotillo, M. G., Garric, G.,
Gasparin, F., Gerin, R., Good, S., Gourrion, J., Grégoire, M., Greiner, E.,
Guinehut, S., Gutknecht, E., Hernandez, F., Hernandez, O., Høyer, J.,
Jackson, L., Jandt, S., Josey, S., Juza, M., Kennedy, J., Kokkini, Z.,
Korres, G., Kõuts, M., Lagemaa, P., Lavergne, T., Cann, B. L., Legeais,
J.-F., Lemieux-Dudon, B., Levier, B., Lien, V., Maljutenko, I., Manzano, F.,
Marcos, M., Marinova, V., Masina, S., Mauri, E., Mayer, M., Melet, A.,
Mélin, F., Meyssignac, B., Monier, M., Müller, M., Mulet, S., Naranjo, C.,
Notarstefano, G., Paulmier, A., Gomez, B. P., Gonzalez, I. P., Peneva, E.,
Perruche, C., Peterson, K. A., Pinardi, N., Pisano, A., Pardo, S., Poulain,
P.-M., Raj, R. P., Raudsepp, U., Ravdas, M., Reid, R., Rio, M.-H., Salon, S.,
Samuelsen, A., Sammartino, M., Sammartino, S., Sandø, A. B., Santoleri, R.,
Sathyendranath, S., She, J., Simoncelli, S., Solidoro, C., Stoffelen, A.,
Storto, A., Szerkely, T., Tamm, S., Tietsche, S., Tinker, J., Tintore, J.,
Trindade, A., van Zanten, D., Verhoef, A., Vandenbulcke, L., Verbrugge, N.,
Viktorsson, L., Wakelin, S. L., Zacharioudaki, A., and Zuo, H.: Copernicus
Marine Service Ocean State Report, J. Oper. Oceanogr., 11, 1–142,
<a href="https://doi.org/10.1080/1755876X.2018.1489208" target="_blank">https://doi.org/10.1080/1755876X.2018.1489208</a>, 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib60"><label>Zeileis et al.(2003)</label><mixed-citation>
Zeileis, A., Kleiber, C., Krämer, W., and Hornik, K.: Testing and dating of
structural changes in practice, Comput. Stat. Data An., 44, 109–123, 2003.
</mixed-citation></ref-html>--></article>
