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  <front>
    <journal-meta><journal-id journal-id-type="publisher">NHESS</journal-id><journal-title-group>
    <journal-title>Natural Hazards and Earth System Sciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">NHESS</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Nat. Hazards Earth Syst. Sci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1684-9981</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/nhess-23-1699-2023</article-id><title-group><article-title>The extremely hot and dry 2018 summer in central and northern Europe from a multi-faceted weather and climate perspective</article-title><alt-title>The extremely hot and dry 2018 summer in central and northern Europe</alt-title>
      </title-group><?xmltex \runningtitle{The extremely hot and dry 2018 summer in central and northern Europe}?><?xmltex \runningauthor{E. Rousi et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Rousi</surname><given-names>Efi</given-names></name>
          <email>rousi@pik-potsdam.de</email>
        <ext-link>https://orcid.org/0000-0003-3191-2793</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff2">
          <name><surname>Fink</surname><given-names>Andreas H.</given-names></name>
          <email>andreas.fink@kit.edu</email>
        <ext-link>https://orcid.org/0000-0002-5840-2120</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Andersen</surname><given-names>Lauren S.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-3080-0503</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Becker</surname><given-names>Florian N.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-2853-5886</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3 aff4">
          <name><surname>Beobide-Arsuaga</surname><given-names>Goratz</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-7775-4906</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff5">
          <name><surname>Breil</surname><given-names>Marcus</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff6 aff7">
          <name><surname>Cozzi</surname><given-names>Giacomo</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Heinke</surname><given-names>Jens</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-5256-0024</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Jach</surname><given-names>Lisa</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0130-9600</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8">
          <name><surname>Niermann</surname><given-names>Deborah</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Petrovic</surname><given-names>Dragan</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-0485-3583</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff10">
          <name><surname>Richling</surname><given-names>Andy</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-1745-2479</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff11">
          <name><surname>Riebold</surname><given-names>Johannes</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8 aff26">
          <name><surname>Steidl</surname><given-names>Stella</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff12 aff27 aff28">
          <name><surname>Suarez-Gutierrez</surname><given-names>Laura</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-0008-5943</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff6 aff13">
          <name><surname>Tradowsky</surname><given-names>Jordis S.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-9059-4292</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff14 aff15">
          <name><surname>Coumou</surname><given-names>Dim</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff16">
          <name><surname>Düsterhus</surname><given-names>André</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-2192-175X</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff17">
          <name><surname>Ellsäßer</surname><given-names>Florian</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-8746-4315</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff18">
          <name><surname>Fragkoulidis</surname><given-names>Georgios</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-1767-4189</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff19 aff20">
          <name><surname>Gliksman</surname><given-names>Daniel</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff11">
          <name><surname>Handorf</surname><given-names>Dörthe</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-3305-6882</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff21 aff29">
          <name><surname>Haustein</surname><given-names>Karsten</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-3126-7851</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff22 aff23">
          <name><surname>Kornhuber</surname><given-names>Kai</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-5466-2059</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9 aff24">
          <name><surname>Kunstmann</surname><given-names>Harald</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Pinto</surname><given-names>Joaquim G.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8865-1769</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Warrach-Sagi</surname><given-names>Kirsten</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff17 aff25">
          <name><surname>Xoplaki</surname><given-names>Elena</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-2745-2467</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association,<?xmltex \hack{\break}?> P.O. Box 60 12 03, 14412 Potsdam, Germany</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe
Institute of Technology, Karlsruhe, Germany</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>International Max Planck Research School on Earth System Modelling
(IMPRS-ESM), Hamburg, Germany​​​​​​​</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Institute of Oceanography, Center for Earth System Research and Sustainability (CEN),<?xmltex \hack{\break}?> Hamburg University, Hamburg, Germany</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Institute of Physics and Meteorology, University of Hohenheim, Stuttgart, Germany​​​​​​​</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Regionales Klimabüro Potsdam, Deutscher Wetterdienst, Stahnsdorf, Germany</institution>
        </aff>
        <aff id="aff7"><label>7</label><institution>Institute of Mathematics, University of Augsburg, Augsburg, Germany​​​​​​​</institution>
        </aff>
        <aff id="aff8"><label>8</label><institution>Deutscher Wetterdienst, Offenbach, Germany</institution>
        </aff>
        <aff id="aff9"><label>9</label><institution>Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe
Institute of Technology,<?xmltex \hack{\break}?> Campus Alpin, Garmisch-Partenkirchen, Germany</institution>
        </aff>
        <aff id="aff10"><label>10</label><institution>Institute of Meteorology, Free University of Berlin, Berlin, Germany</institution>
        </aff>
        <aff id="aff11"><label>11</label><institution>Alfred Wegener Institute, Helmholtz Centre for Polar and Marine
Research, Potsdam, Germany</institution>
        </aff>
        <aff id="aff12"><label>12</label><institution>Max-Planck-Institut für Meteorologie, Hamburg, Germany</institution>
        </aff>
        <aff id="aff13"><label>13</label><institution>Bodeker Scientific, Alexandra, Aotearoa / New Zealand</institution>
        </aff>
        <aff id="aff14"><label>14</label><institution>Department of Water and Climate Risk, Institute for Environmental Studies (IVM),<?xmltex \hack{\break}?> Vrije Universiteit, Amsterdam, the Netherlands</institution>
        </aff>
        <aff id="aff15"><label>15</label><institution>Department of Weather and Climate Models, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the
Netherlands​​​​​​​</institution>
        </aff>
        <aff id="aff16"><label>16</label><institution>Irish Climate Analysis and Research UnitS (ICARUS), Department of
Geography, Maynooth University, Maynooth, Ireland</institution>
        </aff>
        <aff id="aff17"><label>17</label><institution>Centre of International Development and Environmental Research,
Justus Liebig University Giessen, Giessen, Germany</institution>
        </aff>
        <aff id="aff18"><label>18</label><institution>Institute for Atmospheric Physics, Johannes Gutenberg University,
Mainz, Germany</institution>
        </aff>
        <aff id="aff19"><label>19</label><institution>Institute of Hydrology and Meteorology, Faculty of Environmental
Sciences,<?xmltex \hack{\break}?> Technische Universität Dresden, Tharandt, Germany</institution>
        </aff>
        <aff id="aff20"><label>20</label><institution>Institute of Geography, Technische Universität Dresden, Dresden, Germany</institution>
        </aff>
        <aff id="aff21"><label>21</label><institution>Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon,
Hamburg, Germany</institution>
        </aff>
        <aff id="aff22"><label>22</label><institution>Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA</institution>
        </aff>
        <aff id="aff23"><label>23</label><institution>German Council on Foreign Relations, Berlin, Germany</institution>
        </aff>
        <aff id="aff24"><label>24</label><institution>Institute of Geography, University of Augsburg, Augsburg, Germany</institution>
        </aff>
        <aff id="aff25"><label>25</label><institution>Institute of Geography, Justus Liebig University Giessen, Giessen,
Germany</institution>
        </aff>
        <aff id="aff26"><label>a</label><institution>now at: Department of Civil and Natural Resources Engineering,
University of Canterbury,<?xmltex \hack{\break}?> Christchurch, Aotearoa / New Zealand</institution>
        </aff>
        <aff id="aff27"><label>b</label><institution>now at: Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland</institution>
        </aff>
        <aff id="aff28"><label>c</label><institution>now at: Institut Pierre-Simon Laplace, CNRS, Paris, France</institution>
        </aff>
        <aff id="aff29"><label>d</label><institution>now at: Institute for Meteorology, University of Leipzig, Leipzig, Germany</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Efi Rousi (rousi@pik-potsdam.de) and Andreas H. Fink (andreas.fink@kit.edu)</corresp></author-notes><pub-date><day>8</day><month>May</month><year>2023</year></pub-date>
      
      <volume>23</volume>
      <issue>5</issue>
      <fpage>1699</fpage><lpage>1718</lpage>
      <history>
        <date date-type="received"><day>19</day><month>August</month><year>2022</year></date>
           <date date-type="rev-request"><day>23</day><month>August</month><year>2022</year></date>
           <date date-type="rev-recd"><day>14</day><month>February</month><year>2023</year></date>
           <date date-type="accepted"><day>7</day><month>March</month><year>2023</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2023 </copyright-statement>
        <copyright-year>2023</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://nhess.copernicus.org/articles/.html">This article is available from https://nhess.copernicus.org/articles/.html</self-uri><self-uri xlink:href="https://nhess.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://nhess.copernicus.org/articles/.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d1e540">The summer of 2018 was an extraordinary season in climatological terms for northern and central Europe, bringing simultaneous, widespread,
and concurrent heat and drought extremes in large parts of the continent
with extensive impacts on agriculture, forests, water supply, and the
socio-economic sector. Here, we present a comprehensive, multi-faceted
analysis of the 2018 extreme summer in terms of heat and drought in central
and northern Europe, with a particular focus on Germany. The heatwave first
affected Scandinavia in mid-July and shifted towards central Europe in late
July, while Iberia was primarily affected in early August. The atmospheric
circulation was characterized by strongly positive blocking anomalies over
Europe, in combination with a positive summer North Atlantic Oscillation and a double jet stream configuration before the initiation of the heatwave. In terms of possible precursors common to previous European heatwaves, the Eurasian double-jet structure and a tripolar sea surface temperature anomaly over the North Atlantic were  already identified in spring. While in the early stages over Scandinavia the air masses at mid and upper levels were often of a remote, maritime origin, at later stages over Iberia the air masses
primarily had a local-to-regional origin. The drought affected Germany the
most, starting with warmer than average conditions in spring, associated
with enhanced latent heat release that initiated a severe depletion of soil
moisture. During summer, a continued precipitation deficit exacerbated the
problem, leading to hydrological and agricultural drought. A probabilistic
attribution assessment of the heatwave in Germany showed that such events of prolonged heat have become more likely due to anthropogenic global warming. Regarding future projections, an extreme summer such as that of 2018 is expected to occur every 2 out of 3 years in Europe in a <inline-formula><mml:math id="M1" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>1.5 <inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer world and virtually every single year in a <inline-formula><mml:math id="M3" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>2 <inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer world. With such large-scale and impactful extreme events becoming more frequent and intense under anthropogenic climate change, comprehensive and multi-faceted studies like the one presented here quantify the multitude of their effects and provide valuable information as a basis for adaptation and mitigation strategies.</p>
  </abstract>
    
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  </front>
<body>
      

<?pagebreak page1700?><sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e584">Following an anomalously warm and dry spring, the summer of 2018 was
characterized by record-breaking widespread heat and drought across Europe
(Kennedy et al., 2019; Toreti et al., 2019) with intense heatwaves
affecting large parts of Scandinavia (Sinclair et al., 2019)
and central Europe (e.g., Vogel et al., 2019). In Germany, both the months of April–May, as well as the April–July
period, and the entire year were identified as the warmest in the
observational records starting in 1881. Moreover, Germany faced remarkably
prolonged drought from February to November, with 2018 being the fourth
driest year on record (after 1959, 1911, and 1921). A new record was also
set for annual sunshine duration, amounting to 2015 h
(Friedrich and Kaspar, 2019). In Finland, the peak temperature in
summer exceeded 33 <inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, which is extremely unusual for a region
located near the Arctic Circle, breaking historical records of the past 40 years (Liu et al., 2020). In the UK, the summer of 2018
joined 2006 as the hottest on record since 1884. In England itself, this was
the warmest on record, while June 2018 was the driest June for England since
1925 (Kendon et al., 2019). Over the
Iberian Peninsula, a heatwave developed in early August 2018, with this
month being the warmest in the region after 2003
(Barriopedro et al., 2020). The normal eastward
propagation of weather systems was hindered in the summer of 2018 by the recurrent
presence of blocking anticyclones, associated with a particularly meandering
jet stream, which was reflected in the way the heatwave propagated, starting
in Scandinavia (peaking mid-July), then developing in central Europe (end
of July), and ending in Iberia (beginning of August). For the European
continent, 2018 was the second warmest summer on record (following 2010), as
estimated from the CRUTEM4 dataset (Kennedy et
al., 2019), prior to being marginally surpassed by the 2021 summer
(Climate Change Service, 2018, 2021).</p>
      <p id="d1e596">In terms of amplitude, persistence, and spatial extent, the 2018 heatwaves
were comparable to the “mega heatwaves” of 2003 and 2010 over Europe and
Russia (Spensberger et al., 2020; Becker et al., 2022), during which
more than 1 million square kilometers were simultaneously affected by
heatwave conditions (Fink et al., 2004; Barriopedro et al., 2011). But, unlike 2003 and 2010, the exceptionally extreme heat in 2018 occurred under concurrent exceptionally dry conditions, thus making the events in 2018 a spatially and temporally compound extreme (Zscheischler et al.,
2020; Bastos et al., 2021; Ionita et al., 2021). These
co-occurring hot and dry extremes, not only in central Europe but also in
multiple regions of the Northern Hemisphere midlatitudes
(Vogel et al., 2019), caused vast aggregated impacts (Bakke et al.,
2020), ranging from drought-inflicted forest mortality events of an
unprecedented scale (Schuldt et al., 2020; Senf and Seidl, 2021), up to a
50 % reduction in agricultural yields (Toreti<?pagebreak page1701?> et al.,
2019; Beillouin et al., 2020), and increased forest
fire occurrence (San-Miguel-Ayanz et al., 2019) to excess
heat-related human mortality (Pascal et al.,
2021). Compared to previous droughts since 2000, the summer of 2018 occupied the
largest extent of extreme and severe agriculture drought, centered around
Germany, Poland, most of Scandinavia, and the Baltic countries, affecting a
larger extent of boreal forests and high-latitude ecosystems
(Peters et al., 2020). Further, from a temporal point of
view, compared to other droughts of the past 40 years, 2018 was
characterized by the sharpest transition from average-to-wet conditions in
late winter to extremely strong soil water deficits in summer
(Bastos et al., 2020).</p>
      <p id="d1e599">Surface heatwaves are typically co-located with the center of the associated
blocking system (Kautz et al., 2022; their Fig. 2b). If the blocking is intense and persistent, a heatwave will
usually develop. On the other hand, unsteady weather conditions, like
thunderstorms and heavy precipitation, are frequent on the flanks of the
blocking system, which correspond to the air mass boundaries
(Kautz et al., 2022). In fact, summer
extremes can be exacerbated by different components of the Earth system,
such as anomalous atmospheric circulation patterns, oceanic conditions, and
the state of the land surface (Wehrli et al., 2019; Di Capua et al., 2021). The atmospheric circulation during the late spring and summer of 2018 was characterized by the frequent presence of atmospheric blocking and a persistent positive summer North Atlantic Oscillation (sNAO; Drouard et al.,
2019; Li et al., 2020). Among the possible
precursors of European heatwaves, here we analyzed spring sea surface
temperatures (SSTs) over the North Atlantic and soil moisture anomalies over
Europe. In particular, the tripolar North Atlantic SST anomaly pattern is
known to be influenced by the winter NAO, persisting over spring and
affecting European climate in summer (Herceg-Bulić and Kucharski, 2014). The North Atlantic tripolar pattern has been associated with the East Atlantic pattern (Gastineau and Frankignoul, 2015) and Atlantic ridges  (Ossó et al., 2020), leading to decreased summer precipitation (Saeed et al., 2013; Rousi et al., 2021) and increased
summer temperatures over Europe (Chen et al., 2016). Additionally, Duchez et al. (2016) argue that a cold anomaly over the North Atlantic subpolar gyre (SPG) may be associated with a stationary position of the jet stream, enhancing European summer heat extremes. Moreover, soil moisture–temperature feedbacks can amplify heat extremes (Seneviratne et
al., 2010). Through a positive feedback, soil moisture depletion by hot and
dry atmospheric conditions leads to a reduction of evaporative cooling and
suppressed convective available potential energy (CAPE) values, subsequently
limiting the rainfall potential and  further increasing air temperatures
(Miralles et al., 2014, 2018; Prodhomme et al., 2022). Further, Schumacher et al. (2019) highlighted the important role of upwind land–atmosphere feedbacks in addition to local feedbacks, as they can favor heat advection and intensify midlatitude mega heatwaves via soil desiccation.</p>
      <p id="d1e602">Hot and dry summers in Europe are expected to occur more frequently under
anthropogenic global warming (IPCC, 2021). McCarthy et al. (2019) conducted an attribution study for the 2018 summer heatwave in the UK based on Coupled Model Intercomparison
Project Phase 5 (CMIP5)
models and found that the present-day likelihood of such extremes is around
11 %, which has been made 30 times higher due to anthropogenic climate
change, while this likelihood increases to 53 % by the 2050s. Given the
increase in hot and dry extremes in Europe (Manning et al., 2019; Perkins-Kirkpatrick and Lewis, 2020; Markonis et al., 2021) and their further expected increase under continued unmitigated anthropogenic climate change
(Russo et al., 2014, 2015; Spinoni et al., 2018, 2020),
comprehensive weather and climate studies analyzing regional heatwave and
drought characteristics, drivers, and impacts are particularly important.</p>
      <p id="d1e606">Within the German research initiative ClimXtreme, about 140 scientists from
35 institutions joined 39 projects to further understand climate
extremes, focusing on central Europe (<uri>https://climxtreme.net/index.php/en/</uri>, last access: 16 April 2023). Inter-disciplinary task forces were
formed, among which one on heat and drought. This study brings together its
members to study the 2018 European heat and drought from a multi-faceted
weather and climate perspective, making it the first comprehensive and
spatially exhaustive study looking at hot and dry summers over Europe using
different analysis approaches to study (a) the extremeness of and attribution
to anthropogenic climate change (climate perspective), as well as (b) the
synoptic dynamics in concert with the role of slowly varying boundary
conditions at the ocean and continental surfaces (seasonal and weather
perspective). In the following, first, the data and methods are presented
(Sect. 2). Different metrics for the detection and description of the 2018
summer extremes are shown in Sect. 3.1. Then, we present various features of
the atmospheric circulation, including blocking, jet stream state, weather
regimes, Rossby wave activity, and air mass trajectories (see Sect. 3.2).
Next, the role of low-frequency precursors, i.e., SSTs and soil moisture in
spring, in setting the scene and eventually shaping those extremes is
investigated (see Sect. 3.3). Section 3.4 examines the event from a
large ensemble climate model perspective, accompanied by a tailored
attribution analysis that incorporates the length of the heatwave in Germany
based on CMIP6 models. The “Discussion and conclusions” section completes this
paper.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Data and methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Data</title>
      <p id="d1e627">In this paper we use a variety of datasets, including observational,
reanalysis, and model data. We use a common spatial domain for Europe
(30–70<inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 10<inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> W–50<inline-formula><mml:math id="M8" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E) and the
reference period 1981–2010 unless otherwise stated.</p><?xmltex \hack{\newpage}?>
<?pagebreak page1702?><sec id="Ch1.S2.SS1.SSS1">
  <label>2.1.1</label><title>Reanalysis and observational datasets</title>
      <p id="d1e665">ERA5 (Hersbach et al., 2020) and ERA5-HEAT (Di Napoli et al., 2021)
reanalysis datasets were utilized for the calculation of heatwave metrics
(see Sect. 3.1) and the dynamical drivers and their evolution, such as Rossby
wave activity, backward trajectories, double jet streams, atmospheric
blocking, and weather regimes (see Sect. 3.2), as well as for the calculation of precursors,
i.e., SSTs and soil moisture (see Sect. 3.3). E-OBS gridded observational
datasets (Haylock et al., 2008; Cornes et al., 2018) were used for the
calculation of the drought index (standardized precipitation evapotranspiration index (SPEI), see Sect. 3.1) and to estimate the
return period of the heatwave and select equivalent extreme events in CMIP6
model simulations for the attribution study (see Sect. 3.4). Observational
datasets from the German Weather Service (DWD) stations (Kaspar
et al., 2013) were used for the thermopluviogram for Germany (see Sect. 3.1).</p>
</sec>
<sec id="Ch1.S2.SS1.SSS2">
  <label>2.1.2</label><title>General circulation models</title>
      <p id="d1e676">The historical and RCP4.5 simulations of the Max Planck Institute Grand Ensemble
(MPI-GE; Maher et al., 2019) were used to
calculate the cumulative excess heat under recent climate (1979–2021) and
future 1.5 <inline-formula><mml:math id="M9" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (2020–2049) and 2 <inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (2050–2079) warmer
worlds (see Sect. 3.4). The advantage of this dataset is that, apart from
the forced response, it provides an estimate of the internal natural
variability. Historical simulations of several Coupled Model Intercomparison
Project Phase 6 models (CMIP6; Eyring et al., 2016) and
pre-industrial-type simulations (hist-nat) of the same models from the
CMIP6-endorsed Detection and Attribution Model Intercomparison Project
(DAMIP; Gillett et al., 2016) were used for
the probabilistic attribution study (see Sect. 3.4). An overview of the
analyzed CMIP6 models is given in Table S1 in the Supplement.</p>
</sec>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Methods</title>
<sec id="Ch1.S2.SS2.SSS1">
  <label>2.2.1</label><title>Heatwave metrics</title>
      <p id="d1e713">Despite the fact that heatwaves have been a topic of active climate research
for many decades, there is no universal heatwave definition, and there are
multiple metrics and criteria depending on the region, season, and
purpose of the study (Becker et al.,
2022). Here, we define a heatwave as an event of at least 3 consecutive
days during which the 90th percentile of the daily maximum temperature
based on each calendar day is exceeded (Fischer and
Schär, 2010). We chose two different metrics to characterize heatwave
intensity, the cumulative heat, which uses temperature only, and the
cumulative Universal Thermal Climate Index (cUTCI) that represents human
thermal comfort, taking into account temperature, humidity, wind, and
radiation. Cumulative heat and cUTCI refer to the integration of heat
exceedance over the threshold for all heatwave days of a season. In the
present study, only summer months (June to August; JJA) were considered,
hence combining the intensity and persistence of heatwaves
(Perkins-Kirkpatrick and Lewis, 2020). The cUTCI was
calculated for each day as in Błazejczyk et al. (2013), and the 90th percentile of the daily time series
was defined. The cumulative intensity was then calculated as the integration
of the exceedance above this threshold for the whole season.</p>
</sec>
<sec id="Ch1.S2.SS2.SSS2">
  <label>2.2.2</label><title>Drought indicator</title>
      <p id="d1e724">For the characterization and detection of the 2018 drought we present the
standardized precipitation evapotranspiration index (SPEI;
Vicente-Serrano et al., 2014), a widely used
drought indicator. We show two aggregation periods, of 3 and 6 months,
so that two types of droughts can be considered, meteorological (SPEI3) and
agricultural (SPEI6) (Heim, 2002; Zampieri et al., 2017). The SPEI was
calculated with the SPEI R Package (Beguería and
Vicente-Serrano, 2013), based on monthly precipitation sums and monthly mean
maximum and minimum temperatures that are needed for the calculation of the
potential evapotranspiration (PET). The PET was calculated based on the modified
Hargreaves equation (Droogers and Allen, 2002), a
method that corrects the PET by using the monthly rainfall amount as a proxy
for insolation and that is based on the hypothesis that this amount can change the
humidity levels (Vicente-Serrano et al., 2014).
The values obtained by this method are similar to those obtained from the
Penman–Monteith method (Allen et al., 2006).</p>
</sec>
<sec id="Ch1.S2.SS2.SSS3">
  <label>2.2.3</label><title>Atmospheric circulation metrics</title>
      <p id="d1e735">The large-scale atmospheric circulation patterns and the dynamical evolution
of the atmosphere associated with the 2018 extremes were analyzed using
various metrics. First, we looked at the weather regimes during summer in
order to characterize large-scale circulation features. Five summer
circulation regimes were computed with K-means clustering
(Crasemann et al., 2017) applied to ERA5
sea-level pressure (SLP) anomalies for the time period 1979–2018 over the
North Atlantic–European region (30–88<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 90<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> W–90<inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E). Further, blocking frequency anomalies were calculated at a
grid point level based on a hybrid, two-dimensional blocking index. Daily
blocked grid points were identified based on the inversion of meridional
gradients in the 500 hPa geopotential height (gph) field according to a
modified version of the index from Scherrer et al. (2006) and on
areas of strong positive gph anomalies associated with the blocking
detection. Finally, blocking events of a duration of at least 4 d and an
area of 1.5 <inline-formula><mml:math id="M14" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 10<inline-formula><mml:math id="M15" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">6</mml:mn></mml:msup></mml:math></inline-formula> km<inline-formula><mml:math id="M16" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> were selected by a subsequent tracking algorithm described in Schuster et al. (2019).</p>
      <p id="d1e791">Next, we looked at the state of the jet stream. Jet stream states were
identified with the use of self-organizing maps (SOMs), a neural-network-based clustering algorithm<?pagebreak page1703?> (Kohonen, 2013; Rousi et al., 2015).
SOMs were applied on daily ERA5 data of Eurasian (25–80<inline-formula><mml:math id="M17" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N,
25<inline-formula><mml:math id="M18" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> W–180<inline-formula><mml:math id="M19" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E) zonal mean zonal wind data on different
pressure levels (800–100 hPa) for the time period 1979–2020 (see
details in Rousi et al., 2022). Moreover, we applied
the methodology of Fragkoulidis and Wirth (2020) to identify Rossby wave packets and their amplitudes (<inline-formula><mml:math id="M20" display="inline"><mml:mi>E</mml:mi></mml:math></inline-formula>) for the 2018 summer.
The method employs the meridional wind field (<inline-formula><mml:math id="M21" display="inline"><mml:mi>v</mml:mi></mml:math></inline-formula>) at 300 hPa at 2 <inline-formula><mml:math id="M22" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 2<inline-formula><mml:math id="M23" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> resolution, which was taken from the ERA5 data. The visualization of <inline-formula><mml:math id="M24" display="inline"><mml:mi>E</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M25" display="inline"><mml:mi>v</mml:mi></mml:math></inline-formula>
(see Fig. 4) is adaptive to the latitude location of strong Rossby wave
packets, and only the latitudinal belt of 40–90<inline-formula><mml:math id="M26" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N was taken into
account. For each longitude, <inline-formula><mml:math id="M27" display="inline"><mml:mi>E</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M28" display="inline"><mml:mi>v</mml:mi></mml:math></inline-formula> are averaged over 10 grid points that exceed the median of all values within that belt.</p>
      <p id="d1e890">To analyze the origin of the air masses during the 2018 summer heatwave, we
calculated backward trajectories using Lagrangian analysis and the LAGRANTO
tool (Sprenger and Wernli, 2015).
In particular, we calculated 10 d backward trajectories for the levels
between 1000 and 500 hPa in steps of 25 hPa using ERA5 data for three
starting locations in Europe on the respective peak heatwave days. As in
Zschenderlein et al. (2020), starting
points were also taken within the upper-tropospheric blocking anticyclone,
in this case over Scandinavia. These were defined as the grid points where
the anomaly of the vertically averaged potential vorticity (between 500 and
150 hPa, based on monthly climatology) was below <inline-formula><mml:math id="M29" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.7 PVU (1 PVU <inline-formula><mml:math id="M30" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 10<inline-formula><mml:math id="M31" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">6</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> K kg<inline-formula><mml:math id="M32" 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> m<inline-formula><mml:math id="M33" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M34" 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 all grid points that
fulfilled this criterion, trajectories were initialized every 50 hPa between
500 and 150 hPa in the vertical dimension. To exclude starting points in the
stratosphere, only grid points with potential vorticity (PV) <inline-formula><mml:math id="M35" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 1 PVU were considered.</p>
</sec>
<sec id="Ch1.S2.SS2.SSS4">
  <label>2.2.4</label><title>Low-frequency precursors</title>
      <p id="d1e968">In order to analyze low-frequency precursors of the summer of 2018 extremes, we
considered SSTs, total precipitation, and soil moisture in the preceding
months. The SST anomalies, compared to the reference period of 1981–2010,
over the North Atlantic and the seas surrounding Europe (Mediterranean,
North Sea, Baltic Sea) were analyzed for the spring (March to May; MAM) and
summer (June to August; JJA) months of 2018 in ERA5 data. Precipitation and
soil moisture anomalies over Europe were also calculated for the same
seasons in ERA5.</p>
      <p id="d1e971">Additionally, we derived time series for the soil-moisture-latent heat flux
correlation in Germany based on ERA5 reanalysis data with a daily temporal
resolution based on centered 92 d running windows. This approach was used
because soil moisture limitation depends on various factors, such as the
climatic conditions and vegetation characteristics (rooting depth, leaf area
index (LAI), and stomatal conductance), which vary spatially and can change
during the course of a year (Duan et al., 2020).
Therefore, the limitation cannot be easily represented by a unified, fixed
value. The time series were spatially averaged over all land points for
northern Germany and surroundings (51–55<inline-formula><mml:math id="M36" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N and 4–16<inline-formula><mml:math id="M37" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E), as well as southern Germany and surroundings (48–51<inline-formula><mml:math id="M38" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N and
4–16<inline-formula><mml:math id="M39" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E). The German alpine region was not included in the
southern German region because the complex topography that cannot be
accounted for in this study influences the results.</p>
</sec>
<sec id="Ch1.S2.SS2.SSS5">
  <label>2.2.5</label><title>Attribution of the 2018 extreme heat</title>
      <p id="d1e1019">Extreme event attribution typically addresses the question of whether and to
what extent climate change has affected the severity and/or frequency of a
specific extreme weather event (Shepherd, 2016). The most
commonly used approach to extreme event attribution is probabilistic event
attribution (Philip et al., 2020), which compares
climate model simulations under different scenarios, i.e., a factual scenario
which simulates the weather under current and past climate conditions and a
counterfactual scenario which simulates weather under climate conditions
excluding anthropogenic influences.</p>
      <p id="d1e1022">Here we present two kinds of attribution approaches. In the first, we used
the MPI-GE to estimate the probability of exceedance of the 2018 summer heat
levels in the whole European domain for present and future climates, and in
the second, we present a tailored extreme event attribution study for
Germany based on CMIP6 simulations to calculate probability ratios for the
persistent 2018 heat event in Germany.</p>
      <p id="d1e1025">The MPI-GE (Maher et al., 2019) was used to
estimate and compare the probabilities of exceeding the 2018 summer levels
of cumulative heat in the reanalysis data (ERA5, 1979–2021) and under
recent (1979–2021) climate, as well as future 1.5 <inline-formula><mml:math id="M40" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (2020–2049) and
2 <inline-formula><mml:math id="M41" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (2050–2079) warmer worlds. The same heatwave metric and
parameters were used to calculate the cumulative heat as the ones described
above (Sect. 2.2.1). The ERA5 data were regridded to a coarser resolution to
match that of the MPI-GE, and the probabilities were normalized to
percentages (i.e., divided by the total number of years in each period).</p>
      <p id="d1e1046">Then, to estimate how the occurrence probability of the 2018 heatwave in
Germany has been affected by anthropogenic climate change, a tailored
probabilistic attribution study was conducted using CMIP6 simulations. The
historical CMIP6 simulations provide the factual scenario, while hist-nat
simulations from DAMIP provide the counterfactual scenario. The analysis is
based on an attribution system currently under development at DWD within the
ClimXtreme project and involves (1) defining the extreme event, (2) analyzing observational data and estimating the probability/return period of
such an event based on observations, (3) validating the climate model
simulations, (4) preparing and analyzing the climate model simulations, and
(5) calculating a probability ratio between the historical and hist-nat
simulations.</p>
      <?pagebreak page1704?><p id="d1e1050"><?xmltex \hack{\newpage}?>Based on CMIP/DAMIP data available at the computing facility of the German
Climate Computing Center (DKRZ) the most appropriate climate models were
selected for the tailored attribution study by including the ones that had
at least three initializations in the DAMIP archive and passed the
validation tests outlined below for the maximum temperature (<inline-formula><mml:math id="M42" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula>​​​​​​​) that is analyzed in the attribution study. The climatology of <inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula> and the spatial
pattern of the yearly averaged maximum temperature were visually compared
between the models and the gridded E-OBS dataset to evaluate whether the
models are able to represent the climate conditions over Germany.
Additionally, the parameters of a generalized extreme value (GEV)
distribution fitted to the simulation data were compared with a fit to the
E-OBS data to check whether they agree within their uncertainty bounds.
Furthermore, a general consistency check was performed for each model
ensemble. The evaluation procedure is similar to the one used in World
Weather Attribution (WWA) studies (see e.g., Philip et al.,
2020). Simulations of CMIP6 models that passed the validation were further
analyzed (see Table S1 for a list of the models).</p>
      <p id="d1e1076">The following steps are required to calculate the risk ratio: CMIP and DAMIP
<inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula> data from all available initializations of the model were selected for
the German region and for the 30-year timeframe from 1985–2014. The data
were averaged over the region, and a 17 d running mean was calculated,
based on the event definition which is further elaborated on in Sect. 3.4. The
yearly block maxima were then selected from all initializations, and a GEV
fit was used to estimate the probability of heatwaves in the simulation data
that are equivalent to the observed event of 2018. To account for offsets
between observed and simulated temperatures, we analyzed a simulated heat
event which has – in the historical simulations – the same probability as
the observed heatwave, i.e., while the simulated event may not reach the same
temperature as was observed in 2018, the temperature threshold used to
analyze the simulations has the same return period as the observed event
(see also Philip et al., 2020; Tradowsky et al., 2022). To increase the robustness of the results a 1000-member bootstrap was used and a GEV distribution was fitted to each of these 1000 alternative time series. The probability ratios (PRs) were then calculated from the probabilities of such heatwaves in the historical and hist-nat simulations using the GEV fits to the original simulation time series and to the 1000 alternative time series, according to Eq. (1):
              <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M45" display="block"><mml:mrow><mml:mi mathvariant="normal">PR</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">historical</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mtext>hist-nat</mml:mtext></mml:msub></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
            where <inline-formula><mml:math id="M46" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">historical</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is the probability of the event to occur in the historical CMIP scenario, and <inline-formula><mml:math id="M47" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mtext>hist-nat</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> is the probability in the naturalized DAMIP scenario in which anthropogenic greenhouse gas emissions are fixed to pre-industrial times.</p>
      <p id="d1e1138">A probability ratio <inline-formula><mml:math id="M48" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 1 indicates an increase in the probability
of such an event due to anthropogenic climate change, a result which is
typically found for recent heatwaves (see e.g., Stott et al., 2004; Philip et
al., 2022).</p>
</sec>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Detection and description of the 2018 summer extremes</title>
      <p id="d1e1165">The 2018 summer was an extreme season from a climatological perspective
for many regions in Europe. An intense heatwave first affected Scandinavia
in mid-July and then extended towards central Europe and later Iberia,
spanning a total period of 4 weeks. The maximum heatwave duration was
seen in Scandinavian regions, reaching 20 consecutive days (Fig. 1a).
Cumulative heat reached peak values in parts of Norway, Sweden, Germany,
France, Ireland, and the UK (Fig. 1b). The cUTCI index showed periods of
extreme heat stress in Portugal and southwestern Spain; very strong heat
stress in northern and central Germany, central-western Poland, large parts
of France and Iberia; and strong heat stress in most of eastern Europe,
Finland, southern Scandinavia, and parts of the British Isles (Fig. 1c). The
high intensities in Turkey and the Caucasian region were not caused by the
same weather pattern as the event described in this paper and are thus not
discussed here.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e1170">Spatial representation of European heatwave (ERA5) and drought
(E-OBS) in the 2018 summer. <bold>(a)</bold> Maximum heatwave duration in days (grid-point-based, exceedance of 90th percentile of daily maximum temperature). <bold>(b)</bold> Cumulative heat (in <inline-formula><mml:math id="M49" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C). <bold>(c)</bold> Maximum UTCI in the 2018 summer per grid point and respective heat stress category. <bold>(d)</bold> SPEI3 August. <bold>(e)</bold> SPEI6 August. Only SPEI values below <inline-formula><mml:math id="M50" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1 are shown in order to highlight drought conditions. Reference period used in all metrics: 1981–2010.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f01.png"/>

        </fig>

      <p id="d1e1211">In northern and central Europe, the heatwave was preceded and accompanied by
intense drought conditions. As an example, the meteorological drought is
depicted in terms of the SPEI3 and SPEI6 values for August (Fig. 1d, e) that
were particularly low in central and northern Europe. The cumulative effects
of low precipitation and high evapotranspiration lead to lower values of the
SPEI6 index in many European regions compared to SPEI3. The most extreme
values (SPEI6 <inline-formula><mml:math id="M51" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> <inline-formula><mml:math id="M52" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>5) are identified for southern Norway and Sweden.
The thermopluviogram for Germany depicts temperature and precipitation
anomalies for Germany and confirms that the extended warm period of April
to October 2018 was the most exceptional in terms of precipitation deficit
and heat anomaly compared to the reference period (1981–2010) since 1881
(Fig. 2​​​​​​​). When considering different seasonal periods, such as March to
August or June to August only, 2018 remains a very extreme season (see Fig. S1). In summary, while the heatwave was most intense in southern
Scandinavia, 2018 stood out as the most intense compound heat and dry event
in the observational history for Germany, in agreement with
Zscheischler and Fischer (2020).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e1231">Thermopluviogram for the growing season, April to October, of the
years 1881–2022 for Germany, showing the temperature and precipitation
anomalies from the climatological mean (DWD observational data, reference
period 1981–2010). The year 2018 is highlighted with a light green color.
Thermopluviograms for different periods can be found in the Supplement (Fig. S1).</p></caption>
          <?xmltex \igopts{width=213.395669pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f02.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Dynamical drivers and evolution</title>
      <p id="d1e1248">In order to characterize large-scale circulation features for the summer of 2018, we used a number of different and complementary metrics to describe the
multi-faceted characteristics of the event. First, we analyzed the blocking
conditions for this season, as the occurrence of heatwaves is directly
associated with summer blocking or – for the lower latitudes<?pagebreak page1705?> in Europe –
to atmospheric ridges (Woollings et al., 2018; Sousa et al., 2018;
Kautz et al., 2022). Using the blocking
detection algorithm, we confirm that for the 2018 summer, blocking is
detected over Great Britain from late June into the first 10 d of July
as well as over Scandinavian and Ural regions for most days of July (Fig. S2). Compared to the climatological occurrence of blocking frequency, the percentage of blocked days in June–July 2018 was 20 %–60 % higher in the
mentioned areas (Fig. 3a, b), indicating blocking frequency values above the
90th percentile (Fig. S3). This large-scale setup for the summer time
(see e.g., Kautz et al., 2022, their Fig. 2b) leads to the development of a heatwave collocated with the center of the
blocking, while unsteady weather conditions may happen on the block edges.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e1253">Blocking frequency anomalies for <bold>(a)</bold> June and <bold>(b)</bold> July 2018 (shading; contour lines show mean geopotential height at 500 hPa plotted every 50 hPa). <bold>(c)</bold> Eurasian zonal mean zonal wind at 250 hPa for May–September 2018 (shading; 5 d running means centered on each day from 1 May–30 September 2018). The red lines mark the duration of the longest double jet
event (4–25 July 2018). <bold>(d)</bold> NAO index for May–September 2018.</p></caption>
          <?xmltex \igopts{width=441.017717pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f03.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e1276">Hovmöller diagram for the period of 15 June–15 August 2018. The
longitudinal extent of three core heatwave regions (Iberia, central Europe,
Scandinavia), as well as their temperature time series at the 850 hPa level
as standardized anomalies (<inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msup><mml:mi>T</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula>) on the right, are marked in green, orange, and blue, respectively. Periods when <inline-formula><mml:math id="M54" display="inline"><mml:mrow><mml:msup><mml:mi>T</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula> was above the respective 95th
percentiles are shaded. Both temperature (<inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:msup><mml:mi>T</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula>) and meridional wind at the 300 hPa level (<inline-formula><mml:math id="M56" display="inline"><mml:mrow><mml:msup><mml:mi>v</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula>) are anomalies with respect to their smoothed annual cycles. Rossby wave packet amplitude (<inline-formula><mml:math id="M57" display="inline"><mml:mi>E</mml:mi></mml:math></inline-formula>) is depicted in contours from 24 to 38 m s<inline-formula><mml:math id="M58" 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> in steps of 4 m s<inline-formula><mml:math id="M59" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> and <inline-formula><mml:math id="M60" display="inline"><mml:mrow><mml:msup><mml:mi>v</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula> as color shading from <inline-formula><mml:math id="M61" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>30 to 30 m s<inline-formula><mml:math id="M62" 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>. Both fields are
weighted by the cosine of latitude and meridionally averaged over
above-median grid points within the 40–80<inline-formula><mml:math id="M63" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N latitude band
(self-adjusting, depending on the location of the largest amplitudes). Days
with a dominant positive phase of the summer North Atlantic Oscillation
(sNAO+) pattern, double jet days, and blocking days are marked on the left.</p></caption>
          <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f04.png"/>

        </fig>

      <p id="d1e1401">The establishment of a long-lived blocking anticyclone is consistent with
the development of a double jet stream state over Eurasia, with two maxima
of the zonal mean zonal wind at the 250 hPa level, which started as early as
mid-May and persisted until the 25th of July, with only a few days in
between not characterized by double jets (Fig. 3c). The period 4–25 July
was characterized by a continuous persistent double-jet configuration,
according to the SOM-based detection scheme of jet stream states. These 22
consecutive days of double jets make 2018 one of the longest such events in
the study period (1979–2020), the longest being that of 2003
(Rousi et al., 2022; their Fig. 4). The initiation
of the heatwave in Europe happened a few days after the initiation of this
persistent double-jet event (see Fig. 4), highlighting the potential role of
the double-jet structure in preconditioning the flow and favoring the onset
of a heatwave in the region of weak winds between the two jets, where the
blocking anticyclone lies (Rousi et al., 2022). This
large-scale setup typically corresponds to the occurrence of the summer
NAO+ (sNAO+) regime, as confirmed by the circulation regime approach
applied on the 2018 summer. Indeed, most of July 2018 was dominated by a
sNAO+ index (Fig. 3d) and a spatial pattern, typically characterized by a
more northerly location and a smaller spatial scale than its winter
counterpart<?pagebreak page1706?> (Folland et al., 2009). This is in agreement with previous studies (e.g., Drouard et al., 2019) showing a strong positive EOF-based NAO anomaly in this time period that is consistent with large parts of the seasonal anomalies observed during the summer of 2018.</p>
      <p id="d1e1404">The analysis of Rossby wave activity permits the evaluation of the
development of the blocking, NAO+ phase, and the corresponding double-jet
structure for the summer of 2018. Results show an eastward propagation of
Rossby wave packets from the Pacific towards the Atlantic Ocean, the British
Isles, and finally towards the European continent during the last 10 to 15 d of June and before the initiation of the heatwave over Scandinavia
(Fig. 4). On the other hand, this was not the case for August, when the peak
over Iberia occurred, which highlights the different mechanisms involved in
this heatwave, rather than Rossby wave activity coming from the Pacific.
Indeed, heatwaves and precipitation deficits in this location are primarily
associated with amplified subtropical atmospheric ridges rather than
midlatitude blocking situations (see Woollings et al., 2011; Sousa et al.,
2017, 2018).</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F5" specific-use="star"><?xmltex \currentcnt{5}?><?xmltex \def\figurename{Figure}?><label>Figure 5</label><caption><p id="d1e1409">The 10 d backward trajectories in 25 hPa steps between 1000 and
500 hPa for the three location coordinates. <bold>(a)</bold> Utsjoki, Finland,
initialized on 18 July 2018. <bold>(b)</bold> Bernburg, Germany, initialized on 31 July 2018. <bold>(c)</bold> Alvega, Portugal, initialized on 4 August 2018. For every 100 hPa a different color is used for the trajectories. Each black dot is representative of a 24 h time step. <bold>(d)</bold> Geographical locations of the three points.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f05.png"/>

        </fig>

      <p id="d1e1430">Further, a backward-trajectory analysis was conducted to determine the
origins of the air masses that were present during the different heatwave
phases and their evolution. Three grid points were chosen to represent the
three affected areas and time segments of the heatwave: one over Scandinavia
(Utsjoki, Finland) initialized on 18 July 2018, one over central Europe
(Bernburg, Germany) on 31 July, and one over Iberia (Alvega, Portugal) on 4 August 2018 (Fig. 5). The backward trajectories showed the remote origin of
the mid-troposphere air masses, especially in the case of Utsjoki (Fig. 5a),
where it primarily originated over the central North Atlantic. This is also
true for the mid-troposphere air masses in the case of Bernburg (Fig. 5b).
However, in the last 48 h, descending air masses were observed, pointing
to an adiabatic warming by compression. Trajectories starting in the lowest
200 hPa at Bernburg indicate that air masses stemmed from a region to the
south and east close to the starting location, indicating relatively
stagnant air masses as already discussed in
Spensberger et al. (2020). In the
case of Alvega (Fig. 5c), air masses starting between 700 and 1000 hPa
experienced several rising and sinking motions on their way from the south
and southeast (e.g., the Algerian desert, Atlas Mountains, Mediterranean Sea),
towards the Iberian Plateau and coastal regions, thus documenting their
local-to-regional origin, in contrast to the remote origin of the air
masses seen in central and northern Europe, and largely stagnant conditions
(in line with Santos et al., 2015; Sousa et al., 2019).</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F6" specific-use="star"><?xmltex \currentcnt{6}?><?xmltex \def\figurename{Figure}?><label>Figure 6</label><caption><p id="d1e1435">Backward trajectories for 7 d <bold>(a)</bold> and 3 d <bold>(b)</bold>. Backward-trajectory density maps ending on 18 July, initiated in 50 hPa steps between 150 and 500 hPa for grid points within the Scandinavian anticyclone (backward trajectories were initiated from the dotted points inside the red rectangle; the dotted points are those defined by vertically averaged PV anomaly based on monthly climatology <inline-formula><mml:math id="M64" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> <inline-formula><mml:math id="M65" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.7 PVU and PV <inline-formula><mml:math id="M66" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 1 PVU).</p></caption>
          <?xmltex \igopts{width=361.35pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f06.png"/>

        </fig>

      <p id="d1e1472">In order to infer causal hypotheses for the existence of the Scandinavian
block, the trajectory approach was extended to obtain the origins of low
potential vorticity (PV) air masses that formed the upper-tropospheric part
of the Scandinavian<?pagebreak page1707?> anticyclone (see Sect. 2.2.3). For the sake of brevity,
only maps of 7 and 3 d trajectory density on 18 July 2018, around
the maximum heatwave day in Scandinavia, are shown in Fig. 6, but other
days corroborate the inferences below (not shown). Figure 6a shows the
density of 7 d backward trajectories, indicating that air masses were
steered from the western North Atlantic over the British Isles to
Scandinavia. This is in line with the propagation of the corresponding
Rossby wave packet discussed above. Moreover, using the method described in
Zschenderlein et al. (2020,
their Fig. 4), the role of a remote warm conveyor belt is suggested by
ascending, diabatically heated trajectories over the western Atlantic (not
shown); PV is lowered in the warm conveyor belt and then transported in the
upper troposphere into the Scandinavian anticyclone (termed “remote
branch” by Zschenderlein et al., 2020).
Interestingly, high trajectory densities over central to eastern Europe,
which also strongly ascended and were diabatically heated (not shown), point
towards an influence of moist convection observed under an upper-level
trough in this area by feeding low-PV air towards the Scandinavian
anticyclone. Such a “nearby branch” was also mentioned by
Zschenderlein et al. (2020) to be
important for anticyclone persistence over central Europe. However, in the
2018 case the nearby branch is located to the southeast, not to the
southwest as for central Europe; 3 d before the peak of the heatwave,
trajectories almost exclusively stem from this nearby branch, now located
more to the south of the Scandinavian anticyclone (Fig. 6b). Clearly,
determining causal pathways from this analysis is not possible, yet
modeling studies with explicit convection could shed more light on the role
of the remote branch (warm conveyor belt over the western Atlantic) versus
the nearby branch over southeastern Europe for the establishment and
maintenance of the Scandinavian anticyclone.</p>
</sec>
<?pagebreak page1709?><sec id="Ch1.S3.SS3">
  <label>3.3</label><title>Low-frequency precursors</title>
      <p id="d1e1483">When addressing possible precursors for European heatwaves, SST anomalies
over the North Atlantic (Dunstone et al., 2019; Ossó et al., 2020;
Beobide-Arsuaga et al., 2023) and soil moisture anomalies over continental Europe (Quesada et al., 2012) are among the
primary candidates, as outlined in the Introduction. A tripolar SST pattern
with negative anomalies over the subpolar gyre (SPG) was evident in spring
(MAM, northern box of Fig. 7a, b). At the same time, a pronounced
precipitation deficit over Scandinavia in the spring of 2018 was present (Fig. 7c).
The SST tripolar pattern persisted over time, with the cold SPG anomaly
intensifying in summer (JJA, Fig. 7b), and the same is true for the
precipitation deficit, which increased particularly in Germany and central
Europe (Fig. 7d), as also discussed in Toreti et
al. (2019). The soil moisture anomaly for the 2018 spring and summer (Fig. 7e, f)
shows a pattern consistent with the precipitation anomaly.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7"><?xmltex \currentcnt{7}?><?xmltex \def\figurename{Figure}?><label>Figure 7</label><caption><p id="d1e1488">Anomalies of sea surface temperature (SST; <bold>a, b</bold>), precipitation <bold>(c, d)</bold>, and soil moisture <bold>(e, f)</bold> in the ERA5 reanalysis (compared to the reference period 1981–2010) for spring (March to May, MAM; <bold>a, c, e</bold>) and summer (June to August, JJA; <bold>b, d, f</bold>) months. Boxes in <bold>(a)</bold> and <bold>(b)</bold> indicate
the regions for the tripolar SST pattern.</p></caption>
          <?xmltex \igopts{width=230.467323pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f07.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8" specific-use="star"><?xmltex \currentcnt{8}?><?xmltex \def\figurename{Figure}?><label>Figure 8</label><caption><p id="d1e1521"><bold>(a)</bold> Time series of centered 92 d running mean soil moisture averaged over all land points of northern Germany (51–55<inline-formula><mml:math id="M67" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N and 4–16<inline-formula><mml:math id="M68" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E) for the period March–September of 1981–2020. The grey lines denote individual years, the black line denotes the average of 1981–2010, and the blue line denotes 2018. <bold>(b)</bold> The same as <bold>(a)</bold> but for southern Germany (48–51<inline-formula><mml:math id="M69" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N and 4–16<inline-formula><mml:math id="M70" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E). <bold>(c)</bold> Time series of soil-moisture-latent heat flux
coefficients based on 92 d running periods for the growing period covering
March to September for the years 1981–2020 for northern Germany. The grey
lines denote individual years, the black line denotes the average of 1981–2010, and
the red line denotes 2018. Energy limited is related to a correlation coefficient of
<inline-formula><mml:math id="M71" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1, and moisture limited to a correlation coefficient of 1. <bold>(d)</bold> The same as <bold>(c)</bold> but for southern Germany.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f08.png"/>

        </fig>

      <p id="d1e1592">Having established that the large-scale soil moisture anomaly is consistent
with the SST and precipitation anomalies, we investigated the temporal
development of the soil moisture pattern over Germany. Reduced soil moisture
often facilitates the occurrence of summer drought and heatwaves
(Teuling, 2018), as the soil moisture determinant for
evapotranspiration (or lack thereof) directly links to the surface
temperature and relative humidity at the land surface
(Stéfanon et al., 2014; Miralles et al., 2018). Thus, soil moisture
and latent heat flux were used to identify periods of moisture limitation
(denoted by positive correlation coefficients between the two) and wet
conditions (negative correlation coefficients), under which the latent heat
flux is primarily controlled by the atmosphere. The derived time series for
the soil-moisture-latent heat flux correlations are based on daily data
centered on 92 d running periods for Germany (Fig. 8). Additionally,
centered 92 d running mean soil moisture is shown. The time series were
spatially averaged over all land points for northern (Fig. 8a, c) and
southern Germany (Fig. 8b, d). Germany is usually not in the moisture-limited
regime, but extraordinary hydrologic conditions can lead to a shift from an
energy-limited evaporative regime to moisture-limited conditions
(Lo et al., 2021), increasing the surface
temperature and enhancing the sensible heat flux. The soil moisture anomaly
in March 2018 was low all over Germany (Fig. 8a, b) and thus did not yet
limit evapotranspiration and latent heat flux. Warm conditions in spring
caused a high latent heat flux all over Germany, indicating a strong
energy limitation (Fig. 8c, d). High latent heat fluxes, in turn, lead to a
severe depletion of the soil moisture up to a depth of 1 m, starting at the
end of March and continuing until July in northern Germany and mid-August in
southern Germany. The precipitation deficit (Fig. 7c, d) further exacerbated
the drying of the soils and shifted the evaporative regime from
energy-limited to moisture-limited conditions. The latter prevailed between
June and August 2018, indicating that the anomalously dry soils during the
2018 summer further augmented the hot surface temperatures
(Dirmeyer et al., 2021; Orth, 2021).</p>
      <p id="d1e1595">In summary, the observed and modeled spring and early summer SST anomalies
over the North Atlantic and European soil moisture anomaly patterns for 2018
are in line with those identified for other recent hot summers. Moreover,
the dried-out soils and vegetation may have enhanced the maximum
temperatures by leading to anomalous latent heat fluxes not only locally, but also
downwind via advected sensible heat that can lead to abrupt increases in air
temperatures, further enhancing local land–atmosphere feedbacks
(Schumacher et al., 2022).</p><?xmltex \hack{\newpage}?>
</sec>
<?pagebreak page1710?><sec id="Ch1.S3.SS4">
  <label>3.4</label><title>Attribution of the 2018 extreme heat</title>
      <p id="d1e1607">This section evaluates how anthropogenic climate change has affected the
likelihood of similar heatwaves under present climate conditions and how it
will affect their likelihood at global warming levels of <inline-formula><mml:math id="M72" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>1.5 and <inline-formula><mml:math id="M73" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>2 <inline-formula><mml:math id="M74" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C compared to pre-industrial times.</p>
      <p id="d1e1633">As defined by the cumulative heat metric, the 2018 summer was the second
warmest summer over Europe following 2010, surpassed again in 2019 and 2021
(not shown), ranking it the fourth warmest by now. In the period of 1979–2021, ERA5 data exhibit a 7 % likelihood of 2018 cumulative heat levels (black PDF in Fig. 9). MPI-GE, which is shown to adequately represent the
variability and forced anthropogenic changes in observed temperatures
(Suarez-Gutierrez et al., 2018, 2021), is also  able to capture cumulative heat well
(gray PDF in Fig. 9), as compared to ERA5. Under recent climate (1979–2021)
conditions, the 100 members of MPI-GE simulate a 9 % likelihood of
exceeding 2018 levels, making this roughly a 1-in-10-year event. This is in
line with an earlier attribution study by the World Weather Attribution
(WWA) team who found return periods of about 1 in 10 years for Scandinavia
and slightly less in the Netherlands (WWA, 2018). Vogel et al. (2019) also showed that
events of this type, exhibiting concurrent hot temperature extremes over
large parts of the Northern Hemisphere, were unprecedented before 2010, and
it is virtually certain that the 2018 events would not have occurred without
human-induced climate change. Under increased global warming, this likelihood
reaches 69 % in a <inline-formula><mml:math id="M75" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>1.5 <inline-formula><mml:math id="M76" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C world and 96 % in a
<inline-formula><mml:math id="M77" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>2 <inline-formula><mml:math id="M78" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C world (orange and red PDFs in Fig. 9). Thus, conditions as
extreme as the summer of 2018 are projected to occur two out of every three
summers in a 1.5 <inline-formula><mml:math id="M79" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer world, while in a 2 <inline-formula><mml:math id="M80" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer world they occur virtually every single summer. The extreme summer of 2018 represents a fairly average summer in a 1.5 <inline-formula><mml:math id="M81" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer world. In a 2 <inline-formula><mml:math id="M82" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer world, the cumulative heat during the average summer
is twice as large as the 2018 levels, while the most extreme 2 <inline-formula><mml:math id="M83" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer
world summers could exhibit more than 4 times more excess heat compared
to recent climate conditions.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F9"><?xmltex \currentcnt{9}?><?xmltex \def\figurename{Figure}?><label>Figure 9</label><caption><p id="d1e1716">European ERA5 (1979–2021; black) cumulative heat versus MPI-GE
under recent (1979–2021; gray) climate and future <inline-formula><mml:math id="M84" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>1.5 <inline-formula><mml:math id="M85" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (2020–2049; orange) and <inline-formula><mml:math id="M86" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>2 <inline-formula><mml:math id="M87" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (2050–2079; red) warmer worlds compared to pre-industrial time warmer worlds. The 2018 summer from ERA5 data is marked with a white X. Daily maximum temperatures (<inline-formula><mml:math id="M88" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula>) for summer months (June to August; JJA)
over land grid points only. Anomalies with respect to 1981–2010. ERA5 data
regridded to a coarser resolution of MPI-GE. Probabilities are normalized to
percentages (divided by the total number of years in the period). Bin size is
500 <inline-formula><mml:math id="M89" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C.</p></caption>
          <?xmltex \igopts{width=207.705118pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f09.png"/>

        </fig>

      <p id="d1e1779">To estimate how much more likely the heat event of 2018 has become in
Germany in recent decades due to anthropogenic climate change, its
probability ratio was calculated based on historical and hist-nat
(pre-industrial-type) simulations from the CMIP6 archive. In the first step,
we defined the extreme event for which the tailored attribution analysis for
Germany was conducted. We analyzed the maximum daily temperature (<inline-formula><mml:math id="M90" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula>)
averaged for a box over Germany (47.5–55<inline-formula><mml:math id="M91" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 6–15<inline-formula><mml:math id="M92" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E),
and to account for the prolonged heat of 2018, we used the <inline-formula><mml:math id="M93" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula> as a spatial
average over 17 d (<inline-formula><mml:math id="M94" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mo>max⁡</mml:mo><mml:mn mathvariant="normal">17</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>). This length was defined based on the
longest period of consecutive days with <inline-formula><mml:math id="M95" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula> above 30 <inline-formula><mml:math id="M96" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in
German weather stations on record. Using this length resulted in the
longest return period. Thus, annual block maxima of this variable (<inline-formula><mml:math id="M97" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mo>max⁡</mml:mo><mml:mn mathvariant="normal">17</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>)
were constructed within the GEV fit, and<?pagebreak page1711?> the return periods were calculated.
The return period of the 2018 summer <inline-formula><mml:math id="M98" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mo>max⁡</mml:mo><mml:mn mathvariant="normal">17</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> (approximately 31 <inline-formula><mml:math id="M99" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
in E-OBS) was estimated as 108 years, making it a heatwave that is expected
less than once in a lifetime and which can therefore have considerable impacts. It
should be acknowledged that such a return period estimate contains
uncertainties, as the time series used to calculate it are shorter (about 70 years). Following the analysis of observation-based data, the following
models were analyzed: CanESM5, CNRM-CM6-1, ACCESS-ESM1-5, IPSL-CM6A-LR,
HadGEM3-GC31-LL, and MRI-ESM2-0 (see Table S1 for further details on the
models used).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F10" specific-use="star"><?xmltex \currentcnt{10}?><?xmltex \def\figurename{Figure}?><label>Figure 10</label><caption><p id="d1e1896">Probability ratio (PR) of the 2018 summer heatwave occurrence in
Germany in the analyzed CMIP6 models (see Table S1). The black squares show
the PR estimated based on the original simulation time series, and the red
bars show the 5th to 95th PR percentiles calculated from a
1000-member bootstrap. The number of available DAMIP ensemble members is
given together with the model name and the originating institution on the
<inline-formula><mml:math id="M100" display="inline"><mml:mi>y</mml:mi></mml:math></inline-formula> axis. The vertical thick black line indicates a PR <inline-formula><mml:math id="M101" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 1, above which the likelihood of such an event has increased compared to pre-industrial times.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/1699/2023/nhess-23-1699-2023-f10.png"/>

        </fig>

      <p id="d1e1919">The probability ratio of the 2018 summer heatwave occurrence in Germany is
shown for all analyzed models in Fig. 10. For all models the probability
ratio estimated on the original simulation data is larger than 1, meaning
that the probability of such a heatwave has increased due to anthropogenic
climate change. The red bars provide uncertainty ranges bases on the 1000
bootstraps. The best estimate in all analyzed CMIP6 models (black squares)
is <inline-formula><mml:math id="M102" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 2, again in line with the WWA findings despite a rather
different event definition (WWA, 2018). For
readability of the results, the <inline-formula><mml:math id="M103" display="inline"><mml:mi>x</mml:mi></mml:math></inline-formula> axis in Fig. 10 is only extended to a
value of 100, with larger values omitted due to the large uncertainties. In
fact, the upper range of the probability ratios for some models is invalid, as the event had a zero probability of occurrence in the hist-nat scenario,
indicating that such an extended heatwave would have been very improbable
under pre-industrial conditions.</p>
      <p id="d1e1936">In summary, the analysis of the impact of anthropogenic climate change on
the heatwave in the summer of 2018 shows that such heat events have already become
more frequent, i.e., their probability has increased compared to
pre-industrial conditions. Furthermore, it is expected that such heat events
will become even more likely in a warmer world.</p>
      <p id="d1e1939">Drought attribution is notoriously difficult due to the fact that global
models only crudely reproduce convective precipitation, which is the main
mode of rainfall in summer. While evapotranspiration is increasing with
warming, the question of whether or not this can be compensated by stronger
downpours to avoid hydrological (or agricultural) drought cannot be answered
with any degree of certainty at the moment. Drought episodes are expected to
increase (IPCC, 2021) across the world, but the frequency of occurrence and the actual change in risk cannot be quantified yet. Nevertheless, it is likely that the prolonged 2018 drought, followed by two more below-average rainfall years in 2019 and 2020 in Germany, is partially attributable to
human-induced climate change. Given that attributable global warming is
approximately 1.1 <inline-formula><mml:math id="M104" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (2011–2020), corresponding to 100 % of the
observed warming, and warming over land is much more rapid, Europe has
already warmed disproportionately by <inline-formula><mml:math id="M105" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 2 <inline-formula><mml:math id="M106" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C compared
to pre-industrial times, with summer warming being particularly amplified
due to soil moisture feedbacks under increased sensible heat fluxes.
Together with the potential dynamic feedback discussed above, the average
summer <inline-formula><mml:math id="M107" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula> in Europe may well exceed 3 <inline-formula><mml:math id="M108" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C above pre-industrial
conditions already. This is corroborated by a recent WWA study, which
analyzed the UK heat record during the exceptional 2022 heatwave (18–19 July
2022) and found that climate change added 4 <inline-formula><mml:math id="M109" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C to the observed
record <inline-formula><mml:math id="M110" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula>. What used to be a 36 <inline-formula><mml:math id="M111" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C day is now a 40 <inline-formula><mml:math id="M112" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
day (WWA, 2022).</p>
</sec>
</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <label>4</label><title>Discussion and conclusions</title>
      <p id="d1e2035">The extreme heat and drought of the summer of 2018 have been studied from a
multi-faceted weather and climate perspective. We looked at hot and dry
summers over Europe using different analysis approaches to study the
extremeness of and attribution to anthropogenic climate change (climate
perspective), as well as synoptic dynamics in concert with slowly varying
boundary conditions at the ocean and continental surfaces (seasonal and
weather perspective). The 2018 summer is found to be a unique historical
example of persistent heatwave and drought conditions in large parts of
Europe. This is particularly true for northern and central Europe, regions
which – unlike the seasonal drought in the Mediterranean – are historically
not so accustomed to this kind of concurrent hot and dry summer extremes.
The 2018 summer is one more case in a cluster of intense heatwaves facing
Europe over the last few decades (Russo et al., 2015; Becker et al., 2022). The 2018 drought was an intense, large-scale event, promoting strong land–atmosphere coupling that exacerbated the heatwave (Dirmeyer et
al., 2021).</p>
      <?pagebreak page1712?><p id="d1e2038"><?xmltex \hack{\newpage}?>Regarding the large-scale atmospheric conditions conducive of the summer of
2018 extremes, we provided detailed evidence on the blocking anticyclones,
persistent double jet stream configurations, sNAO+ phase, Rossby wave
activity, and different air mass origins. For example, the persistent double
jet stream event, combined with record high positive sNAO
(Drouard et al., 2019), seems to have played a role
in the long duration of the 2018 heatwave. Additionally, according to
Li et al. (2020), the collaborative (not
mutually exclusive) roles of sNAO+ and European blocking could favor the
frequency, persistence, and magnitude of heatwaves over Europe, as the
positive sNAO-related blocking events are quasi-stationary and more persistent
compared to the non-NAO<inline-formula><mml:math id="M113" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> related ones. New evidence is provided regarding
the origin of the low-PV air masses in the upper-tropospheric blocking
anticyclone over Scandinavia; while in its initiation phase, backward-trajectory analyses point to the role of a western North Atlantic warm
conveyor belt, and we provide hints that its maintenance could be supported by
low-PV air stemming from moist convection in the trough flanking the block
to its southeast, i.e., over eastern Europe. However, further analysis is
needed to address the direction of causality behind this link. On the other
hand, our analysis suggests that the later heatwave phase over Iberia has
different drivers, as the air masses originated locally or were advected
from nearby areas (e.g., north Africa) and are not necessarily directly
associated with the propagation and breaking of large-scale Rossby waves as
over Scandinavia (Santos et al., 2015; Sousa et al., 2019).</p>
      <p id="d1e2049">The dominant oceanic and large-scale conditions of the North Atlantic might
have supported the development of the 2018 heatwave
(Dunstone et al., 2019). The physical reasoning in the
relationship between the North Atlantic SST tripole and exceptionally cold
North Atlantic ocean, the jet stream setup, and the occurrence of the heatwave was proposed by Duchez et al. (2016)
based on the summer 2015 event. Here, we documented that similar anomalies
were also observed during the spring of 2018. While the atmospheric forcing
is associated with the anomalous jet stream positions and blocking, they in
turn influence the precipitation patterns over Europe, leading to changes in
the soil moisture content. Although such a process enhances the potential
for a heat extreme, the meteorological factors are the ones that determine
the timing and duration of the heatwave. Dedicated modeling experiments and
causal inference algorithms will be key to test the hypothesis of a causal
link between spring North Atlantic SSTs and subsequent summer extremes in
Europe. Moreover, the patterns of North Atlantic SSTs are acting on top of
the warming background climate, which may further modify the type or the
magnitude of those relationships (McCarthy et al., 2019).</p>
      <p id="d1e2052">The severe soil moisture depletion in Germany between April and July of 2018
reflected the persistently warm and dry conditions and led to anomalously
dry soils in summer. The drought conditions in the soil pushed its state
into the transition zone conditions, in which soil wetness plays a direct
role in influencing the climate by reducing the evaporative cooling effect
at the land surface and thus enhancing hot and dry conditions. The
moisture-limited conditions that prevailed between June and August 2018
indicated that the hot surface temperatures are directly linked to
anomalously dry soils during the 2018 summer period
(Dirmeyer et al., 2021; Orth, 2021).</p>
      <?pagebreak page1713?><p id="d1e2056">We also showed that the summer of 2018 was extreme in the observational record for
Europe and that heat anomalies of this magnitude are expected to occur much
more often in a warmer world, being reached up to almost every year with
global warming of <inline-formula><mml:math id="M114" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>2 <inline-formula><mml:math id="M115" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C. Wehrli et
al. (2020) provided evidence that the anthropogenic background warming was a
strong contributor to the 2018 summer heatwave in the Northern Hemisphere,
highlighting that future extremes under similar atmospheric circulation
conditions at higher levels of global warming would reach dangerous levels.
Our tailored attribution study, which analyzed how the maximum temperature,
averaged over 17 d over Germany, has been impacted by anthropogenic
climate change, showed that the probability of such a prolonged heat event
has increased in all CMIP6 models analyzed here. This adds to previous
attribution studies that analyzed the summer 2018 heatwave in other areas of
Europe and also found an increase in its likelihood under anthropogenic
climate change (McCarthy et al., 2019; Vogel et al., 2019; Leach et al., 2020).</p>
      <p id="d1e2075">Here, we presented a comprehensive study of the extreme hot and dry 2018
summer in Europe, investigating its emergence and evolution with a
combination of conventional and more sophisticated metrics and methods, with
an emphasis on their synoptic-scale atmospheric drivers and a reference to
their potential precursors in spring. Moreover, by assessing the event from
a climate perspective, we provided evidence that anomalous summers of such
extremity have already, and will further, become much more frequent in a
warming world. Overall, this study highlights the added value of
multi-faceted approaches for the analysis of such extreme events and that
collaboration among different fields is crucial both for the
understanding of the process and for the subsequent quantification of impacts. The summer of
2022 was yet another very extreme hot and dry summer that affected Europe,
corroborating the approach of this work and emphasizing the need to carry
out multi-disciplinary impact studies.</p>
</sec>

      
      </body>
    <back><notes notes-type="codeavailability"><title>Code availability</title>

      <p id="d1e2083">Code is available from the authors upon request.</p>
  </notes><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e2089">The ERA5 (<ext-link xlink:href="https://doi.org/10.1002/qj.3803" ext-link-type="DOI">10.1002/qj.3803</ext-link>, Hersbach et al., 2020) and
ERA5-HEAT (<ext-link xlink:href="https://doi.org/10.1002/gdj3.102" ext-link-type="DOI">10.1002/gdj3.102</ext-link>, Di Napoli et al., 2021) reanalysis data are publicly available via the Copernicus Climate Change Service (<ext-link xlink:href="https://doi.org/10.24381/cds.adbb2d47" ext-link-type="DOI">10.24381/cds.adbb2d47</ext-link>, Hersbach et al., 2023). The gridded observational datasets E-OBS (<ext-link xlink:href="https://doi.org/10.1029/2008JD010201" ext-link-type="DOI">10.1029/2008JD010201</ext-link>, Haylock et al., 2008; and <ext-link xlink:href="https://doi.org/10.1029/2017JD028200" ext-link-type="DOI">10.1029/2017JD028200</ext-link>, Cornes et al., 2018) are publicly available on the European Climate Assessment &amp; Dataset website
(<uri>https://www.ecad.eu/download/ensembles/download.php</uri>, ECA&amp;D, 2023). The observational datasets from the German Weather Service (DWD; <ext-link xlink:href="https://doi.org/10.5194/asr-10-99-2013" ext-link-type="DOI">10.5194/asr-10-99-2013</ext-link>, Kaspar et al., 2013) are publicly available on the DWD website under their Open Data Portal (<uri>https://opendata.dwd.de/</uri>, DWD, 2023). The CMIP6 (<ext-link xlink:href="https://doi.org/10.5194/gmd-9-1937-2016" ext-link-type="DOI">10.5194/gmd-9-1937-2016</ext-link>, Eyring et al., 2016; please refer to Table S1 and the reference list in the Supplement for all data citations of each CMIP6 model and run used in this study) and the Max Planck Institute Grand Ensemble (MPI-GE; Maher et al., 2019, <ext-link xlink:href="https://doi.org/10.1029/2019MS001639" ext-link-type="DOI">10.1029/2019MS001639</ext-link>) climate model data are publicly available via the Earth System Grid Federation portal (<uri>https://esgf-data.dkrz.de/projects/cmip6-dkrz/</uri>, DKRZ, 2022b; and <uri>https://esgf-data.dkrz.de/projects/mpi-ge/</uri>, DKRZ, 2022a). The North Atlantic Oscillation (NAO) index time series is publicly available on the National Weather Service Climate Prediction Center website (<uri>https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii</uri>, CPC, 2023).</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e2133">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/nhess-23-1699-2023-supplement" xlink:title="pdf">https://doi.org/10.5194/nhess-23-1699-2023-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e2142">ER and AHF coordinated the inter-disciplinary task force on heat and drought within ClimXtreme and this collaborative paper. ER did the jet stream analysis; prepared Figs. 1, 3c–d, 5d, and 7; curated most of the final figures with contributions from different co-authors (see below); and wrote the first draft of the manuscript with contributions from different co-authors. FNB calculated the UTCI; did the Rossby wave packet and the trajectory analysis; and prepared Figs. 4, 5a–c, and 6. GBA calculated the cumulative heat metric in ERA5 and the SST anomalies; DP calculated SPEI; DN and StS prepared Figs. 2 and S1; AR did the blocking analysis and prepared Figs. 3a, b, S2, and S3; JR did the weather regime analysis; LJ calculated precipitation and soil moisture anomalies in ERA5 and prepared Fig. 8; LSG did the MPI-GE attribution study and prepared Fig. 9; JST did the CMIP6 attribution study and prepared Fig. 10 and Table S1; and GC contributed to the CMIP6 attribution study. All authors followed the analysis from the beginning, contributed text, and edited/commented on the final version of the paper.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e2148">At least one of the (co-)authors is a member of the editorial board of <italic>Natural Hazards and Earth System Sciences</italic>. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e2157">Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><notes notes-type="sistatement"><title>Special issue statement</title>

      <p id="d1e2163">This article is part of the special issue “Past and future European atmospheric extreme events under climate change”. It is not associated with a conference.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e2169">This paper is a collaborative effort within the BMBF ClimXtreme project, for
which the authors acknowledge funding (grant nos. 01LP1901A, 01LP1901C,
01LP191D, 01LP1901E, 01LP1901F, 01LP1902F, 01LP1903J, 01LP1902D, 01LP1902N, 01LP1903C,
01LP1902B, 01LP1904A). Laura<?pagebreak page1714?> Suarez-Gutierrez also received funding from the European Union’s Horizon Europe Framework Programme under the Marie Skłodowska-Curie Actions (grant agreement no. 101064940). André Düsterhus is supported by A4, funded by the Marine Institute (grant no. PBA/CC/18/01). Elena Xoplaki acknowledges support by the H2020 Project CLINT, the Academy of Athens, and the Greek National Network on Climate Change and its Impact (200/937). Georgios Fragkoulidis acknowledges the support of the German Research Foundation (DFG; project no. 445572993). Joaquim G. Pinto thanks the AXA
Research Fund for support. We acknowledge the World Climate Research
Programme, which, through its Working Group on Coupled Modelling,
coordinated and promoted CMIP6. We thank the climate modeling groups for
producing and making their model output available, the Earth System Grid
Federation (ESGF) for archiving the data and providing access, and the
multiple funding agencies who support CMIP6 and ESGF. We acknowledge the
E-OBS dataset from the EU-FP6 project UERRA (<uri>http://www.uerra.eu</uri>, last access: 19 August 2022) and the Copernicus Climate Change Service, as well as the data providers in the ECA&amp;D
project (<uri>https://www.ecad.eu</uri>, last access: 19 August 2022). This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project bb1152. Parts of the data were accessed through the XCES community evaluation system based on Freva technology (Kadow et al., 2021). We thank the two anonymous reviewers for their constructive feedback that substantially improved the manuscript.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e2180">This research has been supported by the Bundesministerium für Bildung und Forschung (grant nos. 01LP1901A, 01LP1901C, 01LP191D, 01LP1901E, 01LP1901F, 01LP1902F, 01LP1903J, 01LP1902D, 01LP1902N, 01LP1903C, 01LP1902B, and 01LP1904A), the Marine Institute (grant no. PBA/CC/18/01), the Deutsche Forschungsgemeinschaft (grant no. 445572993), the European Union’s Horizon Europe Framework Programme under the Marie
Skłodowska-Curie Actions (grant no. 101064940), the H2020 project CLINT, the Academy of Athens, the Greek National Network on Climate Change and its Impact (grant no. 200/937), and the AXA Research Fund (<uri>https://axa-research.org/en/project/joaquim-pinto</uri>, last access: 29 April 2023).
<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
The article processing charges for this open-access<?xmltex \notforhtml{\newline}?> publication were covered by the Potsdam Institute <?xmltex \notforhtml{\newline}?> for Climate Impact Research (PIK).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e2196">This paper was edited by Ricardo Trigo and reviewed by two anonymous referees.</p>
  </notes><ref-list>
    <title>References</title>

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