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  <front>
    <journal-meta><journal-id journal-id-type="publisher">TC</journal-id><journal-title-group>
    <journal-title>The Cryosphere</journal-title>
    <abbrev-journal-title abbrev-type="publisher">TC</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">The Cryosphere</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1994-0424</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/tc-16-1369-2022</article-id><title-group><article-title>The role of föhn winds in eastern Antarctic Peninsula rapid<?xmltex \hack{\break}?> ice shelf collapse</article-title><alt-title>The role of föhn winds in eastern Antarctic Peninsula rapid ice shelf collapse</alt-title>
      </title-group><?xmltex \runningtitle{The role of f\"{o}hn winds in eastern Antarctic Peninsula rapid ice shelf collapse}?><?xmltex \runningauthor{M. K. Laffin et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Laffin</surname><given-names>Matthew K.</given-names></name>
          <email>mlaffin@uci.edu</email>
        <ext-link>https://orcid.org/0000-0002-6079-336X</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Zender</surname><given-names>Charles S.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0129-8024</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>van Wessem</surname><given-names>Melchior</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-3221-791X</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Marinsek</surname><given-names>Sebastián</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Department of Earth System Science, University of California, Irvine, USA</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Department of Computer Science, University of California, Irvine, USA</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Institute for Marine and Atmospheric Research Utrecht (IMAU), Utrecht University, Utrecht, the Netherlands</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Department of Glaciology, Instituto Antártico Argentino, Buenos Aires, Argentina​​​​​​​</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Matthew K. Laffin (mlaffin@uci.edu)</corresp></author-notes><pub-date><day>13</day><month>April</month><year>2022</year></pub-date>
      
      <volume>16</volume>
      <issue>4</issue>
      <fpage>1369</fpage><lpage>1381</lpage>
      <history>
        <date date-type="received"><day>24</day><month>September</month><year>2021</year></date>
           <date date-type="rev-request"><day>25</day><month>October</month><year>2021</year></date>
           <date date-type="rev-recd"><day>22</day><month>March</month><year>2022</year></date>
           <date date-type="accepted"><day>23</day><month>March</month><year>2022</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2022 Matthew K. Laffin et al.</copyright-statement>
        <copyright-year>2022</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://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022.html">This article is available from https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022.html</self-uri><self-uri xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022.pdf">The full text article is available as a PDF file from https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d1e131">Ice shelf collapse reduces buttressing and enables grounded
glaciers to contribute more rapidly to sea-level rise in a warming climate.
The abrupt collapses of the Larsen A (1995) and B (2002) ice shelves on the
Antarctic Peninsula (AP) occurred, at least for Larsen B, when long-period
ocean swells damaged the calving front and the ice shelf was inundated with
melt lakes that led to large-scale hydrofracture cascades. During collapse,
field and satellite observations indicate föhn winds were present on
both ice shelves. Here we use a regional climate model and machine learning
analyses to evaluate the contributory roles of föhn winds and associated
melt events prior to and during the collapses for ice shelves on the AP.
Föhn winds caused about 25 % <inline-formula><mml:math id="M1" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 3 % of the total annual melt in
just 9 d on Larsen A prior to and during collapse and were present during
the Larsen B collapse, which helped form extensive melt lakes. At the same
time, the off-coast wind direction created by föhn winds helped melt and
physically push sea ice away from the ice shelf calving fronts that allowed
long-period ocean swells to reach and damage the front, which has been
theorized to have ultimately triggered collapse. Collapsed ice shelves
experienced enhanced surface melt driven by föhn winds over a large
spatial extent and near the calving front, whereas SCAR inlet and the Larsen
C ice shelves are affected less by föhn-wind-induced melt and do not
experience large-scale melt ponds. These results suggest SCAR inlet and the
Larsen C ice shelves may be less likely to experience rapid collapse due to
föhn-driven melt so long as surface temperatures and föhn occurrence
remain within historical bounds.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e150">Ice shelves, the floating extensions of grounded glaciers, subdue the
discharge of grounded ice into the global ocean (Rignot et al., 2004;
Scambos et al., 2004; Gudmundsson, 2013; Borstad et al., 2016).
Re-examination of past ice shelf collapse events can help to shed light on
the mechanisms of collapse and improve the understanding of ice shelf
dynamics for future projections of sea-level rise (Rignot et al., 2004;
Gudmundsson, 2013; Borstad et al., 2016). The final collapses of the
Larsen A (LAIS) in 1995 and the Larsen B (LBIS) ice shelves in 2002 have
been attributed to decreased structural integrity brought on by a
combination of factors: most notably, regional atmospheric warming (Scambos
et al., 2000; Mulvaney et al., 2012), extended melt seasons (Scambos et al.,
2003), multi-year firn pore space depletion (Kuipers Munneke et al., 2014;
Trusel et al., 2015), melt pond flooding (Glasser and Scambos, 2008; Trusel
et al., 2013; Leeson et al., 2020), crevasse expansion through hydrofracture
(Scambos et al., 2003; Banwell et al., 2013; Pollard et al., 2015; Alley et
al., 2018; Banwell et al., 2019; Robel and Banwell, 2019), glacier
structural discontinuities (Glasser and Scambos, 2008​​​​​​​), basal melt (Pritchard et
al., 2012; Rignot et al., 2013; Depoorter et al., 2013; Schodlok et al.,
2016; Adusumilli et al., 2018), warm meltwater intrusion (Braun and Humbert,
2009​​​​​​​), melting of the ice melange within rifts conducive to rift propagation
(Larour et al., 2021), and regional sea ice loss allowing ocean swell
flexure stress on the calving front (Banwell et al., 2017; Massom et al.,
2018).</p>
      <p id="d1e153"><?xmltex \hack{\newpage}?>While the list of mechanisms that can destabilize ice shelves is extensive,
a conceptual model for rapid ice shelf collapse proposed by Massom et al. (2018) identifies four essential prerequisites for sudden collapse: (1)
extensive surface flooding and hydrofracture, (2) reduced sea ice or fast
ice at the ice shelf front, (3) outer margin or terminus fracturing and
rifting, and (4) initial calving trigger at the ice shelf margin. They
theorize waves led to calving front damage and small calving events that
breached the “compressive arch” of stability of both ice shelves proposed
by Doake et al. (1988). At the same time the ice shelves were covered in
extensive surface melt lakes that were unlikely to drain horizontally
because of the relatively flat surface (Banwell et al., 2014). Satellite
observations and ice shelf stability model studies determined the LBIS was
covered with <inline-formula><mml:math id="M2" display="inline"><mml:mrow><mml:mi mathvariant="italic">&gt;</mml:mi><mml:mn mathvariant="normal">2750</mml:mn></mml:mrow></mml:math></inline-formula> melt lakes that were on average 1 m deep
before collapse (Glasser and Scambos, 2008; Banwell et al., 2013). Ice
shelves inundated with surface melt lakes are susceptible to disintegration
through a process known as hydrofracture, where meltwater applies outward
and downward pressure to the walls and tip of crevasses that can propagate
through the ice shelf (Scambos et al., 2003; Banwell et al., 2013; Bell et
al., 2018; Lhermitte et al., 2020). Furthermore, melt lakes that rapidly
drain by hydrofracture can create fracture patterns that split ice shelves
into sections with aspect ratios that support unstable rollover and
hydrofracture cascades that begin when melt lakes drain and/or calving
occurs at the ice shelf terminus (Scambos et al., 2003; Banwell et al.,
2013; Burton et al., 2013; Robel and Banwell, 2019). The combination of
ocean swell stress on the calving front and extensive melt ponds led to
large-scale hydrofracture cascades that Massom et al. (2018) proposed
ultimately caused the rapid collapse of the LBIS and possibly the LAIS.</p>
      <p id="d1e167">In addition to a lack of sea ice and extensive melt ponds, meteorological
and satellite observations identify clear skies and warm west-northwest
föhn wind at the time of collapse (Fig. 1c–f) (Rott et al., 1998; Rack
and Rott, 2004; Cape et al., 2015; Massom et al., 2018). Föhn winds
form when relatively cool moist air is forced over a mountain barrier, often
leading to precipitation on the windward side of the barrier that dries the
air mass (Grosvenor et al., 2014; Elvidge et al., 2015). As the now drier
air descends the leeward slope it warms adiabatically and promotes melt
directly through sensible heat exchange, and indirectly by the associated
clear skies that allow additional shortwave radiation to reach the surface
in non-winter months (Turton et al., 2017, 2018; Kuipers Munneke et al.,
2018; Elvidge et al., 2020; Laffin et al., 2021). Föhn winds and their
capacity to cause surface melt have been studied extensively on the AP.
Observations and model studies on the LCIS confirm the föhn mechanism
that enhances sensible heat and shortwave radiation and alters local albedo,
which can increase surface melt rates upwards of 50 % compared to
non-föhn conditions (Cape et al., 2015; Elvidge et al., 2015; King et
al., 2015, 2017; Kuipers Munneke et al., 2012, 2018; Bevan et al., 2017;
Lenaerts et al., 2017; Datta et al., 2019; Kirchgaessner, et al., 2021;
Laffin et al., 2021; W. Wang et al., 2021​​​​​​​). Late-season föhn melt reduces
firn pore space and thus pre-conditions ice shelves to form melt ponds that
are responsible for the increased firn density pattern east of the AP
mountains on the LCIS (Holland et al., 2011; Kuipers Munneke et al., 2014;
Datta et al., 2019). Föhn melt climatology studies have aimed to
identify how much melt is caused by föhn and the locations most affected
and found föhn winds account for up to 17 % of melt and are
concentrated in the LCIS inlets (Turton et al., 2017; Datta et al., 2019;
Laffin et al., 2021). Pressure gradient differences across the AP range lead
to föhn winds that funnel through mountain gaps as highly concentrated
föhn jets, particularly in inlets east of the AP range (Luckman et al.,
2014; Elvidge et al., 2015; Kuipers Munneke et al., 2012; Grosvenor et al.,
2014). In addition to enhancing surface melt rates, föhn winds exert force
on sea/fast ice and drag it away from the calving front, thereby exposing
the front to ocean waves (Bozkurt et al., 2018). Climatic studies of the
Larsen B embayment indicate that föhn winds were coincident with
collapse (Rack and Rott, 2004; Leeson et al., 2017). However, it is unknown
if concentrated föhn jets spilled onto the former LAIS and LBIS and, if
so, whether those föhn winds contributed to their collapse. The
following questions, therefore, arise. (1) To what extent did föhn-induced melt
contribute to the surface melt budget on each eastern AP ice shelf? (2) Did
föhn winds and associated melt play a role in triggering the collapses
of the LAIS and LBIS? (3) What are the implications of föhn-induced melt
for the remaining eastern AP ice shelves?</p>
      <p id="d1e170">To address these questions we consider three metrics: Sect. 3.1 explores
the total annual surface melt quantity induced by föhn winds and how
melt is spatially distributed across each ice shelf; Sect. 3.2 identifies
the coincidence of föhn-induced melt preceding and during the collapse
events, and the estimated melt-lake depth in response to melt events.;
Sect. 3.3 identifies the contribution of föhn melt to the
climatological surface liquid water budget, comparing collapsed and extant
ice shelves on the eastern AP. By constructing a timeline of melt and melt
mechanisms and comparing melt metrics with collapsed and extant ice shelves,
we can identify the factors contributing to collapse.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e176">Map of the northern Antarctic Peninsula <bold>(a)</bold> showing locations of
collapsed ice shelves (LAIS – 25 January 1995, LBIS – 9 February 2002), extant
ice shelves (SCAR inlet and LCIS), and föhn jets (Larsen A jet (LA jet),
Larsen B jet (LB jet), Jason Peninsula jet (JP jet), Cabinet inlet jet (CI
jet), Mill inlet jet (MI jet), Whirlwind inlet jet (WI jet), Mobil Oil inlet
jet (MOI jet)) with a MODIS Mosaic overlay. The colored regions indicate how
this study separates ice shelves for climatic analysis. The dotted lines
show the former extent of the Larsen A and Larsen B ice shelves at the time
of collapse. Panels <bold>(b)</bold>–<bold>(f)</bold> are satellite images of the collapses of the
LAIS and LBIS. <bold>(b)</bold> AVHRR (Advanced Very High-Resolution Radiometer) image of
the northern AP 2 years before the collapse of the LAIS showing melt lakes
on the surface of both ice shelves. <bold>(c)</bold> AVHRR image after the collapse of
the LAIS. <bold>(d)</bold> NASA-provided MODIS (Moderate Resolution Imaging
Spectroradiometer) image showing the LBIS days before collapse began. <bold>(e)</bold>
MODIS image showing a föhn wind event (clouds over the western AP, clear
skies over the ice shelves) along with the initial collapse of the LBIS. <bold>(f)</bold>
MODIS image of the complete collapse of the LBIS.</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022-f01.jpg"/>

      </fig>

</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Data and methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Regional Climate Model 2 Simulation (RACMO2)</title>
      <p id="d1e225">We base our analysis on 3-hourly output from simulations by the Regional
Atmospheric Climate Model 2 (RACMO2), version 2.3p2, with a horizontal
resolution of 5.5 km (0.05<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>) focused on the AP from 1979–2018.
RACMO2 uses the physics package CY33r1 of the ECMWF Integrated Forecast
System (IFS)
(<uri>https://www.ecmwf.int/en/elibrary/9227-part-iv-physical-processes/</uri> ECMWF, 2009, last access: 6 April 2022​​​​​​​) in combination with atmospheric dynamics of the
High-Resolution Limited Area Model (HIRLAM). When RACMO2 surface simulations
are compared with AWS observations on the LCIS, surface air temperature has
a slight warm bias likely because model resolution and shortwave–longwave
radiation are over- or underestimated due to underestimation of clouds and
moisture but overall reproduce surface observations (King et al., 2015;
Leeson et al., 2017; Bozkurt et al., 2020; Laffin et al., 2021).</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><?xmltex \opttitle{F\"{o}hn wind detection}?><title>Föhn wind detection</title>
      <p id="d1e249">We use the föhn detection algorithm (FöhnDA) that identifies
föhn winds that cause melt using 12 automatic weather stations (AWSs) on
the AP previously developed and detailed in Laffin et al. (2021).
FöhnDA identifies föhn-induced melt events using binary
classification machine learning when 10 m air temperature (<inline-formula><mml:math id="M4" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>) is greater
than 0 <inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, which ensures it captures föhn events that cause
surface melt. Thresholds for relative humidity (RH) and wind speed (WS) are
more dynamic because high wind speeds and low relative humidity do not
guarantee temperatures above freezing; they only aid to identify föhn.
FöhnDA uses quantile regression to identify these variable thresholds
that take into account the climatology and seasonality at each AWS site.
FöhnDA uses two empirically determined thresholds: the 60th percentile
wind speed and 30th percentile relative humidity, which are 2.85 m s<inline-formula><mml:math id="M6" 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
79 % averaged at all AWS locations. We co-locate AWS with the nearest
model grid cell and use FöhnDA results to train a machine learning (ML) model that detects
föhn winds in RACMO2 output. Our ML model improves the accuracy of
föhn detection by over 23 % when compared to the simple binary
classification method applied to RACMO2 output as described above. A
sensitivity study detailed in Laffin et al. (2021) compares previous
föhn detection methods (Cape et al., 2015; Datta et al., 2019) and shows
that FöhnDA allows us to use in situ observations from AWS and expand
föhn detection with RACMO2 output to regions and times when AWS
observations are not available (Fig. S1, Table S1).</p>
      <p id="d1e280">Föhn jet locations were identified using wind direction and strength
during föhn events (Fig. 2a) and by the surface melt pattern during
föhn (Fig. 3b). The RACMO2 topography pixel size is 5.5 km, which is
sufficient to produce the föhn jets identified on the LCIS (Elvidge et
al., 2015) and allows for new föhn jet identification on the LAIS and
LBIS despite lack of direct observation. However, föhn winds funneled
through local canyons and mountain gaps smaller than 5.5 km are not directly
simulated. Therefore, we consider RACMO2-simulated estimates of surface melt
caused by föhn winds to be conservative and likely greater in regions
where föhn winds are funneled and concentrated.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Ice shelf intercomparison analysis</title>
      <p id="d1e291">We split the ice shelves into areas shown in Fig. 1a (Larsen A, Larsen B,
SCAR inlet, Larsen C (north), and Larsen C) and take the average of all
model grid cells annually to create a climatology of surface melt, melt
rate, melt hours, and surface temperature. We use a two-tailed <inline-formula><mml:math id="M7" display="inline"><mml:mi>t</mml:mi></mml:math></inline-formula>-test statistic
to identify if the mean surface temperature and mean surface melt of both
ice shelves are statistically different from one another at the 95 %
confidence interval. We compare all ice shelves to the LBIS because it was
the most recent collapse event and is adjacent to collapsed and existing ice
shelves. Qualitatively similar results are obtained when comparing all ice
shelves to the LAIS.</p>
      <p id="d1e301">To compare ice shelf liquid water budgets, we use a liquid-to-solid ratio
(LSR) as a crude proxy for available firn air content and can be estimated
as
            <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M8" display="block"><mml:mrow><?xmltex \hack{\hbox\bgroup\fontsize{9}{9}\selectfont$\displaystyle}?><mml:mi mathvariant="normal">LSR</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mtext>total liquid water</mml:mtext><mml:mspace width="0.25em" linebreak="nobreak"/><mml:mo>(</mml:mo><mml:mi mathvariant="normal">snowmelt</mml:mi><mml:mo>+</mml:mo><mml:mtext>liquid
precipitation</mml:mtext><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mtext>total solid precipitation</mml:mtext><mml:mspace linebreak="nobreak" width="0.25em"/><mml:mo>(</mml:mo><mml:mi mathvariant="normal">snow</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mfrac></mml:mstyle><?xmltex \hack{$\egroup}?><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where areas with LSR <inline-formula><mml:math id="M9" display="inline"><mml:mrow><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> represent an ice shelf that receives more
solid precipitation than liquid water and is therefore less likely to
saturate with liquid water and form melt lakes than areas with LSR <inline-formula><mml:math id="M10" display="inline"><mml:mrow><mml:mi mathvariant="italic">&gt;</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>.</p>
</sec>
<sec id="Ch1.S2.SS4">
  <label>2.4</label><title>Sea ice concentration analysis</title>
      <p id="d1e372">We used 3-hourly meteorological data of sea ice concentration from the
European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis
(Hersbach et al., 2020). These data are available at a
horizontal resolution of about 30 km or 0.28<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>. ERA5 is created by
assimilated satellite and in situ observations into ECMWF's Integrated
Forecast System (IFS). We compare sea ice concentration to the occurrence of
föhn wind events to identify how föhn winds impact sea ice
concentration. We measure the mean sea ice concentration of the ocean 90 km
directly east of each ice shelf (Larsen A, Larsen B, and Larsen C) in the
Weddell Sea. We explore the relationship of summer föhn wind occurrence
and summer (DJF) sea ice concentration using a statistical Pearson
correlation method. When föhn winds are present, we compare the mean of
all sea ice concentration pixels in the designated ice shelf region for all
years from 1979 to 2018.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><?xmltex \opttitle{F\"{o}hn jets and melt}?><title>Föhn jets and melt</title>
      <p id="d1e401">Using RACMO2 historical simulations, informed by a machine learning
algorithm (FöhnDA) that is trained with AWS observations (Laffin et al.,
2021), we identify seven recurring föhn jets or “gap winds” that lead
to high surface melt rates on the eastern AP ice shelves (Fig. 2a). Four
of these jets (CI, MI, WI, MOI) have been studied using airborne
observations and model simulations (Grosvenor et al., 2014; Elvidge et al.,
2015). The remaining three jets (LA, LB, and JP) are, to our knowledge,
identified here for the first time. Overall, winds from the west and
northwest direction lead to increased surface melt rates that can be up to
53 % higher than melt when the wind is from other directions (Fig. 2c)
(van den Broeke, 2005). Additionally, the degree to which föhn winds
impact surface melt on each ice shelf varies depending on föhn jet
existence, location, and wind strength (Wiesenekker et al., 2018). These
variations in föhn jet location may provide insight into why SCAR inlet and
the LCIS remain intact while the LAIS and LBIS have collapsed other than the
significant difference in annual surface temperature (Cook and Vaughan, 2010; Bozkurt et al., 2020; Carrasco et al., 2021).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e406"><bold>(a)</bold> The northern AP showing the RACMO2-simulated wind speed and
direction vectors on 24 January 1995, just before the collapse of the LAIS.
Föhn jet locations are indicated with names. <bold>(b)</bold> RACMO2 annual average
föhn melt hour percent of total melt hours, föhn melt percent of
total melt for each ice shelf from 1980–2002, and percent of total hours föhn
winds occur from 1980–2002. <bold>(c)</bold> RACMO2 melt rate as a function of wind
direction averaged for all ice shelf regions on the AP from 1980–2002.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022-f02.png"/>

        </fig>

      <p id="d1e423">Surface melt production is more pronounced under the influence of föhn
jets, particularly for the LA and LB jets, which produce 35.7 % and
31.8 % more melt, respectively, compared to regions not in the path of a
föhn jet on each ice shelf (Fig. 3). Föhn-induced surface melt
accounts for 42 % of the total annual melt between 1979 and 2002 on the
LAIS and 51 % of total melt on the LBIS but only represents 21 % and
25 % of total melt hours on the LAIS and LBIS (Figs. 2b, 3c). In
locations directly influenced by föhn jets, the mean annual
föhn-induced melt was as high as 61 % on the LAIS and 57 % on the
LBIS of total annual melt. By contrast, föhn-induced melt accounts for
only 25 % of 1979–2002 total melt on SCAR inlet and 17 % on the LCIS.
SCAR inlet is not directly impacted by a föhn jet but still experiences
clear skies and weak föhn influence from the overall descending air
during föhn events. The LCIS is affected by numerous föhn jets (CI,
MI, WI, MOI), accounting for up to 40 % of the total annual melt in
Cabinet and Whirlwind inlets, decreasing with distance east of the AP
mountains. The stark contrast in surface melt amount and fraction caused by
föhn winds on collapsed vs. intact ice shelves implicates föhn melt
as a contributor to the LAIS and LBIS collapses. A clearer picture of the
role of föhn winds emerges after we examine föhn-induced melt extent and
timing.</p>
      <p id="d1e427">The spatial distribution and extent of surface melt influence ice shelf
stability. Surface melt and melt lakes near the ice shelf terminus can lead
to calving front collapse and structural instability for the remaining
portion of the ice shelf (Depoorter et al., 2013; Pollard et al., 2015).
Consistent with this mechanism, the LA and LB föhn jets impact a large
spatial area of the LAIS and LBIS and reach the ice shelf calving fronts
(Fig. 3b). SCAR inlet lacks a strong föhn jet/influence and does not
regularly experience large-scale melt lakes even during high-melt years
(Fig. 1b–f). This helps explain why SCAR inlet is still intact, despite
decreased sea ice buttress force and major structural changes observed after
the collapse of the LBIS (Borstad et al., 2016; Qiao et al., 2020). LCIS on
the other hand is impacted by four major jets and regularly experiences
föhn-induced melt lakes, particularly in Cabinet inlet. However, the
vast size of the LCIS limits the amount of föhn-induced melt at the
terminus. The föhn melt mechanism breaks down by mixing with cold air,
which reduces the intensity of the föhn jets from their peak at the base
of the AP mountains to the calving front (Fig. 3b) (Elvidge et al., 2015;
Turton et al., 2018). Having established that föhn winds significantly
enhanced surface melt overall (Cape et al., 2015; Elvidge et al., 2015;
Datta et al., 2019) and at the crucial calving front of LAIS and LBIS, we
now examine the timing of föhn-induced melt events relative to collapse.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e432"><bold>(a)</bold> RACMO2 average annual melt from 1980–2002. <bold>(b)</bold> RACMO2 average
annual föhn wind-induced melt from 1980–2002. <bold>(c)</bold> RACMO2 percent of
total melt concurrent with föhn wind from 1980–2002. <bold>(d)</bold> RACMO2 time
series of the mean annual surface melt on each ice shelf from 1979–2018.
Dashed vertical lines indicate the year in which each ice shelf collapsed.
Note that the Larsen B graph often overlaps the Larsen A curve.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022-f03.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><?xmltex \opttitle{Coincidence of f\"{o}hn winds with collapse}?><title>Coincidence of föhn winds with collapse</title>
<sec id="Ch1.S3.SS2.SSS1">
  <label>3.2.1</label><title>LAIS</title>
      <p id="d1e468">Three föhn wind events occurred on LAIS between 18 and 27 January 1995,
overlapping with the initial phase of the LAIS collapse that began on
25 January (Fig. 4b) (Rott et al., 1998). These föhn events helped
contribute to the collapse of the ice shelf in two ways: (1) enhanced
surface melt rates caused by the LA jet led to extensive melt lakes across
the ice shelf that possibly promoted large-scale hydrofracture cascades
because of the rapid (days to weeks) nature of collapse (Banwell et al.,
2013), and (2) the west-northwest wind direction actively pushed or melted sea
ice and fast ice away from the calving front, allowing ocean waves to reach
the ice shelf terminus (Rott et al., 1996; Massom et al., 2018). The
föhn wind events prior to and during collapse lasted an average of 3 d each and produced increased surface melt greater than any other 9 d
period from 1979–2018, with mean cumulative melt of 268.5 mm w.e. or
25.2 % of the total annual melt in the 1994/1995 melt season. Total melt
during the 1994/1995 melt season was 127 % higher than an average year (474 mm w.e. yr<inline-formula><mml:math id="M12" 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 the 9 d föhn wind event produced 57 % of the total
melt of an average melt year. Therefore this 9 d föhn-induced melt
event and melt year are clearly anomalous in the observational record. We
also identify a negative correlation between the occurrence of föhn
winds and sea ice concentration on all eastern AP ice shelves (Fig. 5a),
which is more correlated with föhn wind occurrence than air temperature
(Fig. 5b). When föhn winds occur on the AP, sea ice concentration
decreases, which is consistent with other wind types in Antarctica (katabatic
winds) that form perennial wintertime polynya (Fig. 5c–e) (Bromwich and Kurtz, 1984​​​​​​​;
Bozkurt et al., 2018; X. Wang et al., 2021​​​​​​​). At the start of the 9 d föhn
event, sea ice concentration east of the LAIS was at or near 100 %, but by
the time collapse began, sea ice concentration dropped significantly (Fig. 5d–e).</p>
      <p id="d1e483">We next examine the contribution of föhn-generated melt to other
observables implicated in the collapse, namely surface liquid water, melt
lake depth, and melt lake extent (Scambos et al., 2003). We estimate the
spatial extent and depth of melt lakes prior to collapse on the LAIS using
satellite images of melt lake surface area combined with model-simulated
available liquid water volume. The cumulative spatial melt pattern between
18 and 27 January 1995 identifies significant melt on the LAIS ranging from
157–356 mm w.e. (Fig. S2a), varying spatially with the influence of the LA
jet. Satellite imagery of the LAIS during the collapse in progress shows melt
lakes were present (Fig. S3). However because the collapse had already
begun, it is likely many of the lakes had drained or had been altered, so
estimating melt lake extent is not possible. However, Advanced Very
High-Resolution Radiometer (AVHRR) imagery on 8 December 1992 provides
high-resolution cloudless images of the ice shelf taken at the end of a
similar föhn-induced melt event during a year when melt was comparable
to the 1994/1995 melt season. Therefore we consider this melt lake extent
analogous to the 1994/1995 melt season (Fig. 4a). We find the melt lake
surface area was likely between 5.1 %–10.8 % (103–219 km<inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>) of the total LAIS surface area (Fig. S2b). Melt lake surface
area is likely underestimated because the image was taken early in the
1992/1993 melt season and does not easily identify small lakes or river
systems. Liquid water pooling on the ice surface is modulated by the local
topography. If we assume all the available surface liquid water during the
9 d melt period, minus evaporation, runoff, and refreeze, forms lakes that
cover the same estimated surface area as the 1992/1993 melt season, we can
estimate melt lake depth during the initial collapse. We find mean melt lake
depth to be between 1.38–6.86 m depending on lake location and föhn
influence, which exceeds the average lake depth of the LBIS lakes prior to
collapse (1 m) (Banwell et al., 2014) and the modeled lake depth (5 m) that
could lead to large-scale hydrofracture cascades, especially under the
influence of the LA jet (Banwell et al., 2013).</p>
</sec>
<sec id="Ch1.S3.SS2.SSS2">
  <label>3.2.2</label><title>LBIS</title>
      <p id="d1e503">A föhn wind event coincided with the initial LBIS collapse on 9 February 2002, with two events just prior to collapse and three additional events
before complete collapse by 17 March 2002 (Fig. 4c). Föhn events in
the LBIS 2001/2002 melt season were relatively short, averaging less than 24 h per event, and produced melt rates 27 % higher than non-föhn
melt that year and 39 % of the average föhn melt rate in all other
years (Fig. 4e). Similar to the LAIS collapse, the off-coast wind direction
and enhanced surface melt rates during the föhn wind event helped push
sea ice away from the calving front and contributed to surface melt lakes
that led to hydrofracture and collapse (Fig. 5a) (Massom et al., 2018).
Additionally, previous high-melt-rate föhn events such as those in the
1992/1993 and 1994/1995 melt seasons likely preconditioned the LBIS through firn
densification to support melt lake formation, discussed in Sect. 3.3.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e508">RACMO2 time series of surface melt production and cumulative melt
during the Antarctic melt season averaged over the indicated ice shelf. Grey
shading indicates the presence of föhn winds. <bold>(a)</bold> 1992/1993 LAIS. <bold>(b)</bold>
1994/1995 LAIS. <bold>(c)</bold> 1992/1993 LBIS. <bold>(d)</bold> 1994/1995 LBIS. <bold>(e)</bold> 2001/2002 LBIS.
Note the surface melt that occurs after the collapse events indicated by the dashed
vertical lines in <bold>(b)</bold> and <bold>(e)</bold> is an estimate of melt quantity if the ice
shelves did not disintegrate.</p></caption>
            <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022-f04.png"/>

          </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><?xmltex \currentcnt{5}?><?xmltex \def\figurename{Figure}?><label>Figure 5</label><caption><p id="d1e541"><bold>(a)</bold> Scatter plot of ERA5 summer (DJF) sea ice concentration and
RACMO2-identified föhn occurrence hours on each ice shelf from 1979–2018.
<bold>(b)</bold> Scatter plot of ERA5 summer (DJF) sea ice concentration and RACMO2 mean
summer air temperature on each ice shelf from 1979–2018. ERA5 sea ice
concentration at the start of a 9 d föhn melt event <bold>(c)</bold>, in the middle of the
event <bold>(d)</bold>, and on the day of initial phase of the LAIS collapse <bold>(e)</bold>. Grey
arrows indicate the mean föhn wind direction, and the numbered boxes indicate
the sea ice study region associated with the adjacent ice shelf for the
correlation analysis (LAIS (1), LBIS (2), LCIS (3)).</p></caption>
            <?xmltex \igopts{width=227.622047pt}?><graphic xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022-f05.png"/>

          </fig>

</sec>
</sec>
<sec id="Ch1.S3.SS3">
  <label>3.3</label><?xmltex \opttitle{F\"{o}hn melt and the surface liquid water budget}?><title>Föhn melt and the surface liquid water budget</title>
      <p id="d1e574">To better understand the role that föhn winds play in eastern AP ice
shelf surface melt and stability, we intercompare melt climatologies and the
surface liquid water budget of all eastern AP ice shelves (Larsen A, Larsen
B, SCAR inlet, Larsen C). A comparison of collapsed with intact ice shelves
yields a clearer picture of the effects föhn winds have on ice shelf
stability. We identify whether annual surface melt production, melt rate,
melt hours, and surface temperature variables from 1980–2002 are
significantly different from the LBIS (Fig. 6 and corresponding two-tailed
<inline-formula><mml:math id="M14" display="inline"><mml:mi>t</mml:mi></mml:math></inline-formula>-test statistics in Table S2). We compare to LBIS because it was centered
between other ice shelves and was the most recent to collapse. Total surface
melt production on every ice shelf except LAIS differs significantly from
LBIS melt (mean annual melt over the ice shelf area: LAIS – 476 mm w.e.,
LBIS – 479 mm w.e., SCAR – 353 mm w.e., Larsen C (north) – 336 mm w.e., LCIS – 238 mm w.e.) (Fig. 6a), which is expected when we consider the latitudinal
location and mean annual air temperature (Fig. 6d) (Table S2). However,
when föhn-induced melt is subtracted from total melt, the mean annual
surface melt production on SCAR inlet and Larsen C (north) is not
statistically different from the LBIS (LAIS – 337 mm w.e., LBIS – 321 mm w.e.,
SCAR – 286 mm w.e., Larsen(north)​​​​​​​ – 278 mm w.e., LCIS – 203 mm w.e.) (Fig. 6b).
In other words, with the exception of föhn-induced melt (Fig. 6c),
melt production on SCAR inlet and LCIS is statistically indistinguishable
at the 95 % confidence interval from LBIS melt production. Föhn-wind-induced surface melt impacted collapsed ice shelves significantly more
than SCAR inlet and the LCIS, which further defines föhn melt as an
important contributor to the LAIS and LBIS melt budget.</p>
      <p id="d1e584">Our analysis of firn density or available firn pore space identifies
significant differences in ice shelves that have collapsed (LAIS, LBIS) and
those that remain intact (SCAR inlet, LCIS). The liquid-to-solid ratio (LSR)
is a crude proxy for available firn air content, with extant ice shelves
(SCAR inlet, LCIS) having an LSR just above 1 for the period 1980–2002 if all
surface melt is included (Fig. 7a). The LSR for LAIS and LBIS is also just
above 1 for this period, though only if föhn-induced surface melt is
excluded (Fig. 7b). When surface melt caused by föhn wind is included,
LSR exceeds 1.5 throughout extensive regions, including the ice shelf
margins, of the LAIS and LBIS. Thus the collapsed ice shelves experienced
climatological LSRs significantly larger than the SCAR inlet and the LCIS,
mainly due to föhn-induced melt. It is important to note that there is
evidence that the LCIS experiences regions of firn densification through
melt processes; however these regions are mostly focused close to the AP
mountains, likely formed from the location of föhn jets (Hubbard et al.,
2016). This result suggests that föhn-induced melt helped precondition
the LAIS and LBIS to produce extensive melt lakes by long-term firn
densification.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><?xmltex \currentcnt{6}?><?xmltex \def\figurename{Figure}?><label>Figure 6</label><caption><p id="d1e589">Box-and-whisker plots intercompare ice shelves with
RACMO2 simulations from 1980–2002. Annual surface melt production <bold>(a)</bold> all
melt, <bold>(b)</bold> non-föhn melt, <bold>(c)</bold> föhn-induced melt. <bold>(d)</bold> Mean annual air
temperature, <bold>(e)</bold> air temperature without föhn winds, and <bold>(f)</bold> air temperature
during föhn winds. Note the LAIS estimates are hypothetical after 1995f but
are still resolved in the model simulations.</p></caption>
          <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022-f06.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7"><?xmltex \currentcnt{7}?><?xmltex \def\figurename{Figure}?><label>Figure 7</label><caption><p id="d1e620">RACMO2 firn liquid-to-solid ratio or mean annual liquid water
divided by mean annual frozen precipitation from 1979–2002 for <bold>(a)</bold> total
melt and <bold>(b)</bold> all liquid water except for föhn-induced melt. Note the LAIS
estimates are hypothetical after 1995 but are still resolved in the model
simulations.</p></caption>
          <?xmltex \igopts{width=199.169291pt}?><graphic xlink:href="https://tc.copernicus.org/articles/16/1369/2022/tc-16-1369-2022-f07.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Discussion</title>
      <p id="d1e644">The north–south temperature gradient present on the eastern AP ice shelves
contributes to the differences in the ice shelf melt regime (Fig. 6).
Warmer ice shelves can be more vulnerable to long-term thinning and retreat
that accelerate disintegration (Scambos et al., 2003; Morris and Vaughan,
2003). However, the temperature gradient alone does not explain the
substantial increase in surface melt on the LAIS and LBIS relative to more
southerly ice shelves. Only with the addition of föhn-induced surface
melt (Fig. 6c) do the LAIS and LBIS stand out significantly from the other
eastern AP ice shelves (Fig. 6a, b). Temperature gradient, however, could
explain why föhn wind events cause less melt on more southern ice shelves
and may cause super melt events on collapsed ice shelves because temperature
is already elevated on more northern ice shelves prior to the effect föhn
has on temperature. With that in mind, we have examined liquid water
processes on the spatiotemporal scales pertinent to AP ice shelf stability.
For instance, the structural flow discontinuities or suture zones, where
tributary glaciers merge together to form an ice shelf, are mechanically
weak points that impact stability (Sandhäger et al., 2005; Glasser and
Scambos, 2008; Glasser et al., 2009). These suture zones are further
weakened through lateral shear depending on the difference in tributary
glacier flow. All ice shelves in the region are composed of numerous outflow
glaciers sutured together, and while some studies suggest this is a major
contributor to ice shelf instability, only two of the ice shelves have
collapsed (Borstad et al., 2016; Glasser and Scambos, 2008). Further
research suggests that marine accretion of ice on the bottom of the ice
shelves, specifically LCIS, may stabilize these suture zones, which may be
why SCAR inlet has remained intact despite major rift formation (McGrath et
al., 2014; Borstad et al., 2016).</p>
      <p id="d1e647">The timing of surface melt and melt enhanced by föhn winds within the
melt season may also provide insight into the fate of LAIS and LBIS,
including why neither ice shelf collapsed in the anomalously strong 1992/1993
melt season (Fig. 3d). Pore space within the upper snow and firn layers
buffers surface melt before lakes begin to form (Polashenski et al., 2017).
Late-season melt is more likely to form surface melt lakes because meltwater
from the preceding fall, winter, and spring has partially or completely
filled available pore space. On both the LAIS and LBIS, 92 % of surface
melt during the 1992/1993 melt season occurred before 9 January when there
was more pore space to buffer the anomalous surface melt than at the onsets
of their collapses in late January 1995 and early February 2002,
respectively (Fig. 4a, c). Melt lakes were present on both ice shelves
throughout the 1992/1993 melt season, though melt production slowed
dramatically after mid-January 1993 (Scambos et al., 2000). The high melt
rates in late November and early December 1992 on the LAIS were perhaps too
early in the melt season, and after too many years of nominal melt, to form
substantial melt lakes and trigger hydrofracture that season. Nevertheless,
the 1992/1993 melt could have preconditioned the shelf for collapse in January 1995. The LBIS collapse began in February 2002 after the surface melt had
returned to nominal 1980s levels for 6 years. How much pore space had
recovered during those 6 years is unknown, and an important question for
future research. Satellite images of surface melt lakes indicate 11 % of
the ice shelf was covered in melt lakes prior to collapse (Glasser and
Scambos, 2008). However, the preceding melt year (2000/2001) had low melt
and high precipitation, which added additional snow and water mass to the
unstable ice shelf (Leeson et al., 2017).</p>
      <p id="d1e650">Another possible reason collapse of the LAIS and LBIS did not occur in the
1992/1993 melt season or other years prior to collapse was a possible
misalignment of the four prerequisites for rapid collapse theorized by
Massom et al. (2018). An AVHRR image of the LAIS taken on 8 December 1992,
just after a series of major föhn wind events that lead to 252 mm w.e.
of surface melt in the 8 d prior to the image (Fig. 4a), shows
significant melt lakes across the LAIS, which make hydrofracture cascades
possible. However, in the same image, sea ice and melange are shown to be at the
calving front, protecting the front from long-period ocean swells that could
trigger collapse. It may have been too early in the melt season to have
substantial gaps in sea ice, the ocean temperature may have been too cold,
ocean circulation could have help stabilize the sea ice at the front, the
föhn winds speed could have been too weak to push the ice away or may
have been in the wrong direction, all of which could have not allowed a
proper trigger for collapse even though substantial melt ponds were present.
Even if there were years or instances when sea ice extent was low and
substantial melt lakes were present, there could have been a lack of long-period ocean swells that are thought to trigger collapse.</p>
      <p id="d1e653">Regardless of other possible contributors to ice shelf instability not
considered here (e.g., basal melting), föhn-induced surface melt and
associated melt lakes, and the off-coast wind direction likely, played an
important role in pushing the LAIS and LBIS past a structural tipping point.
The estimated surface melt lake depth caused by the 9 d föhn melt
event on the LAIS surpassed a melt lake depth identified by modeled and
satellite-derived lake depths before the collapse of the LBIS (Banwell et
al., 2013, 2014). The LAIS was likely the same thickness
(200 m) or thinner at the time of collapse, so the estimate of critical
surface lake depth for the LBIS that is applied to the LAIS may reflect an
upper limit of melt lake depth of stability for the LAIS. Melt lake depth is
likely underestimated because our estimation only accounts for melt during
the 9 d melt event. Melt before this time period already exceeded an
average melt year by 23 % (118 mm w.e.), so melt lakes probably already
existed.</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <label>5</label><title>Conclusions</title>
      <p id="d1e665">The converging lines of evidence in these results show that observed and
inferred föhn-driven melt is present in sufficient amounts, and at the
right locations and times, to cause extensive surface melt lakes, while the
off-coast föhn wind direction pushed sea ice away from the calving
front. The fact that the LAIS and LBIS collapsed catastrophically within
weeks and not through long-term thinning and retreat like other ice shelves
(Prince Gustav, Wordie) suggests sudden disintegration is anomalous and
requires forcings to match vulnerabilities (Scambos et al., 2003). We
conclude that föhn winds and the associated surface melt played a
significant role in the collapses of the LAIS and LBIS, while extant eastern
AP ice shelves are not likely to collapse from föhn-induced melt and
hydrofracture in today's current climate. We have come to these conclusions
with the following forms of evidence.
<list list-type="bullet"><list-item>
      <p id="d1e670">First, both the LAIS and LBIS are impacted by powerful melt-inducing
föhn jets that affect a large spatial portion of each ice shelf and
reach the ice shelf terminus. Surface melt and melt lakes near the ice shelf
terminus can lead to calving front collapse and structural instability for
the remaining portion of the ice shelves (Depoorter et al., 2013; Pollard et
al., 2015). SCAR inlet and the LCIS are not directly affected by a
föhn jet, are too vast to have any significant effect near the terminus,
or are too far south to experience major melt events.</p></list-item><list-item>
      <p id="d1e674">Second, strong föhn winds were present prior to and during collapse for
the LAIS and LBIS. A series of three föhn events on the LAIS lasted 9 d total and produced over 25 % of the total annual melt for the 1994/1995
melt season, while föhn was present prior to and during the collapse of
the LBIS, which enhanced surface melt rates. Enhanced melt filled new and
existing melt lakes above the melt lake depth observed on the LBIS (1 m) and
modeled lake depth (5 m) that could trigger large-scale hydrofracture
cascades. The föhn winds on both ice shelves actively pushed/melted sea
ice away from the calving front, allowing long-period ocean swells to trigger
large-scale hydrofracture cascades on the LBIS and possibly LAIS,
exacerbated by extensive surface melt that originated from the ice shelf
terminus.</p></list-item><list-item>
      <p id="d1e678">Third, in the absence of föhn-wind-induced melt, the surface liquid
budgets of collapsed and intact ice shelves are climatically similar, which
points to föhn winds as a driver of increased surface melt and extensive
melt lakes on collapsed ice shelves. The additional föhn-induced-melt on
the LAIS and LBIS compared to intact ice shelves helped precondition the
LAIS and LBIS to produce extensive melt lakes by long-term firn
densification.</p></list-item></list></p>
      <p id="d1e681">This research clarifies the roles of föhn-induced melt for collapsed and
extant ice shelves on the eastern AP. Future analyses of these ice shelf
collapse events using advanced firn density models coupled with
ice–ocean–atmosphere coupled simulations may be useful to better understand
the role of surface melt in ice shelf instability. Further, the AP föhn
wind regime has remained stable over the past half-century (Laffin et al.,
2021), which points to enhanced surface temperatures and increased liquid
phase precipitation as more important contributors to the future surface
liquid budget on remaining ice shelves and is an important area of future
research (Bozkurt et al., 2020, 2021). However, changes in
climate drivers such as the Southern Annular Mode (SAM), which influences
the north–south movement of the westerlies in the region, may alter the
temperature and föhn occurrence that will likely enhance surface melt in
locations farther south and therefore make more southern ice shelves more
vulnerable (Abram et al., 2014; Zheng et al., 2013; Lim et al., 2016).
Nevertheless, this research highlights a new understanding behind föhn
melt mechanisms for ice shelf collapse and suggests that SCAR inlet and the
LCIS may remain stable so long as surface liquid water from melt and
precipitation remains within historical bounds.</p>
</sec>

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

      <p id="d1e688">We use SciKit-learn, a freely available Python library for the machine learning algorithm in Laffin et al. (2021) (<uri>https://scikit-learn.org/stable/</uri>, last access: 8 April 2022)​​​​​​​ and this study.</p>
  </notes><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e697">RACMO2 model data are available by request at <uri>https://www.projects.science.uu.nl/iceclimate/models/racmo-model.php</uri> (last access: 8 April 2022)​​​​​​​, however, a subset (2001–2018) of the data are hosted online at <ext-link xlink:href="https://doi.org/10.5281/zenodo.3677642" ext-link-type="DOI">10.5281/zenodo.3677642</ext-link> (Van Wessem and Laffin, 2020).
ERA5 data is freely available for download at <uri>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</uri> (ECMWF, 2022).</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e709">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/tc-16-1369-2022-supplement" xlink:title="pdf">https://doi.org/10.5194/tc-16-1369-2022-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e718">MKL and CSZ designed the study. MvW and SM curated the model
simulation output and surface observations. MKL performed statistical data
analysis. MKL wrote the article with valuable input from all authors.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e724">The contact author has declared that neither they nor their co-authors have any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e730">Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e736">We thank Helmut Rott​​​​​​​ for generously providing detailed
in-person observations of the LAIS months before collapse. We also thank the
Institute for Marine and Atmospheric research Utrecht (IMAU) for providing
RACMO2 output. RACMO2 model data are available by request at
<uri>https://www.projects.science.uu.nl/iceclimate/models/racmo-model.php</uri> (last access: 13 December 2021​​​​​​​);
however, a subset (2001–2018) of the data are hosted online at
<ext-link xlink:href="https://doi.org/10.5281/zenodo.3677642" ext-link-type="DOI">10.5281/zenodo.3677642</ext-link> (Van Wessem and Laffin, 2020). This work utilized the
infrastructure for high-performance and high-throughput computing, research
data storage and analysis, and scientific software tool integration built,
operated, and updated by the Research Cyberinfrastructure Center (RCIC) at
the University of California, Irvine (UCI). The RCIC provides cluster-based
systems, application software, and scalable storage to directly support the
UCI research community (<uri>https://rcic.uci.edu</uri>, last access: 22 March 2022​​​​​​​).</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e750">Matthew K. Laffin was supported by the National Science Foundation (grant no. NRT-1633631​​​​​​​) and NASA
AIST (grant no. 80NSSC17K0540). Charles S. Zender was supported by the DOE BER
ESM and SciDAC programs (grant nos. DE-SC0019278, LLNL-B639667, and LANL-520117). JMVW
acknowledges support by PROTECT and was partly funded by the NWO
(Netherlands Organisation for Scientific Research, VENI grant
VI.Veni.192.083).​​​​​​​</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e756">This paper was edited by Xavier Fettweis and reviewed by two anonymous referees.</p>
  </notes><ref-list>
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