# The Extraordinary March 2022 East Antarctica "Heat" Wave. Part II: Impacts on the Antarctic Ice Sheet

Jonathan D Wille, Simon P Alexander, Charles Amory, Rebecca Baiman, Léonard Barthélemy, Dana M Bergstrom, Alexis Berne, Hanin Binder, Juliette Blanchet, Deniz Bozkurt, et al.

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## To cite this version:

Jonathan D Wille, Simon P Alexander, Charles Amory, Rebecca Baiman, Léonard Barthélemy, et al. The Extraordinary March 2022 East Antarctica "Heat" Wave. Part II: Impacts on the Antarctic Ice Sheet. Journal of Climate, 2024, 37 (3), pp.779-799. 10.1175/JCLI-D-23-0176.1. hal-04289361

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# The extraordinary March 2022 East Antarctica “heat” wave, Part II: impacts on the Antarctic ice sheet

Jonathan D. Wille,ª,ᵇ Simon P. Alexander,ᶜ, d¹ Charles Amory,ª Rebecca Baiman,ᶜ Léonard Barthélemy,>f Dana M. Bergstrom,ᶜ* Alexis Berne,ᶜ Hanin Binderᵇ, Juliette Blanchet,ª Deniz Bozkurt,h¹, h² Thomas J. Bracegirdle,ᵢ Mathieu Casado,ⱼ Taejin Choi,ᵏ Kyle R. Clem,¹ Francis Codron,>f Rajashree Datta,ᶜ Stefano Di Battista,ⁿ Vincent Favier,ª Diana Francis,ⁿ Alexander D. Fraser,d¹ Elise Fourré,ⱼ René D. Garreaud,h², o Christophe Genthon,p Irina V.

Gorodetskaya,q, r Sergi González-Herrero, s, t Victoria J. Heinrich,d² Guillaume Hubert,u Hanna Joos,ᵇ Seong-Joong Kim,ᵏ John C. King,ᵢ Christoph Kittel,ª Amaelle Landais,ⱼ Matthew Lazzara,v¹, v² Gregory H. Leonard,w Jan L. Lieser,d³ Michelle Maclennan,e David Mikolajczyk,v¹ Peter Neff,x Inès Ollivier,y, j Ghislain Picard,ª Benjamin Pohl,z Martin F. Ralph,ª Penny Rowe,ᵇ Elisabeth Schlosser,y Christine A. Shields,δ Inga J. Smith,ε Michael Sprenger,ᵇ Luke Trusel,ζ Danielle Udy,d¹, d³, d⁴ Tessa Vance,d¹ Étienne Vignon,p Catherine Walker,η Nander Wever,e Xun Zouª

File generated with AMS Word template 2.0

<footnote>ª Institut des Géosciences de l'Environnement, CNRS/UGA, Saint Martin d'Hères, France</footnote>

<footnote>ᵇ Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland</footnote>

<footnote>ᶜ Australian Antarctic Division, Channel Highway, Kingston, Tasmania 7050, Australia</footnote>

<footnote>d¹ Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, Hobart 7001, Tasmania, Australia</footnote>

<footnote>d² School of Psychological Sciences, University of Tasmania, Australia</footnote>

<footnote>d³ Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, Hobart 7001, Tasmania, Australia</footnote>

<footnote>d⁴ ARC Centre of Excellence for Climate Extremes, University of Tasmania, Private Bag 129, Hobart 7001, Tasmania, Australia</footnote>

<footnote>* Global Challenges Program, University of Wollongong, Wollongong, NSW 2522, Australia & Centre for Ecological Genomics and Wildlife Conservation, Department of Zoology, University of Johannesburg</footnote>

<footnote>e Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, United States</footnote>

<footnote>f Laboratoire d'Océanographie et du Climat, LOCEAN-IPSL, Sorbonne Université, CNRS, IRD, MNHN, Paris 75252, France</footnote>

<footnote>g Environmental Remote Sensing Laboratory (LTE), École Polytechnique Fédérale de Lausanne, Switzerland</footnote>

<footnote>h¹ Department of Meteorology, University of Valparaíso, Valparaíso, Chile</footnote>
h2 Center for Climate and Resilience Research (CR)2, Santiago, Chile

i British Antarctic Survey, Cambridge, United Kingdom

j Laboratoire des Sciences du Climat et de l'Environnement, CNRS-CEA-UVSQ-IPSL, Gif sur Yvette, France

k Korea Polar Research Institute, Incheon 21990, Republic of Korea

l School of Geography, Environment and Earth Sciences, Victoria University of Wellington, New Zealand

m Meteogiornale, Milan, Italy

n Environmental and Geophysical Sciences (ENGEOS) Lab, Khalifa University, P. O. Box 127788, Abu Dhabi, United Arab Emirates.

o Universidad de Chile, Santiago, Chile

p LMD/IPSL, Sorbonne Université, ENS, PSL Research University, École polytechnique, Institut Polytechnique de Paris, CNRS, Paris France

q CIIMAR - Interdisciplinary Centre of Marine and Environmental Research of the University of Porto, Portugal

r CESAM - Centre for Environmental and Marine Studies, University of Aveiro, Portugal

s WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

t Antarctic Group, Agencia Estatal de Meteorología (AEMET), Barcelona, Spain

u The French Aerospace Lab, ONERA/DPHY, University of Toulouse, France

v1 Antarctic Meteorological Research and Data Center, Space Science and Engineering Center, University of Wisconsin-Madison, Madison, Wisconsin

v2 Department of Physical Sciences, School of Engineering, Science, and Mathematics, Madison Area Technical College, Madison, Wisconsin

w National School of Surveying, University of Otago, Dunedin, New Zealand

x University of Minnesota, Saint Paul, Minnesota

y University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway

z Biogéosciences, CNRS / Université de Bourgogne, Dijon, France

a CW3E, Scripps Institution of Oceanography, San Diego, CA

b NorthWest Research Associates, Seattle, WA

c Dep. of Atmospheric and Cryospheric Sciences, Univ. of Innsbruck, Innsbruck, Austria

d Climate and Global Dynamics Lab, National Center for Atmospheric Research, Boulder, CO, USA

e Department of Physics, University of Otago, Dunedin, New Zealand

f Department of Geography, Pennsylvania State University, University Park, PA
Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole,
MA USA

Corresponding author: Jonathan D. Wille, jonathan.wille@env.ethz.ch
Between March 15-19, 2022, East Antarctica experienced an exceptional heatwave with widespread 30-40° C temperature anomalies across the ice sheet. In Part I, we assessed the meteorological drivers that generated an intense atmospheric river (AR) which caused these record-shattering temperature anomalies. Here in Part II, we continue our large, collaborative study by analyzing the widespread and diverse impacts driven by the AR landfall.

These impacts included widespread rain and surface melt which was recorded along coastal areas, but this was outweighed by widespread, high snowfall accumulations resulting in a largely positive surface mass balance contribution to the East Antarctic region. An analysis of the surface energy budget indicated that widespread downward longwave radiation anomalies caused by large cloud-liquid water contents along with some scattered solar radiation produced intense surface warming. Isotope measurements of the moisture were highly elevated, likely imprinting a strong signal for past climate reconstructions. The AR event attenuated cosmic ray measurements at Concordia, something previously never observed. Finally, an extratropical cyclone west of the AR landfall likely triggered the final collapse of the critically unstable Conger Ice Shelf while further reducing an already record low sea-ice extent.

SIGNIFICANCE STATEMENT

Using our diverse collective expertise, we explored the impacts from the March 2022 heatwave and atmospheric river across East Antarctica. One key takeaway is that the Antarctic cryosphere is highly sensitive to meteorological extremes originating from the mid-latitudes and sub-tropics. Despite the large positive temperature anomalies driven from strong downward longwave radiation, this event led to huge amounts of snowfall across the Antarctic interior desert. The isotopes in this snow of warm airmass origin will likely be detectable in future ice cores and potentially distort past climate reconstructions. Even measurements of space activity were affected. Also the swells generated from this storm helped trigger the final collapse of an already critically unstable Conger Ice Shelf while further degrading sea-ice coverage.

INTRODUCTION

Between March 15-19, 2022, East Antarctica experienced an unprecedented heatwave with widespread 30-40° C temperature anomalies peaking on March 18 where record-high maximum temperatures were observed from coastal regions like Dumont d'Urville to the
high Antarctic Plateau like Dome C and Vostok. In the first part of this study (Wille et al. 2023, Part I hereafter), we analyzed the large-scale drivers of that event, as well as its temperature response over the East Antarctic Ice Sheet. These austral, autumnal March temperature extremes rivaled record-high maximum temperatures observed during peak summer, which is very unusual because the Antarctic climate is usually quickly transitioning to winter conditions in March. In Part I, we established that this event was caused by a very intense and persistent atmospheric river (AR; Ralph et al. 2020) accompanied by a very strong atmospheric ridge throughout the depth of the troposphere (Part I). The high-pressure system was enhanced by and contributed in return to channel subtropical/mid-latitude heat and moisture deep into the Plateau of East Antarctica. As the heatwave event unfolded, relatively heavy snowfalls were observed on the Plateau, rain and modest surface melting were observed along some coastal regions, and the small Conger Ice Shelf collapsed. In this second part of our study, we propose to analyze the impacts of this major event on the environment of East Antarctica.

ARs have been shown to cause strong positive anomalies of temperature and humidity across Antarctica (Gorodetskaya et al. 2020; Turner et al. 2022b) and significant mass balance impacts over the Antarctic ice sheet (Wille et al. 2019, 2022, 2021; Adusumilli et al. 2021; Bozkurt et al. 2018; Gehring et al. 2022; Gorodetskaya et al. 2014; Maclennan et al. 2022b,a). Their impacts on surface mass balance range from surface melt induced by enhanced cloud liquid water content and radiative forcing (e.g. in West Antarctica; Wille et al. 2019; Adusumilli et al. 2021; Djouma and Holland 2021), ice-shelf instability from surface melt, ocean swell processes, and storm surge (Wille et al. 2022; Francis et al. 2021), and intense snowfall events in East Antarctica that control inter-annual precipitation variability (Wille et al. 2021; Gorodetskaya et al. 2014). All of the previously documented AR impacts were observed during this heatwave event, but its duration and its intensity as defined by integrated vapor transport (IVT), unprecedented since the beginning of the satellite era, contributed to make it a record-breaking event in multiple areas.

Globally in this study (Part 1 and Part II), we present a detailed analysis of the heatwave's origins and review the multitude of impacts across East Antarctica in order to encapsulate the event's historical nature. We unravel the various atmospheric processes and impacts interacting with one another during this compound event. This is done by combining numerous different datasets and expertise to provide a comprehensive analysis/overview of the March 2022 East Antarctic heatwave and place this event in context with other extreme
Antarctic climate events observed. In this manuscript, we describe: the various impacts on the ice sheet; the overall impacts on the surface mass balance and near surface firn (Section 3); the causality between the AR/heatwave and the collapse of the Conger Ice Shelf along with other sea ice impacts (Section 4); and the mass balance and paleoclimate dating-relevant signatures on the East Antarctic ice sheet (Section 5). Finally, we discuss the event's influences on cosmic ray and paleoclimate measurements (Section 6), concluding with a discussion on the implications of this heatwave for the future Antarctic climate system and the risks of such an event happening more frequently in a warming climate (Section 7).

## 2. Data and methods

### a. Precipitation, melt, and moisture products

To quantify the precipitation associated with the event, we first used MERRA-2 and ERA5 atmospheric reanalysis data. To determine the total amount of precipitation associated with the AR/heatwave event, we integrate precipitation over the Antarctic Ice Sheet (grounded ice and ice shelves) from March 14-18, and for the whole month of March 2022. We compare this event-attributed precipitation to the mean total precipitation in March from 1980-2021. To improve our estimates, we also used melt and precipitation (rainfall and snowfall) during the event from the high-resolution (35 km horizontal gridding) regional climate model Modèle Atmosphérique Régional (MAR) version 3.11 (referred to as MAR hereafter) in its Antarctic setup (Agosta et al. 2019) with the updates described in Kittel et al. (2021). MAR is driven at its lateral boundaries (pressure, wind speed, temperature, specific humidity), at the top of the troposphere (temperature, wind speed) and at the ocean surface (sea-ice concentration, sea surface temperature) by 6-hourly ERA5 reanalysis, and evolves freely in its inner spatial domain, including the snowpack and firn layer. The polar-oriented model physics allows a detailed representation of the interactions between the Antarctic boundary layer, snowfall, and the firn layer, yielding similar snow accumulation rates at the surface irrespective of the driving reanalysis (Agosta et al. 2019). We use MAR rather than reanalysis data because the model has been more extensively evaluated over the continent and shown to be able to reproduce the observed variability in near-surface climate and surface mass balance at the employed resolution (Mottram et al. 2021).

### b. Vertical profile measurements from Dumont d'Urville
At Dumont d'Urville (DDU) station, the evolution of the AR and of the associated landfall was analyzed using radiosonde data following the processing methodology of Vignon et al. (2019). To characterize the vertical structure of the precipitation during the AR, we used data collected by a Micro Rain Radar (MRR) deployed under a radome at the station in 2015. The MRR provides vertical profiles of K-band (24 GHz) reflectivity and Doppler velocity over the first 3,000 m above ground level (a.g.l.) with a resolution of 100 m. Data were processed following the processing chain for snow hydrometeors developed by Maahn and Kollias (2012) and the attenuation due to the radome was estimated and corrected following Grazioli et al. (2017). Further details on the instrument deployment and data processing are provided in Grazioli et al. (2017) and Durán-Alarcón et al. (2019). In addition, we characterized the evolution of the cloud base altitude during the AR using data from a Vaisala CL-31 ceilometer deployed in 2020.

### c. Surface snow modeling

To analyze the combined impacts of the heatwave and of the heavy precipitation on the firn layer, we applied the physics-based, multi-layer, detailed firn model SNOWPACK (Lehning et al. 2002b,a), with recent modifications for application to the polar ice sheets (Keenan et al. 2021; Wever et al. 2022) to investigate the impact of the heatwave on the surface energy balance and the near-surface firn layers. SNOWPACK calculates, among other properties, firn density, microstructure, and water percolation. Densification of the surface layers explicitly takes into account compaction during drifting snow conditions. Typical layer resolution is on the order of 0.5–2 cm near the surface to 1 m at 100 m below the surface. As in previous studies using SNOWPACK for the firn layer (Keenan et al. 2021; Thompson-Munson et al., 2023), SNOWPACK was forced by the MERRA-2 atmospheric reanalysis precipitation, air temperature, relative humidity, wind speed and incoming long- and shortwave radiation (Gelaro et al. 2017), relying on the SNOWPACK-calculated snow surface temperature as well as the parameterized surface albedo (Groot Zwaaitink et al. 2013) to calculate net short- and longwave radiation. This allows SNOWPACK to consider the energy balance consistently with the state of the firn layer, for example regarding the skin temperature, as well as the heat advection within the firn near the surface. We ran SNOWPACK for the Antarctic Ice Sheet on the MERRA-2 grid, but to reduce computational cost, neighboring grid cells with very similar climatology were grouped (Smith et al. 2020). Spinup of the firn column was achieved by repeating the 1980–2021 period until 150 m deep
firn was reached, or the bottom 3 m consisted of solid ice. We then ran until March 31, 2022, after which we extracted the energy balance components (discussed in Part I), density and firn air content between March 14, 0000 UTC to March 19, 0000 UTC. Based on the standard deviation determined over the 42 years between 1980 and 2021 over these exact 5 days, we calculated standard scores, or Z-scores, denoting how many standard deviations the 2022 value deviated from the 1980-2021 mean.

### d. Satellite observations of Conger Ice Shelf and sea ice

We analyzed available satellite imagery to characterize the style and timing of the breakup of the Conger Ice Shelf. Landsat 7 and 8 data provided the primary means to document the progressive retreat of the ice shelf over the last ~20 years. Sentinel 1 radar images were used to assess conditions immediately preceding and following the ice shelf collapse. Likewise, MODIS images were assessed to further constrain the timing of ice shelf collapse.

Daily maps of sea-ice concentration were obtained from the National Snow and Ice Data Center. From these, the change in sea-ice extent after the passage of the AR was calculated.

Landfast sea ice (fast ice) was manually mapped from composite cloud-free NASA moderate-resolution imaging spectroradiometer (MODIS) imagery following Fraser et al. (2020).

### e. Cosmic ray measurements at Concordia Station

In addition to the measurements described above, we used measurements of neutron spectra made at Concordia Station since December 2015 (Hubert 2016) as part of the Continuous High-altitude Investigation of the Neutron Spectra for Terrestrial Radiation Antarctic Project (CHINSTRAP), supported by the French Polar Agency (IPEV). The neutron spectrometer records the neutron spectrum from the thermal region up to the GeV energy range, using multi-sphere spherical 3He proportional counters placed in spherical moderators with different diameters and consisting of high density polyethylene and metallic shells (Cheminet et al. 2012; Hubert et al. 2019). The interaction of cosmic rays with liquid and vapor water content was analyzed using liquid water paths measured by a radiometer operated at Concordia Station in the framework of the HAMSTRAD project (Ricaud et al. 2010).

### f. Snow temperature and isotope measurements/modeling at Concordia Station

Snow temperature measurements recorded hourly at Concordia with 32 Pt100 sensors were used. The sensors were distributed every 5-10 cm in the first meter and then progressively
more sparsely down to depths of 13 m. The accuracy is better than 0° C according to in-lab
calibration (note, however, that the upper sensor may be subject to solar heating). The
relative distance between the sensors is known with subcentimeter precision (enforced by a
rigid support) but the position of the sensor relative to the surface is only measured once a
year and is subject to centimeter scale changes between measurements.

Vapor isotopic composition measurements were made at Concordia Station, as a surrogate for
precipitation isotopic composition, in order to evaluate the imprint of the warm anomaly.
Indeed, at first order, the vapor isotopic composition shares variability with the isotopic
composition of the local precipitation (Leroy-Dos Santos et al. 2020). Continuous
measurements of the water vapor isotopic composition were performed with a Picarro
analyzer (L2130-i), similarly to Casado et al. (2016) and Leroy-Dos Santos et al. (2021). The
raw data provided by the instrument was then corrected for the humidity response of the
instrument and calibrated against the Vienna Standard Mean Ocean Water (VSMOW) scale
to get absolute values (calibration protocol described in Leroy-Dos Santos et al. 2021).
Typically, these measurements are only used in summer (humidity above 200 ppmv) due to
the challenge of interpretation in winter, when humidity is extremely low (often below 50
ppmv). However, during the AR, humidities up to 3000 ppmv were observed, enabling us to
have an unbiased picture of the isotopic composition anomaly induced by the event.

We combined the vapor monitoring with a virtual firn core model to document how the
atmospheric monitoring applies to ice cores. The virtual firn core generator simulates the
signal recorded of the isotopic composition in the snow (Casado et al. 2020). For each
precipitation event, a layer of snow is added to the simulation of the firn formed before, with
a thickness determined by the precipitation amount and an isotopic composition determined
by the temperature, assuming a linear relationship (Stenni et al. 2017), where both
precipitation amount and temperature are determined from ERA5. Since the precipitation
amount varies, the layers are irregular. The vertical profile was then resampled onto a regular,
millimetric-resolution scale before applying diffusion using a simulated density (Herron and
Langway 1980) and a classical isotopic diffusion scheme (Gkinis et al. 2014). Finally, the
firn core block average was computed at a resolution of 1 cm, similarly to what can be
sampled in an ice core.

Table S1 summarizes type and purpose of reanalysis, model, and instrumental data utilized in
this study. See Fig. 1 in Part I for a map showing station locations.
# 3. Ice sheet impacts

## a. Surface mass balance (SMB) impacts

Precipitation over the ice sheet and ice shelves was investigated using MAR regional climate model as well as global reanalysis products. The March transition to winter meant that most of the AR precipitation across East Antarctica fell in the form of snow, resulting in an overall large net-positive SMB gain. The extent and intensity of the moisture transport resulted in an extreme precipitation event over portions of the interior East Antarctic polar desert (see Turner et al. 2019). Over the whole ice sheet, total precipitation in March 2022 was 306 Gt in MAR (43 Gt anomaly relative to 1980-2021), 298 Gt in ERA5 (43 Gt anomaly), and 326 Gt in MERRA-2 (54 Gt anomaly) (Fig. 1, Fig. 2). The event accounted for 32% of the March 2022 ice sheet-integrated total precipitation and up to 90% of the March 2022 total precipitation in local areas of the East Antarctic Ice Sheet (MAR). MAR, ERA5, and MERRA-2 show similar March total values as well as totals specific to this event.

While the AR generated heavy snowfall across the East Antarctic Ice Sheet, rainfall during the event was located primarily over coastal East Antarctica. Although rain events over coastal stations in East Antarctica typically occur a couple of days per year during March (Vignon et al. 2021), this mid-March rainfall was notably intense. Over the whole ice sheet, there was 2.12 Gt of simulated rainfall in MAR during March 2022 (0.49 Gt anomaly relative to 1980-2021) (Fig. 1).

Such an unusual warm air intrusion over East Antarctica also led to surface melting in coastal East Antarctica. ARs are more often associated with surface melt along West Antarctica and the Antarctic Peninsula (Wille et al. 2022, 2019; Adusumilli et al. 2021), but do occasionally trigger melt in coastal Wilkes Land during summer months. However, observing modest melt during the winter transition season is much more unusual. MAR simulated 0.5 Gt of surface melt during the event, which is insignificant compared to the snowfall accumulation, but the occurrence of melt during the winter transition here is noteworthy. This melt occurrence was confirmed using space-borne AMSR-E (Advanced Microwave Scanning Radiometer for EOS) and AMSR2 passive microwave radiometers applying a simple threshold algorithm (Torinesi et al. 2003; Picard and Fily 2006). The results show an exceptional situation for this time of the year, with up to 9 wet days in some locations and a maximal extent of 44000 km² (Fig. S1). This marks the most extensive melt event recorded beyond February 4 in the
Adélie and Wilkes region since satellite observational records began in 1979 (Datta et al. 2023).

The snow-to-rain transition is well visible in the measurements of the K-band MRR radar deployed at DDU. From 0700 to 1300 UTC March 17, a sharp increase in Doppler velocity magnitude (up to 5 ms⁻¹) was noticeable between 1700 and 2100 m (Fig. 3a). This is a typical signature of the melting layer (Brast and Markmann 2020). Its altitude detected from the radar observations is consistent with the radiosoundings which show that temperatures were greater than 0° C up to 2100 m during the day of March 17 (Fig. 3b). A more detailed analysis of the precipitation event from the set of meteorological measurements at DDU is provided in Appendix B.
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 1: The percent of total March (a) precipitation, (c) rainfall, and (e) melt that occurred between March 16-18 simulated by MAR. (b, d, f) The March monthly anomaly of the</figcaption></figure>
respective value with respect to the March mean from 1980-2021. CIS represents the location
of the Conger Ice Shelf

<figure><img src="image_10.png" /></figure>

Fig. 2: Daily precipitation amounts across the Antarctic Ice Sheet from (a) MERRA-2 and (b) ERA5. (c) Daily surface melt and (d) annual cumulative surface mass balance changes from MAR for Wilkes Land, East Antarctica.

<figure><img src="image_11.png" /></figure>

Fig. 3: (a) Time height plot of the mean Doppler velocity (defined positive upward) at DDU on March 17 2022. The thin gray line is the cloud base height detected by the ceilometer. White spaces correspond to periods during which the radar detects no precipitation signal. (b)
Vertical profiles of temperature from the radiosoundings at 0000 UTC, March 17-18.
Altitudes in y-axes are a.g.l.

## b. Firn impacts

We used the SNOWPACK simulations to investigate how the combination of snowfall, local rainfall, high temperatures and areas of surface melt impacted the near surface firn structure on the ice sheet. When considering the total column firn air content (Fig. 4), we found an increase in the area affected by the heatwave, associated with the added firn air content by snowfall (Fig. 4a and S2a, b, c), particularly near the coast. The firn air content increase over large areas was 10-20 standard deviations above the climatological mean (Fig. 4b). To provide a characterization of the firn structure, we analyzed the surface density, defined as the density of the uppermost 10 cm of the firn (Fig. 5). We found that at the onset of the event, the density in the affected area was mostly within the ±2 standard deviations range compared to climatology (Fig. 5a, c). However, at the end of the event, an area could be identified with substantially higher density than the climatology (Fig. 5b, d). We found the strongest decrease in firn height from wind compaction (Fig. S2j, k, l), suggesting that wind erosion and deposition could have been a major driver in higher than normal surface density. Near Dome C, there was an area with above normal settling rates (Fig. S2g, h, i), which could be attributed to warmer firn temperatures. Also near the coast, where melt occurred, higher than normal settling rates were found. Even though the impact of the higher density on e.g. SMB is likely limited, it may have an impact by a reduced potential for future erosion (thus, better locking in of the snowfall in the firn), but it is also something worthy of consideration when using repeat satellite altimetry to investigate the SMB impacts of such an event.

In contrast to the coast, in the interior of the East Antarctic Ice Sheet temperatures were not high enough for melt to occur. Nevertheless, increases in snow temperature had important impacts. Temperature measurements in the snow at Concordia Station (Fig. 6) showed a sharp increase from March 15, with a maximum of -10.6° C reached on March 18 at 500 UTC for the probe nearest the surface (estimated at about 5 ± 5 cm depth). The penetration of this temperature pulse at depth is attenuated and lags in time due to the thermal diffusivity process. The maximum at 15 cm is -27° C, reached 4 h later and it is -42° C at 75 cm, reached 4 days later. A small maximum was still observed at 1.65 m depth 9 days later, but this maximum was close to the temperatures prevailing before the beginning of the event at that depth. Such rapid and strong change of temperature induced thermal gradients of >100°
C m⁻¹ near the surface. This however, is not exceptional compared with the gradients induced by the diurnal cycle of air temperature at Dome C. However, a bit deeper, at 30 cm, sustained gradients of >60° C m⁻¹ were observed during a few days in March where the norm for the diurnal cycle-generated gradients is typically <50° C m⁻¹ even at summer solstice. The consequence of these combined high temperatures and strong thermal gradients was a strong transfer of vapor between snow layers, downward during the temperature rise and upward when the surface was cooling down. This led to marked snow metamorphism and likely contributed to the changes in isotope composition observed during this event.

<figure><img src="image_12.png" /><figcaption>Fig. 4: (a) Change in firn air content over the period March 14 to March 19 2022, and (b) Z-score of the changes in firn air content relative to March 14 to March 19 1980-2021 climatology. Easting and northing are in the EPSG 3031 coordinate system (Antarctic polar stereographic projection).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Fig. 5: Surface firn density anomaly at (a) the onset of the event on March 14 and (b) at the end of the event on March 19, and associated Z-scores (c, d). Surface density is defined as the density of the uppermost 0.1 m of the firn layer. Easting and northing are in the EPSG 3031 coordinate system (Antarctic polar stereographic projection).</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Fig. 6: Hourly snow temperature measurements at Concordia Station using Pt100 sensors.</figcaption></figure>

# 4. Ice shelf and sea ice impacts

## a. Collapse of the Conger Ice Shelf

In the midst of the heatwave in East Antarctica, the small Conger/Glenzer Ice Shelf (hereafter Conger Ice Shelf) located near the AR landfall site in Wilkes Land collapsed (Fig. 7), thus raising questions whether the two events were connected. Previous ARs have been connected to ice shelf collapses on the Antarctic Peninsula (Wille et al. 2022) but have not yet been linked to triggering ice shelf collapse in East Antarctica. ARs and their associated cyclones, have been found to trigger calving events on the Amery Ice Shelf (Francis et al. 2021) and the Brunt Ice Shelf (Francis et al. 2022) where strong offshore winds generated by the cyclones induce oceanward sea slope, which acts dynamically on the ice shelf front leading to its calving.

In January 2022, the Conger Ice Shelf covered approximately 135 km², connecting ice draining from the Knox Coast of East Antarctica at ~103.5° E to a ~7 km-wide pinning point at the southern edge of Bowman Island (Fig. 7a). Over the preceding 20 years, the western ice margin of Conger Ice Shelf had progressively retreated, first losing contact with a smaller, unnamed island after 2011 and in subsequent years retreating further to the east. This retreat left the Conger Ice Shelf in a structurally weakened state through the progressive loss of buttressing provided by the islands.
During January through early March 2022, the western margin of the ice shelf was bounded
by open water, whereas the eastern margin terminated in a mélange of landfast sea ice (fast
ice) and calved icebergs. On March 5, 2022, Sentinel-1 radar imagery recorded a calving
event at the southern (landward) margin of the ice shelf (Table 1). Within the next two days,
the ice shelf calved a series of icebergs including iceberg C-37 at its northern extent, causing
loss of contact with its pinning point on Bowman Island (Fig. 7b). Subsequent imagery on
March 12, 2022 shows the resulting icebergs being swept westward with the main trunk of
Conger Ice Shelf and landfast sea ice to the east still intact. MODIS imagery from March 14
shows the shelf still intact. Two days later on March 16 2022, MODIS imagery showed that
the entire Conger Ice Shelf had collapsed, producing iceberg C-38, and releasing a large
portion of the landfast sea ice to east of the former shelf. Given available data, we expect that
final collapse of the Conger Ice Shelf was underway by March 15, 2022 (Table 1). By March
19 2022 (Fig. 7d), this ice had mostly been swept westward, leaving a broken mélange of ice
and open water in the place where the former ice shelf resided.

In the weeks before the ice shelf collapsed, radar backscatter measurements from Sentinel-1 did not indicate any surface melting on the Conger Ice Shelf or the adjacent sea ice, suggesting collapse was not triggered by surface melt-induced hydrofracturing. Indeed, available Sentinel-1 backscatter indicated surface melting occurring only after the collapse, likely indicating the presence of AR-associated heat at this site on or before March 17. As such, the structural precursor for ice shelf collapse appears to be the dual calving events at the southern margin on March 5 and two days later on March 7 during which the northern edge of the ice shelf became unpinned from Bowman Island. During this period, a stationary and intense cyclone occurred at the mouth of the ice shelf to the west of the AR landfall location (see Fig. 2b in Part I) driving strong anomalous surface winds. The AR on March 15 made landfall just east of the Conger Ice Shelf (see Fig. 4a in Part I).

Assessment of wave swell and wind data from ERA5 show conditions during these two calving
events were not anomalous. However, ERA5 indicates highly anomalous wave swell and
easterly winds on March 14, 2022 (Fig. 8), the day before the presumed ice shelf collapse (see
Bruno et al. (2020) for assessment of ERA5 wave swells). These data are also supported by
observations on March 14 from Casey Station (~335 km east of Conger) that recorded 37 m.s⁻¹
easterly winds and at the Bunger Hills Station (~140 km southwest of Conger) where 19 m.s⁻¹
winds were recorded, despite being relatively sheltered by the plateau. These anomalous
winds were again associated with the intense, stationary cyclone present to the west of the AR.
The collapse appears to have been triggered by oceanward high sea-surface slopes resulting
from the strong offshore winds (e.g., Francis et al. 2022, 2021). As such, these highly
anomalous easterly winds and the associated wave swell likely acted to dislodge the recently
un-buttressed Conger Ice Shelf, leading to its abrupt demise and the rapid, westward advection
of its remnants (Fig. S3).

<figure><img src="image_15.png" /><figcaption>Fig. 7: Twenty years of progressive Conger Ice Shelf retreat followed by its abrupt and near-total collapse in March 2022 as seen in Sentinel 1 radar imagery. Western ice shelf margins over 2002-2022 determined via Landsat imagery. Blue line indicates full ice shelf extent on February 22, 2022. Panels correspond to image dates with (a) March 5, (b) March 7, (c) March 12, and (d) March 19. Brown lines show coastlines and ice sheet grounding lines.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Fig. 8: (a) Zonal winds and (b) wave swell in the vicinity of Conger Ice Shelf in March 2022 as recorded by ERA5 reanalysis. The gray shading is ± one standard deviation from the 1979-2021 mean.</figcaption></figure>

b. Sea ice effects

During late summer 2022, Antarctic wide sea-ice extent was at a satellite-era record minimum, a record reached on 25 February 2022 (Turner et al. 2022a). This was notably evident around the Conger Ice Shelf which was exposed to the open ocean at the time of its collapse, thus allowing swells to destabilize the ice shelf front (see Massom et al. 2018; Wille
et al. 2022; Teder et al. 2022). For March 2022, sea-ice concentration (SIC) was overall lower than normal years, especially in the Ross Sea where SIC was lower by more than 45% (Fig. 9). Turner et al. (2022b) attributed almost half of the signal of the satellite-era record low Antarctic sea-ice extent in late February 2022 to strong offshore winds in October/November 2021 leading to increased sea-ice loss. Victoria Land was within the footprint of the AR event (see Fig. 4 in Part I) while high temperature anomalies extended across Victoria Land and the western Ross Sea (see Fig. 3 in Part I). After the AR event from March 14-18, sea ice overall increased off the Victoria Land and the Ross Sea. When we eliminate climatological fluctuations, the SIC appears to have increased in the same locations but decreased slightly outside the Ross Sea.

For the region of East Antarctica between the Amery Ice Shelf and the Ross Sea (72° E to 180° E), fast-ice extent prior to the landfall of the AR (March 2 to 16) was already at the lowest on record, at ~56,000 km² (as shown in Fig. 10a; i.e. approximately 46,000 km² below the baseline extent for this time of year (Fraser et al. 2020, 2021, 2022). This further declined following the passage of the AR (March 17 to 31) to ~51,000 km² (i.e., less than half of the climatological average for this region at this time of year; ~109,000 km²). The loss of fast ice between these two time periods occurred largely around the Conger Ice Shelf, as indicated in Fig. 10b. Much of this lost fast ice was generally multi-year, and broke out only infrequently (since 2000, only 2011, 2012, 2014 and 2020 saw near-complete breakouts). In the future, fast ice is less likely to remain throughout the summer without the mechanical support of the Conger Ice Shelf (Fraser et al. 2023).

The effects of the AR-associated event were furthermore, exerted on fast ice beyond the Conger Ice Shelf vicinity. Normally fast ice formation begins during March in the McMurdo Sound in the Ross Sea region, however in 2022 the fast ice stabilization (“freeze-up”) in the Sound was significantly delayed (personal communication, Catherine Kircher, Scott Base Winter Field Support 2021-2022, Antarctica New Zealand). As shown by Fig. S4, the mean 2 m temperature on March 18 was elevated in a region outside the broken temperature hatched area, including Ross Island and McMurdo Sound (see Fig. 1 in Part I). Fig. S4 also shows enhanced near surface temperatures for Ross Island and McMurdo Sound for the period 14-20 March 2022. However, those temperatures were still cold enough for sea ice formation to occur, and the ocean would already have been at the freezing point temperature. The more important factor for fast ice formation and possible freeze-up in McMurdo Sound is the wind
strength and direction. Fig. S4 illustrates that maximum wind speeds were south-westerlies (i.e., off-shore winds) that reached 17 m.s⁻¹ at 0400 UTC on March 16, and 17.6 m.s⁻¹ at 1400 on March 16. Further research is required to determine if the AR-associated event preconditioned McMurdo Sound for low extent throughout the winter.

<figure><img src="image_17.png" /><figcaption>Fig. 9: (a) Sea ice concentration average for March 2022 and (b) sea ice concentration difference between pre (March 12-13) and post (March 19-20) AR event. Sea ice in (a) and (b) is only considered when the area has a March monthly concentration greater than 15%. Data are from the National Snow and Ice Data Center.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Fig. 10: (a) Fast ice anomaly distribution for early-mid March, 2022 (day-of-year range 61-75). The anomaly distribution was calculated by subtracting the long term mean fast ice coverage (2000-2018; from Fraser et al., 2020) in early-mid March from the early-mid March 2022 observations. (b) Change in fast ice between early-mid March, 2022 (pre-AR landfall) and late March-early April, 2022 (post-AR landfall, day-of-year range 76-90). The distribution change map was calculated as the difference between these two maps (day-of-year 76-90 minus day-of-year 61-75). 'Formation' indicates ice present in the day-of-year 76-90 map but not present in the earlier map (and vice versa for 'breakout')</figcaption></figure>

<table><thead><tr><td>Date</td><td>Status of fast ice/ice shelves</td></tr></thead><tbody><tr><td>2022-03-05</td><td>Calving of southern portion of Glenzer Ice Shelf</td></tr><tr><td>2022-03-07</td><td>Remaining part of Glenzer Ice Shelf (abutting Bowman Island) calves</td></tr></tbody></table>
<table><caption>Table 1: Timeline of consequential events regarding the Conger Ice Shelf.</caption><tr><td>2022-03-15</td><td>AR landfall east of Shackleton Ice Shelf. Conger Ice Shelf, plus thick, 2,200 km² fast ice breaks out (including thick, multiyear fast ice); eastern flank of fast ice remains</td></tr><tr><td>2022-03-19</td><td>End of AR event in this region</td></tr><tr><td>2022-03-20</td><td>Complete breakout of remaining fast ice east of Conger Ice Shelf</td></tr><tr><td>~2022-03-26</td><td>Fast ice reforms, composed of broken out fast ice</td></tr></table>

# 5. Influences on markers for past climate reconstruction

## a. Impacts of atmospheric moisture on cosmic rays

One rather unexpected observation during the AR event was the novel observation of a discernible impact of large atmospheric water content on cosmic ray measurements at Concordia Station. For context, the primary cosmic rays (CRs) interact with atmospheric atoms, producing secondary particles such as neutrons, protons, muons, pions or electrons (Grieder 2001). In the lower atmosphere and ground, secondary CRs properties can be impacted by the short-term primary CRs changes (magnetic solar event and solar flare). Secondary cosmic-ray-induced-neutrons are influenced by environmental and systematic effects, in particular the atmospheric pressure, the hydrometric environment close to the instrument (snowfall), and the atmospheric water vapor. Another influence concerns the albedo neutron produced by the interaction of air-shower neutrons with the soil. Hydrogen in soil, air and snow determines the amount of ground albedo neutrons in the sensitive energy range from 1 eV to 10 MeV.

The transport of neutrons through matter (atmosphere, soil, ice ...) is profoundly influenced by the presence of hydrogen, in the form of vapor, liquid and solid water. Hydrogen is uniquely effective in moderating (slowing) neutrons by virtue of its low mass and relatively large elastic scattering cross section, which is a measure of the probability of interacting elastically with a neutron. Elastic collisions with hydrogen and other light nuclei progressively moderate a fast neutron until it is either absorbed by a nucleus or is reduced to a velocity on the order of the thermal motions of surrounding molecules, at which point there is no net change in energy through subsequent collisions.
Fig. 11 presents the uncorrected and corrected total neutron fluxes, and the integrated water vapor and liquid water path (IWV and LWP in kg/m²) during the period from March 1-31, 2022. The IWV and LWP (liquid water path) parameters were extracted from a radiometer operated in the framework of the HAMSTRAD project (Ricaud et al. 2010). Corrections applied to the uncorrected flux take into account influences of atmospheric pressure and the water vapor content (Hubert et al. 2019; Rosolem et al. 2013). A magnetic solar event impacted cosmic ray data on March 14-15, without disturbing data during the AR. The neutron flux initially decreases March 13-14 due to a solar magnetic event. Following a brief stabilization, a second decrease in cosmic ray intensity (~ 15%) was observed from March 15, associated with the onset of the AR event, i.e. the liquid water in median/high altitude. The minimum in neutron flux was correlated with the IWV and LWP peaks. A recovery phase occurred and the neutron flux trended towards its pre-event level with the meteorological conditions (IWV, LWP) returning to their baseline. Results show the importance of neutron attenuation during particle transport mechanisms in a highly saturated atmosphere. Monte-Carlo simulations based on nuclear transport of primary cosmic rays in the atmosphere (for example Geant4 or MCNPx tools) allow for studying physical mechanisms, to identify impact of liquid/vapor water on cosmic ray properties, then to determine physical parameters characterizing ARs (hydrogen quantity, dynamics etc.).

Considering the special environmental condition of the Concordia Station (invariance of the neutron albedo, dry environment), this is the first time attenuation caused by atmospheric liquid water has been observed. The CRs measurements on the Antarctic high plateau constitute a new opportunity to investigate rare meteorological events such as ARs. Cosmic ray activity leaves a beryllium-10 (B10) signature on the snow surface which is used in ice-core dating. A discernable AR influence on B10 measurements could potentially lead the way to past climate water vapor reconstructions.
<figure><img src="image_19.png" /></figure>

Fig. 11: Integrated Water Vapor in kg/m² (top, left axis) and Liquid Water Path in g/m² (top, right axis) during March 2022. Uncorrected and corrected neutron flux in n/cm²/s during March 2022. Corrections applied to the uncorrected flux take into account influences of atmospheric pressure and the water vapor content.

b. Past climate reconstruction impacts

The relatively warm and moist air mass responsible for the widespread snowfall led to profound changes in isotope anomaly measurements with implications for past climate reconstructions. Typical summer maximum values of humidity at Dome C are around 1000 ppmv, yet during this AR, mixing ratios of almost 3000 ppmv were observed (Fig. 12a), underscoring the intensity of this event compared to the typical seasonal cycle. Compared to the levels measured in the beginning of March, the event translates into a vapor isotopic composition anomaly of roughly +28‰ for δ¹⁸O compared to the pre-event baseline (Fig. 12b) and +229‰ for δD (same evolution over time as δ¹⁸O, not shown here), both >10 sigma. The anomaly of d-excess is roughly +10‰ (Fig. 12c, <2 sigma). These observations do not
reproduce the strong decrease of vapor d-excess isotopic composition observed in the Arctic at the North Greenland Eemian Ice Drilling site (NEEM, 77.45° N 51.06° W) during an ARin 2012 (Bonne et al. 2015). Considering the low precision for the d-excess measurement here, it is unclear if the results contradict Bonne et al. 2015, or if the uncertainties of the measurement are hiding the feature.

Given the limited observational records across East Antarctica, past climate reconstructions and ensemble model simulations can provide context to the heatwave and its associated impacts on surface mass balance. The δ¹⁸O anomalies could lead to a tremendous signal in ice cores, but it is not clear if such a short signal will be archived in the ice core isotopic composition. To evaluate the impact of the AR in the isotopic signal recorded by an ice core record, we made use of the Virtual firn core generator (see Data and Methods and Casado et al. 2020) and evaluated the isotopic anomaly imprinted during the event. We observed irregular seasonal cycles in which summer maximum values (dark red) vary a lot from one year to the next, while winter minimum values (blue) are relatively similar (Fig. 13a). During some winters, some warm events were visible during which the isotopic composition could be as high as during summer. The AR event we are studying here (marked with an orange diamond) is the only mid-season (fall/spring) warm event, and the only non-summer event during which values above -45‰ are visible. As such, it is almost 4‰ higher than the previous summer isotopic maximum. This suggests that the anomaly associated with this event should be imprinted in ice cores with high enough resolution both from this event and likely events in the past or future, (see Casado et al. 2020), and for which the impact of stratigraphic noise is mitigated (Fisher et al. 1985; Münch and Laepple 2018).

Finally, we evaluated over which time scale this signal could still be detected by comparing the isotopic anomaly between the virtual firn core with the AR imprinted, and a virtual firn core during which the precipitation amount during the event has been set to 0 (virtually preventing the event to leave any imprint on the isotopic composition). The preservation of the signature of this event depends mainly on the resolution of the sampling (diffusion has a negligible effect here). With a sampling corresponding to the monthly scale, the imprint of the event (4‰) is of the same order of magnitude as the signature of the previous summer period (Fig. 13b). For increasing sample length, an anomaly between 1.5 and 2‰ is imprinted for a sampling resolution lower than 2 years. Above this 2-year threshold for the sampling resolution, the anomaly is diluted and decreases rapidly to drop below the detection limit for sampling interval corresponding to a 5-10 year period (Fig. 13c). For the interpretation of the
isotopic signal in ice core records, this extreme event of only several days can lead to a
significant local positive anomaly over the equivalent of several years of snow accumulation.
Taking into account the local temperature-to-isotope relationship (0.46‰.° C⁻¹: Stenni et al.
2016), it would create a positive bias on the temperature reconstruction made from an ice
core at Dome C of roughly 2-3° C at a two-year resolution. Occurrence of such events can
thus introduce important bias on the interpretation of the isotopic paleothermometer, in
particular if the frequency of occurrence of these events changes for warmer or colder
climatic conditions (see Section 5 in Part I). This analysis relies on the accuracy of the
reanalysis data used to feed the virtual firn core generator. ERA5 products are generally
believed to model relatively well extreme events in extra-tropical regions (Lavers et al. 2022)
and should be the most appropriate tool here to evaluate the impact of the March 2022 AR.

Few ice core records spanning more than recent decades have been drilled in the coastal
region impacted by the AR in March 2022, meaning observations of regional snowfall
accumulation variability are confined largely to satellite era estimates (Turner et al. 2019;
Wille et al. 2021; Vance et al. 2016; Thomas et al. 2017). However, the Law Dome ice core
on the Wilkes Coast preserves the longest and best studied annually resolved regional
snowfall accumulation records with which to compare this event. Law Dome's high annual
accumulation rate over the last two millennia has increased in recent decades from 0.67 to
0.75 meters ice equivalent at the Dome Summit South drill site and has been related to the
negative phase of the Southern Annular Mode (SAM; Marshall et al. 2017) and meridional
circulation in the southern Indian Ocean with links to Pacific variability (Roberts et al. 2015;
Jong et al. 2022; Crockart et al. 2021; Vance et al., 2022). Recent studies in this region have
found a generalized pattern of northerly onshore moisture flow stemming from mid-latitude
lows interacting with downstream blocking highs leading to extreme precipitation events and
temperature anomalies in the Wilkes Coast region (Wille et al. 2021; Pohl et al. 2021; Udy et
al. 2022; Jackson et al. 2022). While this northerly moisture source is broadly analogous to
the March 2022 synoptic pattern, the March 2022 event was clearly intensely impacted by
interactions with low latitude extreme events that led to the transport of heat and moisture
well in excess of a 'normal' extreme precipitation event in the Wilkes Coast region. This led
to precipitation over a few days which is comparable to the annual mean accumulation for
much of this region. Unfortunately, instrumentation failure at the Law Dome AWS prevented
us from analyzing if surface melting may have occurred near the ice core drilling site.

<figure><img src="image_20.png" /><figcaption>Fig. 12: Anomaly in (a) H₂O, (b) δ¹⁸O and (c) d-excess during the atmospheric river event recorded at Dome C. The data presented corresponds to the corrected and calibrated data averaged over 15 minutes. The data was then filtered with a 50 ppmv threshold on humidity (limit of instrument detection). The anomaly was calculated from a baseline corresponding to the mean values before the event (when there was reliable data: from March 1-9, 2022, represented by the dotted line at zero), and the shaded areas correspond to ± 2 standard deviations.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>Fig. 13: Simulation of a virtual firn core at Dome C using ERA5 temperature and precipitation amount starting from (a) 1979 and (b) zoom over the most recent period (since March 2015). (c) Isotopic anomaly imprinted by the atmospheric river event in the virtual firn for samples of increasing thickness representing time scales ranging from 1 month to 10 years.</figcaption></figure>

# 6. Discussion and Conclusions

## a. Summary of Main Results

The historically intense AR/heatwave left a large footprint across a very large swath of the East Antarctic Ice Sheet that will be detectable in future paleoclimate records. Much of the surface warming was driven by a large radiative forcing sustained over several days as clouds laden with liquid water traversed deep into the Antarctic interior. Across the East Antarctic ice sheet, widespread rain and surface melt were observed along coastal regions, but the small mass losses were largely outweighed by high snowfall accumulation on the high plateau giving the event a largely net-positive SMB impact. High swells and intense surface winds generated by the extratropical cyclone associated with the AR helped trigger the final collapse of the already critically unstable Conger Ice Shelf while the storm further reduced sea-ice and fast-ice extent which were already at Antarctic-wide record minimums. The moisture advection associated with the AR had an unforeseen impact on cosmic ray measurements at Concordia Station with potential implications for past climate reconstruction. The relatively warm, humid airmass transported over East Antarctica caused $\delta^{18}$O anomalies higher than the previous highest summer isotopic maximum with the ability to generate large positive temperature biases in past climate reconstruction from ice cores.

## b. Implications for long-term changes in Antarctic ARs and impacts
The anthropogenic climate change signal could be arising in some regions of Antarctica, leading to intensified warming events. This is the case of the 2020 February record-breaking event in the Antarctic Peninsula, which was attributed to anthropogenic factors (González-Herrero et al. 2022) and was also caused by an Antarctic AR. The teleconnection with enhanced tropical convection in the SW Indian Ocean that spawned the eventual AR demands that we connect climate changes in the subtropics/mid-latitudes to changes in extreme weather events over Antarctica. In a warmer world, the increased IVT, duration and frequency of warmer air masses could play an important role in the intensification of these extreme events. This leads us to ask whether long-term changes in Antarctic climate extremes come from local changes over the continent or from remote changes further from the continent. Tropical sea surface temperatures during this event, even under La Niña conditions, were warmer than they were 30 years ago (Hughes et al. 2018). These dynamics suggest that long-term changes in the nature of ARs may be a driver for these extreme events and thus there is a pressing need to better understand drivers of extreme events for a changing climate in Antarctica.

How these teleconnections change will influence our understanding of Antarctic ARs in a globally warmed world has implications for global sea level rise. If and when ARs become warmer and wetter, they will combine with basal melting to further destabilize ice sheets and shelves (Pritchard et al. 2012; Holland et al. 2015). ARs can affect mass balance by bringing both precipitation and heat towards the continent, both of which will increase in future warming scenarios (Espinoza et al. 2018; O'Brien et al. 2021). In the frozen interior of Antarctica, this precipitation will usually take the form of snowfall (adding mass), while at increasingly higher temperatures, especially at the margins, this precipitation can take the form of rainfall (Vignon et al. 2021). Both rainfall and surface melt reduce the albedo of the ice sheet surface (adding energy), initiating an ice-albedo feedback which enhances surface melt further. Additionally, even short-lived surface melt which refreezes can have long-term impacts on the characteristics of firn, by increasing density and even forming subsurface melt features. Over ice sheets, this can impact the path that meltwater takes towards the ice sheet edge, while over the ice shelves at the margins, these changes can lead to calving or even catastrophic hydrofracturing. While the loss of ice shelves (which also melt at the ice-ocean interface) does not immediately lead to sea level rise, the loss of their buttressing effect can accelerate the outflux of mass from the grounded ice sheet and increase sea level rise.
Although this AR event and associated temperature anomalies were extraordinarily higher
than anything previously observed over Antarctica, the autumn occurrence and impact over
the coldest part of the continent lead to heavy snowfall and a highly net-positive SMB event.
In fact, this event helped make 2022 a record breaking snowfall year for the entire AIS that
resulted in an overall mitigation of global sea level rise by around ~0.82 mm (Wang et al.
2023; Clem et al. 2023). However, if an AR of similar magnitude hypothetically occurred
over the Thwaites Glacier region during peak summer, the results could potentially have been
damaging for the glacial stability (Wille et al. 2019). Thus if extreme events like what
occurred in March 2022 happen more frequently in the future, it is important to determine
whether they will be a net-positive for Antarctic SMB and act as a negative feedback for sea-
level rise, or whether they will shift towards net-negative SMB events as is currently
observed over Greenland (Mattingly et al. 2018, Mattingly et al. 2023).

Similarly for sea ice, this AR event's timing influenced its impacts, as it occurred soon after a
satellite-era record minimum sea ice extent (25 February 2022: Turner et al. 2022a). This
meant that the implications for sea ice were not as strong as if it had occurred at a different
time. For example, heavy precipitation falling as snow on sea ice can slow sea-ice formation
by creating an insulating layer or increase sea-ice formation through pushing down the sea ice
surface causing flooding and through increasing the surface albedo, whereas rain on sea ice
can melt snow cover affecting sea-ice longevity (Maksym and Markus 2008; Fichefet and
Maqueda 1999; Serreze and Meier 2019). It will be important in future research to examine
effects on sea ice if AR extreme events become more frequent in the future and if similarly
intense AR events precondition sea ice for prolonged low extent.

Overall, the AR associated with the March 2022 heatwave represents a massive anomaly in
terms of moisture and heat transport in a climate system accustomed to large variability in
temperature. This partially explains why this event is associated with the largest temperature
anomaly ever recorded globally (Blanchard-Wrigglesworth et al. 2023) and helped make
2022 a net positive year for Antarctic mass balance (Datta et al. 2023, Wang et al. 2023).
Properly understanding the variability of AR frequency, impacts, and intensity can help
determine the possible range of impacts from future AR events. Such methodologies could
help address the question of high-impact weather events (e.g., Marsigli et al. 2021), identified
as a scientific priority by the World Meteorological Organization. Understanding and
anticipating their effects is an issue of growing importance under a changing climate, and in
the light of the compounding nature of some major events like the March 2022 AR in East Antarctica.

Acknowledgments.

All authors particularly thank the national polar institutes such as US Antarctic Program, the French Polar Agency (IPEV), and the Australian Antarctic Division (AAD), without which none of the data used here would be available. P. M. Rowe and X. Zou are grateful for funding from the NSF Office of Polar Programs under awards 2127632 and 2229392. R. T. Datta is grateful for funding from NSF Office of Polar Programs award 1952199 and NASA award S000885. The PWRF model is developed and is maintained by the Polar Meteorology Group, Byrd Polar and Climate Research Center (BPCRC), The Ohio State University. PWRF simulations were performed on the San Diego Supercomputing Center's COMET resource through AR Program, Phase II 460001361 and III 4600014294 (State of California, Department of Water Resources). J.D.W. and V.F. acknowledge support from the French Agence Nationale de la Recherche projects ANR-20-CE01-0013 (ARCA). C. Amory and C. Kittel thanks the Computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.–FNRS). M.A. Lazzara and D. Mikolajczyk are grateful for support from the NSF Office of Polar Programs under grants #1924730, 1951720, and 1951603. We also wish to thank Bella Onsi for her assistance. This project received grant funding from the Australian Government as part of the Antarctic Science Collaboration Initiative program. I.J. Smith and G.H. Leonard were supported by grant MFP-UOO1825 from the Marsden Fund Council from government funding, administered by the Royal Society of New Zealand, and the Antarctic Science Platform (MBIE SSIF Programmes Investment contract number ANTA1801). C.A.Shields supported by U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research (BER), Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program, DE-SC0022070, National Science Foundation (NSF) IA 1947282, and NCART NSF Cooperative Agreement No. 1852977. S.P. Alexander was supported by the Australian Antarctic Division's Australian Antarctic Science projects 4387 and 4637. We thank the Australian Bureau of Meteorology and the Australian Antarctic Division for providing the AWS data from the Australian Antarctic stations. S. González-Herrero was supported by the research group ANTALP (Antarctic, Arctic, Alpine Environments; 2017-SGR-1102) funded by the Agència de Gestió d'Ajuts Universitaris i de Recerca of the Government of Catalonia. S.-J.
Kim and T. Choi were supported by the Korea Polar Research Institute (KOPRI) project "Understanding of Antarctic climate and environment and assessments of global influence" (PE23030) funded by the Ministry of Oceans and Fisheries. D.Udy was supported by an Australian Research Training scholarship and an Australian Research Council (ARC) Centre of Excellence for Climate Extremes top-up scholarship. T.Vance acknowledges support from the Australian Antarctic Program Partnership (ASCI000002). D.U and T.V also acknowledge support from ARC DP220100606. I. Ollivier was supported by the project DEEPICE. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955750. L. D. Trusel acknowledges support from NASA Cryospheric Sciences Program (Award 80NSSC20K0888). N. Wever was supported by the Swiss National Science Foundation (SNSF Grant 200020-179130). D. Bozkurt acknowledges support from ANID-FONDAP-1522A0001 and COPAS COASTAL ANID FB210021.

## Data Availability Statement.

We thank Météo France for launching daily radiosoundings at Dumont d'Urville station and for making the data freely accessible at

https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=97&id_rubrique=33

MRR observations benefit from support by the French polar institute IPEV to project CALVA 1013. MRR and ceilometer data at Dumont d'Urville station are freely available upon request to Christophe Genthon (christophe.genthon@cnrs.fr). We thank Claudio Durán-Alarcón for his help with the processing of the MRR data. We are grateful for the use of broadband radiation, radiosonde and HAMSTRAD data over Dome C. Broadband radiation data are from L e l'Univers (INSU)/Centre National de la Recherche Scientifique (CNRS), the Institut polaire français Paul-Emile Victor (IPEV), Météo-France and the Centre National d'Etudes Spatiales (CNES). The data for the atmospheric isotopic composition at Dome C in March 2023 is available upon request. These observations are made possible by the support from the French Polar Institute IPEV through the NIVO2 and ADELISE research programs.

## Appendix A

### Further analysis of weather conditions at Dumont d'Urville

Analysis of the coastal precipitation from remote-sensing measurements and radiosoundings at DDU
Fig. A1 shows the evolution of the event from observations at DDU. On 15 March, the radar samples a period of virga with significant reflectivity in altitude but no signal near the ground (panel a). This situation is common at the beginning of precipitation events at DDU when snowfall sublimates in the dry boundary layer (see dotted purple line in panel c). The gradual decrease in the precipitating cloud base altitude corresponds to the transit of the warm front of the weather system above the station (Jullien et al. 2020).

At 0000 UTC, 16 March the warm front moved to the south of DDU and moderate snowfall reached the ground. The radiosounding (red line, panels c to f) shows a well-marked warm layer between 1500 and 2500 m a.g.l. which coincides with a north-westerly wind jet and likely corresponds to the warm conveyor belt. The latter advects a substantial amount of supercooled liquid water over the ice sheet as the air between 800 and 2500 m a.g.l. is saturated with respect to liquid phase (solid red line in panel d).

Between 0700 and 1300 UTC 17 March, a sharp increase in reflectivity and Doppler velocity magnitude (up to 5 m s⁻¹) is noticeable between 1700 and 2100 m (panels a and b). This is a typical signature of a so-called melting layer (Brast and Markmann 2020) and DDU was therefore under the rain. Albeit infrequent, rainfall can occur at DDU in case of maritime intrusions favored by a blocking anticyclone (Vignon et al. 2021) as during the present event. While the near-surface temperature does not exceed 5° C, a ~2000 m height for the melting layer is surprising as a vertical extrapolation with a moist adiabatic gradient would give a 0° C level below 1000 m. The radiosounding of the 18 March, 0000 UTC, i.e. a few hours after the rain, exhibits temperatures > 0° C at almost all levels below 2000 m (blue line, panel c) and one can notice that the melting layer forms within a local elevated thermal inversion between 1700 and 2000 m (see blue line in panel c) with temperature above 0° C at the top. The thermal inversion is located below a jump in relative humidity (panel d) which corresponds to the base of the cloud whose height oscillates between 2000 and 2700 m around the sounding time in ceilometer measurements (see gray line in panels a and b). As the relative humidity in the cloud is saturated with respect to liquid water, the cloud is probably liquid or with a mixed-phase composition. Liquid-containing clouds being optically thick, the thermal inversion and the local temperature maximum > 0° C could be explained by the absorption of upwelling longwave radiation at cloud base. This process may be particularly intense in the present case as the surface temperature is warm and the air below the cloud very dry.
A third phase of precipitation (snowfall) is detected from 18 March, 1900 UTC to 19 March, 1800 UTC. Using the reflectivity-snowfall relationship derived for DDU conditions in Grazioli et al. (2017) at the lowest radar-gate, the total cumulative snowfall equals 9 mm w.e. over the 5 days (rainfall periods have been excluded). This is not a major contribution for the snow accumulation in the sector as the mean annual snowfall amount (estimated from MRR data at DDU over a 2-year period) is about 780 mm w.e. (Jullien et al. 2020).

<figure><img src="image_22.png" /><figcaption>Fig. A1: Panels a and b show the time height plots of the MRR reflectivity (a) and mean Doppler velocity (defined positive upward, b) at DDU between the 15 and 20 March. Vertical colored lines indicate the official time of the radiosoundings but pay attention that balloons are launched about one hour earlier. The thin gray line is the cloud base height detected by the ceilometer. Panels c to f show the vertical profiles of the temperature, relative humidity, wind speed and direction from radiosoundings. The horizontal gray line indicates the altitude of the highest radar gate. Altitudes in y-axes are a.g.l.</figcaption></figure>

REFERENCES

Adusumilli, S., M. A. Fish, H. A. Fricker, and B. Medley, 2021: Atmospheric River Precipitation Contributed to Rapid Increases in Surface Height of the West Antarctic Ice Sheet in 2019. *Geophys. Res. Lett.*, **48**, e2020GL091076,
https://doi.org/10.1029/2020GL091076.

Agosta, C., and Coauthors, 2019: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes. *The Cryosphere*, **13**, 281–296, https://doi.org/10.5194/tc-13-281-2019.

Blanchard-Wrigglesworth, E., T. Cox, Z. I. Espinosa, and A. Donohoe, 2023: The Largest Ever Recorded Heatwave—Characteristics and Attribution of the Antarctic Heatwave of March 2022. *Geophys. Res. Lett.*, **50**, e2023GL104910, https://doi.org/10.1029/2023GL104910.

Bonne, J.-L., and Coauthors, 2015: The summer 2012 Greenland heat wave: In situ and remote sensing observations of water vapor isotopic composition during an atmospheric river event. *J. Geophys. Res. Atmospheres*, **120**, 2970–2989, https://doi.org/10.1002/2014JD022602.

Bozkurt, D., R. Rondanelli, J. C. Marín, and R. Garreaud, 2018: Foehn Event Triggered by an Atmospheric River Underlies Record-Setting Temperature Along Continental Antarctica. *J. Geophys. Res. Atmospheres*, **123**, 3871–3892, https://doi.org/10.1002/2017JD027796.

Brast, M., and P. Markmann, 2020: Detecting the melting layer with a micro rain radar using a neural network approach. *Atmospheric Meas. Tech.*, **13**, 6645–6656, https://doi.org/10.5194/amt-13-6645-2020.

Bruno, M. F., M. G. Molfetta, V. Totaro, and M. Mossa, 2020: Performance Assessment of ERA5 Wave Data in a Swell Dominated Region. *J. Mar. Sci. Eng.*, **8**, https://doi.org/10.3390/jmse8030214.

Casado, M., and Coauthors, 2016: Continuous measurements of isotopic composition of water vapour on the East Antarctic Plateau. *Atmospheric Chem. Phys.*, **16**, 8521–8538, https://doi.org/10.5194/acp-16-8521-2016.

—, T. Münch, and T. Laepple, 2020: Climatic information archived in ice cores: impact of intermittency and diffusion on the recorded isotopic signal in Antarctica. *Clim. Past*, **16**, 1581–1598, https://doi.org/10.5194/cp-16-1581-2020.

Cheminet, A., V. Lacoste, G. Hubert, D. Boscher, D. Boyer, and J. Poupeney, 2012: Experimental Measurements of the Cosmic-Ray Induced Neutron Spectra at Various Mountain Altitudes With HERMEIS. *IEEE Trans. Nucl. Sci.*, **59**, 1722–1730, https://doi.org/10.1109/TNS.2012.2201500.

Crockart, C. K., and Coauthors, 2021: El Niño–Southern Oscillation signal in a new East Antarctic ice core, Mount Brown South. *Clim. Past*, **17**, 1795–1818, https://doi.org/10.5194/cp-17-1795-2021.

Datta, R.T, J.D. Wille, D. Bozkurt, D.E. Mikolajczyk, K.R. Clem, Z. Yin, and M. Macferrin, 2023: Sidebar 6.1: The Antarctic heatwave of March 2022 [in “State of the Climate in 2022”]. *Bull. Amer. Meteor. Soc.*, **104** (9), S333–S335, https://doi.org/10.1175/BAMS-D-23-0077.1.

Djouma, G., and D. M. Holland, 2021: Atmospheric Rivers, Warm Air Intrusions, and Surface Radiation Balance in the Amundsen Sea Embayment. *J. Geophys. Res. Atmospheres*, **126**, e2020JD034119, https://doi.org/10.1029/2020JD034119.

Durán-Alarcón, C., B. Boudevillain, C. Genthon, J. Grazioli, N. Souverijns, N. P. M. van Lipzig, I. V. Gorodetskaya, and A. Berne, 2019: The vertical structure of precipitation at two stations in East Antarctica derived from micro rain radars. *The Cryosphere*, **13**, 247–264, https://doi.org/10.5194/tc-13-247-2019.

Espinoza, V., D. E. Waliser, B. Guan, D. A. Lavers, and F. M. Ralph, 2018: Global Analysis of Climate Change Projection Effects on Atmospheric Rivers. *Geophys. Res. Lett.*, **45**, 4299–4308, https://doi.org/10.1029/2017GL076968.

Fichefet, T., and M. A. M. Maqueda, 1999: Modelling the influence of snow accumulation
and snow-ice formation on the seasonal cycle of the Antarctic sea-ice cover. *Clim. Dyn.*, **15**, 251–268, https://doi.org/10.1007/s003820050280.

Fisher, D. A., N. Reeh, and H. B. Clausen, 1985: Stratigraphic Noise in Time Series Derived from Ice Cores. *Ann. Glaciol.*, **7**, 76–83, https://doi.org/10.3189/S0260305500005942.

Francis, D., K. S. Mattingly, S. Lhermitte, M. Temimi, and P. Heil, 2021: Atmospheric extremes caused high oceanward sea surface slope triggering the biggest calving event in more than 50 years at the Amery Ice Shelf. *The Cryosphere*, **15**, 2147–2165, https://doi.org/10.5194/tc-15-2147-2021.

Francis, D., R. Fonseca, K. S. Mattingly, O. J. Marsh, S. Lhermitte, and C. Cherif, 2022: Atmospheric Triggers of the Brunt Ice Shelf Calving in February 2021. *J. Geophys. Res. Atmospheres*, **127**, e2021JD036424, https://doi.org/10.1029/2021JD036424.

Fraser, A. D., R. A. Massom, K. I. Ohshima, S. Willmes, P. J. Kappes, J. Cartwright, and R. Porter-Smith, 2020: High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018. *Earth Syst. Sci. Data*, **12**, 2987–2999, https://doi.org/10.5194/essd-12-2987-2020.

—, and Coauthors, 2021: Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability. *The Cryosphere*, **15**, 5061–5077, https://doi.org/10.5194/tc-15-5061-2021.

—, and Coauthors, 2022: Antarctic landfast sea ice: Physical, biogeochemical and ecological significance. *Earth Space Sci. Open Arch.*, 100, https://doi.org/10.1002/essoar.10512682.1.

—, and Coauthors, 2023: Antarctic Landfast Sea Ice: A Review of Its Physics, Biogeochemistry and Ecology. *Rev. Geophys.*, **61**, e2022RG000770, https://doi.org/10.1029/2022RG000770.

Gehring, J., É. Vignon, A.-C. Billault-Roux, A. Ferrone, A. Protat, S. P. Alexander, and A. Berne, 2022: Orographic Flow Influence on Precipitation During an Atmospheric River Event at Davis, Antarctica. *J. Geophys. Res. Atmospheres*, **127**, e2021JD035210, https://doi.org/10.1029/2021JD035210.

Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). *J. Clim.*, **30**, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1.

Gkinis, V., S. B. Simonsen, S. L. Buchardt, J. W. C. White, and B. M. Vinther, 2014: Water isotope diffusion rates from the NorthGRIP ice core for the last 16,000 years – Glaciological and paleoclimatic implications. *Earth Planet. Sci. Lett.*, **405**, 132–141, https://doi.org/10.1016/j.epsl.2014.08.022.

González-Herrero, S., D. Barriopedro, R. M. Trigo, J. A. López-Bustins, and M. Oliva, 2022: Climate warming amplified the 2020 record-breaking heatwave in the Antarctic Peninsula. *Commun. Earth Environ.*, **3**, 122, https://doi.org/10.1038/s43247-022-00450-5.

Gorodetskaya, I. V., M. Tsukernik, K. Claes, M. F. Ralph, W. D. Neff, and N. P. M. Van Lipzig, 2014: The role of atmospheric rivers in anomalous snow accumulation in East Antarctica. *Geophys. Res. Lett.*, **41**, 6199–6206, https://doi.org/10.1002/2014GL060881.

—, T. Silva, H. Schmithüsen, and N. Hirasawa, 2020: Atmospheric River Signatures in Radiosonde Profiles and Reanalyses at the Dronning Maud Land Coast, East Antarctica. *Adv. Atmospheric Sci.*, **37**, 455–476, https://doi.org/10.1007/s00376-020-9221-8.

Grazioli, J., J.-B. Madeleine, H. Gallée, R. M. Forbes, C. Genthon, G. Krinner, and A. Berne, 2017: Katabatic winds diminish precipitation contribution to the Antarctic ice mass
balance. *Proc. Natl. Acad. Sci.*, 201707633, https://doi.org/10.1073/pnas.1707633114.

Grieder, P. K. F., 2001: Chapter 1 - Cosmic Ray Properties, Relations and Definitions. *Cosmic Rays at Earth*, P.K.F. Grieder, Ed., Elsevier, 1–53.

Groot Zwaaitink, C. D., A. Cagnati, A. Crepaz, C. Fierz, G. Macelloni, M. Valt, and M. Lehning, 2013: Event-driven deposition of snow on the Antarctic Plateau: analyzing field measurements with SNOWPACK. *The Cryosphere*, 7, 333–347, https://doi.org/10.5194/tc-7-333-2013.

Herron, M. M., and C. C. Langway, 1980: Firn Densification: An Empirical Model. *J. Glaciol.*, **25**, 373–385, https://doi.org/10.3189/S0022143000015239.

Holland, P. R., and Coauthors, 2015: Oceanic and atmospheric forcing of Larsen C Ice-Shelf thinning. *The Cryosphere*, **9**, 1005–1024, https://doi.org/10.5194/tc-9-1005-2015.

Hubert, G., 2016: Analyses of cosmic ray induced-neutron based on spectrometers operated simultaneously at mid-latitude and Antarctica high-altitude stations during quiet solar activity. *Astropart. Phys.*, **83**, 30–39, https://doi.org/10.1016/j.astropartphys.2016.07.002.

—, M. T. Pazianotto, C. A. Federico, and P. Ricaud, 2019: Analysis of the Forbush Decreases and Ground-Level Enhancement on September 2017 Using Neutron Spectrometers Operated in Antarctic and Midlatitude Stations. *J. Geophys. Res. Space Phys.*, **124**, 661–673, https://doi.org/10.1029/2018JA025834.

Hughes, T. P., and Coauthors, 2018: Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. *Science*, **359**, 80–83, https://doi.org/10.1126/science.aan8048.

Jackson, S. L., T. R. Vance, C. Crockart, A. Moy, C. Plummer, and N. J. Abram, 2022: Climatology of the Mount Brown South ice core site in East Antarctica: implications for the interpretation of a water isotope record. *EGUSphere*, **2022**, 1–36, https://doi.org/10.5194/egusphere-2022-1171.

Jong, L. M., and Coauthors, 2022: 2000 years of annual ice core data from Law Dome, East Antarctica. *Earth Syst. Sci. Data*, **14**, 3313–3328, https://doi.org/10.5194/essd-14-3313-2022.

Jullien, N., É. Vignon, M. Sprenger, F. Aemisegger, and A. Berne, 2020: Synoptic conditions and atmospheric moisture pathways associated with virga and precipitation over coastal Adélie Land in Antarctica. *The Cryosphere*, **14**, 1685–1702, https://doi.org/10.5194/tc-14-1685-2020.

Keenan, E., N. Wever, M. Dattler, J. T. M. Lenaerts, B. Medley, P. Kuipers Munneke, and C. Reijmer, 2021: Physics-based SNOWPACK model improves representation of near-surface Antarctic snow and firn density. *The Cryosphere*, **15**, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021.

Kittel, C., and Coauthors, 2021: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet. *The Cryosphere*, **15**, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021.

Lavers, D. A., A. Simmons, F. Vamborg, and M. J. Rodwell, 2022: An evaluation of ERA5 precipitation for climate monitoring. *Q. J. R. Meteorol. Soc.*, **148**, 3152–3165, https://doi.org/10.1002/qj.4351.

Lehning, M., P. Bartelt, B. Brown, and C. Fierz, 2002a: A physical SNOWPACK model for the Swiss avalanche warning: Part III: meteorological forcing, thin layer formation and evaluation. *Cold Reg. Sci. Technol.*, **35**, 169–184, https://doi.org/10.1016/S0165-232X(02)00072-1.

—, —, —, —, and P. Satyawali, 2002b: A physical SNOWPACK model for the Swiss avalanche warning: Part II. Snow microstructure. *Cold Reg. Sci. Technol.*, **35**, 147–167, https://doi.org/10.1016/S0165-232X(02)00073-3.
Leroy-Dos Santos, C., and Coauthors, 2020: A 4.5 Year-Long Record of Svalbard Water Vapor Isotopic Composition Documents Winter Air Mass Origin. *J. Geophys. Res. Atmospheres*, **125**, e2020JD032681, https://doi.org/10.1029/2020JD032681.

—, and Coauthors, 2021: A dedicated robust instrument for water vapor generation at low humidity for use with a laser water isotope analyzer in cold and dry polar regions. *Atmospheric Meas. Tech.*, **14**, 2907–2918, https://doi.org/10.5194/amt-14-2907-2021.

Maahn, M., and P. Kollias, 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. *Atmospheric Meas. Tech.*, **5**, 2661–2673, https://doi.org/10.5194/amt-5-2661-2012.

Maclennan, M. L., and Coauthors, 2022a: Climatology and Surface Impacts of Atmospheric Rivers on West Antarctica. *Cryosphere Discuss.*, **2022**, 1–25, https://doi.org/10.5194/tc-2022-101.

—, J. T. M. Lenaerts, C. Shields, and J. D. Wille, 2022b: Contribution of Atmospheric Rivers to Antarctic Precipitation. *Geophys. Res. Lett.*, **49**, e2022GL100585, https://doi.org/10.1029/2022GL100585.

Maksym, T., and T. Markus, 2008: Antarctic sea ice thickness and snow-to-ice conversion from atmospheric reanalysis and passive microwave snow depth. *J. Geophys. Res. Oceans*, **113**, https://doi.org/10.1029/2006JC004085.

Marshall, G. J., D. W. J. Thompson, and M. R. van den Broeke, 2017: The Signature of Southern Hemisphere Atmospheric Circulation Patterns in Antarctic Precipitation. *Geophys. Res. Lett.*, **44**, 11,580-11,589, https://doi.org/10.1002/2017GL075998.

Marsigli, C., and Coauthors, 2021: Review article: Observations for high-impact weather and their use in verification. *Nat. Hazards Earth Syst. Sci.*, **21**, 1297–1312, https://doi.org/10.5194/nhess-21-1297-2021.

Massom, R. A., T. A. Scambos, L. G. Bennetts, P. Reid, V. A. Squire, and S. E. Stammerjohn, 2018: Antarctic ice shelf disintegration triggered by sea ice loss and ocean swell. *Nature*, **558**, 383–389, https://doi.org/10.1038/s41586-018-0212-1.

Mattingly, K. S., T. L. Mote, and X. Fettweis, 2018: Atmospheric River Impacts on Greenland Ice Sheet Surface Mass Balance. *J. Geophys. Res. Atmospheres*, **123**, 8538–8560, https://doi.org/10.1029/2018JD028714.

—, J. V. Turton, J. D. Wille, B. Noël, X. Fettweis, Å. K. Rennermalm, and T. L. Mote, 2023: Increasing extreme melt in northeast Greenland linked to foehn winds and atmospheric rivers. *Nature Communications*, 14, 1743, https://doi.org/10.1038/s41467-023-37434-8.

Mottram, R., and Coauthors, 2021: What is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates. *The Cryosphere*, **15**, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021.

Münch, T., and T. Laepple, 2018: What climate signal is contained in decadal- to centennial-scale isotope variations from Antarctic ice cores? *Clim. Past*, **14**, 2053–2070, https://doi.org/10.5194/cp-14-2053-2018.

O'Brien, T. A., and Coauthors, 2021: Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment. *J. Geophys. Res. Atmospheres*, *n/a*, e2021JD036013, https://doi.org/10.1029/2021JD036013.

Picard, G., and M. Fily, 2006: Surface melting observations in Antarctica by microwave radiometers: Correcting 26-year time series from changes in acquisition hours. *Remote Sens. Environ.*, **104**, 325–336, https://doi.org/10.1016/j.rse.2006.05.010.

Pohl, B., and Coauthors, 2021: Relationship Between Weather Regimes and Atmospheric Rivers in East Antarctica. *J. Geophys. Res. Atmospheres*, **126**, e2021JD035294, https://doi.org/10.1029/2021JD035294.

Pritchard, H. D., S. R. M. Ligtenberg, H. A. Fricker, D. G. Vaughan, M. R. van den Broeke,
and L. Padman, 2012: Antarctic ice-sheet loss driven by basal melting of ice shelves. *Nature*, **484**, 502–505, https://doi.org/10.1038/nature10968.

Ralph, F. M., M. D. Dettinger, J. J. Rutz, and D. E. Waliser, eds., 2020: *Atmospheric Rivers*. Springer International Publishing,.

Ricaud, P., B. Gabard, S. Derrien, J.-P. Chaboureau, T. Rose, A. Mombauer, and H. Czekala, 2010: HAMSTRAD-Tropo, A 183-GHz Radiometer Dedicated to Sound Tropospheric Water Vapor Over Concordia Station, Antarctica. *IEEE Trans. Geosci. Remote Sens.*, **48**, 1365–1380, https://doi.org/10.1109/TGRS.2009.2029345.

Roberts, J., and Coauthors, 2015: A 2000-year annual record of snow accumulation rates for Law Dome, East Antarctica. *Clim. Past*, **11**, 697–707, https://doi.org/10.5194/cp-11-697-2015.

Rosolem, R., W. J. Shuttleworth, M. Zreda, T. E. Franz, X. Zeng, and S. A. Kurc, 2013: The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System. *J. Hydrometeorol.*, **14**, 1659–1671, https://doi.org/10.1175/JHM-D-12-0120.1.

Serreze, M. C., and W. N. Meier, 2019: The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic. *Ann. N. Y. Acad. Sci.*, **1436**, 36–53, https://doi.org/10.1111/nyas.13856.

Smith, B., and Coauthors, 2020: Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes. *Science*, **368**, 1239–1242, https://doi.org/10.1126/science.aaz5845.

Stenni, B., and Coauthors, 2016: Three-year monitoring of stable isotopes of precipitation at Concordia Station, East Antarctica. *The Cryosphere*, **10**, 2415–2428, https://doi.org/10.5194/tc-10-2415-2016.

—, and Coauthors, 2017: Antarctic climate variability on regional and continental scales over the last 2000 years. *Clim. Past*, **13**, 1609–1634, https://doi.org/10.5194/cp-13-1609-2017.

Teder, N. J., L. G. Bennetts, P. A. Reid, and R. A. Massom, 2022: Sea ice-free corridors for large swell to reach Antarctic ice shelves. *Environ. Res. Lett.*, **17**, 045026, https://doi.org/10.1088/1748-9326/ac5edd.

Thomas, E. R., and Coauthors, 2017: Regional Antarctic snow accumulation over the past 1000 years. *Clim. Past*, **13**, 1491–1513, https://doi.org/10.5194/cp-13-1491-2017.

Thompson-Munson, M., N. Wever, C. M. Stevens, J. T. M. Lenaerts, and B. Medley, 2023: An evaluation of a physics-based firn model and a semi-empirical firn model across the Greenland Ice Sheet (1980–2020). The Cryosphere, **17**, 2185–2209, https://doi.org/10.5194/tc-17-2185-2023.

Torinesi, O., M. Fily, and C. Genthon, 2003: Variability and Trends of the Summer Melt Period of Antarctic Ice Margins since 1980 from Microwave Sensors. *J. Clim.*, **16**, 1047–1060, https://doi.org/10.1175/1520-0442(2003)016<1047:VATOTS>2.0.CO;2.

Turner, J., and Coauthors, 2019: The Dominant Role of Extreme Precipitation Events in Antarctic Snowfall Variability. *Geophys. Res. Lett.*, https://doi.org/10.1029/2018GL081517.

—, and Coauthors, 2022a: Record Low Antarctic Sea Ice Cover in February 2022. *Geophys. Res. Lett.*, **49**, e2022GL098904, https://doi.org/10.1029/2022GL098904.

—, H. Lu, J. C. King, S. Carpentier, M. Lazzara, T. Phillips, and J. Wille, 2022b: An Extreme High Temperature Event in Coastal East Antarctica Associated With an Atmospheric River and Record Summer Downslope Winds. *Geophys. Res. Lett.*, **49**, e2021GL097108, https://doi.org/10.1029/2021GL097108.

Udy, D. G., T. R. Vance, A. S. Kiem, and N. J. Holbrook, 2022: A synoptic bridge linking sea salt aerosol concentrations in East Antarctic snowfall to Australian rainfall.
Commun. Earth Environ., 3, 175, https://doi.org/10.1038/s43247-022-00502-w.

Vance, T. R., and Coauthors, 2016: Optimal site selection for a high-resolution ice core \hack\newline record in East Antarctica. *Clim. Past*, **12**, 595–610, https://doi.org/10.5194/cp-12-595-2016.

Vignon, É., O. Traullé, and A. Berne, 2019: On the fine vertical structure of the low troposphere over the coastal margins of East Antarctica. *Atmospheric Chem. Phys.*, **19**, 4659–4683, https://doi.org/10.5194/acp-19-4659-2019.

—, M.-L. Roussel, I. V. Gorodetskaya, C. Genthon, and A. Berne, 2021: Present and Future of Rainfall in Antarctica. *Geophys. Res. Lett.*, **48**, e2020GL092281, https://doi.org/10.1029/2020GL092281.

Wang, Y., Q. Wu, X. Zhang, and Z. Zhai, 2023: Record-breaking Antarctic snowfall in 2022 delays global sea level rise. *Sci. Bull.*, https://doi.org/10.1016/j.scib.2023.08.055.

Wever, N., E. Keenan, C. Amory, M. Lehning, A. Sigmund, H. Huwald, and J. T. M. Lenaerts, 2022: Observations and simulations of new snow density in the drifting snow-dominated environment of Antarctica. *J. Glaciol.*, 1–18, https://doi.org/10.1017/jog.2022.102.

Wille, J. D., V. Favier, A. Dufour, I. V. Gorodetskaya, J. Turner, C. Agosta, and F. Codron, 2019: West Antarctic surface melt triggered by atmospheric rivers. *Nat. Geosci.*, **12**, 911–916, https://doi.org/10.1038/s41561-019-0460-1.

—, and Coauthors, 2021: Antarctic Atmospheric River Climatology and Precipitation Impacts. *J. Geophys. Res. Atmospheres*, **126**, e2020JD033788, https://doi.org/10.1029/2020JD033788.

—, and Coauthors, 2022: Intense atmospheric rivers can weaken ice shelf stability at the Antarctic Peninsula. *Commun. Earth Environ.*, **3**, 90, https://doi.org/10.1038/s43247-022-00422-9.

—, and Coauthors, 2023: The extraordinary March 2022 East Antarctica “heat” wave. Part 1: observations and meteorological drivers. *Journal of Climate*, Submitted