# West Antarctic surface melt triggered by atmospheric rivers

Jonathan Wille, Vincent Favier, Ambroise Dufour, Irina V Gorodetskaya,

John R G Turner, Cécile Agosta, Francis Codron

► To cite this version:

Jonathan Wille, Vincent Favier, Ambroise Dufour, Irina V Gorodetskaya, John R G Turner, et al. West Antarctic surface melt triggered by atmospheric rivers. Nature Geoscience, 2019, 12 (11), pp.911-916. 10.1038/s41561-019-0460-1. hal-02402395

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# West Antarctic surface melt triggered by atmospheric rivers

Jonathan D. Wille<fnref n="1" />*, Vincent Favier<fnref n="1" />, Ambroise Dufour<fnref n="1" />, Irina V. Gorodetskaya², John Turner³, Cécile Agosta⁴ and Francis Codron⁵

Recent major melting events in West Antarctica have raised concerns about a potential hydrofracturing and ice shelf instability. These events often share common forcings of surface melt-like anomalous radiative fluxes, turbulent heat fluxes and föhn winds. Using an atmospheric river detection algorithm developed for Antarctica together with surface melt datasets, we produced a climatology of atmospheric river-related surface melting around Antarctica and show that atmospheric rivers are associated with a large percentage of these surface melt events. Despite their rarity (around 12 events per year in West Antarctica), atmospheric rivers are associated with around 40% of the total summer meltwater generated across the Ross Ice Shelf to nearly 100% in the higher elevation Marie Byrd Land and 40-80% of the total winter meltwater generated on the Wilkins, Bach, George IV and Larsen B and C ice shelves. These events were all related to high-pressure blocking ridges that directed anomalous poleward moisture transport towards the continent. Major melt events in the West Antarctic Ice Sheet only occur about a couple times per decade, but a 1-2 °C warming and continued increase in atmospheric river activity could increase the melt frequency with consequences for ice shelf stability.

Recent estimates of the Antarctic mass balance revealed a significant acceleration of ice wastage in the West Antarctic Ice Sheet (WAIS)<fnref n="1" />,², where the grounding line retreat over a retrograde slope of the glacier bedrock³ and changes in the warm water fluxes at the base of the glaciers⁴ have driven the recent glacier mass loss. There is concern that large ice shelf collapses on the Antarctic Peninsula (AP) and WAIS could lead to accelerated glacier velocities due to the removal of the buttressing effects on the surrounding glaciers⁵⁻⁷. The risk increases in the presence of surface meltwater ponding, which acted as a primary driver of the ice shelf collapse for Larsen B⁵,⁶ and Larsen C⁸,⁹. Recently, an atmospheric river (AR) was observed to play a critical role in a winter melting event in the AP¹⁰ and led to melt pond formation on the land-fast sea ice in the Larsen A and Larsen B embayments¹⁰. Here we focus on the Antarctic AR climatology in relationship to the total surface melt budget on the ice shelves.

ARs¹¹,¹² represent narrow bands that contain enhanced water vapour fluxes typically associated with the low-level jet ahead of an extratropical cyclone's cold front¹³ and within the cyclone's warm conveyer belt¹⁴,¹⁵. Despite covering around 10% of the globe's surface, ARs account for more than 90% of the meridional moisture transport in the mid-to-high latitudes¹⁶,¹¹. In the polar regions, an AR's ability to transport heat in addition to moisture becomes consequential for the cryosphere. ARs are important contributors to snowfall accumulation in East Antarctica¹⁸, sea ice decline in the Arctic¹⁹,²⁰ and the decreasing ice mass on the Greenland ice sheet due to the enhanced downward longwave radiation and warm air advection²¹⁻²³. Thus, a single AR can cause both strong snowfall and melt and contribute either positively or negatively to the surface mass balance.

We used an AR detection algorithm developed for Antarctica and tested it on a multireanalysis dataset that covered 1979–2017. Using the results from the detection algorithm applied to the European

Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim), we examined daily surface melt observations from the Scanning Multichannel Microwave Radiometer (SMMR) and the Special Sensor Microwave/Imager²⁴ (SSM/I), and simulated melting magnitudes from a regional climate model (RCM) driven by ERA-Interim and especially adapted for polar regions, Modèle Atmosphérique Régional (MAR)²⁵, to produce the first climatology of AR-related surface melting around Antarctica. To analyse the distribution of the ARs and their associated impacts, we divided Antarctica into four evenly spaced quadrants between 0°, 90° E, 180° and 90° W—the Dronning Maud Land and Princess Elisabeth Land, Wilkes Land and Victoria Land, WAIS, AP and the Weddell Sea region. We also divided Antarctica into halves to account for ARs that might travel between the quadrants (Fig. 1c and Supplementary Figs. 1 and 2).

## Summer surface melting

ARs can lead to extensive surface melt in regions that rarely experience melting conditions. Within interior portions of the Ross Ice Shelf along the base of the Transantarctic Mountains, melting conditions were observed for about 3% of the melt season (December and January) days from 1979 to 2017²⁶, whereas AR conditions in West Antarctica were detected in 1.0–2.5% of all the days, or around 12 distinct AR events per year (Fig. 1a,b). Of the melt days in this region, 30–50% occurred on the day of an AR landfall or within one day after landfall (Supplementary Fig. 3a). Further inland and higher up from the Siple Coast into Marie Byrd Land, the total number of observed melt days decreased (between 10 and 100 melt days from 1979 to 2017), but the percentage related to ARs increased to 60–90%. The simulated meltwater percentages from MAR during the melt season are in good agreement with the melting day percentages from satellite observations in the WAIS region; however, the melt

<footnote n="1">¹Institut des Géosciences de l'Environnement, CNRS/UGA, Saint Martin d'Hères, France. ²CESAM—Centre for Environmental and Marine Studies, Department of Physics, University of Aveiro, Aveiro, Portugal. ³British Antarctic Survey, Cambridge, UK. ⁴Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France. ⁵LOCEAN, Sorbonne Université, Paris, France.
*e-mail: Jonathan.Wille@univ-grenoble-alpes.fr</footnote>
<figure><img src="image_2.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 1** | The yearly percentage of six-hourly AR occurrences to make landfall detected in the multireanalysis dataset and points of interest.</figcaption></figure>

a,b, An AR detection algorithm was run in the WAIS (a) and AP-Weddell (b) quadrants. These quadrants represent the partition of West Antarctica (c). The dashed lines represent the linear regression of the mean AR landfall occurrence between all the reanalysis datasets. The asterisks represent the reanalysis datasets with a horizontal resolution too coarse for the AR detection algorithm, so the AR detection algorithm was run with an integrated water vapour continuity requirement of 3° latitude instead of 1° for these datasets. The reanalysis datasets displayed in a and b are ERA-I, ERA-Interim reanalysis; JRA-55, Japanese 55-year Reanalysis; CFSR, Climate Forecast System Reanalysis; MERRA-2, Modern-Era Retrospective Analysis for Research and Applications, Version 2; NCEP/NCAR V1, National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis Version 1; NCEP/DOE V2, National Centers for Environmental Prediction/Department of Energy Reanalysis Version 2.

extent in MAR extends 300 m higher in Marie Byrd Land compared to the SMMR and SSM/I observations. Overall, from the simulated meltwater magnitudes from MAR, which range from 100 to 500 mm water equivalent over the region during the study period, 20–40% of the total melt on the Ross Ice Shelf and 40–100% of the total melt from 300 m to 1,500 m elevation in Marie Byrd Land occurred during an AR landfall or within 24 hours after the landfall (Fig. 2a).

When ARs make landfall over WAIS, they trigger melting through various physical mechanisms. Intrusions of high integrated water vapour (IWV) (also known as precipitable water) filaments that come from the South Pacific Ocean lead to the development of mixed-phase clouds with high liquid and ice water paths. In the composite spatial and temporal evolution of an AR landfall life cycle, it is

clear that melting is most closely related with the anomalously high cloud liquid and ice water content and therefore downward longwave radiation, which is slightly compensated by the net shortwave radiation with the anomalous sensible heat fluxes (SHFs) generally low (Fig. 2c,d and Supplementary Fig. 4a,b). Mixed-phase clouds act to reduce radiative cooling during the night-time by increasing the downward longwave radiation when the incoming shortwave radiation is small, which has been shown to decrease meltwater refreezing²⁷. Towards the Amundsen Sea coast, a strong surface melting is also intensified by rainfall brought by ARs (Supplementary Fig. 5a,b), which thus decreases the surface albedo and leads to a greater chance of additional melt in the region (Fig. 2c). Further inland, on the Ross Ice Shelf, there is often a slight delay between the initial AR landfall
<figure><img src="image_5.png" /><figcaption>b</figcaption></figure>

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

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

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

<figure><img src="image_9.png" /></figure>

**Fig. 2** | Surface melt associated with ARs and average AR life cycle on landfall. **a,b,** The percentage of total surface meltwater from 1979-2017 according to MAR that occurred when an AR made landfall and within 24 h after landfall during summer (December and January) (**a**) and winter (March–October) (**b**). **c–e,** The AR landfall dates were calculated by running the detection algorithm over West Antarctica (WAIS quadrant + AP–Weddell quadrant) using ERA-Interim data. The panel plots are a composite of anomalous IVT, IWV, surface melt magnitude (upper panels), surface albedo, total cloud water path (TCWP) and downward longwave radiation (DLW) (middle panels), and the net shortwave (SW) radiation and turbulent heat fluxes (lower panels) during AR events near to the Edward VII Peninsula (summer) (**c**), further inland along the Siple Coast (summer) (**d**) and on the Larsen C Ice Shelf (winter) (**e**) using MAR data (Methods). The averaged 3 h anomaly of all the events (light lines) and the 1-day running mean of anomalies (dark lines) are displayed over the 16 days of the initial landfall detection (red triangles). Also shown are the mean values for the 6 days that precede the ARs (dashed lines). The locations for **c** and **d** are plotted on **a**, and the location of **e** is plotted on **b** (black circles). w.e., water equivalent.

and the melting response along with an increase in surface albedo, likely in response to fresh snowfall (Fig. 2d). If the strong integrated vapour transport (IVT) associated with an AR is perpendicular to the coastal mountain ranges near the Amundsen Sea coast, then a resulting föhn wind can enhance the surface SHFs through isentropic drawdown in the leeward regions of Marie Byrd Land and the Edward VII Peninsula²⁸. After the AR makes landfall and dissipates, the residual high precipitable water and resulting mixed-phase clouds continue to cause surface melt for around five days until the airmass is transported away (Fig. 2c,d). Such phenomena were observed during a recent and intense melt event that occurred in January 2016 over the Ross Ice Shelf and Marie Byrd Land²⁹,³⁰, in which a multiday AR event was detected (Supplementary Fig. 6). There appears to be little relation between the AR intensity (measured through the maximum IVT at landfall) and the magnitude of the 2 m temperature anomalies, so the effects of increasing IVT and IWV (not shown) on the temperature anomalies are negligible after the initial 2 m temperature increase on crossing the threshold for AR detection (Supplementary Fig. 7a).

## Winter surface melting

Although summer melting from solar radiation dominates the annual melt budget over the AP³¹, recent winter melting events on the AP account for nearly one-quarter of the annual surface melt

due to local mesoscale climatic features³². From March to October in the years 1979–2017, the total meltwater magnitudes on the AP ice shelves were around 1,000 mm (and exceeded 100 mm during some AR events, as in March 2015 (Supplementary Fig. 8a)) along the base AP mountain range, according to MAR. During these winter months (March–October), ARs and their residual moisture accounted for 40–60% of the melt days on the Larsen B and Larsen C ice shelves along the central portion of the eastern AP and represent 20–40% of the melt magnitude, according to MAR (Supplementary Figs. 2b and 3c). Further south along the outlet glaciers on the Wilkins Coast, 90–100% of winter melt days and melt magnitude are linked with AR activity. An AR signature is also present on the western AP, where 40–80% of winter melt days and similarly melt magnitudes on the Wilkins, Bach and George VI ice shelves occurred during AR conditions. The largest AR-associated winter melt magnitudes appeared in the eastern side of the AP along the base of the mountains and edge of the Larsen C Ice Shelf (Supplementary Fig. 8a,b), probably in response to the föhn wind. Winter melting does appear outside the AP, such as in the Pine Island Glacier region, but at much smaller and insignificant magnitudes compared to those within the AP.

As during the AR-related summer melting conditions in Marie Byrd Land, the presence of a high IWV, cloud liquid water paths
<figure><img src="image_10.png" /></figure>

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

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

**Fig. 3** | Atmospheric variables associated with an AR that contributed to the 25–30 May 2016 melt event. **a–d**, The purple contours show the AR shape at the peak of the event at 12.00 (coordinated universal time) on 25 May 2016 with the spatial distribution of IVT (**a**), IWV (**b**), cloud net radiative forcing at the surface (**c**) and SHF (**d**), all from ERA-Interim. The contours represent the 500 hPa geopotential height contours (**m**) and the dark blue contour and black arrows represent the AR shape and AR IVT (reference magnitude arrow included (ref.)), respectively, according to the original Antarctic AR tracking algorithm¹⁸.

and resulting downward longwave radiation provides considerable melt energy during winter melt events on the AP (Fig. 2e). However, given the local topography and persistent westerly flow, föhn winds that descend the leeward side of the AP mountain ranges play a more crucial role in triggering winter melt events³³. When ARs make landfall perpendicular to the mountain ranges, IVT is orographically lifted over the windward side of the mountains and generates precipitation and latent heat release, which act to enhance the föhn wind on the leeward side. The drying of the leeside downslope wind causes sublimation, which leads to a slightly negative latent heat flux (LHF), but a much larger surface SHF¹⁰,³² (Fig. 2e and Supplementary Fig. 4d) that combines with high downward longwave radiative fluxes (Supplementary Fig. 4c). The increased wind shear mixes the boundary layer and prevents the development of a surface inversion over the ice, which thus increases the efficacy of the downward turbulent fluxes³⁴. Strong SHFs along with significant longwave radiative forcing were observed during two major AR-related melt events over the AP in winter (Fig. 3c,d and Supplementary Figs. 9c,d and 10c,d). The peak surface melting occurred in conjunction with the maximum anomalies in IVT, downward longwave radiation and SHF and dissipated more quickly compared to the summer melting on WAIS (Fig. 2e). In addition, 30–50% of the winter rainfall over the AP ice shelves during the study period was associated with AR activity, which thus enabled further melting through warming the snowpack (Supplementary Fig. 5c). Unlike during the summer AR events in Marie Byrd Land, there is a positive relationship between the AR intensity (measured through the maximum IVT at landfall) and the maximum positive 2 m temperature anomalies (Supplementary Figs. 7b,c).

## Large-scale atmospheric background

Previous research has already highlighted the importance of atmospheric blocking for WAIS surface melting events²⁹,³⁵ and we found that a nearly identical pattern holds true for the meridional moisture transport within ARs that cause melt in Antarctica. The dominant large-scale atmospheric signal during an AR landfall along the entirety of the Antarctic coastline is a blocking ridge upstream of the AR's position (Supplementary Fig. 11). Such a blocking pattern preceded the WAIS January 2016²⁹ and AP 25–30 May 2016 and 22–28 March 2015 melt events.

The observed atmospheric blocking during AR activity in the WAIS quadrant (Fig. 4a) resembles the geopotential height anomalies observed during the Pacific–South American pattern (PSA1)³⁶. The PSA1 pattern has been shown to correlate with melting events in WAIS³⁵ and is related to the El Niño–Southern Oscillation variability as it is coupled with tropical convection anomalies within the western Pacific³⁷,³⁸. We observe a significant positive trend in the AR frequency in the WAIS quadrant from the beginning of the twenty-first century that is similar to an observed increase in blocking activity in the Amundsen Sea during the same period, which was attributed to the PSA1 pattern³⁵ (Fig. 1a and Supplementary Tables 1 and 2). There is also a clear shift of the Amundsen Sea Low to the west towards the Ross Sea during times when ARs made landfall in the WAIS quadrant (not shown), which further indicates a connection with the PSA1 pattern³⁹. The observed geopotential height anomalies are also similar to the anomalies associated with the Interdecadal Pacific Oscillation, which was also known to drive warming on the Ross Ice Shelf¹⁰.
<figure><img src="image_14.png" /></figure>

**Fig. 4** | Corresponding conditions during the detected ARs in WAIS. **a**, Composite 500 hPa geopotential height anomalies. **b**, The 2 m temperature anomalies associated with Ross Ice Shelf melting events (December 1982, December 1988, December 1991, January 2005 and January 2016) during the study period. **c**, Composite 2 m temperature anomalies during the ARs that made landfall in the WAIS quadrant for December and January from 1979 to 2017. **d**, The difference in 2 m temperature anomalies associated with the Ross Ice Shelf melting events (December 1982, December 1988, December 1991, January 2005 and January 2016) in **b** and the AR temperature anomalies in **c** (essentially **b** minus **c**). The geopotential height and 2 m temperatures are from ERA-Interim.

## AR and melt-event temperature anomalies

A composite analysis of the ARs that made landfall in the WAIS quadrant during December and January shows an average 2 m temperature anomaly of -4–5°C in parts of the Ross Ice Shelf and Marie Byrd Land when compared to the 1979–2017 average (Fig. 4c). However, melt events are rare in these interior and higher elevation regions. For example, there were about five major melting events on the Ross Ice Shelf and Marie Byrd Land during the study period and the algorithm detected ARs during three of those (December 1982, January 2005 and January 2016). However, when the temperature anomalies associated with these melting events on the Ross Ice Shelf and Marie Byrd Land (Fig. 4b) are compared against the anomalies associated with all the ARs to reach the WAIS coastline, the difference between them is around 1–2°C (Fig. 4d). Predicted future warming¹¹ could close this difference in temperature anomalies between the ARs and melt events and make AR-related melting more frequent. Owing to the effect of the föhn wind and orientation of the coastline, the temperature anomaly is -7–10°C warmer during the winter melting events on the AP (May 2016 and March 2015) than it is when ARs make landfall on average (Supplementary Fig. 12).

## Implications of AR behaviour changes

ARs are a critical factor in surface melting on the major ice shelves of West Antarctica. ARs trigger surface melting due to the associated increases in IWV and cloud liquid and ice water content, which warm the surface via an increased downward longwave radiation, decreased surface albedo due to rainfall (with a potential impact during the summer (Supplementary Section 3)) and increased SHFs. The last is both due to the heat transport over a cold surface and a combination of the AR forcing adiabatic warming from lee-side föhn winds. Currently, the impact of ARs on the melt is limited, given that melt events occur only about a couple times per decade on the Ross Ice Shelf and Marie Byrd Land. However, we suggest that continued warming⁴² and an increase in AR activity¹³ could lead to an increased melt frequency. The vulnerable ice shelves on the AP⁴⁴ at which AR-related surface melts occur are already observed to have highly saturated firn layers and thus an increased surface melt could lead to an enhanced surface runoff, ponding¹⁵ and hydrofracturing⁴⁴,⁴⁶. Already, two of these ice shelves, Larsen B⁵,⁶ and Wilkins⁴⁷, affected by the AR-related melt have collapsed and progressively disintegrated, respectively. Equally as important, this study highlights the necessity to examine changes in extreme events in addition to changes in the mean climate when studying future changes in the Antarctic surface mass balance.

## Online content

Any methods, additional references, Nature Research reporting summaries, source data, statements of code and data availability and associated accession codes are available at https://doi.org/10.1038/s41561-019-0460-1.

Received: 5 February 2019; Accepted: 30 August 2019;
Published online: 28 October 2019

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## Acknowledgements

This study is part of the PhD project of J.D.W. conducted at the Université Grenoble Alpes. We acknowledge support from the Agence Nationale de la Recherche, projects ANR-14-CE01-0001 (ASUMA), ANR-16-CE01-0011 (EAIIST) and ANR-15-CE01-0015 (AC-AHC2). I.V.G. thanks FCT/MCTES for the financial support to CESAM (UID/AMB/50017/2019) through national funds. C.A. acknowledges support from the Fondation Albert 2 de Monaco under project Antarctic-Snow (2018–2020). C.A. performed the MAR simulations during her Belgian Fund for Scientific Research (F.R.S.-FNRS) research fellowship. Computational resources were provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the F.R.S.-FNRS under grant no. 2.5020.11.

## Author contributions

J.D.W. devised the study and led the writing of the manuscript using input from the co-authors. A.D. supplied the reanalysis datasets and aided in the development of the AR detection algorithm. I.V.G. advised on the physics of ARs, applied the second AR detection algorithm, analysed the data, plotted and provided descriptive text for Fig. 3 and Supplementary Figs. 6, 9 and 10. C.A. performed the MAR simulations and advised on its implementation. V.F., J.T., and F.C. contributed to the development of the study and the preparation of the manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

Supplementary information is available for this paper at https://doi.org/10.1038/s41561-019-0460-1.

Correspondence and requests for materials should be addressed to J.D.W.

Peer review information Primary Handling Editor(s): Heike Langenberg.

Reprints and permissions information is available at www.nature.com/reprints.

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© The Author(s), under exclusive licence to Springer Nature Limited 2019
## Methods

**The AR detection algorithm.** To analyse the ARs, we created a detection algorithm based on six-hourly specific humidity fields integrated between 300 and 900 hPa atmospheric pressure levels to create an IVT term:

$$ \text{IWV} = -\frac{1}{g} \int_{900 \text{ hPa}}^{300 \text{ hPa}} q \, dp \quad (1) $$

where $q$ (kg kg⁻¹) is the specific humidity, $g$ (m s⁻²) is the gravitational acceleration and $p$ is the atmospheric pressure (hPa). As the strong and persistent katabatic wind from the ice sheet plateau reduces the boundary layer specific humidity and is not uniform across the Antarctic coastal regions, removing the lowest section of the atmosphere in the algorithm reduces the risk of AR detection biases in Antarctica¹⁸,⁴⁸. The algorithm searches for grid cells between 45° and 80° S for which the IWV belongs in the 98th percentile of all the monthly climatological IWV values for a particular model grid cell from 1979 to 2017. Using a percentile requirement relative to location accounts for the decreased saturation capacity closer to the Antarctic continent, as demonstrated in a previous Antarctic AR study¹⁸. If contiguous grid cells verify the same criteria, they become a 'meridional segment'. If the meridional segments of anomalously high IWV have no discontinuities greater than 1° and extend at least 20° in the meridional direction, the algorithm outputs the corresponding date into an index of AR dates. If a detected AR reaches the latitude of the coastline or further south, it is classified as an AR landfall.

The AR detection algorithm we designed for Antarctica follows the outline of other AR detection algorithm schemes found in the literature in terms of computation type (conditional for each time step), geometry requirements (length), threshold requirements (relative base of IWV anomalies) and temporal requirements⁴⁹ (time slice). The second AR detection algorithm depicted in Fig. 3 and Supplementary Figs. 6, 9 and 10 is the algorithm described in a previous Antarctic AR study¹⁸ and is also included in the Atmospheric River Tracking Method Intercomparison Project⁴⁹ and verified in a global AR study⁵⁰. Although there are some important differences in how the two algorithms detect ARs, they both agree well in determining the shape and position of the AR approaching the ice sheet. The original AR algorithm developed for Antarctica¹⁸ compares instantaneous IWV against zonal mean IWV with a threshold determined by the maximum atmospheric moisture holding capacity calculated using temperature profiles and applies geometric criteria on the length and width of the detected ARs. The maximum atmospheric moisture holding capacity is expressed as the saturated precipitable water:

$$ W_s = -\frac{1}{g} \int_{900 \text{ hPa}}^{300 \text{ hPa}} q_s(T) \, dp \quad (2) $$

where $q_s(T)$ is the saturation specific humidity (kg kg⁻¹) for the corresponding temperature $T(K)$ at each $p$ level of the ERA-Interim reanalysis according to the Clausius–Clapeyron relation. IVT (kg m⁻¹ s⁻¹), shown in Fig. 3 and Supplementary Figs. 6, 9 and 10, was calculated as:

$$ \text{IVT} = -\frac{1}{g} \int_{900 \text{ hPa}}^{300 \text{ hPa}} q V \, dp \quad (3) $$

where V is the wind vector (m s⁻¹). The algorithm utilized in this study detected 90% of the ARs detected by the original AR algorithm in East Antarctica from 2009 to 2012¹⁸. We also developed an IVT-based AR detection algorithm that generated similar results in the AR detection and surface melt attribution compared to the IWV-based algorithm described in this study (Supplementary Figs. 14 and 15). Currently, the Atmospheric River Tracking Method Intercomparison Project is attempting to enhance the state of AR science through compiling various AR detection algorithms (www.cgd.ucar.edu/projects/artmip/).

**Surface melt data.** The daily surface melt observations were obtained from the dataset²⁴ now extended until 2018 derived from passive microwave radiometer data provided by SMMR and SSM/I at a resolution of 25 × 25 km². The passive microwave signal is sensitive to surface melting, that is, the presence of liquid water in the first metre of the firn, which causes large changes in brightness temperature. The surface melting dataset²⁴ is based on the brightness temperature threshold between dry snow and melting snow, calculated taking into account the variability in dry snow emissivity, and provides the status of melted or not melted for every pixel.

To obtain more detailed information on the melt magnitudes and rainfall, we utilized MAR³¹, a RCM run at a 35 × 35 km² horizontal resolution over the whole Antarctic continent with 120 s time steps and outputs every 6 h (ref. ²⁵). The MAR RCM includes the multilayer snow model Crocus²⁵, which calculates meltwater at the surface when it reaches the melting point in combination with a surplus of energy (a deficit results in refreezing). The presence of meltwater alters the snow characteristics, for example, the type and size of the snow grains and the albedo. Percolation through the snowpack is determined through a tipping bucket method based on snow density. We used 30 snow or ice layers of variable thicknesses from the surface to a 20 m depth⁵³. This version of MAR has been extensively tested over

the Antarctic ice sheet³⁵ and recently utilized by the Ice Sheet Mass Balance Inter-comparison Exercise team².

When comparing the times when an AR was detected according to the detection algorithm against the times of surface melting in the SMMR and SSM/I satellite observations and MAR, we looked 24 h into the future to account for residual moisture after an AR landfall. For the daily surface melt satellite observations, this meant including the day an AR made landfall plus the next day in the time record. For the MAR melt simulations, this meant including the time an AR made landfall plus the next four six-hourly time steps when making the time record. We accounted for instances in which ARs were detected in consecutive time steps to ensure that the same time step was not counted twice. We performed the same procedure for MAR rainfall.

**Reanalysis and RCM datasets.** The datasets used to test the algorithm were ERA-Interim⁵⁴, JRA-55⁵⁵, CFSR⁵⁶, MERRA-2⁵⁷, NCEP/NCAR V1⁵⁸ and NCEP/DOE V2⁵⁹. We also utilized an interpolated version of the JRA-55 over a lower spatial resolution, but all other specifications remained the same. Further information on the reanalyses is given in Supplementary Tables 1 and 2. Given the previously demonstrated abilities of ERA-Interim with regards to moisture biases³⁵, the results in this article were created by applying the AR detection algorithm to IWV calculated by ERA-Interim. We also developed an IVT-based AR detection algorithm (Supplementary Fig. 14) in addition to the IWV-based algorithm used in this study that generated similar results in the AR detection and surface melt attribution (Supplementary Figs. 14 and 15).

As some of the reanalysis datasets (JRA-55 interpolated to lower spatial resolution, NCEP/NCAR V1 and NCEP/DOE V2) have horizontal resolutions that are too coarse for a 1° continuity requirement, a second version of the algorithm with a continuity requirement of 3° was applied to all the reanalysis datasets. The less-strict continuity requirement slightly increased the amount of detected ARs and slightly increased the positive trend in yearly AR occurrences (Supplementary Table 2 and Supplementary Fig. 2).

We performed a composite analysis using the time indices provided by the AR detection algorithm and applied them to the ERA-Interim 2 m temperature, 500 hPa geopotential height and mean sea level pressure. ERA-Interim was also used to analyse various atmospheric variables that can potentially contribute to the melt occurrence, which included cloud vertically integrated liquid and ice water paths, net longwave and shortwave radiative fluxes (used to calculate the cloud net radiative forcing at the surface), skin temperature and SHFs and LHFs.

For the temporal composite plots (Fig. 2c-e), the spatial composite plots (Supplementary Fig. 4) and rainfall plots (Supplementary Fig. 5), we utilized MAR²⁵ IWV, IVT, downward longwave radiation, surface albedo, net shortwave radiation, TCWP (cloud water plus cloud ice paths), SHF, LHF, rainfall and surface melt. For Fig. 2c-e, an AR event begins when an AR makes landfall (set to day 0) and is examined over the preceding six days and the following ten days after landfall. If a new AR detection occurs within the following ten days after the landfall, then it is considered as the same event. For each event, the anomaly is computed with regard to the mean value observed during the preceding six days of AR detection. In Fig. 2c,d, the composite of the AR events comes from using AR landfalls in the WAIS quadrant. In Fig. 2e, we only included ARs that made landfall on the AP and did not use a predefined quadrant.

## Data availability

The MAR data are publicly available from https://doi.org/10.5281/zenodo.3362277. The daily surface melt satellite observations are publicly available at http://pp.ige-grenoble.fr/pageperso/picardgh/melting/

## Code availability

The scripts for the AR detection algorithms discussed in this paper are available at https://github.com/jwille45/Antarctic-lab. Additional versions of the algorithm will be made available as they are completed.

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