Föhn-induced melting over Larsen C modulated by atmospheric river shape, direction and landfall location

Domain: biology
Published: 2026-04-03
Editorial outcome: major_revision

Abstract
Recent decades have seen record-high temperatures on the Antarctic Peninsula (AP) due to combined atmospheric rivers (ARs) and föhn warming. While ARs frequently enhance föhn, not all events cause surface warming over the entire Larsen C Ice Shelf (LCIS). Using high-resolution Polar WRF simulations, we examine the relationship between ARs and föhn over the AP during austral summers and identify four distinct AR shapes associated with föhn-induced surface warming over the LCIS: zonal-perpendicular, zonal-like, convex, and concave. Zonal-like ARs associated with coupled low-high pressure systems and convex ARs linked to blocking highs produce strong föhn warming across the entire LCIS, primarily affecting its northern and southern sectors, respectively. In contrast, zonal-perpendicular and concave ARs generate moderate-to-weak warming, owing to either weaker AR intensity or AR curvature. Although downward shortwave radiation dominates surface warming, enhanced moisture suppresses its increase from föhn-induced cloud clearance while enhancing downward longwave radiation near mountain gaps. Sensible heat flux also contributes substantially along the mountain foothills. As ARs intensify under climate change, their interaction with föhn over the AP can critically influence the future stability of coastal ice shelves.

Introduction
The Antarctic Peninsula (AP) has warmed by around 3°C over the past 60 years, despite a brief cooling since the late 1990s1,2. Notably, the northern AP (north of 70S) experienced a summer warming rate of +0.15 °C per decade from 1959 to 2021, accompanied by multiple extreme warming events3. Extreme warming events like this have intensified surface melting over AP ice shelves, contributing to past collapses4,5 and threatening the last remaining eastern AP ice shelf—the Larsen C Ice Shelf (LCIS; see Fig. 1 for a map of the study area). The LCIS experienced an exceptionally long melt season in 2019/20206 and a second peak in surface melting across the AP in February 20223, both associated with record-high surface temperatures. It is also one of the most vulnerable Antarctic ice shelves to potential surface-melt-induced collapse by 2100, as the projected southward shift of AP temperatures beyond the melt threshold7 could expose the LCIS to conditions similar to those preceding the Larsen A (1995) and Larsen B (2002) collapses. Any collapse of the LCIS could trigger increased discharge of grounded ice due to the loss of its substantial buttressing capacity, which would result in sea-level rise8.

On the synoptic scale, atmospheric rivers (ARs) can transport substantial amounts of heat and moisture from lower latitudes to the AP. They often form within Rossby waves3,9,10, which can be excited by anomalous convection over the central tropical Pacific10,11. For example, upstream Rossby wave packets along the polar jet in the Southern Ocean can induce low-pressure anomalies over the Amundsen and Bellingshausen Seas and high-pressure anomalies east of the Drake Passage, promoting AR landfall over the AP12. Although ARs are rare events in Antarctica, they are the primary contributors to intense water vapor transport in this region and play an important role in its surface mass balance9. For instance, approximately 60% of the calving events over the AP between 2000 and 2020 were triggered by AR-induced surface melting9. Moreover, surface warming over the LCIS can also be significantly influenced by local orographic forcing, and especially from the föhn effect13. For example, föhn-induced warming between 1979 and 2018 contributed up to 18% of the annual surface melting over the western region of the LCIS, where it meets the base of the AP13,14. The föhn effect can enhance temperatures and surface melting over the LCIS through several physical processes, including isentropic drawdown, latent heat release from windward-side condensation, mechanical mixing, and radiative heating15. Also, the western region of the LCIS frequently experiences low-level föhn jets, which are narrow gap flows associated with föhn conditions14,16. Föhn jets originating from the eastern AP are typically warmer than the ambient air on the leeside, and have the potential to enhance surface warming14,17.

Over the AP, the co-occurrence of ARs and föhn events can intensify surface melting and further compromise ice shelf stability, as demonstrated in recent studies that examined several combined cases3,18–21. However, not all AR events lead to widespread föhn warming or extensive surface melting. This is especially the case for the southern LCIS19, which has been less impacted by recent extreme heat waves over the northern AP that are related to the compounding effects of ARs and föhn22,23. For example, the magnitude of föhn-related temperature increases is primarily influenced by the upstream atmospheric conditions that are responsible for the associated “flow-over” regime13,15,17,23. Yet how these conditions are influenced by synoptic circulation patterns such as ARs is poorly understood19,23.

A key unanswered question is therefore how varying characteristics of ARs influence föhn mechanisms over the LCIS and, consequently, its surface warming patterns. For instance, given that föhn-induced warming over the LCIS is a consequence of upstream flow encountering the steep orographic barrier of the AP17,19,23,24, the characteristics of ARs—such as their shape (e.g., straight or curved), vertical profile (e.g., height of AR core), direction, landfall location, frequency, strength, and duration—are major considerations that could affect this. Furthermore, the abundant moisture transport associated with ARs influences cloud radiative effects, a key driver of surface melting24. However, while prior AR studies have primarily examined their frequency over the AP25, substantial uncertainty remains regarding how AR shape, vertical structure, and landfall location influence föhn-induced surface warming over the LCIS. This study addresses this research gap using high-resolution Polar WRF (PWRF) simulations (up to 1.2 km) to examine 37 austral-summer AR events over the Antarctic Peninsula from 2001–2022 (Fig. 1a,b), providing a representative sample of AR characteristics. This resolution adequately resolves terrain-modified flow over the complex AP topography, which is essential for capturing the occurrence and intensity of föhn-induced surface warming17 and is poorly represented in coarse-resolution global reanalysis datasets (~25–50 km)3,21,26.

Results
Identification of distinct AR shapes associated with föhn-induced warming over the LCIS

Using the gridded Enhanced AR Scale dataset that we developed for this study27,28, which is based on integrated vapor transport (IVT) fields calculated using ERA5 output (see Methods and Supplementary Materials), we identified 38 AR events during austral summers from 2001 to 2022 that occurred over the AP region29 (Supplementary Table S1). Each of the 38 ARs had a ranking that varied from 1 to 3 on the Enhanced AR Scale, which for Antarctica indicates moderate to strong AR events (Supplementary Table S1)28,30. Of these events, 37 made landfall on the upwind/western side of the AP, while one AR event made landfall on the LCIS and was subsequently removed from the study. Thus, our following study focuses on the 37 events that made landfall upstream (Supplementary Fig. S1 and Table S1). These included four events with sequences of back-to-back landfalling ARs, referred to as AR-family events (Supplementary Fig. S1 and Table S1). Consistent with previous studies, 8 out of these 37 events occurred after 2020, reflecting the recent increase in reported extreme weather events over the AP region3,19,20,22. All 37 AR events are simulated using PWRF with a horizontal resolution of up to 1.2 km (comprising a total of 3120 h of PWRF simulations).

Initial examination of 6-hourly IVT and mean sea level pressure (MSLP) snapshots based on PWRF outputs from all 37 AR events29 reveals five distinct AR shapes over the AP region (Figs. 1c and Supplementary Fig. S1, Tables S1 and S2): zonal-perpendicular (promoting perpendicular airflow toward the mountain range upstream of the LCIS), zonal-like, concave (cyclonically curved), convex (anticyclonically curved), and fully meridional (nearly straight north-south airflow). Note that most of the AR events exhibited more than one shape due to changes in regional circulation. To focus specifically on the co-occurrence of ARs and föhn events over the LCIS associated with the different AR shapes, we subsequently select a subset of AR events that resulted in the LCIS experiencing föhn-induced surface warming and classify them according to the Enhanced AR Scale28 (see Methods and Supplementary Materials for details; Supplementary Table S1). Among the 37 AR events, 23 events exhibited identifiable föhn-induced warming periods over the LCIS (comprising 1165 h of PWRF simulations; hereafter referred to as “AR-föhn events”), including three events classified as AR1, fourteen as AR2, and six as AR3 (the latter including three prolonged AR-family events). Not surprisingly, the AR-föhn events associated with föhn-induced warming over the LCIS tended to make landfall over the central or southern AP (Supplementary Fig. S1). The average warming over the LCIS associated with the 23 AR-föhn events, defined as the increase in hourly maximum 2 m temperature (T2max) after removal of the diurnal cycle, varies from around 4.1 °C for AR1 events to 5.6 °C for AR2 events and 7.1 °C for AR3 events (Supplementary Table S1).

Next, cluster analysis31 of the 500 hPa geopotential height and IVT fields from the simulations of the 23 AR-föhn events (1165 h) is used to investigate the distinct AR shapes associated with these, which confirms the importance of zonal-perpendicular, zonal-like, convex, and concave AR shapes (Fig. 2). The fully meridional AR shape was discarded from our analysis due to its infrequent occurrence and relatively smaller impact on the LCIS29. For this AR shape, the regional flow is mostly parallel to the AP, so the flow component perpendicular to the AP23, and hence the development of föhn, is relatively limited over the LCIS. Consequently, the results presented hereafter focus on four AR shapes: zonal-perpendicular, zonal-like, convex, and concave (Fig. 2).

The cluster analysis shows that AR-föhn events over the LCIS are most frequently driven by zonal-perpendicular (45%) and zonal-like AR shapes (21%), associated with a coupled low-high pressure system (Fig. 2a,b,e,f). These are followed by convex AR shapes (19%) associated with an upwind blocking high over the AP (Fig. 2c,g), and concave AR shapes (15%) that are more likely influenced by a low-pressure system near Alexander Island (Fig. 2d,h). The linkage between AR shape and synoptic circulation is stronger for zonal-perpendicular and convex AR shapes, and weaker for zonal-like and concave AR shapes. Moreover, the IVT cluster analysis indicates that zonal-perpendicular and zonal-like AR shapes that trigger the föhn-induced warming over the LCIS often make landfall over the central AP (Fig. 2e,f), convex AR shapes favor landfall over the southern sector (Fig. 2g), whereas concave AR shapes tend to make landfall further north (Fig. 2h). Furthermore, concave AR shapes exhibit the highest IVT magnitudes (>650 kg m-1 s-1; Fig. 2h), followed by zonal-like AR shapes (>500 kg m-1 s-1; Fig. 2f) and convex AR shapes (>400 kg m-1 s-1; Fig. 2g). Zonal-perpendicular AR shapes exhibit the lowest IVT among all AR shapes (~200 kg m-1 s-1; Fig. 2e). We also note that while the cluster analysis may not fully distinguish all AR shapes, it clearly separates convex and zonal-like AR shapes, likely due to their distinct circulation characteristics.

Föhn-induced Surface Warming Patterns Over the LCIS Associated with Each AR shape

We then analyze hourly rates of change in 2 m temperature (T2) from the simulations based on the IVT clusters to investigate the surface warming characteristics over the LCIS associated with each AR shape (Fig. 3a-d). Note that the diurnal cycle is removed to better isolate the combined impacts of ARs and föhn-induced warming. The zonal-perpendicular, zonal-like, and convex AR shapes all produce distinct spatial patterns of föhn-induced warming across the LCIS; zonal-like ARs yield the strongest warming in the north, followed by convex ARs with stronger warming in the south. In particular, the zonal-perpendicular AR shape, despite being theoretically expected to produce the strongest föhn warming over the LCIS due to near-perpendicular flow across the mountain barrier, results in only moderate temperature increases (see case study below). The zonal-like AR shape was associated with the highest positive temperatures over the northern LCIS along the edge of the AP mountain chain. This is partly due to the curvature of the AP towards the east at its northern tip (Fig. 1), which means that zonal-like AR shapes making landfall over this region have relatively strong flow impinging nearly perpendicular to the mountain barrier (see their19 Fig. S4), implying more flow-over regimes and subsequently stronger föhn warming. Despite having lower IVT intensity than concave and zonal-like ARs, the convex AR shape still favors strong warming over the southern sector (Fig. 2), highlighting the unique impact of this AR shape (see case study below). The concave AR shape, however, produces the weakest föhn warming, confined to the northern LCIS near the ice-shelf edge. This is likely owing to its landfall location and the curved geometry of the AP, which enhance mountain blocking and thereby limit the extent of föhn warming.

To further diagnose the surface energy balance (SEB) associated with these warming patterns, we analyzed hourly averaged SEB components based on IVT clusters. While this approach does not directly quantify the rate of temperature change during ARs and föhn-induced warming, it helps elucidate the dominant warming patterns over the LCIS. Consistent with previous studies showing that isentropic drawdown, mechanical mixing, and radiative heating are key processes driving föhn-induced warming over the AP17, sensible heat flux (SHF) together with downward SW and downward longwave (LW) radiation dominate the SEB over the LCIS (Fig. 3)13,19,32, i.e., more important than other components like latent heat flux (Supplementary Fig. S2a-d). Downward SW radiation exhibits a strong diurnal cycle, with peak values more than 800 W m-2 and the hourly mean contribution of ~220 W m-2 (Fig. 3e-h). Net SW radiation shows an overall positive contribution (Supplementary Fig. S2e-h). In contrast, downward LW radiation provides a more sustained contribution to surface warming over the LCIS (∼300 W m-2; Fig. 3i-l); however, net LW radiation remains negative due to enhanced upward LW emission from the warming surface (Supplementary Fig. S2i–l).

For the zonal-perpendicular AR shape (i.e., moderate warming over the entire LCIS), downward SW radiation contributes ~230 W m-2 over the northern LCIS, particularly near the ice-shelf edge adjacent to the ocean, while SHF provides a more moderate contribution that is largely confined to the mountain foothills (Fig. 3e,m). Downward LW radiation contributes more than 270 W m-2 to the southern LCIS near the mountain foothills (Fig. 3i). For the zonal-like AR shape (i.e., strong warming over the entire LCIS, particularly over the north), downward SW contributions are similar to those in the zonal-perpendicular AR shape, while downward LW radiation and SHF are stronger, likely responsible for the warming pattern (Fig. 3f,j,n). The substantial SHF contribution for the zonal-like AR shape near the mountain foothills over the northwestern LCIS (exceeding 100 W m-2; Fig. 3n) is likely driven by enhanced isentropic drawdown and mechanical mixing in response to stronger föhn-induced downslope winds13. For the convex AR shape (i.e., strong warming over the entire LCIS, particularly over the south), downward SW radiation exhibits relatively high values, particularly over the northern LCIS (exceeding 250 W m-2; Fig. 3g). Downward LW radiation is moderate (~240 W m-2; Fig. 3k). In addition, the strongest SHF occurs near the mountain base over the central and southern LCIS (approaching 200 W m-2; Fig. 3o), forming stripe-like patterns that may be associated with gap flows. Finally, for the concave AR shape (i.e., weak warming over the northern LCIS), moderate downward SW values occur over the northern LCIS, although values are higher over its northern sector (up to 270 W m-2) compared to its southern sector (less than 200 W m-2; Fig. 3h). Downward LW radiation is the strongest among all AR shapes (up to 300 W m-2; Fig. 3l). Moreover, SHF values associated with this AR shape are generally low across the LCIS (~20 W m-2) but increase substantially over its northern edge (~80 W m-2; Fig. 3p). Although SEB components exhibit relatively high values, indicating a warmer background state, AR-induced föhn warming has limited contribution under the concave AR shape.

How Does the AR Shape Influence Föhn Conditions and Warming over the LCIS?

To better understand how the physical processes associated with föhn warming over the LCIS are influenced by the AR shape, we now examine two representative AR-föhn events featuring zonal-perpendicular and convex shapes for detailed analysis (Figs. 4-7). This approach enables a clearer differentiation of the key föhn processes associated with a particular AR shape, which is slightly limited using the cluster analysis due to the different shapes not always being well distinguished from each other, especially for the zonal-perpendicular shape. Additionally, these two AR shapes are chosen because of their widespread AR-föhn impacts over the LCIS, which is especially apparent for the convex shape (Fig. 3). Selection of these two events as representative AR-föhn events29 for these categories was based on examination of 6-hourly IVT and hourly T2 fields (Supplementary Table S1), which show that both are three-day AR2 events, associated with pronounced föhn-driven warming over the LCIS during these periods. The zonal-perpendicular AR-föhn event occurred from 1-3 Dec 2008 and the convex AR-föhn event from 22-24 Feb 2013. Notably, the 2013 convex event exhibited the strongest föhn warming over the LCIS among all convex-dominated AR events (T2max increased by 8.4 °C; Table S1) and ranked third among all events considered. The 2008 zonal-perpendicular event represents a strong föhn warming within its shape category (T2max increased by 4.4 °C; Table S1). Hereafter, these cases are referred to as the “zonal-perpendicular” and “convex” events, respectively.

The zonal-perpendicular AR event made landfall over the central AP with IVT <500 kg m-1 s-1 (Fig. 4a) and its warm core between 200 and 700 m above sea level showed temperatures of up to 2 °C (Fig. 5a). It was driven by a north-south orientated low-high pressure couplet situated to the west of the AP, which produced strong zonal flow with 10 m wind speeds (W10) exceeding 30 m s-1 upwind of the mountain barrier (Fig. 4a,c and Supplementary Fig. S3a-d). The strong upstream low-level winds associated with the AR (especially its core) decelerated as they approached the AP (Fig. 5a) and were redirected around the orography (Figs. 4c, 5a, and Supplementary Fig. S3a-d), i.e., upstream blocking. Above this layer, the upstream flow was deflected vertically over the AP, resulting in warm and strong downslope winds over the leeside (Figs. 5a, 6a, and Supplementary Fig. S3a-d). Consistent with this, the trajectory analysis shows that nearly all upstream air parcels originating within the elevated AR core reached the leeside (Figs. 4b and 6a,c). This led to widespread warming across the entire LCIS, with T2max of 10.6 °C (Fig. 4c and Supplementary Fig. S3a-d). Further warming from pronounced föhn jets also occurred, which took the form of distinct narrow bands over the central LCIS (Fig. 4c and Supplementary Fig. S3a-d). Upwind precipitation over the AP was relatively strong, likely due to the substantial orographic lift, with 12 h accumulations up to 63.7 mm (Figs. 4d and 5a).

Surface warming over the LCIS during this event became more pronounced after 06Z on Dec 2 (Fig. 7e), which coincided with an increase in maximum SHF (Fig. 7a) and surface downward LW radiation (Fig. 7d). The increase in downward LW over the LCIS is likely due to the broad-scale advection of the AR-associated moisture or cloudiness across the AP (Figs. 4a, 6c and 7b), although this was much reduced near the base of the AP, due to föhn-induced cloud clearance (Supplementary Fig. S4a,b). The highest values of SHF over the LCIS are at the base of the AP (Supplementary Fig. S4c), which is consistent with the föhn-induced downslope winds enhancing turbulent mixing over this region (Fig. 5a). Also noteworthy is evidence that the föhn jets also introduced enhanced SHF over the LCIS (Supplementary Figs. S3a-d and S4c). These jet areas were associated with a relative reduction in downward SW and an increase in downward LW compared to the rest of the LCIS, which is likely due to the channeling of enhanced AR-associated moisture and cloudiness from the upwind side of the AP through the mountain gaps to the leeside (Supplementary Fig. S4a,b).

In comparison, the convex AR event made landfall over the southern AP (Fig. 4e) and was driven by a blocking high-pressure system over the Bellingshausen Sea29. It also featured higher IVT values (> 700 kg m-1s-1; Fig. 4e) than the zonal-perpendicular event, and a warm core between 100 and 1000 m above sea level showing temperatures of up to 1 °C (Fig. 5b). This event was associated with markedly less upstream flow being vertically deflected over the AP (Figs. 5b and 6b) compared to the zonal-perpendicular event, and consequently substantial amounts of low-level flow being redirected around the AP (Fig. 4g and Supplementary Fig. S3e-h). However, the comparatively limited vertical deflection was still sufficient to cause warm and strong downslope winds over the leeside (Figs. 5b and 6b), which resulted in a T2max of 14.7 °C over the LCIS (Fig. 4g and Supplementary Fig. S3e-h). Note that the relative lack of vertical deflection over the AP could be related to the elevated warm core of the AR (Fig. 5b) resulting in very stably stratified conditions upstream, as well as the presence of the blocking high-pressure system. Additionally, the weaker orographic lifting of the moist upstream flow resulted in the moderate precipitation over the upstream side of the AP, with 12 h accumulations up to 29.5 mm (Figs. 4h and 5b).

Surface warming over the LCIS during this event exhibited a ~15°C median temperature increase within 12 h, starting around 06Z on Feb 22 (Fig. 7j). This rapid warming was accompanied by a peak in maximum SHF, enhanced surface downward LW, and reduced surface downward SW (Fig. 7f-i and Supplementary Fig. S4d,e), which suggests that it’s due to a combination of föhn effects as well as the broad-scale advection of the AR-associated warmth, moisture, and cloudiness across the AP (Figs. 4e and 6b,d). Because SHF exhibited strong spatial variability and affected only limited areas, its median value remained relatively stable during the event. Over the southern LCIS, where AR-associated moisture exerted the strongest influence, enhanced downward LW could exceed the contribution from downward SW29. Compared to the zonal-perpendicular event, the convex event notably produced stronger warming not only near the surface but also throughout the lower troposphere, reaching up to 2 km above the surface and extending farther from the mountain base (Fig. 5b), which is also consistent with the AR itself having a substantial impact on the leeside of the AP. Föhn jets were also present over the southern LCIS during this event with limited influence on the northern sector (Supplementary Fig. S3e-h, and S4f).

Finally, we calculated the total föhn-induced warming over the LCIS within a 9 h window for both the zonal-perpendicular and convex events, along with the relative contributions from individual föhn mechanisms to further quantify the underlying dominant processes (Table 1). Here, föhn-induced warming is associated with a median temperature increase of 5.1°C over the LCIS for the zonal-perpendicular event, and a slightly stronger warming of 5.6°C for the convex event. In both events, isentropic drawdown is the dominant mechanism, contributing a median value over the LCIS of 15.3°C for the zonal-perpendicular event and 22.8°C for the convex event, i.e., consistent with the convex event ultimately producing stronger warming than the zonal-perpendicular event. Latent heat release from windward-side condensation plays a secondary role in both events, contributing a median value of less than 1°C warming over the LCIS, which is perhaps consistent with much of the AR-associated moisture reaching the LCIS through mountain gaps. However, in both cases there was also a substantial contribution over the LCIS from the combined radiative and sensible heat flux term, which is negative and thus offset the isentropic drawdown contribution, with median values of −8.5 °C for the zonal-perpendicular event and −14.9 °C for the convex event. The large negative values for both events are consistent with the broad-scale advection of the AR-associated warmth, moisture, and cloudiness across the AP resulting in a reduction in downward SW, despite the positive contributions from the SHF.

In addition, this study also briefly examined representative events for the concave and zonal-like AR shapes, which occurred in Feb 2003 and Feb 2022, respectively3,19. Both events made landfall over the northern AP, with IVT values around 1000 kg m-1 s-1. The associated surface warming was therefore mainly confined to the northern AP, especially around Scar Inlet. Both events were also associated with strong upwind precipitation, with 12 h accumulations of 254.6 mm for the concave event and 183.1 mm for the zonal-like event, i.e., consistent with the large IVT values. See Supplementary Fig. S5 for more details.

Methods
Reanalysis Data and Observations

The 5th major atmospheric reanalysis (ERA5)55 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) was used to develop the gridded Polar AR dataset used to select AR events27. This study also used the ERA5 output, pre-processed by the NSF National Center for Atmospheric Research (NCAR), as input to drive the high-resolution PWRF simulations (see below)56.

Downward LW and SW were measured at Escudero Station (62.2°S, 58.97°W), which is situated north of the AP on King George Island. Downward LW was measured with a Kipp & Zonen pyrgeometer and downward SW using a Kipp & Zonen SMP21-V global hemispherical irradiance pyranometer. More details on these measurements and calculation of clear sky downward radiative fluxes are given in previous research57,58. Observations from automatic weather stations situated on the LCIS from AWS14 (67.02°S, 61.5°W, northern LCIS), AWS15 (67.57°S, 62.15 °W, southern LCIS), Spring Point (64.29°S, 61.05°W), and AWS17 (65.93°S, 61.85°W) are also used to evaluate PWRF’s performance near the surface (see Fig. 1b for site locations).

PWRF simulations and model evaluation

PWRF, developed and maintained by the Polar Meteorology Group at the Byrd Polar and Climate Research Center, The Ohio State University, is a regional climate model designed for polar regions based on WRF59,60. PWRF follows the standard WRF workflow but incorporates multiple updates to its physical parameterizations. This study employed PWRF V4.3.3 to generate hourly output using a downscaling approach with three nested domains: 30 km (domain 1), 6 km (domain 2), and 1.2 km (domain 3) spatial resolutions (see Fig. 1b). All PWRF outputs in this study were initialized at 00Z each day using ERA5 data for surface and lateral boundary conditions, with the first 24 h discarded as spin-up time (only forecasts from 24 h to 47 h were used).

The detailed physical parameterization settings are listed in Table S3. The two-moment Morrison-Milbrandt P3 (P3) scheme was selected for its reliable estimation of liquid water path and LW radiation61,62. The Mellor-Yamada-Nakanishi-Niino (MYNN) scheme63 was used for the atmospheric boundary layer. Both LW and SW radiation were represented by the Rapid Radiative Transfer Model (RRTMG)64. The Kain-Fritsch scheme65 was applied for the parameterization of convection in domain 1 at 30 km spatial resolution, but was switched off for the other higher-resolution domains as convection is resolved explicitly. The Noah-MP model66 scheme was used for land surface processes.

In addition, PWRF simulations incorporated surface albedo observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topography data from the Reference Elevation Model of Antarctica (REMA)67 to better capture surface changes and improve the representation of airflow modifications by local topography68. The MODIS albedo, produced from 16 days of Terra and Aqua observations by the National Aeronautics and Space Administration (NASA), has been validated in surface melting studies over West Antarctica, demonstrating improved surface temperature estimation69,70. Incorporating the MODIS albedo also helped reduce the systematic cold bias over the Tibetan Plateau in WRF71. REMA provides a high-resolution surface elevation dataset with up to 8-meter spatial resolution, derived from stereophotogrammetry and satellite imagery67. This model setup has been extensively tested in previous Antarctic AR studies and demonstrated robust performance, particularly in simulating SEB and temperature3,19,57,72–74.

To further assess the accuracy of the PWRF model for this study, output from the simulation of the AR event from 22 to 24 February 2013 was compared to standard meteorological observations at AWS14 and AWS15 on the LCIS. This comparison showed that PWRF relatively accurately captured the temperature increase over the LCIS associated with föhn warming, with an average bias of less than -1.5°C (Supplementary Fig. S7). However, like other high-resolution models14, PWRF exhibits a larger bias in near-surface wind, likely due to model instability. Additionally, downward SW, downward LW, and total radiative fluxes from ERA5 and PWRF are compared to observations75,76 during an AR2 event in January 2022 (Supplementary Fig. S8). Clear-sky simulations are also shown from ERA5 and (for downward LW only) based on simulations using radiosonde profiles. PWRF outperforms ERA5 in both downward SW and LW at Escudero for this event, and consistently demonstrates superior performance across several other AR events during the Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) campaign period30,57, likely due to its more advanced microphysical scheme and higher spatial resolution. Additional model evaluations of surface meteorological variables, based on available AWS observations during AR events, are summarized in Supplementary Table S4. Future research will help further improve PWRF simulations by exploring alternative reanalysis datasets for initial and boundary conditions, refining model physical parameterizations, and incorporating additional observations.

Methodology Overview

In general, this study was conducted following these steps to select and investigate AR-föhn events: 1) used the gridded Enhanced AR dataset27 to identify all AR events influencing the AP from 2001 to 2022 during austral summer, and categorized them based on AR shapes29; 2) performed high-resolution PWRF simulations for each identified AR cases; 3) analyzed the PWRF outputs to identify model time steps associated with föhn-driven warming over the LCIS (AR-föhn patterns); 4) conducted an analysis of all AR-föhn patterns; 5) performed detailed case studies of four representative AR-föhn events, for zonal-perpendicular, convex, zonal-like, and concave AR shapes, focusing primarily on the first two. Detail selection criteria are provided in the Supplementary Materials. This study restricts event selection to years after 2001 because our high-resolution simulations incorporate MODIS albedo observations, which are only available after February 2000.

This study employed k-means clustering analysis and air parcel trajectory analysis. K-means clustering analysis31 was applied to hourly 500 hPa geopotential height and IVT fields when AR-föhn affected the LCIS. Then surface variables such as T2 and SEB components were investigated based on IVT-defined clusters. This approach helps characterize föhn-induced surface warming impacts associated with different AR characteristics, rather than being limited to individual events that may exhibit multiple AR shapes. Notably, the cluster analysis exhibited limitations in completely distinguishing among zonal-like, zonal-perpendicular and concave AR shapes. To address this limitation, we included individual case studies.

For two representative AR-föhn events for zonal-perpendicular and convex AR shapes, which impacted the entire LCIS, we performed trajectory analysis using Read Interpolate Plot (RIP4) with PWRF simulation data as input, allowing us to identify and quantify the contribution of each föhn mechanism under the influence of an intense AR. The RIP4 is a tool developed and maintained by Mark Stoelinga at NCAR and the University of Washington. It tracks air parcel movement and computes meteorological parameters along trajectories. Previously, RIP4 has been used in research yielding reliable results, such as examining the mesoscale topographic influence on tropical cyclone tracks77 and quantifying leeside föhn warming mechanisms78.

Detection and Quantification of Atmospheric Rivers

To detect ARs, we use the Enhanced AR Scale28 developed at the Center for Western Weather and Water Extremes (CW3E) at Scripps Institution of Oceanography (Supplementary Fig. S1), which is based on both IVT value (Eq. 1) and the AR duration at a specific location. The regular AR scale33 ranges from AR1 (weak) to AR5 (exceptional) and is designed for the middle latitudes. In addition to the regular ARI scale, the Enhanced AR Scale is extended for the polar regions with lower IVT thresholds of 100, 150, and 200 kg m-1 s-1, accounting for low-IVT cases in these areas. For Antarctica, a ranking of AR1 to AR3 on the Enhanced AR Scale indicates moderate to strong events (IVT thresholds > 250 kg m-1 s-1). This scale was first tested during the YOPP-SH Winter Targeted Observing Periods and was proven effective in accurately reflecting the strength and duration of ARs28,30. See Supplemental Materials for further details (Supplementary Figs. S9 and S10).

In this study, we utilized this Enhanced AR Scale28 and calculated a new hourly gridded dataset based on IVT from ERA5 reanalysis data. We then computed the regional mean of this gridded dataset within the target area (60S to 70S, 45W to 75W; indicated by the purple dashed box in Fig. 1 and the red dashed box in Supplementary Fig. S1a). Only AR events with a regional mean AR Scale exceeding AR1 were selected for high-resolution PWRF simulations.

1 10 1 10
𝐼𝑉𝑇 = √( ∫ 𝑞𝐮d𝑝)2 +( ∫ 𝑞𝐯d𝑝)2 (1)
𝑔 𝑔
1000 1000

where g is the gravity acceleration constant (m s−2), u and v are zonal and meridional wind components (m s−1), q is specific humidity (kg kg−1), and p is the pressure (hPa).

Identification and Quantification of Föhn Warming

Föhn warming over the LCIS is manually detected based on hourly time series of maximum T2 over the LCIS and 6-hourly snapshots of T2 and W10. Vertical cross-sections of temperature and wind speed were used to further confirm the presence of downslope winds on the leeside. Two key criteria were applied (details see Supplementary Materials):

• The maximum T2 over the LCIS increased by more than 3 °C for over 6 hours after removing the diurnal cycle, with warming detected near the mountain foothills and the leeside significantly warmer than the upwind side. The start of warming is defined as the onset of the maximum T2 increase, and the end as when maximum T2 decreases by more than 2 °C from its peak (Supplementary Fig. S11).
• The W10 over the LCIS increased during the event21, especially for regions not affected by föhn jets.

No fixed threshold was applied to wind speed changes21, as multiple cases exhibited gap flows that also led to substantial increases in wind speed. Combined AR-föhn events do not necessarily cause a decrease in relative humidity (RH) on the leeside, as moisture can reach this region via gap flows or spillover. Therefore, RH changes were not used as a detection metric.

This study also quantified the contribution of each föhn mechanism to surface warming15 for two representative AR-föhn events, zonal-perpendicular and convex AR shapes. The quantification is based on trajectory analysis. Nine-hour backward trajectories were initiated near the surface over the LCIS (Point B) and traced to the upwind side of the AP (Point A), with associated meteorological variables tracked along the airflow. Similar trajectory-based approaches have been widely used in previous analyses of Antarctic föhn events15,69. The corresponding formulations are given in Eqs. (2–8):

Föhn Effect (FE) = 𝑇𝐵 −𝑇𝐶 (2)
Isentropic Drawdown (ID) = 𝜃𝐴 −𝑇𝐶 (3)
Thermodynamic Term (TT) = (𝜃𝑒𝐴 −𝜃𝐴)−(𝜃𝑒𝐵 −𝜃𝐵) (4)
Sensible Heat Flux (SHF) = 𝜃𝑒𝐵 −𝜃𝑒𝐴 −∫ ∆𝜃 (5)
Radiative Heating (RAH) = ∫ ∆𝜃 (6)
Correction Term (CT) = 𝑇𝐵 − 𝜃𝐵 (7)
FE = ID+TT+SHF+RAH+CT (8)

where T is temperature, 𝜃 is potential temperature, 𝜃𝑒 is equivalent potential temperature. Point A, marking the end of the backward trajectory, is located in the undisturbed upstream flow over the ocean, more than ~200 km upstream of the AP—exceeding the typical Rossby radius of deformation (Fig. 6b). Point B, the start of the trajectory, is situated in the region of earliest surface melting over the LCIS, approximately 2 m above the surface (Fig. 6b). Point C lies directly beneath Point A (sharing the same latitude and longitude) but at the same elevation as Point B, with associated meteorological variables interpolated from the PWRF simulations to this location (Fig. 6b).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study explores the relationship between atmospheric river (AR) characteristics and foehn-induced surface warming over the Larsen C Ice Shelf (LCIS) in Antarctic Peninsula (AP) using a gridded AR dataset and high-resolution Polar-WRF model simulations for each AR event over 2001-2022. Four distinct AR shapes are identified, and respective physical mechanisms are proposed to explain their impact on foehn warming. The results are novel and significant, which should be of interest to the readership of Nature Communications. The findings have great potential implications for Earth system modeling of Antarctic ice shelf stability and loss. However, there are some technical concerns related to the methodology and analysis. I cannot recommend it for publication in the current form. The following specific comments need to be satisfactorily addressed.\n1) When referring to AR shape and landfall location, I would picture the geometric shape of long, narrow moisture corridor upon landfalling over AP, but the schematic illustration in Figure 2 got me confused. Not sure whether the AR pathway represents the moving path of AR. How is the AR pathway for each event identified from the ERA5 data and drawn in Fig. 2a? What do the dates and the length of each line mean?\n2) PWRF configuration: How are the model domains aligned with the zonal and meridional bands near AP? There are a couple of related concerns:\na. When nudging u and v winds in the PWRF simulations to 6-hourly ERA5, should the ERA5 grids be remapped and spatially interpolated to the PWRF x and y directions? How?\nb. Please clarify how the domain alignments may affect the PWRF simulated AR pathways and shape patterns that are defined relative to the zonal lines. The Fully Zonal AR shape/pathway depicted in Fig. 2b doesn’t align with the zonal lines (gray dashed lines).\n3) Figure 1: embedding the D01 at the corner of D02, with the shared axes, is very confusing. I suggest moving it out of D02 and marking its own latitude-longitude ranges.\n4) The Froude (Fr) number, being used to depict the AP topographic flow blocking, might be able to objectively quantify the impact of AR shape and landfall location on foehn-induced warming. Why was Fr number arbitrarily calculated using the mean U calculated in the small blue box? How the was the elevation range 900 m – 1100 m identified?\n5) I tend to believe that the AR landfalling location and wind speed (near the height of the topographic elevation range) might be more important to foehn-induced warming than the specific AR patterns (identified by the IVT) and moisture content. Taking the convex AR (2013-02-23), if most of the AR moisture is sourced from distant ocean along an elevated pathway, it’s hardly convincing that the AR moisture can have a more significant impact on the LCIS through SHF and downward SW processes than the downward LW radiation. Please comment on this.\n6) Reanalysis products can be much biased over polar regions. I wonder if the authors tried a different reanalysis product than ERA5 for driving the PWRF model.", "reviewer_2": "This study selected cases of Atmospheric Rivers (ARs) that could accompany föhn phenomena in the Larsen C Ice Shelf (LCIS) region from 2001 to 2022 in the Antarctic Peninsula (AP), and investigated the föhn mechanisms based on the trajectory shapes of the ARs. For detailed analysis of these mechanisms, high-resolution downscaling data (1.2 km) were generated using Polar WRF. Using this dataset, the ARs were classified by shape, and the warming patterns in the AP region were examined. The föhn mechanisms were analyzed in detail for two representative AR cases.\nGiven the recent rapid increase in the frequency of föhn-induced warming events on the Antarctic Peninsula due to accelerated climate change, this study aims to significantly contribute to understanding and addressing Antarctic warming.\nMajor Concern\nThis study classified AR shapes using K-means clustering, examined the characteristics of each cluster, and then analyzed the mechanisms of föhn impacts through two detailed AR case studies. The analysis of the föhn mechanisms in these cases is a very important part of the research, as it quantitatively examines surface warming depending on the föhn processes. However, the reason for selecting the fully zonal and convex-shaped ARs, as well as the rationale for choosing the two corresponding cases, is insufficient. It would be necessary to select the target shapes based on at least some quantitative evidence, such as the frequency of each AR or a quantitative comparison of the intensity of warming over LCIS. Similarly, in case selection, providing quantitative selection criteria such as LCIS temperature thresholds would strengthen the justification.\nMinor Comments\nL40: thereby have -> thereby having a\nL116: shapes -> Shapes\nL120: Why was the analysis period set from 2001 to 2022? Since ERA5 data cover a longer period, and reanalysis-based studies typically begin from 1979, the reason for choosing 2001 as the starting year should be clarified.\nL273: 9 hour -> 9-hour\nL308: determines -> determine\nIn the methods section, a more detailed description of the K-means clustering approach is needed. Was the number of k predetermined?\nThe term “fully zonal” seems clear in the context of shape classification, but since the trajectories are not strictly parallel to latitude, the use of “fully” may not be linguistically precise.\nIn k-means cluster analysis, typical information provided in other clustering studies—such as the number of ARs per cluster, mean duration, IVT, and degree of warming—appears to be missing.", "reviewer_3": "Summary：\nIn \"Föhn-Induced Melting over Larsen C Modulated by Atmospheric River Shape, Direction and Landfall Location,\" Zou et al. analyze the relationship between ARs and föhn over the Antarctic Peninsula using the high-resolution Polar WRF simulations, and identify four distinct AR shapes that cause föhn-induced surface warming over the LCIS: fully zonal, zonal-like, concave, and convex. They first analyze statistical results related to the landfall positions, pressure fields, IVT, and impacts on temperature and radiative fluxes for each AR type. And then they conduct detailed case studies of two specific AR shapes—fully zonal and convex—examining changes in AR-related wind fields, shortwave and longwave radiation, and associated föhn events. Their findings indicate that convex ARs produce the strongest föhn warming, while fully zonal ARs, due to their accompanying moisture and cloud cover, suppress the increase in föhn-driven shortwave radiation, resulting in more typical warming magnitudes. In contrast, zonal-like and concave ARs exert greater influence on the northern part of the LCIS.\nThe two key takeaway messages of the manuscript appear to be:\n1) ARs making landfall on the LCIS can be categorized into four distinct types: fully zonal, zonal-like, concave, and convex.\n2) Convex ARs cause the most intense föhn warming; fully zonal ARs lead to typical increases in temperature; and zonal-like and concave ARs have a more pronounced impact on the northern LCIS.\nOverall assessment:\nInvestigating the types of ARs making landfall on the LCIS and their respective impacts on föhn warming is an interesting and valuable topic that could significantly advance our understanding of ARs in Antarctica. The study is generally well-presented, engaging, and supported by high-quality figures. However, for a journal with the high profile of Nature Communications, the two key takeaways in their current form are somewhat limited and would benefit from being framed to convey broader implications. Although the results are intriguing and potentially important, I have several major concerns that should be thoroughly addressed. While these issues are, in principle, resolvable, the extent of additional analysis and revisions required leads me to lean toward a recommendation of \"reject and resubmit,\" particularly if this were a discipline-specific journal.\n1） The AR detection method requires significant clarification. The manuscript notes the use of IVT but fails to specify the algorithmic details—such as the threshold type (fixed, relative, or none), geometric constraints, and whether a single or composite method was applied. Since AR statistics are notoriously dependent on the detection methodology (as robustly shown by ARTMIP studies), it is crucial that the authors utilize multiple algorithms and include a discussion on this uncertainty. This remains necessary even considering the potential uniqueness of polar ARs.\n2） The study employs high-resolution Polar WRF to simulate 37 AR-Föhn events identified from ERA5 data (2001-2022). While this approach is valuable, the limited sample size of 37 events—further subdivided into four (effectively five) distinct AR types—results in a small number of cases per category. This raises concerns regarding the statistical robustness and generalizability of the findings. To enhance the reliability of the conclusions, I recommend conducting additional supplemental PWRF simulations to increase the sample size for each AR type.\n3） While the study appropriately applies the Gridded Enhanced AR Scale for classification, the potential of this framework is not fully realized. The analysis focuses on AR2 in cases but overlooks the other categories. To leverage the scale effectively, the authors should discuss the prevalence of AR1 and AR3 events (evident in Fig. 2a) within each shape category and analyze how AR intensity, combined with shape, modulates the hydrological and thermal impacts on the LCIS.\n4） The Background section currently provides separate introductions to the warming effects on the Antarctic Peninsula (AP), the impacts of ARs and föhn on the ice sheet, and the influence of AR-föhn events on AP temperatures. For a journal of Nature Communications' stature, this presentation is somewhat lengthy and could be significantly streamlined to present a more focused and concise narrative. And the Summary section primarily reiterates the main findings, resulting in repetition rather than synthesis. To meet the high standards of Nature Communications, this section should be evolved into a robust Discussion. It should delve into the implications of the results, address uncertainties, and outline unresolved questions that warrant future investigation\n5） The core novelty of the study lies in the classification of ARs and the subsequent analysis of the distinct atmospheric and radiative impacts of the four types. While valuable, this may not sufficiently constitute a major conceptual advance expected for a high-impact journal. To significantly strengthen the manuscript's contribution, I recommend expanding the analysis to investigate the downstream effects of these AR-föhn events on sea ice melt.\nAdditional specific comments:\n1) Figs 1 and 2: I suggest consolidating Figure 1 and Figure 2b into a single, more comprehensive figure. Meanwhile, Figure 2a could be moved to the supplementary information.\n2) Lines 185-186 and Fig.4: It would be helpful to see the spatial patterns of the latent heat flux and longwave radiation to better evaluate their roles in the surface energy budget and the resulting melting processes.\n3) Lines 480-481: Please include a brief explanation of the physical significance of Fr values, specifically stating what Fr > 1 and Fr < 1 represent in the context of this study.\n4) Lines 236-238 and Fig. 8: For the Fully Zonal AR type, the temporal evolution appears consistent with the patterns of IVT and downward longwave radiation. However, the SHF exhibits a lag of approximately 6 hours. To understand this discrepancy, it would be informative to examine the profile of diabatic heating.\n5) Lines 264-268 and Fig.8: For the convex AR case, the SHF does not exhibit a pronounced change and its temporal evolution does not align closely with the observed variations in 2-meter temperature.\n6) Lines 283-297: These two sentences can be combined and made more concise.\n7) Lines 287-289: Is SHF also a reduction?\n8) Table S1: suggested to add the AR type.\n9) Table S2: Lack of \"fully meridional\"\n10) Data: While the model performance is convincingly demonstrated for two representative cases, this validation would be strengthened by a more comprehensive evaluation. Could the authors please present statistical metrics (e.g., mean bias, correlation) that aggregate the results across all 37 simulated AR-föhn events?"}, "author_response": "Thank you very much for the valuable comments. We have addressed all of Reviewer #1’s suggestions and provide point-by-point responses below.\n1) When referring to AR shape and landfall location, I would picture the geometric shape of long, narrow moisture corridor upon landfalling over AP, but the schematic illustration in Figure 2 got me confused. Not sure whether the AR pathway represents the moving path of AR. How is the AR pathway for each event identified from the ERA5 data and drawn in Fig. 2a? What do the dates and the length of each line mean?\nWe examine IVT fields at 6-hourly intervals and manually identify the dominant AR pattern associated with each event (we replaced the previous terminology “AR pathway”, which was confusing). These results are shown in Fig. S1 (originally Fig. 2a).\nTaking the 7 February 2022 event as an example, the AR initially exhibits a concave shape, rapidly transitions to a zonal-like shape, and eventually evolves into a fully meridional shape. The dominant AR shape during this event is zonal-like, with peak impact occurring around 00 UTC on 8 Feb 2022. Accordingly, we represent the dominant AR configuration using the orange arrow (panel e; see also Fig. 1c, originally Fig. 2a). We include more explanation in the Supplementary Materials.\n1\n“Supplementary Figure S1. Dominant AR shape for each AR-föhn event impacting the AP during austral summers from 2001 to 2022, as well as corresponding dates and ranking on the Enhanced AR Scale1. AR shapes of AR-family events are shown by dashed lines and a “F” after the date. If an AR-family event includes multiple shapes, the secondary shape is indicated by a dashed line. Also shown is the model topography (m) from D02 at 6 km, based on the REMA dataset. LCIS in (b) refers to the Larsen C Ice Shelf. The dominant AR shape is manually defined based on the IVT field, following the AR axis at the peak of each event.”\n2) PWRF configuration: How are the model domains aligned with the zonal and meridional bands near AP?\nThank you for this point. Our domain configuration follows a standard regional climate model setup for the Antarctic Peninsula and will provide reliable AR simulations.\nFurther details are provided below.\nThere are a couple of related concerns:\na. When nudging u and v winds in the PWRF simulations to 6-hourly ERA5, should the ERA5 grids be remapped and spatially interpolated to the PWRF x and y directions? How?\nYes—interpolation is applied to convert the ERA5 fields to the model grid so that nudging can be performed during the simulations.\nAs the reviewer probably knows, the WRF workflow consists of five steps: geogrid, ungrib, metgrid, real, and wrf. Although PWRF incorporates several modifications to physical parameterizations, it still follows this standard WRF workflow.\nDuring the metgrid step, the meteorological fields extracted by ungrib are horizontally interpolated to the model grid defined in geogrid1. In the subsequent wrf step, the PWRF simulation is nudged toward ERA5 fields (intermediate data generated by metgrid) to 1 https://www2.mmm.ucar.edu/wrf/users/wrf_users_guide/build/html/wps.html).\n3\nreduce model degradation. The nudging configuration used in our study is typical for weak-to-moderate synoptic-scale constraint.\nThis approach has been tested in prior studies and is recommended by the PWRF development team. We apologize for reiterating model details the reviewer may already be familiar with, and we hope this explanation adequately addresses the question.\nTo maintain conciseness while avoiding confusion, we now include brief explanations of this procedure in both the main text and Table S3.\nLn 429-430: “PWRF follows the standard WRF workflow but incorporates multiple updates to its physical parameterizations.”\nSupplementary Table S3: “Every 6 hours; nudging to u, v wind, temperature and water vapor from ERA5 fields (preprocessed and interpolated after the metgrid step)8,9 for model level 40 (~400 hPa) and above…”\nb. Please clarify how the domain alignments may affect the PWRF simulated AR pathways and shape patterns that are defined relative to the zonal lines. The Fully Zonal AR shape/pathway depicted in Fig. 2b doesn’t align with the zonal lines (gray dashed lines).\nThe term “fully zonal” is ambiguous in the polar context, as zonal flow implies circulation around the Antarctic continent. We have changed it to “zonal-perpendicular” to emphasize that the flow is oriented perpendicular to the mountain range upstream of the LCIS, thereby promoting strong cross-barrier airflow.\nIn addition, our domain configuration follows a fairly standard regional climate model setup for the Antarctic Peninsula (AP; e.g., Listowski and Lachlan and Lachlan-Cope 2017, Luo et al. 2020). This design keeps the AP near the center of the domain, ensuring that key regions such as Larsen C are not influenced by lateral boundary effects.\nOur simulation results are not sensitive to the PWRF domain configuration because:\n1. D01 and D02 domains are sufficiently large to include most of the Southern Ocean.\n2. Domain boundaries are chosen to minimize topographic truncation, thereby reducing the risk of model instability (although some truncation of the terrain is unavoidable).\n● Listowski, C. and Lachlan-Cope, T. (2017): The microphysics of clouds over the Antarctic Peninsula – Part 2: modelling aspects within Polar WRF, Atmos. Chem. Phys., 17, 10195–10221, https://doi.org/10.5194/acp-17-10195-2017.\n● Luo, L., Zhang, J., Hock, R., & Yao,Y. (2021): Case study of blowing snow impacts on the Antarctic Peninsula lower atmosphere and surface simulated with a snow/ice enhanced WRF model. J. Geophys. Res. Atmos.,126, e2020JD033936. https://doi.org/10.1029/2020JD033936\n4\n3) Figure 1: embedding the D01 at the corner of D02, with the shared axes, is very confusing. I suggest moving it out of D02 and marking its own latitude-longitude ranges.\nThank you for the suggestion. We have merged this comment with a related comment from another reviewer. Accordingly, Figure 1 has been updated, and Figure 2a has been moved to the Supplementary Materials as Fig. S1.\n“Fig. 1. PWRF model domains and overview of four AR shapes: a) D01 with a horizontal resolution of 30 km; b) D02 and D03 with resolutions of 6 km and 1.2 km, respectively; c) Classification of all events shown in Supplementary Fig. S1 into distinct AR shapes, which are zonal-perpendicular, zonal-like, convex, and concave. In panel b, the purple dashed line marks the AR detection zone; Spring Point, AWS 14, AWS 15 and AWS 17 are shown as red crosses, and the Escudero station as a purple dot. Topography in panels b-c is from the 6-km WRF D02 domain based on Reference Elevation Model of Antarctica (REMA). Larsen C denotes the Larsen C Ice Shelf.”\n4) The Froude (Fr) number, being used to depict the AP topographic flow blocking, might be able to objectively quantify the impact of AR shape and landfall location on foehn-induced warming. Why was Fr number arbitrarily calculated using the mean U calculated in the small blue box? How the was the elevation range 900 m – 1100 m identified?\nThank you for this suggestion. After careful discussion among all authors, we decided to remove the Froude number calculation to avoid potential confusion, as the trajectory analysis and cross sections already clearly indicate whether low-level blocking occurs on the upwind side.\nTo answer the reviewer’s question, the small blue box was selected because (1) it is sufficiently far from the Domain 3 boundary to avoid potential model instability, and (2) it lies more than ~200 km from the mountain peak—beyond the typical Rossby radius of deformation. The average elevation is defined based on the mountain peak height along the AP. Height is estimated based on the AR landfall location and the corresponding mountain elevation.\n5) I tend to believe that the AR landfalling location and wind speed (near the height of the topographic elevation range) might be more important to foehn-induced warming than the specific AR patterns (identified by the IVT) and moisture content. Taking the convex AR (2013-02-23), if most of the AR moisture is sourced from distant ocean along an elevated pathway, it’s hardly convincing that the AR moisture can have a more significant impact on the LCIS through SHF and downward SW processes than the downward LW radiation. Please comment on this.\nLandfall location & Wind speed VS. AR shape.\nYes, we agree with the reviewer that landfall location and wind speed are indeed very important factors, together with wind direction, airflow curvature, and the vertical position of the AR core, as these collectively determine whether the flow can traverse the mountain barrier and the magnitude of the resulting föhn warming. We believe that AR shape encapsulates these key features to a certain extent and also links to the larger-scale circulation patterns.\nWe added the following discussion to the Discussion section regarding this comment. Please check Ln 358-376.\nSEB analysis for the convex AR\nWe believe that our findings are in line with the reviewer’s perspective; however, this was not clearly articulated in the original manuscript.\nDownward LW radiation can exceed the impact of downward SW, typically where AR-associated moisture exerts the strongest influence (e.g., the 2013 convex event). The contribution from downward LW radiation is more consistent, whereas downward SW radiation exhibits a pronounced diurnal cycle. However, downward SW radiation has a much higher absolute value during the daytime. Please see the detailed response below and the corresponding revisions in the manuscript.\nWhen we look at the LCIS as a whole.\nFollowing panels are selected from Fig. 7.\n1. Downward SW typically exhibits larger magnitudes than other SEB components over the Antarctic Peninsula during austral summer, featuring a strong diurnal cycle. In the convex AR case, although downward SW remains the dominant SEB component, it shows a weakening trend over the course of the event.\n2. By contrast, downward LW increases steadily during the event; however, it does not become the dominant component on average because its absolute contribution is generally smaller than that of downward SW.\n3. SHF displays strong spatial variability and doesn’t have the diurnal cycle. Its maximum values can locally exceed those of both downward SW/LW.\nAs the reviewer pointed out, in regions where AR-induced moisture exerts the strongest influence—such as the southern LCIS during the convex event—downward LW can temporarily exert a stronger influence than downward SW.\nLn 293-296: “Because SHF exhibited strong spatial variability and affected only limited areas, its median value remained relatively stable during the event. Over the southern LCIS, where AR-associated moisture exerted the strongest influence, enhanced downward LW could exceed the contribution from downward SW (not shown).”\nWe added the following content to the Discussion section regarding this comment.\nLn 342-344: “Increased surface downward SW radiation along the LCIS margin is consistent with föhn-induced cloud clearing, while enhanced AR-related moisture and downward LW radiation near mountain gaps (Fig. 3) can locally exceed the SW contribution.”\nWe also highlighted the importance of downward LW radiation, for example:\nLn 189-194: “Consistent with previous studies showing that isentropic drawdown, mechanical mixing, and radiative heating are key processes driving föhn-induced warming over the AP18, sensible heat flux (SHF) together with downward SW and downward longwave (LW) radiation dominate the SEB over the LCIS (Fig. 3)14,20,33, i.e., more important than other components like latent heat flux (Supplementary Fig. S2a-d).”\n6) Reanalysis products can be much biased over polar regions. I wonder if the authors tried a different reanalysis product than ERA5 for driving the PWRF model.\nThank you for this insightful comment. Indeed, reanalysis products can exhibit substantial biases over polar regions. Among currently available datasets, ERA5 is considered one of the most reliable for Antarctica, particularly over the Antarctic Peninsula. As shown by Bromwich et al. (2024), ERA5 temperature trends closely match observations at the Antarctic Peninsula stations Faraday/Vernadsky, Esperanza, and Orcadas. In addition, ERA5 has been widely used for dynamic downscaling studies in 7\npolar regions (e.g., Bozkurt et al., 2018; Xue et al., 2022; Zou et al., 2023), providing a robust foundation for high-resolution simulations.\nThis study generated approximately 40 TB of output after cleanup (nearly 100 TB of data during processing) and consumed about three million RUs. Nevertheless, we appreciate the reviewer’s suggestion and agree that a systematic evaluation of different reanalysis products for AR studies would be a valuable direction for future research. We have therefore added the following context in the Data section.\nLn 471-473: “Future research will help further improve PWRF simulations by exploring alternative reanalysis datasets for initial and boundary conditions, refining model physical parameterizations, and incorporating additional observations.”\nThis study selected cases of Atmospheric Rivers (ARs) that could accompany föhn phenomena in the Larsen C Ice Shelf (LCIS) region from 2001 to 2022 in the Antarctic Peninsula (AP), and investigated the föhn mechanisms based on the trajectory shapes of the ARs. For detailed analysis of these mechanisms, high-resolution downscaling data (1.2 km) were generated using Polar WRF. Using this dataset, the ARs were classified by shape, and the warming patterns in the AP region were examined. The föhn mechanisms were analyzed in detail for two representative AR cases.\nGiven the recent rapid increase in the frequency of föhn-induced warming events on the Antarctic Peninsula due to accelerated climate change, this study aims to significantly contribute to understanding and addressing Antarctic warming.\nThank you very much for the valuable comments. We have addressed all of Reviewer #2’s suggestions and provide point-by-point responses below.\nMajor Concern\n1) This study classified AR shapes using K-means clustering, examined the characteristics of each cluster, and then analyzed the mechanisms of föhn impacts through two detailed AR case studies. The analysis of the föhn mechanisms in these cases is a very important part of the research, as it quantitatively examines surface warming depending on the föhn processes. However, the reason for selecting the fully zonal and convex-shaped ARs, as well as the rationale for choosing the two corresponding cases, is insufficient. It would be necessary to select the target shapes based on at least some quantitative evidence, such as the frequency of each AR or a quantitative comparison of the intensity of warming over LCIS. Similarly, in case selection, providing quantitative selection criteria such as LCIS temperature thresholds would strengthen the justification.\nThank you for pointing this out. Following the reviewer’s suggestion, we refined our analysis in a more quantitative manner and added a detailed, step-by-step description of the workflow to clarify the methodology. The “Supplementary Material: Selection Criteria and Analysis Method for AR Events and Föhn-Warming Periods” now includes a comprehensive description of the procedures used to identify AR events, define föhn-warming periods, perform k-means clustering analysis, and select the two representative cases.\nBelow we outline the key questions addressed in this response:\n● How does foehn warming period decide?\nFor each AR event, we plotted a time series of the maximum IVT and the maximum 2m temperature over the LCIS only (from PWRF simulations). To objectively identify föhn-induced warming, the diurnal cycle was removed from the temperature time series (solid red), and warming periods were selected only if they persisted for more than 6 hours and exhibited a temperature increase exceeding 3 °C (highlighted by the pink shading).\n9\n● Why select fully-zonal and convex?\nConvex. Although relatively rare, this AR shape produces persistent warming across the entire LCIS, particularly over the southern part.\nZonal-perpendicular (originally fully zonal): This pattern is selected due to our hypothesis that near-perpendicular flow impinging on the mountain range leads to stronger föhn warming.\nZonal-like: This is a well-studied AR shape. Previous studies (e.g., Zou et al. 2023; Gorodetskaya et al. 2024) have comprehensively examined the February 2022 zonal-like event.\nConcave: The AR-induced föhn warming response associated with this pattern is less consistent and on average weak. We frequently observe barrier-jet features in these cases, and further analysis might be needed.\nLn 180-185: “Despite having lower IVT intensity than concave and zonal-like ARs, the convex AR shape still favors strong warming over the southern sector (Fig. 2), highlighting the unique impact of this AR shape (see the case study below). The concave AR shape, however, produces the weakest föhn warming, confined to the northern LCIS near the ice-shelf edge. This is likely owing to its landfall location and the curved geometry of the AP, which enhance mountain blocking and thereby limit the extent of föhn warming.”\n● Why those two cases?\nThe 2013 convex event was selected first because it ranked third in föhn warming among all events (8.4 °C increase in T2 ; see figure above) and ranked second when max\nAR-family events were excluded (Table S1). This also motivated our focus on the convex AR shape.\nThe 2008 zonal-perpendicular event was selected subsequently based on three criteria:\n• It is a 3-day AR2 event, comparable in duration & intensity to the convex case.\n• It is a relatively strong föhn-warming event.\n• It exerts a clear warming impact over the central and southern LCIS.\n*Other strong zonal-perpendicular events not selected: Case 18 (2011) is an AR3 event, and Case 32 (2021) primarily affects the northern LCIS.\nTable S1 now provides additional information supporting our case-selection decisions.\nLn 236-242: “The zonal-perpendicular AR-föhn event occurred from 1-3 Dec 2008 and the convex AR-föhn event from 22-24 Feb 2013. Notably, the 2013 convex event exhibited the strongest föhn warming over the LCIS among all convex-dominated AR events (T2 increased by 8.4 °C; Table S1) and ranked third among all events max\nconsidered. The 2008 zonal-perpendicular event represents a strong föhn warming within its shape category (T2 increased by 4.4 °C; Table S1). Hereafter, these cases max\nare referred to as the “zonal-perpendicular” and “convex” events, respectively.”\nMinor Comments\nL40: thereby have -> thereby having a\nCorrected\nL116: shapes -> Shapes\nCorrected\nL120: Why was the analysis period set from 2001 to 2022? Since ERA5 data cover a longer period, and reanalysis-based studies typically begin from 1979, the reason for choosing 2001 as the starting year should be clarified.\nYes, it is because of the availability of MODIS albedo data.\nIn Method section, we added an explanation as the reviewer suggested:\nLn 484-485: “This study restricts event selection to years after 2001 because our high-resolution simulations incorporate MODIS albedo observations, which are only available after February 2000.”\nL273: 9 hour -> 9-hour\nCorrected\nL308: determines -> determine\nCorrected\nIn the methods section, a more detailed description of the K-means clustering approach is needed. Was the number of k predetermined?\nYes, the number of k is predetermined as 4, after some pre-analysis (3-5). Also, it better captures all patterns we observed in the 6hrly IVT and 500hPa geopotential height figures we generated (archived at AMRDC).\n11\n• Zou, X. et al. Selected austral summer Atmospheric River cases over the Antarctic Peninsula, 2001 - 2022. Antarctic Meteorological Research and Data Center https://doi.org/10.48567/exak-2810 (2025).\nIn the Supplementary Material: Selection Criteria and Analysis Method for AR Events and Föhn-Warming Periods\n“K-means clustering analysis uses the Hartigan and Wong (AS-136) algorithm10, which partitions multidimensional data points into 𝐾 clusters by minimizing within-cluster variance. It was applied separately to 500-hPa geopotential height and IVT for all 1,165 selected hours of the PWRF D02 simulations and for additional pre-2001 events from the ERA5 reanalysis dataset (not shown). The number of clusters (k = 4) was determined based on manual inspection of all 6-hourly IVT fields, which indicated that four distinct AR shapes occur during AR-induced föhn-warming periods.”\nThe term “fully zonal” seems clear in the context of shape classification, but since the trajectories are not strictly parallel to latitude, the use of “fully” may not be linguistically precise.\nThank you for the suggestion. Another reviewer raised a similar concern; thus, we changed it to “zonal-perpendicular” now.\nIn k-means cluster analysis, typical information provided in other clustering studies—such as the number of ARs per cluster, mean duration, IVT, and degree of warming—appears to be missing.\nThank you very much for this valuable suggestion. We re-performed the analysis:\n• We applied a more quantitative event-selection procedure (see Supplementary Materials)\n• We examined surface impacts by compositing surface variables (T2, SHF, and SWD, LWD, LH, LWnet, SWnet) according to the IVT-based clusters.\nWe added the percentages to indicate the fraction of time steps assigned to each cluster. Because a given AR event evolves over its life cycle, a single AR case can contribute to multiple clusters at different stages of development.\nAll related analyses and conclusions have been revised accordingly. Please check Ln 197-234.\nSummary：\nIn \"Föhn-Induced Melting over Larsen C Modulated by Atmospheric River Shape, Direction and Landfall Location,\" Zou et al. analyze the relationship between ARs and föhn over the Antarctic Peninsula using the high-resolution Polar WRF simulations, and identify four distinct AR shapes that cause föhn-induced surface warming over the LCIS: fully zonal, zonal-like, concave, and convex. They first analyze statistical results related to the landfall positions, pressure fields, IVT, and impacts on temperature and radiative fluxes for each AR type. And then they conduct detailed case studies of two specific AR shapes—fully zonal and convex—examining changes in AR-related wind fields, shortwave and longwave radiation, and associated föhn events. Their findings indicate that convex ARs produce the strongest föhn warming, while fully zonal ARs, due to their accompanying moisture and cloud cover, suppress the increase in föhn-driven shortwave radiation, resulting in more typical warming magnitudes. In contrast, zonal-like and concave ARs exert greater influence on the northern part of the LCIS.\nThe two key takeaway messages of the manuscript appear to be:\n1) ARs making landfall on the LCIS can be categorized into four distinct types: fully zonal, zonal-like, concave, and convex.\n2) Convex ARs cause the most intense föhn warming; fully zonal ARs lead to typical increases in temperature; and zonal-like and concave ARs have a more pronounced impact on the northern LCIS.\nThank you very much for the valuable suggestions. We have provided point-by-point responses to all of Reviewer #3’s comments.\nWe also note that some of our conclusions have been updated following the application of a stricter selection criterion (more quantitative) for identifying föhn warming and the adoption of a more robust analysis procedure (see Supplementary Material).\nOverall assessment:\nInvestigating the types of ARs making landfall on the LCIS and their respective impacts on föhn warming is an interesting and valuable topic that could significantly advance our understanding of ARs in Antarctica. The study is generally well-presented, engaging, and supported by high-quality figures. However, for a journal with the high profile of Nature Communications, the two key takeaways in their current form are somewhat limited and would benefit from being framed to convey broader implications. Although the results are intriguing and potentially important, I have several major concerns that should be thoroughly addressed. While these issues are, in principle, resolvable, the extent of additional analysis and revisions required leads me to lean toward a recommendation of \"reject and resubmit,\" particularly if this were a discipline-specific journal.\nThank you for pointing this out. We agree that broader implications are important for a journal such as Nature Communications, and we have added the following materials to strengthen the broader impact of the paper:\n1) Background: We trimmed back this section to focus on key messages.\n14\n2) Results: We refined the AR–föhn event selection algorithm, improved the k-means clustering framework, and extended some analysis beyond 2001 using ERA5 reanalysis data. These additions help assess the representativeness of our event subset and suggest that incorporating additional AR characteristics may have broader relevance for future Antarctic AR studies.\n3) Discussion: This section has been completely rewritten to incorporate all key points raised by the reviewers.\n1) The AR detection method requires significant clarification. The manuscript notes the use of IVT but fails to specify the algorithmic details—such as the threshold type (fixed, relative, or none), geometric constraints, and whether a single or composite method was applied. Since AR statistics are notoriously dependent on the detection methodology (as robustly shown by ARTMIP studies), it is crucial that the authors utilize multiple algorithms and include a discussion on this uncertainty. This remains necessary even considering the potential uniqueness of polar ARs.\nShields, C. A., Payne, A. E., Shearer, E. J., Wehner, M. F., O’Brien, T. A., Rutz, J. J., Leung, L.R., Ralph, F. M., Collow, A. B. M., Ullrich, P. A. Ullrich, Dong, Q., Gershunov, A., Griffith, H., Guan, B., Lora, J. M., Lu, M., McClenny, E., Nardi, K. M., Pan, M., Qian, Y., Ramos, A. M. Ramos, Shulgina, T., Viale, M., Sarangi, C., Tomé, R., Zarzycki, C. (2023). Future atmospheric rivers and impacts on precipitation: Overview of the ARTMIP Tier 2 high-resolution global warming experiment. Geophysical Research Letters, 50, e2022GL102091. https://doi.org/10.1029/2022GL102091\nCollow, A.B., Shields, C.A., Guan, B., Kim, S., Lora, J.M., McClenny, E.E., Nardi, K., Payne, A., Reid, K., Shearer, E. J. , Tome, R., Wille, J.D., Ramos, A.M., Gorodetskaya, I.V., Leung, L.R., O’Brien, T.A., Ralph, F.M., Rutz, J. Ullirich, P.A., Wehner, M., (2022) An Overview of ARTMIP’s Tier 2 Reanalysis Intercomparison: Uncertainty in the Detection of Atmospheric Rivers and their Associated Precipitation, Journal of Geophysical Research, Atmospheres, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JD036155.\nO’Brien, Travis Allen and Wehner, Michael F and Payne, Ashley E. and Shields, Christine A and Rutz, Jonathan J. and Leung, L. Ruby and Ralph, F. Martin and Marquardt Collow, Allison B. and Guan, Bin and Lora, Juan Manuel and et al., (2022) Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment. JGR-A https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JD036013.\nRutz, J.J, Shields, C.A., Lora, J.M, Payne, A.E., Guan, B., Ullrich, P., O'Brien, T., Leung, L.-Y., Ralph, F.M., Wehner, M., Brands, S., Collow, A., Goldenson, N., Gorodetskaya, I., Griffith, H., Hagos, S., Kashinath, K., Kawzenuk, B., Krishnan, H., Kurlin, V., Lavers, D., Magnusdottir, G., Mahoney, K., McClenny, E., Muszynski, G., Nguyen, P.D., Prabhat, Qian, Y., Ramos, A.M., Sarangi, C., Sellars, S., Shulgina, T., Tome, R., Waliser, D., Walton, D., Wick, G., Wilson, A., Viale, M.: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology, Journal of Geophysical Research-Atmospheres, https://doi.org/10.1029/2019JD030936, 2019.\nThank you for pointing this out. Uncertainty in AR detection is important and remains an active area of research.\nIn this study, we focus on impactful ARs over the AP, a region characterized by lower water vapor saturation capacity, where both meridional and zonal moisture transport are important. Our detection method is based on the Enhanced AR Scale, which was widely used during the Year of Polar Prediction (YOPP) Southern Hemisphere Targeting Observation Period and has provided reliable estimates of AR impacts. Thus, we believe our AR selection results are robust for this study.\nTo address reviewer’s comments, we completed the following tasks:\n1) Added a detailed section in the Supplementary: Selection Criteria for AR Events and Föhn-Warming Periods, to fully explain how we select all impactful AR-föhn events. We also cross-checked all our selection against another independent dataset, Guan and Waliser (2024), to confirm its reliability. Validation figures for all cases are currently archived at AMRDC2.\nCase #35 as an example\n2https://amrdcdata.ssec.wisc.edu/dataset/selected-austral-summer-atmospheric-river-cases-over-antarctic-peninsula-2001-2022\n• Guan, B., Waliser, D.E. A regionally refined quarter-degree global atmospheric rivers database based on ERA5. Sci Data 11, 440 (2024).\nhttps://doi.org/10.1038/s41597-024-03258-4\n• Guan, B. [Data] Global Atmospheric Rivers Database, Version 4. UCLA Dataverse https://doi.org/10.25346/S6/ZSW7UN (2025).\n2) We added more context about uncertainties in AR detection in the Discussion section, which include all suggested citations here. Also, several authors of this study are currently contributing to a separate manuscript specifically targeting uncertainties in polar AR detection (Different metrics, spatial variations, AR families, etc). That effort, we believe, will provide a more comprehensive assessment.\nPlease check Ln 371-379.\n2) The study employs high-resolution Polar WRF to simulate 37 AR-Föhn events identified from ERA5 data (2001-2022). While this approach is valuable, the limited sample size of 37 events—further subdivided into four (effectively five) distinct AR types—results in a small number of cases per category. This raises concerns regarding the statistical robustness and generalizability of the findings. To enhance the reliability of the conclusions, I recommend conducting additional supplemental PWRF simulations to increase the sample size for each AR type.\nWe appreciate this comment and would like to provide additional context that may have been missing in the original manuscript.\nTo properly address this comment, we completed the following tasks:\n1. Expanded the analysis using ERA5 reanalysis data (1979-2001), demonstrating that our conclusion of four distinct AR shapes is robust.\n2. Explained why we believe the 37 cases are sufficient to represent all AR shapes, noting that a single AR event may exhibit multiple shapes.\n3. Demonstrated that all available austral-summer cases after 2001 are included in this study.\nIf we understand correctly, Reviewer #3 may have the following detailed concerns, all of which are important to address.\n● Do four distinct AR patterns persist in the extended dataset?\nYes. We extended a similar IVT k-means clustering analysis for selected AR events based on ERA5 reanalysis data to show that 4 AR shapes still present.\n• Different spatial resolutions: PWRF D02 has 6-km horizontal resolution with 71 vertical levels, whereas ERA5 has ~31-km horizontal resolution with 38 pressure levels.\n• Different grid systems and map projections (global vs RCM grid).\n• Our PWRF sample is additionally filtered to include only time steps with föhn warming detected over the LCIS (the detailed workflow is now included in the Supplementary).\n*We did not apply an identical T2 filter to ERA5 because it is known to underestimate föhn-related warming (Gorodetskaya et al. 2023), which would bias the event selection. Thus, the ERA5 K-means clustering includes time steps that do not affect the LCIS, typically ARs making landfall farther north.\nDespite these limitations, both datasets still exhibit broadly consistent AR patterns, suggesting the key synoptic regimes are robust.\nPlease check Ln 379-385\n● Further proof of the representativeness of our PWRF dataset.\nIndividual AR events can exhibit multiple AR patterns, and although our analysis includes 37 AR events (over 3,000 hours of simulations), the dataset encompasses a sufficiently broad range of AR patterns to support robust conclusions. Several representative examples are shown here.\nThis is a 7-day February 2006 AR family event characterized by three back-to-back ARs in the PWRF simulation. The event encompasses four AR patterns, excluding the convex one. We present snapshots of the different AR patterns together with the corresponding MSLP fields.\nIn the updated K-means clustering analysis, percentages denote the fraction of time steps assigned to each cluster; all clusters contain more than 100 time steps (See Fig. 2).\n● Have we exhausted all available AR events over the Antarctic Peninsula region?\nYes. For the Antarctic region, ARs are far less frequent than in the mid- and low latitudes; in other words, the number of naturally occurring impactful ARs is inherently limited. Wille et al. (2025), using meridional IVT alone, show that ARs typically occur for only about 3 days per year at a given Antarctic coastal location, with notable spatial variability (left panel in the figure below). Zhang et al. (2024), which accounts for both meridional and zonal IVT, similarly finds an average of ~3 ARs exceeding AR1 intensity per year at a typical coastal site (right panel in the figure below).\nWe also note that this study simulates all available austral summer AR events of at least AR1 intensity using high-resolution modeling, producing approximately 40 TB of output after cleanup (nearly 100 TB of raw data generated during processing). In total, about three million RUs were used to generate this dataset. These simulations are highly computationally intensive due to the high spatial resolution, long integration periods, and comprehensive variable set.\nThe focus of this study is not to exhaustively simulate all AR events—particularly weaker events, which tend to have smaller impacts and are more sensitive to the choice of AR selection criteria. This study aims to provide a sufficiently representative dataset to highlight the importance of AR characteristics, which we believe is achieved in the revised manuscript.\nRelated contexts are revised.\n3) While the study appropriately applies the Gridded Enhanced AR Scale for classification, the potential of this framework is not fully realized. The analysis focuses on AR2 in cases but overlooks the other categories. To leverage the scale effectively, the authors should discuss the prevalence of AR1 and AR3 events (evident in Fig. 2a) within each shape category and analyze how AR intensity, combined with shape, modulates the hydrological and thermal impacts on the LCIS.\nThank you for this great suggestion. Our emphasis on AR2 events is motivated by the following considerations:\n● Among the 10 AR1 events, only 3 exhibit detectable föhn warming over the LCIS.\n● Six AR3 events are identified, half of which are prolonged AR-family events; these complex cases warrant dedicated investigation and are therefore deferred to future work.\n● The remaining 22 events are AR2 events, which form the core of our analysis.\nLn 130-138: “Among the 37 AR events, 23 events exhibited identifiable föhn-induced warming periods over the LCIS (comprising 1165 h of PWRF simulations; hereafter referred to as “AR-föhn events”), including three AR1, fourteen AR2, and six AR3 events (the latter including three prolonged AR-family events). Not surprisingly, the AR-föhn events associated with föhn-induced warming over the LCIS tended to make landfall over the central or southern AP (Supplementary Fig. S1). The average warming over the LCIS associated with the 23 AR-föhn events, defined as the increase in hourly maximum 2 m temperature (T2 ) after removal of the diurnal cycle, varies from around 4.1 °C for max\nAR1 events to 5.6 °C for AR2 events and 7.1 °C for AR3 events (Supplementary Table S1).”\n4) The Background section currently provides separate introductions to the warming effects on the Antarctic Peninsula (AP), the impacts of ARs and föhn on the ice sheet, and the influence of AR-föhn events on AP temperatures. For a journal of Nature Communications' stature, this presentation is somewhat lengthy and could be significantly streamlined to present a more focused and concise narrative. And the Summary section primarily reiterates the main findings, resulting in repetition rather than synthesis. To meet the high standards of Nature Communications, this section should be evolved into a robust Discussion. It should delve into the implications of the results, address uncertainties, and outline unresolved questions that warrant future investigation\nPlease kindly check Ln 1-102 & Ln 340-420.\n5） The core novelty of the study lies in the classification of ARs and the subsequent analysis of the distinct atmospheric and radiative impacts of the four types. While valuable, this may not sufficiently constitute a major conceptual advance expected for a high-impact journal. To significantly strengthen the manuscript's contribution, I recommend expanding the analysis to investigate the downstream effects of these AR-föhn events on sea ice melt.\nWe appreciate this comment and agree with the reviewer that sea ice is an important subject for Antarctic AR studies. However, this topic is very complicated as sea ice can be impacted through multiple ways:\n1. Dynamically: depending on the wind direction and sea ice distribution, AR can either increase sea ice thickness & decrease sea ice coverage or vice versa.\n2. Thermodynamically: AR can significantly affect cloud formation thus radiative effects, with the potential for both warming and cooling impacts (Rowe et al. 2025a,b; Zou et al. 2023).\n3. Oceanic forcings.\n4. Impacts from precipitation (rainfall vs. snowfall).\nA comprehensive response to these questions is better suited for a separate paper, which the lead author is currently preparing (experimental model studies targeting the impacts of SST and sea ice on AR development). As such, we expand the discussion of sea-ice implications and identify them as priorities for future work. We also highlight the potential application of AR shape along the broader Antarctic coastline, which is the subject of ongoing research.\nLn 395-409: “Incorporating AR characteristics is essential for assessing surface impacts along the entire Antarctic coastline, as combined AR-föhn warming has been reported beyond the AP44. This compound impact not only affects ice-shelf stability but may also trigger the disintegration and advection of sea ice in the downstream20. Different AR configurations, characterized by distinct wind regimes and thermodynamic conditions45, together with oceanic forcing46, may be key to understanding sea-ice coverage and thickness over the AP. Under a warming and more moisture-rich future climate, stronger ARs may penetrate farther inland, facilitating greater moisture intrusion and more intense winds along the coast of the Antarctic continent47. A higher probability of stronger ARs could consequently result in more frequent and severe surface warming events48,49 and sea ice retreat45,50. Therefore, on individual ice shelves along the Antarctic coastline, those regional-scale atmospheric processes that are poorly resolved in global climate models will become increasingly important for ice shelf stability51 and sea ice conditions52,53, ultimately influencing Antarctic ice loss and global sea-level rise. This further highlights the need to develop high-resolution regional coupled models that explicitly resolve interactions among Earth system components.”\n1) Figs 1 and 2: I suggest consolidating Figure 1 and Figure 2b into a single, more comprehensive figure. Meanwhile, Figure 2a could be moved to the supplementary information.\nThank you for pointing this out. We have merged this comment with a related comment from another reviewer. Accordingly, Fig. 1 has been updated, and Fig. 2a has been moved to the Supplementary Materials as Fig. S2.\n“Fig. 1. PWRF model domains and overview of four AR shapes: a) D01 with a horizontal resolution of 30 km; b) D02 and D03 with resolutions of 6 km and 1.2 km, respectively; c) Classification of all events shown in Supplementary Fig. S1 into distinct AR shapes, which are zonal-perpendicular, zonal-like, convex, and concave. In panel b, the purple dashed line marks the AR detection zone; Spring Point, AWS 14, AWS 15 and AWS 17 are shown as red crosses, and the Escudero station as a purple dot. Topography in panels b-c is from the 6-km WRF D02 domain based on Reference Elevation Model of Antarctica (REMA). Larsen C denotes the Larsen C Ice Shelf.”\n“Supplementary Figure S1. Dominant AR shape for each AR-föhn event impacting the AP during austral summers from 2001 to 2022, as well as corresponding dates and ranking on the Enhanced AR Scale1. AR shapes of AR-family events are shown by dashed lines and a “F” after the date. If an AR-family event includes multiple shapes, the secondary shape is indicated by a dashed line. Also shown is the model topography (m) \n23\nfrom D02 at 6 km, based on the REMA dataset. LCIS in (b) refers to the Larsen C Ice Shelf. The dominant AR shape is manually defined based on the IVT field, following the AR axis at the peak of each event.”\n2) Lines 185-186 and Fig.4: It would be helpful to see the spatial patterns of the latent heat flux and longwave radiation to better evaluate their roles in the surface energy budget and the resulting melting processes.\nThank you for this suggestion. Following the reviewer’s recommendation, we have updated both Figs 3 and S2. Related analysis has been updated too. Please check Ln 197-234:\n“Fig. 3. Based on the k-means clustering shown in Fig. 2, composites of (a-d) the hourly mean rate of change of 2 m temperature ( 𝒅𝑻𝟐 ; ´100 °C h-1), (e-h) hourly average surface 𝒅𝒕\n24\ndownward shortwave radiation (W m-2), (i-l) hourly average surface downward longwave flux (W m-2) and (m-p) hourly average sensible heat flux (W m-2) over the LCIS for zonal-perpendicular (a,e,i,m), zonal-like (b,f,j,n), concave (c,g,k,o) and convex (d,h,l,p) AR shapes. The diurnal cycle has been removed from the 2 m temperature, and values are multiplied by a factor of 100 for visualization.”\n“Supplementary Figure S2. Based on the k-means clustering of Integrated Vapor Transport (IVT) shown in Fig. 2, composites of (a-d) hourly average latent heat flux (W m-2), (e-h) hourly average net shortwave radiation (W m-2) and (i-l) hourly average net longwave radiation (W m-2) for zonal-perpendicular (a,e,i), zonal-like (b,f,j), convex (c,g,k) and concave (d,h,l) AR shapes.”\n3) Lines 480-481: Please include a brief explanation of the physical significance of Fr values, specifically stating what Fr > 1 and Fr < 1 represent in the context of this study.\nThank you for this suggestion. After careful discussion among all authors, we decided to remove the Froude number calculation to avoid potential confusion, as the trajectory analysis and cross sections already clearly indicate whether low-level blocking occurs on the upwind side.\n25\n4) Lines 236-238 and Fig. 8: For the Fully Zonal AR type, the temporal evolution appears consistent with the patterns of IVT and downward longwave radiation. However, the SHF exhibits a lag of approximately 6 hours. To understand this discrepancy, it would be informative to examine the profile of diabatic heating.\nThank you for this suggestion. We believe this behavior arises from the strong spatial variability of SHF. Enhanced SHF is confined to regions near the mountain foothills or associated with föhn jet structures and is largely limited to the southern LCIS. If we look at the maximum SHF instead of median value, we can see a good match for both events, especially when IVT increases within the red box as shown in the figure below.\n“Fig. 7. Box plots of six-hourly changes over the LCIS of (a-e) zonal-perpendicular and (f-j) convex AR events, occurring in December 2008 and February 2013 respectively, and based on PWRF simulations. (a,f) Sensible Heat Flux (SHF; W m-2), (b,g) IVT (kg m-1 s-1), (c,h) surface downward shortwave radiation (Downward SW; W m-2), (d,i) surface downward longwave radiation (Downward LW; W m-2), and (e,j) 2 m temperature (T2; °C) from 00Z 1 to 23Z 3 Dec and from 00Z 22 to 23Z 24 Feb, respectively. The colored solid line in each panel shows the median value, while the green dashed line in (a,f) indicates the maximum value.”\n26\n5) Lines 264-268 and Fig.8: For the convex AR case, the SHF does not exhibit a pronounced change and its temporal evolution does not align closely with the observed variations in 2-meter temperature.\nAs shown above, the median SHF (solid green) in Fig. 7 (originally Fig. 8) does not capture this localized enhancement, whereas the maximum SHF value exhibits a pronounced increase between 00 UTC on 22 February and 12 UTC on 23 February 2013.\nWe have added the following text:\nLn 293–295: “Because SHF exhibited strong spatial variability and affected only limited areas, its median value remained relatively stable during the event.”\n6) Lines 283-297: These two sentences can be combined and made more concise.\nRevised as\nLn 314–317: “However, in both cases there was also a substantial contribution over the LCIS from the combined radiative and sensible heat flux term, which is negative and thus offset the isentropic drawdown contribution, with median values of −8.5 °C for the zonal-perpendicular event and −14.9 °C for the convex event.”\n7) Lines 287-289: Is SHF also a reduction?\nSHF on average has a positive impact.\nRevised as:\nLn 320: “...resulting in a reduction in downward SW, despite the positive contributions from the SHF.”\n8) Table S1: suggested to add the AR type.\nThank you for this suggestion. The dormant AR shape and change of T2 over the max\nLCIS are both added for each event in Supplementary Table S1.\n*Please note that each event may exhibit multiple shapes; this table reflects only the dominant one.\n9) Table S2: Lack of \"fully meridional\"\nYes, the fully meridional case we selected is particularly interesting and therefore warranted a deeper investigation. Although this event is classified as an AR2 and IVT is detected over the Larsen C Ice Shelf, surface air temperature over Larsen C barely increases. This investigation is ongoing, and we plan to submit the related study to Atmospheric Science Letters in 2026.\n10) Data: While the model performance is convincingly demonstrated for two representative cases, this validation would be strengthened by a more comprehensive evaluation. Could the authors please present statistical metrics (e.g., mean bias, correlation) that aggregate the results across all 37 simulated AR-föhn events?\nThank you for this suggestion. We have included additional evaluations based on available observations in Supplementary Table S4.\nThe same modeling configuration was used in Zou et al. (2023), Gorodetskaya et al. (2023), Wille et al. (2024a,b), and Rowe et al. (2025a,b), all of which demonstrate reliable model performance.\n28"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have effectively addressed all previously raised concerns and suggestions. They have thoroughly revised the manuscript, incorporating the necessary changes and improvements. I appreciate the diligence and clarity demonstrated in the revisions. I have no further questions or concerns regarding the content, significance, or quality of the paper. In my opinion, the manuscript now meets the required standards of the journal, and I recommend it for publication.", "reviewer_2": "All of my concerns have been addressed by the authors.\nI just have comments about a few grammatical errors in the sentences.\nL109 : Table. S1 -> Table S1\nL194, L212 : exhibites -> exhibits\nL248 : approach -> approached\nL338 : shows -> show", "reviewer_3": "Thank you for your efforts in addressing all my initial comments. I am satisfied with most of the revisions. However, I still have a few remaining concerns and suggestions, as outlined below:\n（1） The definition and selection criteria for \"AR–föhn events\" could be more clearly articulated. I suggest providing a more detailed description of the identification process in the Methods section, including specific thresholds, time windows, and spatial extents, to enhance reproducibility and facilitate comparison with future studies.\n（2） While the cluster analysis successfully identified four distinct AR shapes, the authors also note that some shapes (e.g., zonal-perpendicular and concave) are not always clearly distinguishable. I recommend including a discussion on this limitation and, if possible, exploring complementary classification methods or diagnostics to further validate the grouping.\n（3） Regarding the AR scale classification, it would be helpful to explicitly highlight in the main text the limitations associated with AR1 and AR3 events, and to indicate whether future work will specifically address AR3 events in more detail.\n（4） Please ensure that all abbreviations appearing in figures and tables are spelled out in full at their first occurrence, in accordance with standard journal guidelines."}, "author_response": "Reviewer #2 (Remarks to the Author):\nAll of my concerns have been addressed by the authors.\nI just have comments about a few grammatical errors in the sentences.\nL109 : Table. S1 -> Table S1\nL194, L212 : exhibites -> exhibits\nL248 : approach -> approached\nL338 : shows -> show\nThank you for pointing out these errors. They have been corrected in the revised manuscript.\nReviewer #3 (Remarks to the Author):\nThank you for your efforts in addressing all my initial comments. I am satisfied with most of the revisions. However, I still have a few remaining concerns and suggestions, as outlined below:\nThank you for all the suggestions. Please see the point-by-point responses below.\nThe definition and selection criteria for \"AR–föhn events\" could be more clearly articulated. I suggest providing a more detailed description of the identification process in the Methods section, including specific thresholds, time windows, and spatial extents, to enhance reproducibility and facilitate comparison with future studies.\nThank you for this suggestion. The requested information has now been added to the Methods section. We have also deposited example code together with the dataset to enhance reproducibility and facilitate comparisons with future studies.\nLn 514-515: “For Antarctica, a ranking of AR1 to AR3 on the Enhanced AR Scale indicates moderate to strong events (IVT thresholds > 250 kg m-1 s-1).” - specific thresholds\nLn 519-520: We then computed the regional mean of this gridded dataset within the target area (60S to 70S, 45W to 75W; indicated by the purple dashed box in Fig. 1 and the red dashed box in Supplementary Fig. S1a). - spatial extents\nLn 532-533: “The maximum T2 over the LCIS increased by more than 3 °C for over 6 hours after removing the diurnal cycle…” - specific thresholds\nLn 534- 535: “The start of warming is defined as the onset of the maximum T2 increase, and the end as when maximum T2 decreases by more than 2 °C from its peak.” - time windows\nWhile the cluster analysis successfully identified four distinct AR shapes, the authors also note that some shapes (e.g., zonal-perpendicular and concave) are not always clearly distinguishable. I recommend including a discussion on this limitation and, if possible, exploring complementary classification methods or diagnostics to further validate the grouping.\nThank you for this suggestion. We have revised this and are actively looking for different methods to better classify AR shape (e.g., Self-organizing map). This will be a value aspect for our future investigation.\nLn 390– 391: “More advanced machine-learning techniques, such as self-organizing maps39, may help better characterize AR shape and curvature.”\nRegarding the AR scale classification, it would be helpful to explicitly highlight in the main text the limitations associated with AR1 and AR3 events, and to indicate whether future work will specifically address AR3 events in more detail.\nThank you for this suggestion. Some limitations and uninvestigated aspects remain for AR3, as several of these cases belong to AR-family events.\nRevised as suggested:\nLn 379-380: “Notably, three of the four AR-family events belong to AR3, warranting further investigation of this category and its compound impacts.”\nPlease ensure that all abbreviations appearing in figures and tables are spelled out in full at their first occurrence, in accordance with standard journal guidelines.\nRevised as suggested (e.g., Figs. 2 and 3). When space in the figure is limited, the full variable name is provided in the caption."}]

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