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# Atmospheric Response to Antarctic Coastal Polynyas

Matthias Noël, Sébastien Masson, clement rousset

► To cite this version:

Matthias Noël, Sébastien Masson, clement rousset. Atmospheric Response to Antarctic Coastal Polynyas. Journal of Geophysical Research: Atmospheres, 2025, 130 (14), 10.1029/2025jd043319. hal-05266957

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Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

10.1029/2025JD043319

**Key Points:**

* The atmospheric response to the opening of Antarctic coastal polynyas in Prydz Bay, East Antarctica, is explored using the Weather Research and Forecasting model

* Polynyas release heat, boost convection, increase boundary layer thickness, create a low-pressure anomaly and strengthen surface winds

* A momentum budget reveals a balance between vertical mixing, pressure gradient forces, and vertical mixing above coastal polynyas

**Correspondence to:**

M. Noel, matthias.noel@locean.ipsl.fr

**Citation:**

Noel, M., Masson, S., & Rousset, C. (2025). Atmospheric response to Antarctic coastal polynyas. *Journal of Geophysical Research: Atmospheres, 130*, e2025JD043319. https://doi.org/10.1029/2025JD043319

Received 8 JAN 2025

Accepted 2 JUL 2025

© 2025. The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Atmospheric Response to Antarctic Coastal Polynyas

M. Noel¹, S. Masson¹, and C. Rousset¹

¹UMR 7159 CNRS-IRD-MNHN, LOCEAN-IPSL, Sorbonne Université, Paris, France

**Abstract** The lack of sea ice in Antarctic coastal polynyas enables strong ocean-to-atmosphere heat fluxes and intense sea-ice production. Although these features are primarily driven by intense offshore winds, their impact on local atmospheric conditions remains poorly understood. This study employs a high-resolution polar Weather Research and Forecasting model configuration over the Prydz Bay region, East Antarctica, home to two major coastal polynyas: Cape Darnley and Mackenzie Bay polynyas. Sensitivity experiments over three winters (2014–2016) comparing simulations with and without polynyas reveal a marked and spatially confined atmospheric response. Polynya openings induce substantial surface heat release (up to 700 W m⁻²), warming near-surface air by over 5 K and triggering convection and clouds. This results in a thickening of the atmospheric boundary layer and the development of stronger surface winds (up to 4 m s⁻¹) that converge toward a low-pressure anomaly. A particular emphasis is placed on the mechanisms controlling wind anomalies, using a dedicated wind tendency analysis. This diagnosis highlights that the processes involved depend on location and altitude within the polynya region and involve a balance between vertical mixing, horizontal advection, and pressure gradient forces—vertical mixing acting as the initiating factor. This study provides a novel modeling framework used to isolate the atmospheric response to coastal polynyas, overcoming limitations of previous studies and coarse-resolution models. It offers new insights into the role of polynyas in modulating regional climate and improving understanding of the processes driving their dynamics.

**Plain Language Summary** Coastal polynyas are areas of open ocean near the Antarctic coast that form in winter when strong winds push sea ice away. These polynyas are crucial for sea-ice production and dense water formation, which contribute to the creation of Antarctic Bottom Water, an important component of global ocean circulation. However, the physical processes driving polynya dynamics are not fully understood. In this study, we used high-resolution computer simulations to investigate the atmospheric response to coastal polynyas in East Antarctica, focusing on Cape Darnley Polynya and Mackenzie Bay Polynya. We show that polynyas significantly impact local weather conditions. Heat released from the open polynyas (up to 700 W/m²) warms the air above by up to 5°C, triggering convection and cloud formation. This leads to stronger surface winds (up to 4 m/s) that converge into a low-pressure area. Our findings provide new insights into the role of coastal polynyas in shaping the local climate. These results suggest that the atmospheric response to polynya openings may influence polynya dynamics, creating a feedback loop with potential implications for regional and global climate processes.

## 1. Introduction

During winter, the polar regions are predominantly covered by consolidated sea ice, with only a few small areas remaining as open water or covered by thin ice. These regions are referred to as “leads” when they are ephemeral linear openings no wider than 1 km (Barry et al., 1993), and “polynyas” when they are larger, recurring, areas of open water, sometimes spanning several hundred kilometers (D. Barber et al., 2001; Smith et al., 1990). Polynyas are typically classified into two types: open-water (or sensible heat) polynyas, and coastal (or latent heat) polynyas (D. G. Barber & Massom, 2007). Open-water polynyas form when sea ice melts due to warm water upwelling to the surface, whereas coastal polynyas are created by mechanical forces, such as winds and ocean currents, which drive sea ice away from the coast (e.g., Williams et al., 2007; Zwally et al., 1985). The location of these polynyas is influenced by topographic features such as coastlines, ice shelves, grounded icebergs, or landfast ice (e.g., Morales Maqueda et al., 2004). This study focuses on Antarctic coastal polynyas.

Despite their relatively small size and intermittent occurrence, Antarctic coastal polynyas exert a significant influence on the ocean and atmosphere. Southern Ocean coastal polynyas, despite covering only about 1% of the maximum sea-ice extent, account for approximately 10% of total sea-ice production (Tamura et al., 2008). The ocean releases a substantial amount of heat into the atmosphere above polynyas, reaching up to several hundreds
of W m⁻² (e.g., Maykut, 1978; Willmott et al., 2007), which in turn promotes sea-ice formation through intense surface cooling. This process is self-sustaining due to the persistent offshore winds that continually push the sea ice away, earning coastal polynyas the nickname “ice factories.” This intense ice production leads to brine rejection, which aids in the formation of Dense Shelf Water, a precursor to Antarctic Bottom Water (AABW) which in turn fills the ocean's abyssal regions and accounts for 30%–40% of the global ocean volume (Johnson, 2008).

From an atmospheric perspective, one of the most significant impacts of open-ocean and coastal polynya formation is the substantial increase in turbulent heat fluxes (e.g., Dare & Atkinson, 1999). These fluxes often exceed 200 W/m² (e.g., Ayres et al., 2024; Golledge et al., 2025; Moore et al., 2002; Renfrew et al., 2002; Roberts et al., 2001; Smith et al., 1983; Steffen, 1986; Stössel et al., 2008; Weijer et al., 2017). The presence of polynyas also enhances moisture fluxes at the surface (Minnett & Key, 2007), which can lead to cloud formation in cold air. These clouds reduce shortwave radiation and increase longwave downward radiation (Pinto & Curry, 1995), potentially creating a positive feedback that promotes further polynya formation (Curry & Ebert, 1992; Uttal et al., 2002). For example, by combining satellite data and reanalysis over the North Water polynya, Monroe et al. (2021) found that the surface Cloud Radiative Effect (CRE) was +18 W/m² larger over the polynya compared to the surrounding sea ice.

The opening of a coastal polynya also significantly affects mesoscale atmospheric motions, particularly by accelerating surface winds across the polynya (e.g., Gallée, 1997; Morales Maqueda et al., 2004; Vihma, 1995). This acceleration can be attributed to several physical processes. First, strong turbulent heat fluxes generate plumes that mix high-momentum air downward, thus accelerating surface winds, as demonstrated by Dare and Atkinson (1999, 2000) in their simplified 2D atmospheric model. Second, pressure gradients created by low pressure anomalies above polynyas may also favor wind speed up. The importance of this low-pressure anomaly is still debated. It was hypothesized as a crucial process over the open-ocean Weddell Sea polynya by Timmermann et al. (1999) in their coupled sea ice-mixed layer and lower atmosphere model. Pressure anomalies over this polynya are typically around 2 to 8 hPa when either NCEP-NCAR reanalysis data (Moore et al., 2002) or modeling studies (Ayres et al., 2024; Glowienka-Hense, 1995; Weijer et al., 2017) are used. Third, strong temperature contrasts between the polynya and the Antarctic continent drive an onshore breeze toward the polynya (Gallée, 1997; Kottmeier & Engelbart, 1992; Savijärvi, 2011). Fourth, the reduced aerodynamic surface roughness over the ocean compared to the surrounding ice pack facilitates the wind acceleration (e.g., Andreas et al., 1984). The resultant winds speed up causes divergence and downdrafts on the upwind side while creating convergence and updrafts on the downwind side.

Polynyas are essentially warm regions within a cold environment, so we could expect similar physical processes to occur between warm ocean eddies and the atmosphere. Both features involve heat transfer and dynamic interactions that can influence atmospheric circulation and surface winds in comparable ways. The impact of sea surface temperature (SST) fronts has been extensively studied in the context of eddy-atmosphere interactions in the global ocean (e.g., Chelton & Xie, 2010). These studies using satellite observations have shown a strong correlation between SST fronts and, not only the speed of the surface winds but also the curl and divergence. More recently, the influence of fronts on the atmospheric boundary layer (ABL) and vertical wind structure was reviewed by Seo et al. (2023). They showed that the ABL thickens and a secondary circulation counter to the main flow develops at the SST front when winds blow from a cold to a warm sea. These findings will be considered in relation to our polynya study to determine whether or not the same processes are observed. By comparing the dynamics of SST fronts with those in polynyas, we can assess whether similar atmospheric responses, such as boundary layer thickening and secondary circulation, occur in both environments.

The difficulty in obtaining observations in ice-covered regions and the complexity of air-sea interaction processes over polynyas (encompassing small scale interactions between the ocean, atmosphere, and sea ice), explains why a clear understanding of the atmospheric response to Antarctic coastal polynyas remains elusive. Many studies on this topic have relied on simplified, often 2D models, driven by either averaged atmospheric conditions in the region (Dare & Atkinson, 1999, 2000; Gallée, 1997) or have been limited to geostrophic responses (Timmermann et al., 1999). On the other hand, studies using fully coupled ocean-ice-atmosphere models tend to integrate all the feedback mechanisms between the different components, rather than isolating the atmospheric response (e.g., Weijer et al., 2017). Most studies investigating how the atmosphere responds to polynyas (Ayres et al., 2024; Dare & Atkinson, 1999, 2000; Kottmeier & Engelbart, 1992; Moore
et al., 2002) have done so in the case of open-water polynyas, which are characterized by different atmospheric conditions compared to coastal polynyas, such as the absence of katabatic winds or katabatic jumps (D. G. Barber & Massom, 2007). Finally, although some studies (e.g., Dare & Atkinson, 1999, 2000) have examined the effect of polynyas on winds and identified the physical mechanisms responsible for wind acceleration, the quantification of their respective contributions remains absent.

This study investigates the impact of the opening of Antarctic coastal polynyas on the atmosphere, thereby
addressing the following research questions:

* How does the surface heat balance above polynyas differ from that of the surrounding sea-ice-covered regions?

* What are the implications for local meteorological conditions, particularly in terms of air temperature, boundary layer convection, cloud formation, and wind patterns?

* What are the dominant mechanisms driving changes in atmospheric dynamics in response to polynya opening?

To answer these questions, we adopt a modeling approach focused on Prydz Bay, a region selected for its two coastal polynyas (Nakata et al., 2021), which may exhibit distinct atmospheric responses: (a) the Cape Darnley Polynya (CDP), the second most productive Antarctic coastal polynya in terms of sea-ice formation (Tamura et al., 2008) and a major source of AABW (Ohshima et al., 2013), and (b) the Mackenzie Bay Polynya (MBP), located near Amery Ice Shelf landmarks and subject to colder atmospheric conditions than the CDP. We build a series of regional simulations using the Weather Research and Forecasting (WRF) model at 3 km resolution and present an exhaustive analysis of the atmospheric response to the opening of these polynyas, including the heat budget, air mass characteristics, atmospheric dynamics and their drivers.

The paper is structured as follows: Section 2 introduces the model and outlines the sensitivity experiments. Section 3 is devoted to the validation of the simulations. Section 4 presents the results of the sensitivity experiments. Section 5 concludes the study.

## 2. Data and Methods

### 2.1. Polar WRF: Prydz Bay Configuration

This study uses the WRF model, specifically the Polar version 4.3.3 (Bromwich et al., 2013), which includes physical packages tailored for polar regions. Given the crucial role of ice and snow in the surface atmospheric heat budget, we place particular emphasis on accurately representing these elements in our analysis.

#### 2.1.1. Domain and Geography

The model employs a two-way nested downscaling approach. The parent domain (d01) uses a 1/4° Mercator projection centered at 73°E, 67.2°S, with a child domain (d02) having a 1/12° resolution (approximately 3 km at Cape Darnley). The model configuration is shown in Figure 1. Unless otherwise stated, most of the analyses presented in this study are conducted over the child domain.

The vertical grid consists of 60 eta levels, with a nominal resolution of 10 m near the surface. Twenty of these levels are located below 1,000 m, and the model top is set at 10 hPa. In our simulations, we replace the default WRF topography and land use data sets over Antarctica with the Reference Elevation Model of Antarctica (REMA) topography and the AntarcticaLC2000 land use data (Howat et al., 2019). This high-resolution (1 km) data set provides more accurate and detailed representations of Antarctic terrain and surface cover, thereby improving the model's ability to simulate key atmospheric processes in the region, such as katabatic winds.

#### 2.1.2. Parameterizations

Following Vignon et al. (2019) and Xue et al. (2022), we utilize WRF with the following physical parameterizations: the Rapid Radiative Transfer Model (RRTMG, Iacono et al., 2008), the Noah Multi-Parameterization land surface model (He et al., 2023), the Mellor-Yamada-Nakanishi-Niino (MYNN) planetary boundary layer scheme (Nakanishi & Niino, 2009) along with its associated surface layer scheme (Olson et al., 2021), and the Kain-Fritsch cumulus scheme (Kain, 2004). Two microphysics schemes were tested: the scheme proposed by
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Polar WRF domains and topographic elevation map. Domains d01 (black line) and d02 (red line) use a Mercator projection of 1/4° and 1/12° of resolution (resp.). Magenta symbols correspond to the positions of the four weather stations that are used for validation (Davis, Mawson, Taishan, and Zhongshan). The orange and blue boxes delineate the boundaries of the Cape Darnley (Cape Darnley Polynya) and Mackenzie Bay (Mackenzie Bay Polynya) polynyas zones (see Section 2.3.1). The dotted lines represent the positions of vertical cross sections (see Section 4).</figcaption></figure>

Vignon et al. (2021) and the default Polar WRF scheme (Morrison et al., 2009). We selected the Vignon et al. (2021) scheme as it produces drier air, which aligns more closely with observations (not shown).

The surface layer scheme employs stability functions based on the “Kansas” form, using the formulations of Grachev et al. (2000) and Chenge and Brutsaert (2005). Surface roughness is allowed to vary temporally as a function of surface type (open ocean or sea ice). To evaluate the role of surface roughness heterogeneity, we performed a sensitivity test with fixed roughness lengths over sea ice and open water (not shown). This evaluation is further discussed in Section 4.4.2.

Surface fluxes are calculated following the Polar WRF methodology. For each ocean grid cell, fluxes are computed separately over sea ice and open water, with the land surface and surface layer schemes called independently for each surface type. These fluxes are then weighted by the local sea-ice concentration and combined to yield grid-cell averaged values, accounting for the mixed influence of both surfaces. The planetary boundary layer, microphysics, and cumulus schemes then operate using these averaged fluxes.

### 2.1.3. Sea-Ice Boundary Conditions

To prescribe lower boundary conditions in the model, reliable observations of sea-ice concentration are required, as well as a reasonable estimate of sea-ice and snow thicknesses.

For sea-ice concentration, we use the University of Bremen product based on the ARTIST Sea Ice (ASI v5.4) algorithm applied to data from the Advanced Microwave Scanning Radiometer 2 (AMSR2). This data set provides daily sea-ice concentration at a resolution of 3.125 km and spans the period from 2013 to the present (Melsheimer & Sreen, 2019). However, AMSR2 data may exhibit inaccuracies in detecting coastal features and landfast ice due to sensor resolution limitations, proximity to land-sea boundaries, or overlapping satellite tracks.
Consequently, large grounded icebergs such as D15, the Amery Ice Shelf, and some landfast ice regions are not properly represented (see Appendix A). To address this, we corrected the AMSR2 coastline using the circum-Antarctic landfast sea-ice data set from Fraser et al. (2020). This product has a 1 km resolution and provides biweekly landfast ice coverage, including the outlines of ice shelves and icebergs, from 2000 to 2018. In our simulations, we set sea-ice concentration to 100% where landfast ice is identified.

To obtain accurate estimates of sea-ice thickness in polynyas we use the SMOS Level-3 sea-ice thickness product from the European Space Agency (ESA v3.3; European Space Agency, 2023). This product combines L-band (1.4 GHz) brightness temperatures with a thermodynamic sea-ice model and a radiative transfer model (Kaleschke et al., 2024; Tian-Kunze et al., 2014). It detects thin ice up to approximately 1 m, which is particularly suitable for young ice surrounding polynyas. Daily estimates are available from 2010 onward for the freezing season (March to October) at a spatial resolution of 12.5 km.

For snow thickness, we assume that new sea ice (up to 20 cm) is snow-free. For thicker ice, we use monthly estimates derived from the Altimetric Snow Depth (ASD) product, which exploits differences in radar penetration depths between the Ka-band SARAL/AltiKa and the Ku-band CryoSat-2 sensors (Garnier et al., 2021). To match the ASD snow depth with SMOS ice thickness, we compute an average snow-to-ice thickness ratio from the ASD data set over the Antarctic pack during winter. We find a mean snow depth of approximately 10% of the sea-ice thickness (not shown), a ratio we apply in our simulations. The resulting snow depths range from 2 to 14 cm.

WRF includes a subgrid-scale parameterization of snow and sea-ice thickness distributions, which simulates the increase of heat conduction through unresolved thin ice. In our setup, this leads to an increase in conductivity by a factor of 1.65 (as done for single ice category simulations using SI3, e.g., Fichefet & Maqueda, 1997).

Sea ice and snow-covered sea ice albedo are prescribed as a constant value of 0.82.

### 2.1.4. Other Boundary Conditions: ERA5

The ERA5 reanalysis is used to prescribe SST and to force the model at the lateral boundaries, including wind vectors, moist potential temperature, humidity, geopotential height, and dry air mass. ERA5 is produced by the European Center for Medium-Range Weather Forecasts (ECMWF) and represents the latest generation of reanalysis data (Hersbach et al., 2020). It offers a horizontal resolution of approximately 31 km and includes 137 vertical levels extending from the surface up to 0.01 hPa. ERA5 is also used to evaluate the performance of our reference simulation (see Section 3). According to Caton Harrison et al. (2022), ERA5 outperforms other reanalyzes—such as MERRA-2 and JRA-55—in representing wind and temperature profiles near the Antarctic coast. However, it still misses some features of coastal winds, particularly strong surface winds exceeding 20 m/s. Additionally, ERA5 exhibits a known warm bias of approximately 5–10 K over sea ice, especially over the Arctic winter ice pack (Batrak & Müller, 2019). A similar warm bias of around 5 K has also been identified over Antarctic sea ice (Z. Wang et al., 2024). This bias has been attributed to an oversimplified representation of the cryosphere—a uniform 1.5 m sea-ice thickness without snow—and to deficiencies in cloud cover representation (Z. Wang et al., 2024). These limitations lead us to consider two additional data sets in our validation process (see next section).

### 2.1.5. Time Integration

For all simulations, the model is run over three austral winter seasons (June to September) for the years 2014–2016. The analysis period spans from 15 June to 15 September. Each winter, the simulations are initialized on 1 June using the observational data sets described above. The reference simulation, hereafter referred to as WRF REF, is forced at its lateral boundaries using 6-hourly interpolated data. This experiment is evaluated in Section 3 using the observational data sets presented in the following section.

## 2.2. Validation Data

### 2.2.1. Weather Station Measurements

Few long-term in situ observations of Antarctic surface meteorology are available due to the region's harsh environmental conditions and limited accessibility. In this study, we use air temperature and wind speed measurements from manual weather stations at Mawson and Davis (Barnes-Keoghan, 2000), and from Automatic
<caption>**Table 1**</caption>

Mean Value and Standard Deviation of the Surface of Cape Darnley Polynya (CDP) and Mackenzie Bay Polynya (MBP) Polynyas

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Observations</th><th colspan="5">WRF REF (threshold)</th></tr><tr><th>70%</th><th>75%</th><th>80%</th><th>85%</th><th>90%</th></tr></thead><tbody><tr><td>CDP area (10<sup>3</sup> km<sup>2</sup>)</td><td>11.6 ± 4.6</td><td>6.6 ± 3.3</td><td>9.3 ± 4.4</td><td><strong>10.5 ± 4.7</strong></td><td>12.1 ± 5.2</td><td>14.2 ± 5.7</td></tr><tr><td>MBP area (10<sup>3</sup> km<sup>2</sup>)</td><td>4.7 ± 2.7</td><td>2.9 ± 1.7</td><td>4.4 ± 2.7</td><td><strong>5.0 ± 2.9</strong></td><td>5.9 ± 3.2</td><td>7.1 ± 3.5</td></tr></tbody></table>

Note. Polynya surfaces from observations are provided by Nakata et al. (2021), from March to October for the period 2003–2010. Surfaces from REF on the child grid d02 were calculated as a function of threshold sea-ice concentrations (as described in Section 2.3.1) (from April to October period 2014–2016). The chosen detection threshold is 80%. The surface areas associated with this threshold are the closest to the observations (see bold values in the table).

Weather Stations (AWS) at Panda and Zhongshan (Y. Wang et al., 2023) (see Figure 1 for their geographic location).

Sensors are installed at approximately 2 m above the ground, measuring temperature at all stations and wind speed at Taishan. At Davis, Mawson, and Zhongshan, wind speed is measured at a height of 10 m. The temporal resolution is 1 min at the Australian stations and 3 hr at the AWS. The uncertainty in temperature measurements is estimated to be ±0.5°C (Y. Wang et al., 2023). The uncertainty in wind speed is ±1 m/s at the Taishan and Zhongshan stations (Ding et al., 2022; Y. Wang et al., 2023), and ±0.5 m/s at the Davis and Mawson stations (Barnes-Keoghan, 2000).

The Mawson station lies on an isolated coastal rocky outcrop, where the ice sheet rises steeply from coastal ice cliffs up to the interior plateau. In contrast, Davis is situated east of the Amery Ice Shelf, on a large ice-free area with a gradual slope ascending southwestward toward the ice cap (Alexander & Murphy, 2015; Vignon et al., 2019). The Zhongshan and Taishan stations are part of the PANDA transect between Zhongshan and Dome A, near longitude 77°E (Ding et al., 2022). Zhongshan is located in a coastal region with steep terrain (Shengkai et al., 2008), whereas Taishan lies on the interior plateau with a gentle slope and an elevation of approximately 2626 m (Minghu et al., 2011). Winds are strong and predominantly katabatic at these sites (Ding et al., 2022). As we will discuss in Section 3.1, the contrasting environments of these stations require careful consideration when comparing WRF and ERA5 simulations, particularly at Zhongshan and Davis, where significant spatial and temporal wind variability is expected.

### 2.2.2. AntAir ICE

In addition to localized in situ data, we use the AntAir ICE near-surface air temperature product (Nielsen et al., 2023) to validate our simulations (see Section 3). AntAir ICE provides daily air temperature estimates on a 1 km horizontal grid. The product is derived from a linear regression between MODIS skin temperature observations (Barnes et al., 2003) and near-surface air temperature measurements from AWS stations. Contamination by clouds is filtered out in this data set so that AntAir ICE only includes temperature estimates over cloud-free areas covered by sea ice. According to Nielsen et al. (2023), AntAir ICE demonstrates higher accuracy than the ERA5 reanalysis in reproducing in situ air temperature, particularly in terms of mean bias.

## 2.3. Sensitivity Experiment: Closing of Polynyas

To investigate the atmospheric response to polynya openings, we first need to accurately detect the Cape Darnley (CDP) and Mackenzie Bay (MBP) polynyas.

### 2.3.1. Detection of Polynyas

Based on the map of observed polynya occurrence from Nakata et al. (2021), we define two regions corresponding to CDP and MBP (see Figure 1), where polynyas are recurrently detected. In general, a polynya is identified when the sea-ice concentration drops below a specified threshold. We use the modified AMSR2 product (see Section 2.1.3) as the reference sea-ice concentration data set for three austral winters (April-October), from 2014 to 2016. A sensitivity analysis is conducted using commonly applied thresholds in the literature (ranging from 70% to 90%; e.g., Mohrmann et al., 2021). Mean polynya areas corresponding to each threshold are summarized in Table 1.
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Concentration (a, b) and sea-ice thickness (c, d) for WRF REF (a, c) and WRF CP (b, d) simulations, averaged over three winters (2014–2016). The dotted (resp. dashed) lines, which are identical across all figures, delimit areas where polynyas are open more than 30% (resp. 80%) of the time in the WRF REF simulation.</figcaption></figure>

The estimated extents of CDP and MBP are highly sensitive to the selected threshold, ranging from 6,600 to 2,900 km², respectively, to nearly double these values—up to 14,200 and 7,100 km² (Table 1). By comparing our estimates with those of Nakata et al. (2021), who used AMSR-E/Aqua Level 2A brightness temperature data, we find the closest agreement when using an 80% threshold for both polynyas. In this case, the discrepancy is reduced to only ~1,000 km² for CDP and ~400 km² for MBP, and the associated standard deviations appear realistic.

For the remainder of this study, we therefore use the 80% threshold to detect polynyas. In the all relevant figures (Figures 2–12), the frequency of polynya openings is represented by dotted and dashed contours, indicating opening occurrences of respectively 30% and 80%.

In this paper, the values defined as “averaged over CDP or MBP” correspond to daily data spatially averaged over the region corresponding to the daily polynya in the REF experiment (defined with the 80% threshold mentioned above). The resultant daily time series from 15 June to 15 September, yearly from 2014 to 2016, is then temporarily averaged and the standard deviation is used to provide the error bar estimate.

### 2.3.2. Closing of Polynyas

In order to isolate the atmospheric response which is solely due to the polynya's opening, we build a sensitivity experiment called Closed Polynya (WRF CP). In this case, we change the sea ice boundary conditions so that polynyas are no longer tracts of open water, but instead are artificially covered by sea ice. To do so, we modify the WRF boundary conditions inside the polynyas, which are determined by an ice concentration threshold of 80% in WRF REF. These selected regions are extended by four grid points in order to smooth the transition at the polynya boundaries. There, the sea-ice concentration and thickness values are replaced by the surrounding values using an iterative filling algorithm. The use of Gaussian smoothing with progressively larger kernels during the filling
<figure><img src="image_6.png" /><figcaption>**Figure 3.** 2 m air temperature (panels a–d; left) and 10 m wind speed (panels e–h; right) from WRF (blue, y-axis) and ERA5 (orange, y-axis) compared to in situ measurements (x-axis) at the four weather stations during the three winters (15 June to 15 September) 2014–2016. The lines represent the linear regressions of WRF and ERA5 relative to the observations, whereas the shaded areas indicate the lower and higher limit within a 28 km radius around the stations. The black dashed lines correspond to the $y = x$ line.</figcaption></figure>

process ensures smooth and realistic values in the filled areas. Snow thickness on sea ice is adjusted based on the ice thickness, following the method outlined in Section 2.1.3. The resulting sea ice field from this CP experiment is shown in Figure 2 together with the WRF REF experiment. Although WRF REF exhibits polynyas covered by 59% of ice on average for the CDP, this number reaches 98% in the WRF CP experiment. Similarly, ice thickness
increases from 24 cm in WRF REF to 69 cm in WRF CP. For Mackenzie Bay, the results are comparable, with ice coverage increasing from 63% to 98% and ice thickness rising from 25 to 58 cm.

Comparing the reference simulation (WRF REF) and the Closed Polynya (WRF CP) experiments allows us to filter out large-scale atmospheric signals and isolate the local atmospheric responses specifically associated with the opening of the polynyas. This approach ensures that the observed differences are solely due to the presence or absence of polynyas. This atmospheric response is highly localized over the polynya areas. Elsewhere in the domain, the WRF REF and WRF CP simulations remain very similar. An evaluation of this similarity is provided in Appendix B.

### 3. Model Validation

To validate the realism of our simulations, we compare surface temperature and wind speed from our WRF REF simulation with data from the weather stations AntAir-ICE and ERA5. To ensure consistency, all data must be taken at the same height above the ground and within the same geographic area. For temperature, all data represent estimates taken between the surface and a height of 2 m, which is close enough to be considered comparable. For wind speed, most data are obtained at a height of 10 m, except at Taishan, where wind speed must be extrapolated to 10 m using a power-law wind profile under the assumption of neutral atmospheric conditions (Dong et al., 2020; Paulson, 1970; Sanz Rodrigo, 2011).

Comparing station measurements with gridded model outputs is challenging in areas with strong spatial variability (such as land-sea interfaces and steep terrain) common to most station sites in this study. To account for this, we use the nearest grid point from WRF REF and ERA5, and compute median, minimum, and maximum values within an envelope of 28 km radius (up to 137 points for WRF REF and 5 for ERA5). The 28 km radius was chosen so as to match the spatial resolution of ERA5, thereby allowing for the inclusion of at least 5 grid points.

#### 3.1. Validation Against Weather Station Data

Scatter plots of 2 m air temperature and 10 m wind speed from WRF and ERA5, compared to in situ measurements at four meteorological stations (Mawson, Davis, Zhongshan, and Taishan, see Figure 1) are presented in Figure 3 for the winters (mid-June to mid-September) of 2014–2016. Correlation values and mean biases (with ± standard deviation) are also provided in Figure 3. Time series of these variables for the winter of 2015 are shown in Appendix C (Figure C1).

The WRF REF simulation demonstrates strong skill in reproducing surface temperature at meteorological stations, with mean biases comparable to those of ERA5, which assimilates observations from the Davis, Mawson, and Zhongshan stations. Notably, at Taishan (the only station not assimilated in ERA5) WRF outperforms ERA5, showing a smaller warm bias of +0.8 K compared to +3.5 K in ERA5, consistent with the findings of Zhu et al. (2021). The largest cold biases in WRF are observed at Davis and Mawson (–2.6 and –2.3 K, respectively), though these sites exhibit significant spatial heterogeneity that likely contributes to the observed discrepancies (Figures B1a, B1b, 3a, and 3b). In addition, WRF captures temporal variability effectively, with correlation coefficients exceeding 0.9 across all stations. A domain-wide evaluation of simulated surface temperatures, using the AntAir ICE data set, is presented in Section 3.2.

Regarding surface wind speed, both WRF and ERA5 exhibit a tendency to underestimate strong winds and overestimate weak winds (Figures 3e–3h, right panels). However, WRF clearly outperforms ERA5 at Mawson and Zhongshan in representing high wind conditions. Analysis of daily variability during 2015 (Figure C1) reveals that spatial heterogeneities lead to a greater spread in wind speed than in temperature. Furthermore, although ERA5 underrepresents extreme wind events exceeding 20 m/s along the Antarctic coast (see also Caton Harrison et al., 2024), WRF more accurately simulates the highest wind speeds, particularly at Davis, Mawson, and Zhongshan (Figures 3e–3g).

Although WRF generally provides a good representation of temperature and wind speed at the stations, it also offers a more realistic simulation of strong wind gusts compared to ERA5, despite a tendency to underestimate the most intense coastal winds.
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Near-surface temperature differences averaged over the three winters from 2014 to 2016 (15 June to 15 September) between AntAir-ICE and (a) WRF, (b) ERA5. The purple contours correspond to areas occupied by landfast ice. Dotted and dashed lines delimit polynya boundaries (see Figure 2).</figcaption></figure>

## 3.2. Validation Against AntAir-ICE

For this comparison, the AntAir ICE product is projected onto the ERA5 and WRF grids using a bilinear interpolation (Figure 4). Since AntAir ICE provides reliable temperature estimates only over surfaces covered by ice or snow, ocean grid cells with a mean winter sea-ice concentration below 95% are excluded from the analysis. Over the Antarctic continent, WRF outperforms ERA5, exhibiting a lower mean bias of approximately 1.5 K, compared to 3.5 K for ERA5. This difference is particularly pronounced over the Amery Ice Shelf where WRF shows minimal bias while ERA5 displays a substantial warm bias of 3 K. Over sea ice, both models demonstrate similar performance, with mean biases around 2 K for WRF and 3 K for ERA5. In regions of landfast ice (outlined in purple in Figure 4), however, biases are significantly larger, with mean values reaching +5 K for ERA5 compared to +3 K for WRF.

The WRF simulation performs well within the study domain, accurately capturing wind intensity and variability along the coast, representing surface temperature fields across the region, and reproducing temporal temperature variability at station locations. These results support our confidence in using WRF for the subsequent analyses in this study.
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Meteorological variables from the WRF REF simulation averaged over the three winters from 2014 to 2016 (a) is the temperature at 2 m height in °C, (b) is the wind speed at 10 m height in m/s, (c) is the relative humidity at 2 m height in %, (d) is the atmospheric boundary layer height in m, and (e) is the net surface heat flux (positive upward) in W/m². Dotted and dashed lines delimit polynya boundaries (see Figure 2).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 6.** Potential temperature on vertical sections through (a) Cape Darnley Polynya (CDP) and (b) Mackenzie Bay Polynya (MBP) in WRF REF. The figures are obtained by averaging the quantities plotted over the study period (from 15 June to 15 September from 2014 to 2016). The vertical white line delimits the coastline. The shaded bars below the sections provide information on surface ice conditions. Dark purple corresponds to the zone where the polynya is open over 80% of the time, and the light purple corresponds to an opening threshold of 30% of the study time.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Analysis of the surface heat budget averaged over the three winters of the experiment. (a) The difference WRF (REF-CP) of the net surface heat flux. Dotted and dashed lines delimit polynyas boundaries (see Figure 2). (b) Decomposition of the surface heat flux balance (rounded to the nearest 5 W/m², positive upward) averaged over Cape Darnley Polynya and Mackenzie Bay Polynya for the simulations: WRF REF and WRF CP. LW and SW refer to longwave and shortwave radiation, respectively. The cross-hatched area represents the cloud contribution to the downward longwave radiation.</figcaption></figure>

# 4. Impact of Polynyas on the Atmosphere

In this section, we analyze the impact of polynya openings on several key atmospheric features, including the radiative surface balance, air mass characteristics, boundary layer dynamics, and local atmospheric circulation. We first characterize the average atmospheric conditions in our reference simulation before assessing the changes induced by polynya openings.

## 4.1. Averaged Regional Atmospheric Conditions

### 4.1.1. General Atmospheric Conditions

Figure 5 shows the general meteorological fields simulated by WRF REF, averaged over the three winters. Three distinct geographical areas can be identified: (a) the continent, (b) the pack-ice region, and (c) the polynyas (CDP and MBP). Over the continent, winter surface cooling and the high elevation of the Antarctic plateau lead to extremely cold temperatures, from -19°C near the coast down to -46°C for regions above 1,000 m (Figure 5a). Although the Amery Ice Shelf has a low elevation (below 200 m), its average surface temperature is -28°C. The prevailing surface winds are the offshore katabatic winds that are particularly strong over the steepest slopes. For instance, they exceed 15 m/s southwest of Cape Darnley, whereas remaining below 10 m/s over the flat Amery Ice Shelf (Figure 5b). Consistent with the extremely cold temperatures, the Antarctic plateau ranks among the driest regions on Earth, with specific humidity values as low as 0.24 g/kg, corresponding to a mean relative humidity of 57% (Figure 5c).

Airflow undergoes modification upon crossing the coastline due to abrupt changes in surface conditions. At Cape Darnley, the ABL thickens by more than 100 m, exceeding 350 m just a few kilometers offshore (see Figure 5d). This sudden change is caused by a “katabatic jump” or Loewe phenomenon (Ball, 1956; D. G. Barber & Mas-som, 2007; Pettré & André, 1991), but the polynya also plays a key role in the boundary layer thickening, as discussed later in Section 4.3.2. The effect of the “katabatic jump” is more limited off the Amery Ice Shelf, +250 m, as its gentle slope limits the amplitude of the slope discontinuity at the coast. Further offshore, although the presence of sea ice limits surface exchanges between the ocean and the atmosphere, the temperatures over the pack ice are nonetheless higher than along the coastline, averaging around -20°C. Humidity is comparatively higher as well, though still moderate, with mean values around 0.82 g/kg, corresponding to 83% relative humidity.

Over polynyas, the reduced sea-ice cover enhances surface heat flux exchanges, resulting in values that are substantially higher than those over the surrounding pack ice. For example, mean heat fluxes above the CDP reach
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Temperature and relative humidity difference between the two experiments (WRF REF-WRF CP), averaged over the total experiment duration (from 15 June to 15 September from 2014 to 2016). Panels (a) and (b) are anomaly (WRF REF-WRF CP) maps: (a) 2 m temperature in °C and (b) 2 m relative humidity in %. Dotted and dashed lines delimit polynya boundaries (see Figure 2). Panels (c) and (d) correspond to differences of potential temperature in vertical cross-sections above Cape Darnley Polynya and Mackenzie Bay Polynya defined by the dashed lines in Figure 1. For panels (c) and (d), the white dashed (dotted) lines correspond to the WRF CP (WRF REF) atmospheric boundary layer height. The vertical white line delimits the coastline. The shaded bars below the sections provide information on surface ice conditions. Dark purple corresponds to the zone where the polynya is open over 80% of the time, and the light purple corresponds to an opening threshold of 30% of the study time.</figcaption></figure>

approximately 400 W/m², with local maxima up to 680 W/m², compared to around 50 W/m² over adjacent pack ice (Figure 5e). The enhanced ocean-to-atmosphere heat flux over the two polynyas leads to a local increase in 2 m air temperature of over 5°C within just a few tens of kilometers, accompanied by a near doubling of specific humidity. Wind speeds above the polynyas exceed 16 m/s, with maxima reaching 18 m/s. In addition, the presence of open water promotes atmospheric convection, resulting in a boundary layer height exceeding 700 m (Figure 5d)—a feature that will be examined in more detail in Section 4.2.

Over the ocean, the wind pattern is influenced by the Coriolis effect, causing a westward deflection of the flow (Figure 5b). Additionally, a pronounced “wind cape” effect is observed west of Cape Darnley, where local topography contributes to a further intensification of the winds.

### 4.1.2. Contrasting Atmospheric Conditions Over CDP and MBP

Striking differences can be observed in Figure 5 between CDP and MBP. In particular, MBP is colder (−21°C) than CDP (−17°C) (Figures 5a and 6). This temperature contrast is primarily driven by the colder air masses originating from the Amery Ice Shelf (see Section 4.1.1 and Figure 6). As a result, the atmospheric structure over
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Atmospheric boundary layer (ABL) height and cloud fraction difference between the two experiments (WRF REF-WRF CP), averaged over the total experiment duration (from 15 June to 15 September 2014–2016). Panel (a) is a difference map of ABL height (in m). Dotted and dashed lines delimit polynya boundaries (see Figure 2). Panels (b) and (c) are vertical atmospheric cross sections through Cape Darnley Polynya and Mackenzie Bay Polynya showing the difference in cloud fraction (REF-CP). The two vertical sections are defined by the dashed lines in Figure 1. The dotted and dashed curves correspond to the ABL heights for the REF and CP simulations, respectively. The vertical black line delimits the coastline. As before, the bars below the sections provide information on surface ice conditions: dark purple corresponds to a polynya opening frequency threshold of 80%, and light purple to a threshold of 30% of the study time.</figcaption></figure>

MBP is more stratified. The vertical mixing coefficient ($K_M$) is twice as low at MBP (32 m$^2$ s$^{-1}$) compared to CDP (64 m$^2$ s$^{-1}$) in WRF REF. This temperature difference results in weaker convective development over MBP, as evidenced by a shallower ABL height of 520 m compared to 680 m over CDP. Although MBP exhibits higher relative humidity (76%) compared to CDP (68%), the warmer air over CDP holds more specific humidity (71 g/kg) than over MBP (61 g/kg).

The difference in atmospheric characteristics between the two polynyas will be a recurring topic of discussion throughout the paper.

In the remainder of this paper, we seek to further investigate the atmospheric conditions outlined above and their strong relationship with polynya openings. To this end, we compare results from the WRF REF experiment with those from WRF CP sensitivity experiment (see Section 2.3.2).

## 4.2. Impact on Surface Heat Fluxes

This section examines the response of surface heat fluxes to polynya openings. Figure 7a presents the spatial distribution of the winter mean difference in upward heat flux between the WRF REF and WRF CP experiments. As anticipated, polynya openings lead to a substantial increase in ocean-to-atmosphere heat transfer, with additional fluxes reaching up to 625 W/m$^2$ in the core of the CDP and 690 W/m$^2$ in the MBP. The impact is highly localized over the polynyas, with negligible changes just a few kilometers beyond their edges. When averaged over each polynya area (see Section 2.3.1), the additional heat release amounts to approximately 320–335 W/m$^2$: increasing from 80 to 400 ± 30 W/m$^2$ over CDP, and from 100 to 435 ± 30 W/m$^2$ over MBP (Figure 7b).

The difference between WRF REF and WRF CP in net heat flux is primarily driven by the sensible heat flux, which is minimal when polynyas are ice-covered but reaches 230–280 W/m$^2$ in WRF REF, accounting for approximately 70% of the ~320 W/m$^2$ net heat flux difference (Figure 7b). This difference in sensible heat flux is primarily driven by the large temperature contrast between the surface conditions in the two experiments: surface temperature from -18 to -28°C in WRF CP, compared to near-freezing ocean surface temperatures around -1.8°C in WRF REF. Notably, the sensible heat flux over the MBP is about 50 W/m$^2$ larger than over the CDP, a difference attributed to the colder air temperatures above MBP (see Section 4.1.2).

The second major contributor to the heat flux difference is the latent heat flux. Although sublimation over sea ice is minimal, evaporation from open water in polynyas releases 75–90 W/m$^2$ in WRF REF, accounting for approximately 20% of the net heat flux difference. The larger latent heat flux observed over CDP compared to MBP is consistent with the higher specific humidity at CDP (see Section 4.1.2).

Net longwave radiation accounts for approximately 10% of the total net heat flux difference. In contrast, shortwave radiation remains negligible in this budget due to minimal solar insolation during the austral winter.
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Horizontal wind velocity and surface pressure difference between the two experiments (WRF REF-WRF CP), averaged over the total experiment duration (from 15 June to 15 September from 2014 to 2016). Panels (a) and (b) are anomaly (WRF REF-WRF CP) maps: (a) horizontal wind velocity averaged over the total experiment duration and (b) surface pressure. On the panel (a), streamlines give the direction of the wind difference at the surface above the ocean. Dotted and dashed lines delimit polynya boundaries (see Figure 2).</figcaption></figure>

The relatively modest contribution of longwave radiation results from the opposing effects of its upward and downward components. Warmer surface temperatures in WRF REF enhance upward longwave emission by ~40 W/m², whereas increased cloud cover and water vapor content over polynyas (see Section 4.3.2) boosts downward longwave radiation by ~10 W/m², yielding a net longwave difference of about 30 W/m². To disentangle the contribution of clouds to the radiative budget from that of water vapor content in the atmospheric column, we compute the CRE. This calculation is based on the separation of radiative fluxes into full-sky and clear-sky components. Our results indicate that the polynya opening leads to a positive CRE anomaly, meaning that clouds enhance the downward radiative flux at the surface (see cross-hatched area in Figure 7b). More specifically, the CRE anomaly due to polynya opening averages +4.2 W/m² over CDP and +5.7 W/m² over MBP. Surface CRE anomalies reach maxima of +10.0 and +12.4 W/m², respectively. These values indicate that clouds contribute approximately 10% to the net surface radiative budget. This is consistent with the positive feedback associated with polynya-induced cloud formation reported by Monroe et al. (2021), who found slightly higher surface CRE anomalies (+18 W/m²) over polynyas compared to surrounding sea ice.

Throughout the simulation, the balance between radiative and turbulent fluxes remains generally stable. However, a few short-lived events (about 9 in total, each lasting a few days) are identified, during which the downward longwave radiative flux increases by up to 60 W/m² in absolute terms. These events coincide with increased near-surface temperature and humidity, and are also marked by a sharp drop in sensible heat flux, which falls below 100 W/m². They resemble the “atmospheric river” intrusions studied by Fonseca et al. (2023) in relation to the Terra Nova Bay polynya. No significant increase in polynya extent is observed during these events, suggesting that the atmospheric anomalies may not always translate into surface responses. Further investigation is required to elucidate the mechanisms involved.

It is important to note that the flux values discussed above are temporal averages. During peak periods within the polynya cores, total surface heat fluxes reach up to 1,500 W/m², with sensible heat fluxes exceeding +1,000 W/m² and latent heat fluxes around 300 W/m². These values are in line with previous estimates from the literature (e.g., Golledge et al., 2025), which report that combined turbulent heat fluxes over Antarctic polynyas can range between 500 and 1,000 W/m².
<figure><img src="image_14.png" /></figure>

**Figure 11.** Difference WRF (REF-CP) in meridional wind on vertical sections through (a) Cape Darnley Polynya (CDP) and (b) Mackenzie Bay Polynya (MBP). The vector fields represent anomalies of vertical and meridional wind velocities, normalized by the heights and south-north axis lengths of the grid cells. The two vertical sections used are defined as dashed lines in Figure 1. The figures are obtained by averaging the quantities plotted over the study period (from 15 June to 15 September from 2014 to 2016). The black dashed (dotted) lines correspond to the WRF CP (REF) atmospheric boundary layer heights, respectively. The vertical black line is the coastline. As before, the bars below the sections provide information on surface ice conditions: dark purple corresponds to a polynya opening frequency threshold of 80%, and light purple to a threshold of 30% of the study time.

## 4.3. Impact on Air Masses and the Boundary Layer

### 4.3.1. Thermodynamic Modifications

A direct consequence of the heat release into the atmosphere is the warming of the lower layers of the atmosphere over the polynyas and downstream of the prevailing wind. As the cold continental air mass advects over the polynyas, it progressively warms, reaching peak temperatures near the northern polynya edge, as illustrated in Figure 8a. The warming is significant with an average of 4.0 ± 0.9°C over CDP and 3.2 ± 1.1°C over MBP and with maximum increases of 5.0 and 4.2°C, respectively (Figure 8a). The stronger anomaly at CDP can be attributed to its larger size and its position somewhat downstream of MBP (Figure 5a), which warms a portion of the air mass arriving at CDP.

Furthermore, during the simulation, the temperature anomaly exceeds 11°C over both CDP and MBP. This magnitude is consistent with values reported in previous studies (e.g., Ayres et al., 2024).

In contrast to the very localized heat flux anomalies, the surface temperature response extends well beyond the polynyas themselves. For example, a 1.5°C anomaly remains detectable up to 200 km northwest of CDP. Temperature cross-sections over the polynyas (Figures 8c and 8d) reveal the vertical extent of the anomalies. Above CDP, a temperature anomaly of 0.5°C persists up to 450 m, whereas over MBP, it is confined to approximately 300 m, which can be attributed to the stronger atmospheric stratification over MBP compared to CDP (Section 4.1.2). The temperature anomaly also propagates vertically as the air mass moves over the polynya. At the polynya edge, the anomaly is vertically homogeneous, suggesting strong vertical mixing near the surface (see Section 4.3.2 below).

As for temperature, the presence of a polynya markedly enhances atmospheric humidity through increased moisture fluxes from the ocean surface. Relative humidity increases by up to 10% (+0.30 g/kg; Figure 8b).

### 4.3.2. Convective Processes

The warming of air masses over polynyas initiates convection within the ABL, leading to a thickening of this layer. Figure 9a illustrates the spatial distribution of ABL height difference (WRF REF-WRF CP). Over CDP, the ABL height increases by up to 320 m, effectively doubling compared to the baseline (Figure 9a). In contrast, the increase over MBP is more modest, approximately 140 m, reflecting weaker convective activity and a more limited atmospheric response.
<figure><img src="image_15.png" /><figcaption>**Figure 12.** Difference WRF (REF-CP) in decomposition of the meridional wind anomaly by the dominant terms of the momentum budget, namely horizontal advection, vertical diffusion, and pressure gradient at two heights: at the first level model and at 200 m above the surface (panels a–c and e–g), for the 2–3 August 2014 experiment. Meridional wind anomaly maps at the same heights are shown (panels d and h). Sea-ice concentration, 2 m temperature, atmospheric boundary layer height and surface pressure anomalies are shown for the same experiment (panels i–l, respectively). The numbers in panel (b) define the four study regions used in Section 4.4.2 and in Figure 13, and correspond to the same regions shown in the schematic in Figure 14. Dotted and dashed lines delimit polynya boundaries (see Figure 2).</figcaption></figure>

The intensification of boundary layer convection, combined with the enhanced surface moisture flux (Section 4.3.1), leads to vigorous cloud formation (Figures 9b and 9c). Over CDP, clouds develop into elongated plumes reaching altitudes of up to 2000 m, with a core centered around 600 m. In contrast, the atmospheric response over MBP is more subdued due to more stable background conditions (Section 4.1.2), resulting in denser but shallower clouds, limited to approximately 400 m. Maximum cloud fractions are observed over the polynya cores; however, due to prevailing winds, cloud cover remains above 10% even 320 km offshore from the CDP. Please note that cloud composition has not been addressed in this study. For this type of analysis, a more in-depth investigation regarding phase distribution and spatial variability is required.

The observed convective enhancement is associated with intensified turbulent activity within the boundary layer. In particular, the vertical mixing coefficient increases substantially over CDP, from $16 \text{ m}^2 \text{ s}^{-1}$ in WRF CP to $63 \text{ m}^2 \text{ s}^{-1}$ in WRF REF (not shown), indicating a more efficient redistribution of momentum and heat. The consequences of this enhanced vertical mixing on boundary-layer dynamics are further discussed in Section 4.4.2.
<figure><img src="image_16.png" /><figcaption>**Figure 13.** Vertical profiles of meridional wind speed (in black) and mixing coefficient $K_M$ (in orange) along the vertical section through Cape Darnley Polynya defined in Figure 1, from the first model level (approximately 10 m above the surface) to 500 m above the surface. Each of the four zones is defined in Figure 12b and described in Section 4.4.2. The solid line corresponds to the WRF REF experiment and the dashed line to the WRF CP experiment. Zone profile 1 is 50 km from the coast inland, profile 2 is 3.5 km from the coast above the polynya, profile 3 is 50 km from the coast above the polynya and profile 4 is 200 km from the coast downstream of the polynya.</figcaption></figure>

## 4.4. Impact on the Dynamics

### 4.4.1. Winds: Surface and Vertical Structure

The opening of coastal polynyas exerts a strong influence on surface wind patterns. As shown in Figure 10a, wind speeds increase significantly over the polynyas, exceeding 4 m s⁻¹ near the coastline. In contrast, small negative anomalies (~-0.4 m s⁻¹) appear to the north of each polynya, resulting in divergent winds on the upwind (continental) side and convergence along the downwind, offshore edges. These structures are associated with low-pressure anomalies, including a notable low-pressure signal of -0.55 hPa over the northern edge of CDP (Figure 10b), which corresponds to the convection anomaly described in Section 4.3.2.

Over MBP, the pressure anomaly is weaker (-0.2 hPa), consistent with colder atmospheric conditions in the region (Section 4.1.2). Nonetheless, the resulting pressure gradients are comparable in magnitude to those observed at CDP (not shown).

During the simulation, the sea-level pressure anomaly over CDP reaches -3.3 hPa. This falls within the range of previous estimates, from ~1 hPa (Ayres et al., 2024) to 3-4 hPa (Glowienka-Hense, 1995; Weijer et al., 2017) and up to 6-8 hPa in extreme cases (Moore et al., 2002). These findings support the hypothesis from Timmermann et al. (1999) that coastal polynyas induce localized low-pressure systems.

Upstream of CDP, over the continent, a small positive pressure anomaly (~0.05 hPa) develops. This is attributed to the reinforcement of the katabatic jump over CDP (Section 4.1), which arises from interactions between pressure anomalies and enhanced convection. This mechanism is unique to CDP, as the relatively flat topography of the Amery Ice Shelf prevents similar katabatic structures from forming.

Similar wind anomalies to those observed at the surface are seen at higher altitudes (Figure 11). A positive wind anomaly, corresponding to a northward wind, is confined to the area between the surface and 100 m height, extending over CDP from the coastline to approximately 100 km offshore. In contrast, the slowdown of winds
<figure><img src="image_17.png" /></figure>

**Figure 14.** Schematic summary of the physical processes involved in the atmospheric response to the opening of a coastal polynya. This opening is primarily driven by very intense surface winds: the “katabatic winds” originating from the continent and blowing along slope lines (black arrows). The opening of the polynya results in an upward ocean-to-atmosphere heat flux over the ice-free area (red arrow). This heat flux warms the air mass passing over the polynya, represented by the red background. This warming induces convection in the atmospheric boundary layer, leading to vertical mixing (spiral blue arrows) and an increase in boundary layer height (dashed black line). Furthermore, the increase in surface moisture, which is then transported by convection, facilitates cloud formation. At the northern edge of the polynya, a pressure anomaly develops, resulting in surface wind convergence. The opening of the polynya accelerates surface winds over the polynya (black arrows) and creates two circulation anomaly cells upstream and downstream of the polynya (blue arrows). These wind anomalies result from a balance between vertical mixing, advection, and pressure gradient forces, with vertical mixing acting as the initiating driver.

(southward anomaly) north of the polynya has a much larger vertical extension, reaching up to 500 m and occupying most of the ABL, with the strongest anomalies around 200–300 m. Over the polynya, the anomalous southward flow lies on top of the northward flow, resulting in vertical motion, reaching 30 cm/s. The associated wind convergence zone corresponds to the negative surface pressure anomaly shown in Figure 10b.

Above the polynya, two distinct recirculation cells form as a result of this convection (Figure 11). The first cell, rotating anticlockwise, induces descending air near the coast, whereas the second, rotating clockwise, causes ascending air near the northern edge of the polynya. These structures are analogous to those described by Seo et al. (2023). This article reviewed ocean-atmosphere interactions studies, including the atmospheric response to SST eddies and fronts. A flow crossing a polynya exhibits a response analogous to the “cool-to-warm” and “warm-to-cool” scenarios described in Figure 1 of Seo et al. (2023).

The MBP induces similar patterns to those modeled in CDP, but with wind acceleration more confined to the surface.

As mentioned in Section 4.3.2, clouds provide information about the motion of air masses over the polynyas. In Figure 9, the recirculation cell is clearly visible. At CDP (Figure 9b), an “anvil” structure is observed, with part of the cloud forming above the mainland at altitudes greater than 800 m. To the north, part of the cloud follows the top of the second anomaly cell near 800 m altitude.

### 4.4.2. Winds: Dynamical Drivers

Several studies have investigated the physical processes responsible for the increase of surface winds above polynyas, highlighting mechanisms such as vertical turbulent mixing (Dare & Atkinson, 1999, 2000) and pressure gradient force (Timmermann et al., 1999). To better understand the main drivers behind wind modifications between WRF REF and WRF CP, we analyze here the different tendency terms governing the temporal evolution of wind, following the framework described by Lehner (2012), and based on the dynamical equation implement in
WRF (Skamarock et al., 2019). This analysis is applied to a canonical 2-day event of polynya opening in the WRF REF experiment. Several key meteorological fields exhibit similar patterns during this event (Figures 12i–12l) and in the mean over the study period (Figures 2a, 8a, 9a, and 10b), such as 2 m air temperature and ABL height (Figures 8a and 9a). A more detailed description of the methodology is given in Appendix D. Following this approach, our analysis focuses on the dominant terms contributing to the meridional wind speed: advection, vertical diffusion, pressure gradient force and the Coriolis term. The latter, however, will not be discussed in detail here.

Four zones are defined (see Figure 12b for an example of the CDP case), based on the distinct characteristics of the advection and vertical diffusion tendency anomalies (Figures 12a, 12b, 12e, and 12f). The first zone corresponds to the continental area located upstream of the polynya. The second zone covers the coastal section of the polynya, characterized by a strong positive surface vertical diffusion tendency anomaly (Figure 12b). The third zone extends over the central part of the polynya, where the anomalies are reversed, with a positive advection and pressure gradient tendency and a negative vertical diffusion tendency at the surface (Figures 12a, 12b and 12c). Finally, zone 4 corresponds to the region downstream of CDP. The delineation of each region is presented in the summary schematic in Figure 14.

(1) In the first zone (Figure 13a), the opening of the polynya has no significant impact on the katabatic winds blowing over the continent. The vertical wind profiles in WRF REF and CP are identical in both experiments, with a near-surface speed of 13.5 m/s and a maximum speed of 23.5 m/s reached at an altitude of 150 m.

(2) In the second zone (Figure 13b), the wind speed profiles begin to diverge between REF and CP. In REF, wind speed increases near the surface and decreases up to an altitude of 200 m relative to the CP experiment. This change is primarily attributed to intensified vertical mixing, as evidenced by the signals in the coastal regions in Figures 12b and 12f, which show a positive difference of the vertical diffusion tendency near the surface and a negative difference at 200 m. These vertical mixing differences are primarily driven by the enhancement of the vertical mixing coefficient in REF ($K_M$, as shown in Figure 13b) and by changes in surface roughness—open ocean in REF versus sea ice in CP. To quantify the relative contribution of these two processes, we conducted two supplementary experiments, similar to WRF REF and WRF CP but wherein the surface roughness was maintained as a constant corresponding to the pack ice. These simulations demonstrate that changes in surface roughness had a minimal impact on wind speed, altering it by less than 10% (data not shown).

The effects of vertical mixing are partly compensated by horizontal advection, whose WRF REF-WRF CP difference exhibits the opposite sign to that of vertical diffusion, as shown in Figures 12a and 12e.

Finally, the pressure term is significantly weaker, and thus its contribution is relatively minor.

(3) In the third zone (Figure 13c), the wind profiles are flatter compared to those in zone 2, with a more uniform profile for WRF REF. Wind changes are more pronounced compared to zone 2, particularly at 200 m altitude, where wind speeds are approximately 3 m/s lower in REF than in CP. Near the surface, the larger wind speed in REF compared to CP is primarily driven by the advection term, further supported by the pressure gradient tendency, whereas the vertical mixing acts to dampen this increase (Figures 12a–12c). At 200 m altitude, REF northward wind is weaker than in CP as a result of horizontal advection (Figure 12e), whereas the pressure gradient partly counteracts this reduction (Figure 12g) and the vertical mixing is barely significant (Figure 12f).

To explain these tendencies, each physical process must be considered individually. In zone 3, the positive advection difference at the surface (Figure 12a) enhances the positive surface wind anomalies (Figure 12d) whereas, at 200 m, the negative advection difference (Figure 12e) reinforces the negative wind anomalies (Figure 12h). These changes in the differences of advection are mainly driven by the wind anomalies themselves, positive at the surface versus negative at 200 m. In addition, zone 3 is located south of the maximum low-pressure anomaly (Figure 12l).

Consequently, the difference in pressure gradient terms is positive both at the surface and at 200 m (Figures 12c and 12g). We hypothesize that the pressure gradient contribution reinforces the effect of horizontal advection near the surface, while counteracting it at 200 m.
Finally, the interpretation of the difference in the non-linear vertical mixing term is complex, due to the opposing effects of a larger vertical mixing coefficient ($K_M$) and a weaker vertical wind shear in REF compared to CP. The larger $K_M$ in REF seems to be the key driver to explain the limitation of wind speed differences at the surface by the vertical diffusion.

(4) In the fourth zone of the polynya (Figure 13d), (downstream of the polynya), below 350 m altitude, wind speeds in REF are lower than in CP, and the vertical wind profile is more uniform. This reduced speed is driven by the pressure term (Figure 12c) which arises from the low-pressure anomaly associated with the polynya opening (Figure 12l).

## 5. Conclusion

The objective of this study is to investigate the atmospheric response to coastal polynya openings and to identify the fine-scale processes that govern this response. We focus on the Prydz Bay region, East Antarctica, which hosts two polynyas with distinct atmospheric regimes: the CDP and the MBP. To investigate these processes, we employed a high-resolution (~3 km) polar WRF configuration with two-way nesting centered over coastal polynyas, spanning three austral winters (2014–2016). This modeling framework provides a detailed representation of the atmospheric response to polynya openings, allowing us to move beyond the limitations of previous studies, which often relied on single-event analyses or coarse-resolution data sets. By combining this setup with targeted sensitivity experiments, we isolate and quantify the specific contribution of polynyas to local atmospheric conditions—an aspect that remains challenging to constrain using observations or reanalyzes alone. Overall, this approach enables a more robust and generalizable assessment of the mechanisms driving air-sea interactions in polar coastal environments.

The model demonstrates excellent performance, accurately reproducing both the temporal variability and the mean meteorological conditions (including surface temperature and wind) over the Antarctic continent and sea ice. Comparison with observations from four meteorological stations indicates that WRF exhibits low mean biases, comparable to those of the widely used ERA5 reanalysis, with a maximum bias of –2.6 K at Davis Station. Correlation coefficients exceed 0.9 for temperature and 0.8 for wind speed. Both WRF and ERA5 tend to underestimate strong wind events and overestimate low wind speeds relative to *in situ* measurements. However, WRF outperforms ERA5 in representing the strongest winds. At the basin scale, WRF surface temperatures were evaluated against the AntAir ICE product, showing a warm bias of 1.5 K over the continent (which is 1 K lower than ERA5), and 2.5 K over sea ice. This high degree of consistency provides confidence in the model's ability to represent the key meteorological processes discussed in this study.

To isolate the atmospheric response specifically attributable to the opening of coastal polynyas, two numerical experiments were designed. The reference simulation (WRF REF) uses sea-ice concentration from AMSR2 and thin ice thickness from the SMOS Level-3 product. In the sensitivity experiment, referred to as the Closed Polynya case (WRF CP), the sea-ice boundary conditions are modified so that the polynyas are artificially covered by sea ice, eliminating open water in these regions. The atmospheric impact of polynya openings is then assessed by analyzing the differences between WRF REF and WRF CP. They are illustrated in Figure 14.

Our results show that the opening of polynyas induces substantial surface heat fluxes from the ocean to the atmosphere, reaching values as high as +700 W/m² at the core of the polynyas, with average fluxes of 401 W/m² over CDP and 436 W/m² over MBP. This enhanced heat release warms the lower atmospheric layer by several degrees, with temperature anomalies reaching up to +5.0°C over CDP. This warming intensifies turbulent activity by increasing vertical mixing, ranging from 16 m² s⁻¹ in WRF CP to 63 m² s⁻¹ in WRF REF. This mixing, in turn, promotes convection, which leads to a thickening of the ABL by approximately 400 m. It also contributes to the development of a surface low-pressure anomaly, with a maximum amplitude of –0.55 hPa at the northern edge of CDP. The opening of polynyas also acts as a source of atmospheric moisture, increasing specific humidity by up to +0.30 g/kg. This moisture is transported vertically to the top of the boundary layer by convection and contributes to cloud formation, with resultant cloud cover extending as far as 320 km from the polynya.

A key focus of this study has been the role of winds. Beyond quantifying wind changes, we provide a comprehensive analysis of the underlying mechanisms driving these modifications. The opening of polynyas leads to a strengthening of surface winds within the polynya region, with anomalies exceeding +4 m/s. As the air masses exit the polynya, wind speeds decrease slightly, and anomalies of approximately –0.4 m/s are observed to the north of each polynya. At higher altitudes, wind speeds predominantly decrease. These wind anomalies
contribute to the development of divergence and convergence zones, which in turn promote the formation of two
vertical recirculation cells, located upstream and downstream of the polynyas. We show that the mechanisms
driving these wind changes involve a subtle balance between three tendency terms: vertical mixing, pressure
gradient, and advection. This balance varies depending on the location within the polynya. The polynya area is
therefore subdivided into two distinct regions, each characterized by specific processes (zones 2 and 3 in
Figures 12b and 14). In zone 2 (near the coast), wind speed changes are primarily driven by enhanced vertical
mixing, caused by increased mixing coefficients ($K_M$). In zone 3 (central part of the polynya), wind changes at
altitude are explained by the advection term, whereas at the surface they result from the combined effects of the
pressure gradient and horizontal advection terms. Finally, outside the polynya (zone 4 in Figures 12b and 14), the
observed wind deceleration is predominantly explained by the pressure gradient.

These results suggest the existence of a positive feedback mechanism that may contribute to sustaining polynya openings. This feedback is primarily driven by the intensification of surface winds, atmospheric warming, and the associated changes in surface heat fluxes. Together, these interactions are likely to enhance sea-ice production within the polynyas. To quantify the magnitude and impact of this atmospheric feedback on polynya dynamics, however, a fully coupled approach that integrates ocean, sea ice, and atmospheric processes is required. Such an assessment could be achieved using high-resolution coupled models, such as the one proposed by Malyarenko et al. (2023).

**Appendix A: Correction of Sea-Ice Concentration Product**

The AMSR2 sea-ice concentration product exhibits several notable inaccuracies (Figure A1a). First, coastlines are poorly represented, particularly along ice shelves—most notably in the extension of the Amery Ice Shelf—as well as around icebergs, such as iceberg D15 located between 81°E and 82°E. These inaccuracies result in the artificial appearance of areas with low sea-ice concentration, often close to 0%. Second, in landfast ice regions—especially along the coast west of Cape Darnley between 62°E and 65°E (Figure A1b)—the AMSR2 product reports sea-ice concentrations around 80%, which is inconsistent with the expected values for such zones.

To correct these errors, the coastline delineation provided by Fraser et al. (2020) (Figure A1b) is adopted.
Additionally, within landfast ice zones, the sea-ice concentration is manually adjusted to 100%. The corrected
sea-ice concentration field resulting from these adjustments is presented in Figure A1c.

<figure><img src="image_18.png" /></figure>

**Figure A1.** Example of the correction made to the sea ice concentration product used at the surface on 12 September 2016 (see Section 2.1.3): (a) Sea ice concentration from the Advanced Microwave Scanning Radiometer 2 (AMSR2) product before correction, (b) surface type category from the Fraser et al. (2020) product, (c) sea ice concentration from the AMSR2 product and corrected with the Fraser et al. (2020) product.
## Appendix B: Spatially Localized Response

It is important to emphasize that WRF REF and WRF CP simulations remain largely similar across most of the domain, with significant differences only occurring locally near the polynyas. This is illustrated in Figure B1, which shows the spatial distribution of the correlation coefficient for 10-m wind speed between WRF-REF and WRF-CP over the entire simulation period. Lower correlations are confined to the polynya regions, whereas 86% of the domain exhibits a correlation exceeding 0.98. This strong agreement away from the polynyas is primarily due to the unchanged lateral boundary conditions (ERA5) and the relatively large model domain compared to the size of the polynyas.

However, it should be noted that because this study uses a limited-area model constrained by lateral boundary conditions, it does not assess potential impacts of coastal polynyas on large-scale atmospheric circulation beyond the model domain. Such large-scale feedback remains beyond the scope of this work.

<figure><img src="image_19.png" /><figcaption>**Figure B1.** Correlation of the 10-m wind speed between WRF REF and WRF CP from 15 June to 15 September 2014–2016. Dotted and dashed lines delimit polynyas boundaries (see Figure 2).</figcaption></figure>
## Appendix C: Comparison of ERA5 and WRF Temperature and Wind Speed With Observations

A comparison between ERA5 and WRF REF temperature and wind speed data against station measurements is available below from 15 June to 15 September 2015.

<figure><img src="image_20.png" /><figcaption>**Figure C1.** 2-m temperature (panels a–d; left) and 10-m wind speed (panels e–h; right) from surface observations (black lines), ERA5 (orange lines), and WRF (blue lines) at the four weather stations (Mawson, Davis, Zhongshan, and Taishan) during the three winters (15 June to 15 September) 2014–2016. The time series for ERA5 and WRF correspond to the grid points closest to the stations. The shaded areas represent the minimum and maximum values within a 28-km radius around the stations.</figcaption></figure>
## Appendix D: Description of the Momentum Budget

The study of dynamic drivers is based on an analysis of the terms of the dynamic equation. We focus solely on the meridional wind component, since winds in the region are predominantly oriented from south to north (see Section 4.1). The WRF dynamics equation is described by Skamarock et al. (2019). The horizontal meridional component of the momentum of these equations is a follows:

$$ \partial_t V = \text{ADV} + \text{VD} + \text{PG} + \text{COR} + \text{res} \quad (\text{D1}) $$

with $V = v \times \mu_d$, where $v$ is the meridional wind speed and $\mu_d$ the mass of the dry air per unit area in the column. Note that the $\partial_*$ sign indicates partial differentiation according to the * coordinate.

Equation D1 gives the relationship for expressing meridional wind acceleration coupled to mass at each point in space: $\partial_t V$. The terms on which this equation depends are described below:

* Advection (ADV), which in this situation is proportional to the simplified term as follows: $-v \times \partial_y v$. This term is therefore linked to wind speed and its horizontal gradient, representing the transport of the wind with itself.

* Pressure gradient force (PG), expressed as: $-\mu_d \alpha \partial_y p + \frac{\alpha}{\alpha_d} \partial_\eta (p \partial_y \phi)$ where $\alpha$ ($\alpha_d$) is the inverse density (dry) air and $p$ is the pressure. $\eta = (p_{dh} - p_{dhi}) / \mu_d$ is the vertical coordinate used by WRF. $\phi$ is the geopotential. This term depends on horizontal gradient of pressure and geopotential heights, and is therefore sensitive to horizontal gradients of the potential temperature.

* Vertical diffusion (VD), otherwise known as vertical turbulent mixing, depends on the simplified expression: $\partial_z (K_M \partial_z v)$, where $K_M$ is the vertical diffusion coefficient (m$^2$ s$^{-1}$) and is more important in areas of strong vertical mixing. This term tends to homogenize the vertical wind speed profile, depending on the intensity of the vertical mixing.

* Coriolis effect (COR) refers to the influence of Earth's rotation on the flow. Therefore, this term is not a driver of the atmospheric response to a polynya but rather a consequence.

* The residual (res) are the sum of the curvature, the horizontal diffusion and the acoustic time step correction terms. These terms are negligible compared to the other four described above.

These terms are extracted directly from the WRF model output using the dynamic term decomposition, a method previously applied in several studies (Chen et al., 2020; Moisseeva, 2010; Moisseeva & Steyn, 2014; Potter et al., 2018), and originally developed by Lehner (2012).

Here, we extract the momentum budget terms associated with $v$ only, obtained by dividing the momentum budget terms of $V$ by $\mu_d$. Note that this manipulation does not concern terms derived from WRF's physical parameterizations (e.g., the vertical diffusion term), which give terms directly associated with $v$. The total balance of the momentum budget terms is not strictly preserved, but allows us to reconstruct the wind speed term $v$. Furthermore, to calculate the time average of the terms, we use a double time sum described by Oerder et al. (2016).

To quantify the contribution of each dynamical term to the meridional wind response associated with polynya opening, we adopt a simplified framework in which the transition from a closed to an open polynya occurs between two consecutive days. Specifically, a series of short, 2-day simulations is performed. The difference in the momentum budget terms is computed between the mean state of the first day (closed polynya) and that of the second day (open polynya), and is compared to a control experiment in which the polynya remains closed over both days. These two experiments are referred to as WRF REF and WRF CP, respectively, consistent with the naming convention used throughout the paper.

A specific event, representative of the average atmospheric response to polynya opening, is selected to illustrate the analysis. This case study, which spans from 2 August to 3 August 2014, is presented in Section 4.4.2 (see Figure 12).

## Data Availability Statement

The land use and topography data over Antarctica used in this study are derived from the Reference Elevation Model of Antarctica (REMA) and AntarcticaLC2000 land cover classification (Howat et al., 2019), available at
https://www.envidat.ch/#/metadata/rema-topography-and-antarctalc2000-for-wrf. Sea ice concentration data are from the Advanced Microwave Scanning Radiometer 2 (AMSR2) at 3.125 km daily resolution (Melsheimer & Spreen, 2019), accessible at https://doi.pangaea.de/10.1594/PANGAEA.898400. Circum-Antarctic landfast sea ice data are provided by Fraser et al. (2020) at https://data.aad.gov.au/metadata/AAS_4116_Fraser_fastice_circumantarctic. Sea ice thickness data up to ~1 m from SMOS are provided by Kaleschke et al. (2024) and European Space Agency (2023), available at https://earth.esa.int/eogateway/catalog/smos-l3-sea-ice-thickness. Snow thickness on sea ice is taken from the ASD product (Garnier et al., 2021), accessible at https://www.aviso.altimetry.fr/en/data/products/ice-products/altimetry-sea-ice-products-from-ctoh.html. Atmospheric reanalysis data are from ERA5 (Hersbach et al., 2020) and are available from the ECMWF at https://www.ecmwf.int. The AntAir ICE near-surface air temperature data set (Nielsen et al., 2023) is available at https://doi.org/10.1594/PANGAEA.954750. AWS data for the Panda and Zhongshan stations are provided by Y. Wang et al. (2023) and can be accessed at https://amrdcdata.ssec.wisc.edu/dataset/antaws-dataset. Meteorological data for the Mawson and Davis stations are from Barnes-Keoghan (2000), accessible at https://data.aad.gov.au/metadata/records/Antarctic_Meteorology. The original WRF code is open source (https://github.com/wrf-model/WRF). All the simulation data presented in this study are available upon request to the authors.

## Acknowledgments

This work used the GENCI A0150113051 computing resources. We thank Etienne Vignon for his helpful discussions and advice on setting up polar WRF. We are grateful to Simon Alexander and the Australian Bureau of Meteorology for distributing the data at the Mawson and Davis stations. We thank Laetitia Drago for her help in designing the summary diagram, and Katherine Hutchinson for her feedback on the manuscript. We also thank two anonymous reviewers whose insightful comments helped to improve the manuscript.

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