# Antarctic Water Stable Isotopes in the Global Atmospheric Model LMDZ6: From Climatology to Boundary Layer Processes

Niels Dutrievoz, Cécile Agosta, Sébastien Nguyen, Amaëlle Landais, Elise Fourré, Christophe Leroy-dos Santos, Mathieu Casado, Valérie Masson-Delmotte, Jean Jouzel, Bénédicte Minster, et al.

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## ► To cite this version:

Niels Dutrievoz, Cécile Agosta, Sébastien Nguyen, Amaëlle Landais, Elise Fourré, et al. Antarctic Water Stable Isotopes in the Global Atmospheric Model LMDZ6: From Climatology to Boundary Layer Processes. Journal of Geophysical Research: Atmospheres, 2025, 130 (5), 10.1029/2024JD042073. hal-05029172

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Submitted on 11 Apr 2025

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# JGR Atmospheres

## RESEARCH ARTICLE

10.1029/2024JD042073

**Key Points:**

* LMDZ6iso, a global atmospheric model, is assessed in Antarctica against surface snow, precipitation and water vapor isotopic observations

* Spatial, seasonal and diurnal isotopic variations of LMDZ6iso are evaluated with these observations

* Diurnal cycles of water vapor isotopes are used to identify key processes at play in the model

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

N. Dutrievoz, niels.dutrievoz@lsce.ipsl.fr

**Citation:**

Dutrievoz, N., Agosta, C., Risi, C., Vignon, É., Nguyen, S., Landais, A., et al. (2025). Antarctic water stable isotopes in the global atmospheric model LMDZ6: From climatology to boundary layer processes. *Journal of Geophysical Research: Atmospheres*, 130, e2024JD042073. https://doi.org/10.1029/2024JD042073

Received 26 JUL 2024
Accepted 6 FEB 2025

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## Antarctic Water Stable Isotopes in the Global Atmospheric Model LMDZ6: From Climatology to Boundary Layer Processes

Niels Dutrievoz<fnref n="1" />, Cécile Agosta<fnref n="1" />, Camille Risi<fnref n="2" />, Étienne Vignon<fnref n="2" />, Sébastien Nguyen<fnref n="1" />, Amaëlle Landais<fnref n="1" />, Elise Fourré<fnref n="1" />, Christophe Leroy-Dos Santos<fnref n="1" />, Mathieu Casado<fnref n="1" />, Valérie Masson-Delmotte<fnref n="1" />, Jean Jouzel<fnref n="1" />, Thomas Dubos<fnref n="2" />, Inès Ollivier³, Barbara Stenni⁴, Giuliano Dreossi⁴, Mauro Masiol⁴, Bénédicte Minster<fnref n="1" />, and Frédéric Prié<fnref n="1" />

**Abstract** Observations of water stable isotopes in Antarctic surface snow, precipitation and water vapor are key for improving our understanding of the atmospheric water cycle and past climate reconstructions from ice cores. In this study, we use isotopic observations in Antarctica to assess the skill of the isotope-enabled atmospheric general circulation model LMDZ6, nudged to ERA5 above the boundary layer (1980–2023 period). The model has no significant bias for time-mean temperature and snow accumulation over the ice sheet. Sensitivity test on parameterized supersaturation strength highlights its opposite effect on precipitation δ¹⁸O and d-excess. Selecting an intermediate supersaturation strength resulted in a minimal bias for surface snow δ¹⁸O across the continent, with a reduced but systematic positive bias in surface snow d-excess (~5‰). We then assessed seasonal and diurnal isotope variability with daily precipitation and continuous vapor isotopes at Dumont d'Urville (DDU, coastal station) and Concordia (inland station). On a seasonal scale, LMDZ6iso accurately reproduces the seasonal cycle of precipitation δ¹⁸O and d-excess at both stations. Moving from statistical evaluation to physical analysis, we use the individual process contributions to boundary-layer water vapor isotopes to identify the main drivers controlling the clear-sky isotopic daily cycles. At Concordia, daily isotope variations are mainly driven by surface sublimation, whereas at DDU they are driven by surface sublimation and advection by the katabatic flow. Our results suggest that to further improve water isotopes in LMDZ6iso, fractionation during surface sublimation should be included and fractionation at condensation for low temperature should be better constrained.

**Plain Language Summary** The study of water isotopes in Antarctica is key to improve our understanding of the atmospheric water cycle and to reconstruct past climates from ice cores. Climate models equipped with water isotopes are a valuable tool to help interpret the water isotope signal in firn and ice archives, but they need to be thoroughly evaluated to test their reliability. In our study, we evaluate the LMDZ6iso global atmospheric model against surface snow isotopes over the Antarctic continent, and against daily precipitation and continuous water vapor isotopes at two East Antarctic stations: Dumont d'Urville, at the coast, and Concordia, inland. These observations are used to assess simulated spatial, seasonal and diurnal isotopic variations. Then, we analyze individual process contributions to boundary layer water vapor isotopes to identify the key drivers of clear-sky isotopic daily cycles, and to understand the discrepancies between the model and the observation. Our results suggest that to further improve water isotopes in LMDZ6iso, efforts should focus on including isotopic exchanges during sublimation and condensation for low temperature.

## 1. Introduction

Stable isotopes of water (H¹⁶O, HD¹⁶O, H¹⁷O and H¹⁸O) serve as natural tracers to examine past climate changes (Jouzel et al., 2007; Landais et al., 2008; Petit et al., 1999) and the current atmospheric hydrological cycle (Bonne et al., 2019; Galewsky et al., 2016). These fields of application are made possible by water stable isotope fractionation, which occurs during phase changes due to the different thermodynamic properties of heavy and light isotopes. There are two types of fractionation that overlap in their effects: thermodynamic fractionation or

<footnote n="1">¹Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France,</footnote>

<footnote n="2">²Laboratoire de Météorologie Dynamique - IPSL, Sorbonne Université/CNRS/Ecole Normale Supérieure - PSL Université / Ecole Polytechnique - Institut Polytechnique de Paris, UMR 8539, Paris, France, ³Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway, ⁴Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venezia, Italy</footnote>

<footnote>© 2025. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.</footnote>
equilibrium fractionation (which is the only fractionation process when humidity is at saturation) and kinetic fractionation or non-equilibrium fractionation which acts in addition to equilibrium fractionation when relative humidity is not at saturation. As air masses gradually cool when they move toward higher latitudes, condensates are enriched in heavy isotopes, and the remaining water vapor is gradually depleted in heavy isotopes. Here, we focus on H₂¹⁶O, H₂¹⁸O and HDO, and we adopt the standard δ notation to describe the proportion of heavy stable water isotopes compared to light stable water isotopes:

$$ \delta = \left( \frac{R_{sample}}{R_{VSMOW}} - 1 \right) \cdot 1000 \qquad (1) $$

With δ (in ‰) corresponding to either δ¹⁸O or δD, Rsample representing the ratio of the number of heavy molecules to the number of H₂¹⁶O molecules in the sample and RVSMOW this same ratio in the Vienna Standard Mean Ocean Water (VSMOW) international reference. From this first order behavior, the amplitude of variations in δD is 8 times those of δ¹⁸O (Craig, 1961). The deviation from this behavior is measured by the deuterium excess (d-excess):

$$ d\text{-excess} = \delta\text{D} - 8 \cdot \delta^{18}\text{O}. \qquad (2) $$

The d-excess is highly sensitive to kinetic fractionation (Dansgaard, 1964), making it an effective indicator of conditions at water vapor sources and non-equilibrium processes such as condensation under conditions of supersaturation respect to ice, or re-evaporation of precipitation (Jouzel & Merlivat, 1984).

In Antarctica, the analysis of spatio-temporal variations in water isotopes using samples of surface snow, precipitation and water vapor is of significant interest. This analysis is conducted with the dual objectives of gaining a deeper understanding of the isotopic signal archived in ice cores (EPICA community members, 2004) and in deciphering the contributions of different atmospheric processes in driving the water cycle (Dittmann et al., 2016; Schlosser et al., 2017; Servettaz et al., 2020), including boundary layer processes (Bréant et al., 2019; Ritter et al., 2016). Atmospheric general circulation models (AGCMs) equipped with water isotopes simulate the effects of fractionation and transport on the isotopic composition of water within the atmospheric hydrological cycle. As a result, they are essential for improving our understanding of the factors that affect isotope signatures on the scale of meteorological, seasonal and interannual processes, and on the scale of past climate variations within a physically coherent framework. Several AGCMs have been used in Antarctica to study the isotopic composition of snow and precipitation, for example, GISS (Schmidt et al., 2007), ECHAMwiso (Ollivier et al., 2024; Werner et al., 2011), MUGCM (Noone & Simmonds, 2002), IsoGSM (Yoshimura et al., 2008), HadCM3 (Holloway et al., 2018), MIROC5-iso (Kino et al., 2024) and LMDZiso (Cauquoin et al., 2019; Risi et al., 2010). The present study focuses on the LMDZ6 AGCM, in the same configuration as in the IPSL-CM6 earth system model (Boucher et al., 2020) used for phase 6 of the Coupled Model Intercomparison Project CMIP6 (Eyring et al., 2016). Previous versions of LMDZiso have been used in Antarctica, for example, LMDZ4iso to study the sensitivity to the advection scheme for the simulation of water isotopes in precipitation over Antarctica (Cauquoin et al., 2019) and LMDZ5Aiso to study the post-depositional processes affecting the isotopic composition of surface snow (Ritter et al., 2016). The incorporation of water isotopes in LMDZ6 (LMDZ6iso) has been made with the same philosophy and methodology as in previous versions (Risi et al., 2010), but it had to adapt to the substantial developments of the physics between versions 5A and 6 (Hourdin et al., 2020). Such developments include, among others, the incorporation of a mass-flux scheme for representing the mixing in convective boundary layers, new formulations for deep convection triggering, an original parameterization of cold pools, advances in the turbulent diffusion parameterization with a specific focus on Antarctica (Vignon et al., 2018), and general improvements of the cloud scheme (Madeleine et al., 2020).

Previously, climate models have primarily been evaluated with surface snow and precipitation samples (Goursaud et al., 2018; Masson-Delmotte et al., 2008). Water isotope observations enable us to evaluate the skill of atmospheric models and identify systematic biases. Given their sensitivity to phase change and mixing process from the evaporative source, during transport and cloud formation to post depositional processes, water isotopes observations provide complementary process-based information compared to traditional meteorological and snow physical observations. For instance, studies have identified that isotope-enabled AGCMs demonstrate a lack of δ¹⁸O depletion over the Antarctic plateau (Bong et al., 2024; Cauquoin et al., 2019; Masson-Delmotte
et al., 2008). This bias has been attributed to the representation of cloud microphysics and an inadequate representation of large-scale water vapor advection (Cauquoin et al., 2019; Masson-Delmotte et al., 2008).

Here, for the first time, we leverage novel in situ observations of water vapor isotopes to evaluate atmospheric processes within an AGCM. In recent years, advances in commercial laser instruments and their adaptation for polar environment have facilitated continuous measurements of atmospheric water vapor isotopes in Antarctica, leading to studies of isotopic exchanges in the Antarctic boundary layer (Bagheri Dastgerdi et al., 2021; Bréant et al., 2019; Casado et al., 2016; Kurita et al., 2016; Leroy-Dos Santos et al., 2023; Ritter et al., 2016). These novel observations have profoundly improved our understanding of Antarctic boundary layer processes, highlighting the importance of studying the isotopic composition of water vapor in addition to analyses of precipitation and surface snow. In Greenland, the analysis of the isotopic composition of vapor using ECHAM5-wiso, IsoGSM and LMDZ4iso models revealed that simulated isotope biases observed in Greenland cannot be attributed to temperature or humidity biases. Instead, they appear to reflect structural issues common to models for the isotopic composition of the marine boundary layer (Steen-Larsen et al., 2017). At Concordia station (DC), located on the East Antarctic Plateau, the analysis of the observed isotopic composition of vapor, precipitation, surface snow and firn has shown that the isotopic composition of water in ice cores is controlled by both the isotopic signal from precipitation and a local signal from snow metamorphism (Casado et al., 2018). At the Dumont d'Urville station (DDU), located at the continent's oceanic margin, studies of vapor isotopic composition have been conducted at both summer (Bréant et al., 2019) and annual (Leroy-Dos Santos et al., 2023) scales, and demonstrated the importance of the daily cycle and katabatic winds in summer, and of synoptic intrusions in winter, in influencing the isotopic composition of vapor. These water vapor data sets are crucial new resources for evaluating models with explicit water isotope diagnostics because they enable direct observation-model comparison at sub-daily to annual time scales.

In this study, we evaluate the AGCM LMDZ6iso in Antarctica from a classical climatological way to process-oriented analyses. Our objective is to provide an assessment of the model's strengths and limitations through a multi-time scale evaluation, encompassing spatial, seasonal and diurnal isotopic variations. We present data and methods in Section 2. In Section 3.1, we first identify the supersaturation parameter that gives the best compromise between simulated and observed surface snow isotopes. We then proceed to evaluate the simulation over the Antarctic ice sheet at a climatic time scale (1980–2023 time-average). In Section 3.2, we focus on evaluating daily precipitation water isotopes at the Dumont d'Urville coastal station and at the Concordia continental station at a seasonal time scale. In Section 3.3 we evaluate the isotopic composition of water vapor at the two stations during summer daily cycles. Finally, in Section 4, we analyze the processes controlling the daily isotopic variability of water vapor in the model.

## 2. Material and Methods

### 2.1. Observations

#### 2.1.1. Meteorological and Accumulation Observations

We use meteorological observations monitored at the coastal station Dumont d'Urville and the continental station Concordia, 3,233 m above sea level (see locations in Figure 1). At Dumont d'Urville, the Meteo France weather station provides meteorological data since 1956, at 3-hourly and hourly resolution, notably 2 m air temperature (in K) and 2 m specific humidity (in kg kg⁻¹) (Table 1). At Concordia, we use temperature and specific humidity observations from the lowest level (2.8 m, Table 1) of the 45 m meteorological profiling system installed at Concordia (Genthon et al., 2013). These measurements are collected by thermohygrometers with aspirated shields, which are not affected by radiation biases at low wind speed (Genthon et al., 2011). Additional information on the observing system, instruments, sampling, and results can be found in Genthon et al. (2021).

To evaluate snow accumulation over Antarctica, we use accumulation observations of the GLACIOCLIM-SAMBA data set detailed in Favier et al. (2013) and updated by Wang et al. (2016), which follows the quality-control methodology defined by Magand et al. (2007). It includes 3,043 reliable snow accumulation observations averaged over more than 3 years (see location in Figure 1b). We add accumulation estimates from Medley et al. (2014), retrieved over the Amundsen Sea coast (Marie Byrd Land) with an airborne-radar method combined with ice-core glaciochemical analysis. For model-data comparison, we compute the snow accumulation as precipitation minus evaporation in LMDZ. First, we linearly interpolate LMDZ at the observation location for
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Top: (a) Surface temperature and (b) accumulation in LMDZ6iso (1980–2023 time-average, background map) and in observations (colored dots). Bottom: LMDZiso (y-axis) versus observation (x-axis) for (c) surface temperature and (d) accumulation. The black line is for linear regression, the red cross and the black cross indicate observed and modeled values at Concordia and Dumont d’Urville station respectively.</figcaption></figure>

the same time interval as in observations. Then, for a fair comparison between point observations and model gridded outputs, we average the observed accumulation data on LMDZ grid cells, which we do by weighting each accumulation by its corresponding duration, as in Agosta et al. (2019). At the end, we obtain 401 grid-average accumulation observations.

### 2.1.2. Water Isotopes in Surface Snow, in Precipitation and in Vapor

We use a set of 497 observations of δ¹⁸O and δD in surface snow across the continent, collected over a period of 40 years, and south of 60°S (Figure 3), associated with the annual mean temperature estimated from firn temperature or automatic weather stations (Figure 1), as described in Masson-Delmotte et al. (2008). The protocol for

<caption>**Table 1**</caption>

Altitude Above Ground Level of Meteorological and Isotopic Observations and of LMDZ First Atmospheric Level

<table><thead><tr><th></th><th>Concordia</th><th>Dumont d'Urville (land)</th><th>Dumont d'Urville (ocean)</th></tr></thead><tbody><tr><td>Meteorological observations</td><td>2.8 m</td><td>2.0 m</td><td>2.0 m</td></tr><tr><td>Water vapor isotopes</td><td>2.0 m</td><td>2.0 m</td><td>2.0 m</td></tr><tr><td>LMDZ (first level)</td><td>6.7 m</td><td>8.4 m</td><td>9.1 m</td></tr></tbody></table>
collecting surface snow varies significantly between sites. The database reports the isotopic composition of both direct precipitation samples at some sites and for the large majority, from firn cores at least 1 m deep covering part of the year or even several years, depending on the location. For more details on sampling methods, see Masson-Delmotte et al. (2008). For model-data comparison between point observations and model gridded outputs, as for the accumulation observations, we average the observed isotopic data set on LMDZ grid cells, and obtain 180 grid-average isotopic observations.

We also use daily precipitation samples collected at Dumont d'Urville and Concordia (Dreossi et al., 2023; Ollivier et al., 2024). At Dumont d'Urville, precipitation is collected near the station on a daily basis and covers the period from 2019 to 2020 with significant gaps. The precipitation samples are shipped back once a year to the LSCE and stored at -20°C. Measurements are performed using a Picarro L2130-i laser spectrometer operating in liquid mode (Leroy-Dos Santos et al., 2023). At Concordia, the sampling site is located approximately 800 m from the station to prevent contamination from human activities. Samples are manually taken every day between 9 a.m. and midday local time (UTC+8), and kept frozen until analysis. The collection and analysis protocol is described in detail in Dreossi et al. (2023) and Ollivier et al. (2024). The data set covers the period from 2008 to 2021. Concordia precipitation samples are returned annually to the Ca' Foscari University of Venice and are stored at -20°C until the analysis. For model-data comparison and to avoid numerical divergence, we have only selected model outputs associated with a precipitation quantity greater than 0.01 kg m⁻² day⁻¹ at Concordia, and greater than 0.15 kg m⁻² day⁻¹ at Dumont d'Urville, chosen as the 10% percentile of modeled daily precipitation at each site.

Finally, we are also using continuous measurements of the isotopic composition of the near-surface atmospheric water vapor at Dumont d'Urville and Concordia. At Dumont d'Urville, isotopic composition of water vapor is measured by a Picarro infrared cavity spectrometer (L2130-i) at 2 m above ground level (AGL) (Table 1) since December 2018. The data set and the calibration steps applied to the raw data are described in Leroy-Dos Santos et al. (2023). At Concordia, water vapor is pumped 2 m AGL (Table 1) and the isotopic composition has been continuously measured every summer since 2018 by a Picarro infrared cavity spectrometer (L2130-i). The calibration steps applied to the raw data are described in Leroy-Dos Santos et al. (2021).

## 2.2. The Isotope-Enabled General Circulation Model LMDZ6iso

### 2.2.1. Model Configuration and Simulations

We use the AGCM LMDZ6 described in Hourdin et al. (2020), version 20231022. trunk, with the NPv6.3 physical package (Hourdin et al., 2023), nearly identical to the IPSL-CM6A atmospheric setup (Boucher et al., 2020) used for phase 6 of the Coupled Model Intercomparison Project CMIP6 (Eyring et al., 2016). The simulation was performed with the so-called standard Low Resolution (LR) grid of LMDZ6 with a horizontal resolution of 2.0° in longitude and 1.67° in latitude (144 × 142 longitude-latitude grid). We use 79 vertical levels, with the first atmospheric level located approximately 10 m AGL (Table 1).

We nudge LMDZ6iso 3D fields of temperature and wind toward the 6-hourly ERA5 reanalysis (Hersbach et al., 2020) with a relaxation time of 3 hr, except below the sigma-pressure level equivalent to 850 hPa above sea level, where no nudging is applied in order to let the model's physics and dynamics to be expressed in the boundary layer. Surface ocean boundary conditions are derived from monthly mean sea surface temperature and sea-ice concentration fields from the ERA5 reanalysis. The reference simulation covers the period 1979–2023 but it is analyzed over the period 1980–2023 (12-month spin-up period).

### 2.2.2. Isotopic Processes

Isotopic processes implemented in LMDZ6 are detailed in Risi et al. (2010) and a brief summary is given hereafter. We focus specifically on the isotopes $H_2^{16}O$, $H_2^{18}O$ and HDO in surface snow, precipitation, and water vapor. The LMDZ6 model uses the Van Leer moisture advection scheme to passively transport water isotopes (Risi et al., 2010; Van Leer, 1977). Equilibrium fractionation coefficients between water vapor and liquid or ice water are taken from Merlivat and Nief (1967) and Majoube (1971). The kinetic effects associated with sea surface evaporation are derived in accordance with Merlivat and Jouzel (1979), and those associated with snow formation and supersaturation are based on the work of Jouzel and Merlivat (1984).
In LMDZiso, the isotopic composition of the surface snow (over glacier surfaces) is equivalent to a snow bucket which averages snowfall since the beginning of the simulation. The only process that can modify the isotopic composition of surface snow is fractionation during the condensation of surface vapor, as there is no fractionation during sublimation, as commonly assumed in isotope-enabled AGCMs (Hoffmann et al., 1998). In our study, we approximate the isotopic composition of surface snow by the isotopic composition of cumulative precipitation over the period considered.

Supersaturation represents an essential process for water isotope fractionation (Dütsch et al., 2019). However, in LMDZ, saturation adjustment is employed to convert the excess of vapor into ice or liquid as soon as the partial vapor pressure exceeds saturation, at each time step, thereby suppressing supersaturation. As a consequence, LMDZ underestimates relative humidity relative to ice during periods of observed supersaturation, as frequently observed at Concordia (Vignon et al., 2018). As shown in Jouzel and Merlivat (1984), surface snow d-excess in Antarctica cannot be properly represented in atmospheric models if they don't take supersaturation into account during fractionation at condensation. As recommended in this study, supersaturation is parameterized in LMDZ as a simple linear relationship to temperature, for temperatures below freezing, and is used only to compute the fractionation coefficient:

$$Si = 1 - \lambda \cdot (T - T_{\text{freeze}}) \quad (3)$$

where Si is the supersaturation during ice condensation (relative humidity with respect to ice, no unit), λ is a tuning parameter, ranging between 0 K⁻¹ for no supersaturation with respect to ice, to 0.006 K⁻¹ for high supersaturation with respect to ice, T is the air temperature (in K) and Tfreeze is the freezing temperature (273.15 K). The choice of λ parameter depends on the models used: 0.002 K⁻¹ in iCAM5 (Nusbaumer et al., 2017), 0.003 K⁻¹ for MIROC5-iso (Okazaki & Yoshimura, 2019a), or 0.005 K⁻¹ for HadCM3 (J. Tindall et al., 2009). We performed the 44-year reference simulation with λ = 0.004 K⁻¹, and we assessed the sensitivity of LMDZ to the λ parameter with two 16-year long simulations (2008–2023), using λ values of 0.002 and 0.006 K⁻¹, initialized in 2008 with the reference simulation.

Conditions leading to supersaturation are related to the absence of pre-existing ice and the low density of ice nuclei. Crystal growth is then limited by the diffusion of molecules from the vapor toward the ice nuclei and not by the quantity of moisture or energy available. As the diffusion of the heavy isotopes (HD¹⁶O, H¹⁷O and H¹⁸O) is lower than that of H¹⁶O, it results in kinetic fractionation during ice condensation in clouds. To estimate this fractionation, Jouzel and Merlivat (1984) established a theoretical framework to take account of non-equilibrium condensation into ice by defining the kinetic fractionation coefficient α^cin as:

$$\alpha^{\text{cin}} = \frac{Si}{1 + \alpha^{\text{eq}} \cdot \frac{D}{D_{\text{iso}}} \cdot (Si - 1)} \quad (4)$$

where Si is the supersaturation, α^eq is the equilibrium fractionation coefficient, and D and D^iso are the diffusivities of normal water and the isotope respectively (in m² s⁻¹).

In this study, we focus on the effect of supersaturation on the isotopic composition of Antarctic snow. However, large uncertainties remain about equilibrium fractionation coefficients for low temperatures (Hillaire-Marcel et al., 2021). In LMDZiso, the fractionation coefficients are computed based on the formulations of Majoube (1971a) for δ¹⁸O and Merlivat and Nief (1967) for δD. These formulations were experimentally derived between 0 and -40°C, and extrapolated to temperatures below -40°C.

## 2.3. Water Isotope Budget Using Tendencies

To analyze the processes that control the atmospheric water budget and the isotopic composition of water vapor δ¹⁸O in the boundary layer, we use LMDZ water vapor tendencies. They correspond to the changes in time of each water vapor isotope (in kg kg⁻¹ s⁻¹), associated with each process that controls the water cycle: large-scale advection, large-scale condensation and evaporation, shallow convection, deep convection (negligible in
Antarctica), and vertical turbulent diffusion (which includes surface sublimation and condensation). The variation in time of the δ of atmospheric vapor associated to each process, $dδ/dt|_{process}$ (in %c s⁻¹), is derived as follows:

$$ \left. \frac{d\delta}{dt} \right|_{process} = \left. \frac{1}{q} \cdot \frac{dq}{dt} \right|_{process} \cdot (\delta_{process} - \delta) \quad (5) $$

where q is the specific humidity in the atmosphere before the process (in kg kg⁻¹), δ is the atmospheric vapor δ¹⁸O before the process (in %c), dq/dt|_{process} is the specific humidity tendency associated to the process (in kg kg⁻¹ s⁻¹), and δ_{process} is the isotopic composition of this specific humidity tendency at each time step (in %c). The derivation of this equation is detailed in Appendix A.

The total atmospheric water vapor budget and vapor isotopic budget correspond to the sum of contributions of all processes and are computed as follows:

$$ \frac{dq}{dt} = \sum_{process} \left. \frac{dq}{dt} \right|_{process} \quad (6) $$

$$ \frac{d\delta}{dt} = \sum_{process} \left. \frac{d\delta}{dt} \right|_{process} \quad (7) $$

where dq/dt is the change in time of atmospheric specific humidity (in kg kg⁻¹ s⁻¹) and dδ/dt is the change in time of the δ of atmospheric vapor (in %c s⁻¹).

# 3. Model Evaluation and Sensitivity Tests

## 3.1. Mean Antarctic Climate: Temperature, Accumulation and Surface Snow Water Isotopes

Before investigating the ability of LMDZ6 to simulate the isotopic composition of surface snow, we explore the ability of the model to simulate the mean surface temperature and the snow accumulation over the ice-sheet. Then we evaluate LMDZ mean climate state over the Antarctic continent for surface snow δ¹⁸O and d-excess. Model outputs are averaged over 1980–2023 without consideration of sample time of observations, except for accumulation (see Method). The model accurately depicts the spatial distribution of mean surface temperature, with a strong correlation with observations (R² = 0.96, linear regression slope of 1.0) and a cold mean bias of 1.4 K (Figures 1a and 1c). The simulated snow accumulation present a low bias compared to the observational data set (mean bias of +12.2 kg m⁻² yr⁻¹, which represents 8.7% of the mean observed accumulation), independently of the altitude (Figures 1b and 1d).

In order to assess the influence of supersaturation on kinetic fractionation, we examine the relationship between d-excess and δD in the modeled surface snow for different λ values, similarly as done with other isotope-enabled atmospheric models (Masson-Delmotte et al., 2008; Risi et al., 2010; Werner et al., 2011). We use the three simulations with λ = 0.002 K⁻¹, λ = 0.004 K⁻¹ and λ = 0.006 K⁻¹ over the period 2008–2023. We observe that the modeled δD-d-excess relationship in surface snow is the closest to observation for the highest λ (0.006 K⁻¹, Figure 2a), meaning for high parameterized supersaturation. The effect of λ on surface snow δD-d-excess relationship is the strongest for inland sites (δD ≤ −300‰). At Concordia station (observed surface snow δD = −393.8‰), the d-excess deviation between the observation and the simulation with λ = 0.006 K⁻¹ is −1.1‰, whereas it is 6.6‰ for λ = 0.004 K⁻¹ and 14.7‰ for λ = 0.002 K⁻¹ (Figure 2a, red circle, Table 2). At the ice sheet margins (δD ≥ −250‰), modeled surface snow δD-d-excess relation is less dependent of λ and closer to observations.

The choice of parameterized supersaturation through λ also directly impacts Rayleigh distillation, with higher supersaturation inducing weaker fractionation at condensation. Consequently, for the inland sites (large distillation), a higher λ induces a higher surface snow δ¹⁸O, with LMDZ being closer to observation for the smallest λ (λ = 0.002 K⁻¹, Figure 2b and Figure S2 in Supporting Information S1). At Concordia, the mean bias in surface snow δ¹⁸O between LMDZ and observation with λ = 0.006 K⁻¹ is 4.3‰, while it is 2.6‰ with λ = 0.004 K⁻¹ and
<figure><img src="image_5.png" /><figcaption>**Figure 2.** (a) Surface snow d-excess (y-axis) versus δD (x-axis) and (b) Surface snow δ¹⁸O (y-axis) versus Temperature (x-axis), in observations (black dots) and in LMDZiso (2008–2023 time average, blue dots), for λ = 0.002 K⁻¹ (light blue), λ = 0.004 K⁻¹ (medium blue), and λ = 0.006 K⁻¹ (dark blue), at the locations shown in Figures 1a, 3a and 3b. The quadratic regression lines (a) and the linear regression (b) are indicated by solid lines in the color of the data they represent. The filled red dot indicates the observational value collected at Concordia, while the red circles filled with blue indicate the value of the model output at Concordia for the corresponding λ.</figcaption></figure>

0.8‰ with λ = 0.002 K⁻¹ (Figure 2b, Table 2). Therefore, in order to balance the representation of δ¹⁸O and d-excess, we chose to tune the simulation with the supersaturation parameter λ = 0.004 K⁻¹. This choice will be further discussed later in this study.

We now compare the isotopic composition of the LMDZ surface snow for the full period (1980–2023), for a λ parameter of 0.004 K⁻¹, to observed surface snow isotopic composition (see methods) without consideration of their sample time, averaged per model grid cell. The simulation does not show any statistically significant trends in the mean annual precipitation δ¹⁸O and d-excess for the period 1980–2023 (not shown). The 1980–2023 mean differs to the 2008–2023 mean by a maximum of 2.5% for snow δ¹⁸O and less than 9.0% for snow d-excess (not shown). The LMDZ model effectively simulates the spatial distribution of surface snow mean annual δ¹⁸O over the continent (R² = 0.95, Figure 3), with a bias equals to 0.0‰ considering the whole ice sheet, but with a positive bias inland, where observed δ¹⁸O is lower than −45‰, and a negative bias in the coastal regions, when δ¹⁸O is higher than −40‰. With regard to surface snow d-excess, LMDZ simulates a significantly higher d-excess than observed, except at the ice sheet margins, with a maximum positive bias of LMDZ d-excess of 11.9‰.

In summary, the isotopic simulations are highly dependent on the supersaturation parameter. Consequently, the simulation with intermediate supersaturation (λ = 0.004 K⁻¹) was selected as it offers the optimal compromise to correctly simulate both δ¹⁸O and d-excess in surface snow (Table 2). Analysis of the logarithmic d-excess (dln, as defined in Dütsch et al. (2017)) produced the same relative bias across simulations, supporting the selected value of λ. It should be noted that this spatial evaluation does not account for the model's bias in seasonal variations. In

<caption>**Table 2**</caption>

Influence of the Supersaturation Parameter λ on the Mean Isotopic Composition of LMDZ Surface Snow (2008–2023 Time Average) in Antarctica Compared to Observations

<table><thead><tr><th></th><th>Observations</th><th>Δ(λ = 0.002 K<sup>-1</sup>)</th><th>Δ(λ = 0.004 K<sup>-1</sup>)</th><th>Δ(λ = 0.006 K<sup>-1</sup>)</th></tr></thead><tbody><tr><td>δ<sup>18</sup>O (‰)</td><td>-39.4 ± 0.8</td><td>-1.1 ± 1.1</td><td>-0.1 ± 1.0</td><td>0.9 ± 1.0</td></tr><tr><td>δ<sup>18</sup>O-DC (‰)</td><td>-50.3</td><td>0.8</td><td>2.6</td><td>4.3</td></tr><tr><td>d-excess (‰)</td><td>6.6 ± 0.3</td><td>9.5 ± 0.7</td><td>4.9 ± 0.4</td><td>0.4 ± 0.3</td></tr><tr><td>d-excess-DC (‰)</td><td>8.8</td><td>14.7</td><td>6.6</td><td>-1.1</td></tr></tbody></table>

Note. The Δ values indicate the mean bias in surface snow between the model and the observations. δ¹⁸O and d-excess indicates the bias over the continent, while δ¹⁸O-DC and d-excess-DC correspond to the bias at the Concordia station. The error is calculated as the standard deviation divided by the square root of the number of samples (N = 180).
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Top: (a) Surface snow $\delta^{18}$O and (b) surface snow d-excess in LMDZ6iso (1980–2023 time-average, background map) and in observations (colored dots). Bottom: LMDZiso (y-axis) versus observation (x-axis) for (c) $\delta^{18}$O and (d) d-excess in surface snow. The dots' color in the bottom panel corresponds to the mean annual temperature estimated from firn temperature or automatic weather stations. The black line is for linear regression, the red cross and the black cross indicate observed and modeled values at Concordia and Dumont d'Urville station respectively.</figcaption></figure>

the next section, we evaluate LMDZ at the seasonal scale by analyzing the isotopic composition of daily pre-
cipitation at Concordia and Dumont d'Urville.

**3.2. Daily Precipitation Water Isotopes at Concordia and Dumont d'Urville**

For quantifying model-observation differences in daily precipitation, we consider the daily average of the isotopic
composition of precipitation in the model, without weighting by the precipitation amount, compared to the
isotopic composition of daily precipitation samples. We exclude daily observations with a negative d-excess from
our analysis, as it is a marker of fractionation at sublimation of the snow samples before their collection (Ollivier
et al., 2024).

At Concordia station, the observation of water isotopes in daily precipitation spans the 2008–2021 period (Dreossi et al., 2023; Ollivier et al., 2024). To evaluate the impact of supersaturation on kinetic fractionation directly in the precipitation at Concordia, we analyze the relationship between d-excess and δD in the modeled daily precipitation for the three λ values, all year long and during summers (December–January–February, DJF) and winters (June–July–August, JJA). We observe that over the entire period, the daily precipitation δD-d-excess relationship is best represented for λ = 0.004 K⁻¹, with a mean LMDZ bias in d-excess of 1.9‰, compared to 14.4‰ for
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Concordia station daily precipitation d-excess (y-axis) versus δD (x-axis), for the period 2008–2021, in observations (black dots) and in LMDZiso (blue dots), for λ = 0.002 K⁻¹ (dark blue), λ = 0.004 K⁻¹ (medium blue), and λ = 0.006 K⁻¹ (light blue), for (a) year-round, (b) DJF, and (c) JJA. The quadratic regression lines are indicated by solid lines in the color of the data they represent. The filled red dot indicates the observational mean for the analyzed period, while the red circles filled with blue indicate the model output means for the same period corresponding to the respective λ. The gray points correspond to observations with negative d-excess, and are not included in the quadratic regressions.</figcaption></figure>

λ = 0.002 K⁻¹ and -9.9 ‰ for λ = 0.006 K⁻¹ (Figure 4, Table 3). Seasonal analysis indicates that there is low influence of the supersaturation parameter on daily precipitation d-excess in summer (DJF, Figure 4b), and that the discrepancy between LMDZ and observations for the different λ values is predominantly attributable to the cold season (JJA, Figure 4c, Table 3). We note that the bias in daily precipitation δ¹⁸O is more pronounced in the simulation with λ = 0.004 K⁻¹ than in the simulation with λ = 0.002 K⁻¹ independently of the season (Table 3).

In summary, δD-d-excess relationship in daily precipitation at Concordia station shows the best model- observation agreement for intermediate supersaturation strength (λ = 0.004 K⁻¹) despite a positive bias in δ¹⁸O. As previous studies have shown that surface snow d-excess is affected by post-depositional processes in Antarctica (Casado et al., 2018; Ollivier et al., 2024), related to fractionation during sublimation in summer, and as these post-deposition processes are currently not included in LMDZ, we consider the evaluation against daily precipitation d-excess in Concordia to be more robust than against spatial surface snow. This confirms our decision to use the LMDZ simulation with λ = 0.004 K⁻¹ as our reference.

The daily precipitation δ¹⁸O and d-excess exhibit clear seasonal cycles in both observations and our reference simulation (Figures 5a and 5d, Figure S1 in Supporting Information S1), with maximum δ¹⁸O-minimum d-excess

<caption>**Table 3**</caption>

Influence of the Supersaturation Parameter λ on the Mean Isotopic Composition of LMDZ Daily Precipitation at Concordia Station Compared to Observations

<table><thead><tr><th></th><th>Observations</th><th>Δ(λ = 0.002 K<sup>-1</sup>)</th><th>Δ(λ = 0.004 K<sup>-1</sup>)</th><th>Δ(λ = 0.006 K<sup>-1</sup>)</th></tr></thead><tbody><tr><td>δ<sup>18</sup>O (‰)</td><td>-56.1 ± 0.2</td><td>1.3 ± 0.3</td><td>4.0 ± 0.3</td><td>6.5 ± 0.2</td></tr><tr><td>δ<sup>18</sup>O-JJA (‰)</td><td>-60.2 ± 0.3</td><td>2.6 ± 0.5</td><td>6.0 ± 0.5</td><td>9.2 ± 0.5</td></tr><tr><td>δ<sup>18</sup>O-DJF (‰)</td><td>-48.4 ± 0.3</td><td>1.1 ± 0.4</td><td>2.2 ± 0.4</td><td>3.2 ± 0.3</td></tr><tr><td>d-excess (‰)</td><td>16.2 ± 0.3</td><td>14.4 ± 0.4</td><td>1.9 ± 0.3</td><td>-9.9 ± 0.3</td></tr><tr><td>d-excess-JJA (‰)</td><td>19.0 ± 0.5</td><td>15.3 ± 0.9</td><td>-0.9 ± 0.7</td><td>-16.0 ± 0.6</td></tr><tr><td>d-excess-DJF (‰)</td><td>12.3 ± 0.5</td><td>7.5 ± 0.6</td><td>2.9 ± 0.5</td><td>-1.6 ± 0.5</td></tr></tbody></table>

*Note.* In the Observations column, the values indicate the average δ¹⁸O and d-excess observed for the period indicated. The columns with the Δ shows the mean bias in daily precipitation between the model and the observations without weighting by the precipitation amount for λ = 0.002 K⁻¹, λ = 0.004 K⁻¹, and λ = 0.006 K⁻¹. δ¹⁸O and d-excess indicates the mean bias for the period 2008–2021, δ¹⁸O-JJA and d-excess-JJA correspond to the mean bias for austral winter months (JJA), and δ¹⁸O-DJF and d-excess-DJF are the average biases calculated during austral summer months (DJF). To remove d-excess outliers, only observed values associated with a positive d-excess within three times the standard deviation of the mean are included in the calculations. The error is calculated as the standard deviation divided by the square root of the number of samples (N = 1319, N-JJA = 368, N-DJF = 252).
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Concordia station daily precipitation δ<sup>18</sup>O (a) and d-excess (d), for the period 2017–2021, in observations (black dots) and in LMDZis0 (blue dots) for λ = 0.004 K<sup>-1</sup>, and comparison between the observed δ<sup>18</sup>O (b, c) and d-excess (e, f) in precipitation and the corresponding model outputs for DJF and JJA months on the period 2008–2021, respectively. The dots' color corresponds to the modeled amount of precipitation (kg m<sup>-2</sup> day<sup>-1</sup>), and the black line indicates the linear regression. The gray points correspond to observations with negative d-excess, and are not included in the linear regressions. Time series for the full period (2008–2021) are shown in Figure S1 of Supporting Information S1.</figcaption></figure>

in summer, and minimum δ¹⁸O-maximum d-excess in winter. In DJF, the mean LMDZ δ¹⁸O and d-excess of precipitation is slightly higher than the observed one (LMDZ mean bias of 2.2‰ for δ¹⁸O and 2.9‰ for d-excess, Figures 5b and 5e, Table 3). In JJA, LMDZ simulates a 6.0 ‰ larger precipitation δ¹⁸O than observed, while d-excess is simulated with a bias of -0.9‰ (Figures 5c and 5f, Table 3).

We perform the same daily precipitation analysis at Dumont d'Urville for the period 2019–2020. Although the DDU station is located on an island 5 km away from the continent, in LMDZ the station is at the interface between an oceanic grid cell (with 1% glacier and 99% ocean and sea ice) and a land ice grid cell (with 90% land ice and 10% ocean and sea ice), the mean elevation in the latter being 1,032 m above sea level (Figure 6). No clear seasonal cycle is visible in daily precipitation δ¹⁸O and d-excess for this period in the observation nor in the model (Figures 7a and 7d). In the case of daily precipitation δ¹⁸O, there is a greater concordance between the model and the observations for the oceanic grid cell (bias of 0.2‰, Figure 7c) than in the continental grid cell (bias of -7.1‰, Figure 7b). The LMDZ precipitation d-excess is close to the observations in both grid cells, with a model bias of 0.9‰ in the continental grid cell and -0.4‰ in the oceanic grid cell (Figures 7e and 7f).

**3.3. Daily Cycle of Water Vapor Isotopes at Concordia and Dumont d'Urville in Summer**

We focus on daily cycles of water vapor isotopes at Concordia and Dumont d'Urville in order to highlight
boundary layer processes over several days for a statistically robust interpretation. We choose to restrict our study
to December 2018 at Concordia and December 2019 at Dumont d'Urville, during periods of clear sky (without
large-scale moisture advection, white background in Figures 8 and 9), which we arbitrarily defined by LMDZ
incoming longwave radiation fluxes lower throughout the day than 140 W m⁻² at Concordia and lower than
270 W m⁻² at Dumont d'Urville. At Concordia, it corresponds to the periods 1–19 and 25–30 December 2018
<figure><img src="image_9.png" /><figcaption>**Figure 6.** (a) LMDZ grid cells around Dumont d'Urville station and mean sea ice concentration in the ocean fraction of grid cells. The DDU station is indicated by the red dot. The green cell corresponds to the ocean cell closest to DDU (99% of ocean), while the brown cell corresponds to the land ice cell closest to DDU (90% of land ice). (b, c) Changes in the fractions of ocean, sea ice and land ice in the continental cell (b) and the oceanic cell (c) during December 2019.</figcaption></figure>

(Figure 8). At Dumont d'Urville, the period considered is 5–14, 18–20, 23–24 and 26–29 December 2019 (Figure 9). Additionally, a large-scale warm and moist advection event (or atmospheric river) was observed from 20 to 24 December 2018 at Concordia (Figure 8, gray rectangle). The event, characterized by a strong downward

<figure><img src="image_10.png" /><figcaption>**Figure 7.** Dumont d'Urville station daily precipitation $\delta^{18}\text{O}$ (a) and d-excess (d), for the period 2019–2020, in observations (black dots) and in LMDZiso (green triangles in the ocean grid cell and brown triangles in the land grid cell) for our reference simulation ($\lambda = 0.004 \text{ K}^{-1}$), and comparison between the observed $\delta^{18}\text{O}$ (b, c) and d-excess (e, f) in precipitation and the corresponding model outputs for the land grid cell (b, e) and in the ocean grid cell (c, f) respectively. The dots' color corresponds to the modeled daily amount of precipitation (kg m$^{-2}$ day$^{-1}$), and the black line indicates the linear regression.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Evolution of (a) incoming longwave flux, (b) incoming shortwave flux, (c) temperature (°C), (d) specific humidity (g kg⁻¹) from the first level of the Concordia meteorological tower (2.8 m AGL) and (e) δ¹⁸O (‰) from the Concordia Picarro instrument (2.0 m AGL). The gray rectangle indicates a period of cloudy conditions. The blue line represents the model data (first level: 6.7 m AGL), while the black line corresponds to the observations. The LMDZ simulation is carried out with λ = 0.004 K⁻¹. The red (blue) bars indicate the calculated uncertainty due to the isotopic ratio versus humidity dependence on the corrected δ¹⁸O during periods with maximum (minimum) humidity described in Leroy-Dos Santos et al. (2021).</figcaption></figure>

incoming longwave flux at the surface and an increase in temperature, specific humidity, and isotopic signal, is
not analyzed in this article.

At Concordia, during December 2018 clear-sky days, LMDZ6iso simulates the mean temperature with a warm bias of 1.5 K (5% higher than observed) and the mean specific humidity with a wet bias of 0.04 g kg⁻¹ (14% higher than observed). The mean water vapor δ¹⁸O during the same period is -66.9‰ in the observations and -62.2‰ in LMDZ6iso (Figures 8 and 10). The daily cycles of specific humidity and water vapor δ¹⁸O are aligned between model and observations, but lag behind the modeled incoming solar radiation by 4 hrs (Figures 10b and 10c). The 2-m temperature amplitude is 3% larger in LMDZ6iso than in observations (9.5 K observed, 9.8 K modeled), and the specific humidity amplitude in the model is 3% larger to that observed (0.25 g kg⁻¹ observed, 0.26 g kg⁻¹ modeled). The modeled daily cycle of water vapor δ¹⁸O exhibits an amplitude that is more than two times higher than the observed amplitude (4.0‰ observed, 10.7‰ modeled). While a similar comparison was conducted for d-excess, it was inconclusive due to the magnitude of observational uncertainties, similar to the magnitude of model biases (not shown).

At Dumont d'Urville, during December 2019 clear-sky days, the LMDZ oceanic grid cell has a bias of -1.1 K and the continental grid cell a bias of -9.8 K, partly explained by the altitude for the latter (Figures 9c and 10d). Regarding specific humidity, the LMDZ oceanic cell has a bias of 0.5 g kg⁻¹, while the continental grid cell has a bias of -0.6 g kg⁻¹ (Figures 9d and 10e). The δ¹⁸O of water vapor in the continental grid cell accurately
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Evolution of (a) incoming longwave flux, (b) incoming shortwave flux, (c) temperature (°C), (d) specific humidity (g kg⁻¹) from the DDU meteorological station and (e) δ¹⁸O (‰) from the DDU Picarro instrument. The gray rectangles indicate periods of cloudy conditions. The black line is for observation, the green line corresponds to the model for the ocean cell and the brown line the model for the land cell. The mean uncertainty for the observed δ¹⁸O is estimated to be 0.8‰.</figcaption></figure>

reproduces the observed daily variation with an amplitude 23% lower than that observed (observed amplitude 7.9‰), and exhibits a mean negative bias of 8.2‰. The LMDZ oceanic grid cell exhibits a reversed diurnal cycle compared to observations, with the maximum δ¹⁸O of water vapor being reached at the minimum of insolation in the model, and a positive bias of 7.1‰ (Figures 9e and 10f). This unexpected result requires more in-depth analysis and evaluation to rule out any model issue before further interpretation.

## 4. Boundary Layer Processes Driving Water Isotope Daily Cycles at Concordia and Dumont d'Urville

After evaluating the model on different temporal and spatial scales, we use it to gain a more detailed understanding of the modeled processes that control the δ¹⁸O cycle of surface vapor at Concordia and Dumont d'Urville. This physical analysis of the model is based on the process decomposition described in Section 2. Its aim is to identify potential biases in the model and to lay the foundations for a better physical understanding of the observations. Here we interpret the modeled daily cycles of water vapor δ¹⁸O at Concordia and Dumont d'Urville using the process decomposition described in Section 2. For Concordia, the analysis focuses on the period 11–17 December 2018, while for Dumont d'Urville, we use the 6–12 December 2019 period. The daytime period is
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Average daily cycles of (a, d) temperature (°C), (b, e) specific humidity (g kg⁻¹) and (c, f) δ¹⁸O (‰) during clear sky periods (see Figures 8 and 9 for identification of these periods) at Concordia (top row) and Dumont d'Urville (bottom row). The envelopes in light colors represent the standard deviation of the data obtained over the different days within each period. The yellow dotted line corresponds to the maximum modeled insolation (3a.m. UTC at Concordia and 2a.m. UTC at Dumont d'Urville) and the gray dotted line corresponds to the minimum modeled insolation (3p.m. UTC at Concordia and 2p.m. UTC at Dumont d'Urville). At Dumont d'Urville, the green envelope is indicative of the ocean cell and the brown envelope is indicative of the land cell.</figcaption></figure>

defined as the maximum insolation ±6 hr, while the nighttime period is defined as the minimum insolation ±6 hr. At Concordia, the maximum and minimum insolation correspond at 3 a.m. UTC and 3 p.m. UTC, respectively. At Dumont d'Urville, the corresponding times are 2 a.m. UTC and 2 p.m. UTC.

At Concordia, LMDZ shows synchronized daily variations of temperature and humidity in the first 500 m AGL, with temperature and humidity increasing during the day up to an altitude of 300 m AGL and decreasing at night (minimal incoming solar radiation), with a strongest decrease close to the surface (Figures S3a and S3b in Supporting Information S1). From air humidity tendencies, we observe that during the day, surface sublimation and turbulent mixing brings moisture up to 200 m AGL and contributes to enrich the vapor δ¹⁸O (Figures 11a and 11d). The moisture added into the atmosphere by surface sublimation is transported in upper atmospheric layers, around 600 m AGL, by shallow convection, which leads to depletion of vapor δ¹⁸O from the surface to 200 m AGL, and an enrichment of the vapor between 400 and 600 m AGL (Figures 11b and 11e). Due to the decrease in temperature with altitude, the vapor condenses between 400 and 600 m AGL, depleting the water vapor δ¹⁸O at these altitudes (Figures 11c and 11f). The atmospheric condensation tendency comes from the grid-scale cloud scheme, where positive condensation corresponds to sublimation of falling precipitation. This process corresponds to the liquid phase or mixed phase cumulus clouds, which are commonly observed during the day in Dôme C (Ricaud et al., 2020). During the night, atmospheric condensation, surface condensation and turbulent mixing induce a decrease in air humidity to a height of 50 m AGL, leading to a decrease in vapor δ¹⁸O (Figures 11a, 11c, 11d and 11f). However, given that the LMDZ condensation scheme does not account for supersaturation, while supersaturation has been observed during summer at Concordia (Genthon et al., 2022), it is highly likely that the occurrence of night-time surface fog is overestimated in the model.

We quantify the contribution of each process to the amplitude of the daily δ¹⁸O water vapor cycle in the surface atmospheric layer (0–6.7 m AGL) by integrating the averaged daily cycle associated to each process in time. By summing the integrated processes that occur during both daytime and nighttime periods, it is possible to recover the amplitude of variation in the vapor δ¹⁸O (10.7‰, Table 4). During the day, the surface vapor δ¹⁸O is primarily enriched by surface sublimation (+13.3‰, 124% of the amplitude) and atmospheric condensation (+1.0‰, 9% of the amplitude). Additionally, shallow convection depletes surface vapor δ¹⁸O by 3.5‰ (33% of the amplitude). During the night, the depletion of vapor δ¹⁸O is primarily driven by atmospheric condensation (−6.2‰, 58% of the amplitude) and surface condensation (−5.7‰, 53% of the amplitude, Figure 12b, Figure S6b in Supporting Information S1, Table 4).
<figure><img src="image_14.png" /><figcaption>**Figure 11.** Decomposition of the rate of change of the different processes controlling specific humidity (upper line: a, b, c) and vapor $\delta^{18}$O (lower line: d, e, f) as a function of time at Concordia station. (a, d) correspond to surface sublimation and turbulent mixing, (b, e) to shallow convection, and (c, f) to atmospheric condensation. It should be noted that the sign of changes is relative to water vapor so the surface sublimation is positive (increase in water vapor) and the atmospheric condensation is negative (decrease in water vapor).</figcaption></figure>

At Dumont d'Urville, the LMDZ model shows an increase in temperature during the day to an altitude of 300–400 m AGL, and a significant cooling and drying at night in the first 400 m AGL in the continental grid cell, and in the first 200 m AGL in the oceanic grid cell (Figures S3c and S3e in Supporting Information S1). This diurnal change in temperature is concurrent with an increase in specific humidity during the day to an altitude of 400 m AGL in the continental grid, and 400–600 m AGL in the oceanic grid, and a decrease in humidity throughout the atmospheric column at night (Figures S3d and S3f in Supporting Information S1). The specific humidity tendencies in DDU are controlled by surface sublimation and shallow convection during the day, similarly as in Concordia, and by an important contribution of advection at night (Figure S5 in Supporting Information S1). The lack of atmospheric condensation at DDU is probably related to the clear advection of dry air from the continent, combined with an increase in temperature due to adiabatic processes, which lead to an undersaturated atmospheric column (Figure S4 in Supporting Information S1).

<caption>**Table 4**</caption>

Contribution of Processes to the Variation in $\delta^{18}$O of Surface Vapor Computed From Average Daily Cycles at Concordia, Dumont d'Urville (Land) and Dumont d'Urville (Ocean) Integrated Over Day and Night (11–17 December 2018 for Concordia and 6–12 December 2019 for Dumont d'Urville)

<table><thead><tr><th rowspan="2"></th><th colspan="2">DC</th><th colspan="2">DDU (land)</th><th colspan="2">DDU (ocean)</th></tr><tr><th>Day</th><th>Night</th><th>Day</th><th>Night</th><th>Day</th><th>Night</th></tr></thead><tbody><tr><td>Surf. sublimation (‰)</td><td>13.3</td><td>-5.7</td><td>25.6</td><td>18.1</td><td>14.8</td><td>13.8</td></tr><tr><td>Advection (‰)</td><td>0.1</td><td>0.3</td><td>-13.2</td><td>-22.8</td><td>-10.1</td><td>-9.4</td></tr><tr><td>Atmos. condensation (‰)</td><td>1.0</td><td>-6.2</td><td>0.0</td><td>-0.5</td><td>0.0</td><td>0.0</td></tr><tr><td>Shallow convection (‰)</td><td>-3.5</td><td>0.0</td><td>-6.8</td><td>-0.1</td><td>-8.0</td><td>-0.6</td></tr><tr><td>Sum (‰)</td><td>10.9</td><td>-11.6</td><td>5.6</td><td>-5.3</td><td>-3.3</td><td>3.8</td></tr><tr><td>δ<sup>18</sup>O diurnal cycle amplitude (‰)</td><td>10.7</td><td></td><td>6.2</td><td></td><td>4.0</td><td></td></tr></tbody></table>

Note. At Concordia, the daytime period is centered around 3 a.m. UTC (maximum insolation ± 6h) whereas at Dumont d'Urville, the daytime period is centered around 2 a.m. UTC. The $\delta^{18}$O diurnal cycle amplitude is defined as the difference between the daily maximum and minimum values of the simulated signal.
<figure><img src="image_15.png" /><figcaption>**Figure 12.** Diurnal average of the contributions to the specific humidity tendency (a, c, e) and vapor $\delta^{18}$O tendency (b, d, f) in the lowest atmospheric level, for Concordia (top row), Dumont d'Urville—land ice grid cell (medium row) and Dumont d'Urville—ocean grid cell (bottom row) during clear sky periods (11–17 December 2018 for Concordia and 6–12 December 2019 for Dumont d'Urville). The black line represents the signal obtained from the model output, while the dashed line represents the sum of the contributions. The individual contributions to the rate of change of humidity and $\delta^{18}$O are shown in color: orange for vertical turbulent diffusion and surface sublimation, blue for advection, purple for atmospheric condensation and red for shallow convection. It should be noted that the sign of changes is relative to water vapor so the surface sublimation is positive (increase in water vapor) and the atmospheric condensation is negative (decrease in water vapor). The yellow and gray rectangles represent the day and night periods, respectively.</figcaption></figure>

At DDU, the surface vapor (0–10 m AGL, Table 1) is enriched in $\delta^{18}$O exclusively by surface snow sublimation (+43.7‰ for day and night in the continental grid cell, and +28.6‰ for day and night in the oceanic grid cell, Figures 12d and 12f, Figures S6d and S6f in Supporting Information S1, Table 4). The depletion of surface vapor $\delta^{18}$O is dominated by the advection and the shallow convection. In the continental grid cell, the influence of advection on the depletion of the $\delta^{18}$O signal is nearly twice as pronounced at night as during the day, with a depletion of –22.8‰ compared to –13.2‰, respectively, which corroborates the findings of Bréant et al. (2019).
Summary of the evaluation of LMDZ6iso

<figure><img src="image_16.png" /><figcaption>**Figure 13.** (a) Summary of the evaluation of LMDZ6iso in Antarctica, through the study of surface snow across the continent and daily precipitation at Concordia, for the three 16-year simulations (L2-16 years for $\lambda = 0.002 K^{-1}$; L4-16 years for $\lambda = 0.004 K^{-1}$, L6-16 years $\lambda = 0.006 K^{-1}$, 2008–2023) and the 44-year reference simulation (L4-44 years, 1980–2023). The color of each box indicates the mean bias between the model and the observations. The section “Surface snow” shows mean bias in surface snow $\delta^{18}$O and d-excess across the continent and at Concordia station (DC) (Section 3.1). The section “Daily precipitation DC” corresponds to mean bias in daily precipitation $\delta^{18}$O and d-excess at Concordia station, for all year, for austral summer months (DJF) and for austral winter months (JJA), for the period 2008–2021 (Section 3.2). (b) Summary of the evaluation of LMDZ6iso at Concordia (DC) and Dumont d’Urville (DDU) stations through the study of isotopic water vapor (Section 3.3): the “all” column corresponds to the average bias of $\delta^{18}$O vapor computed for clear-sky days of December 2018 in DC and December 2019 in DDU. $\Delta$-day indicates the bias in daily cycle amplitude during the day (computed as mean during the day minus mean over the period). $\Delta$-night is the same for the night. $\Delta$ corresponds to the bias in $\delta^{18}$O daily amplitude (computed as mean during day minus mean during night). (c) Summary of the processes controlling the modeled isotopic composition of surface vapor at Concordia during daily cycle (Section 4). The processes of surface sublimation and turbulent mixing are shown in orange, atmospheric condensation is shown in purple and shallow convection is shown in red. Numbers are taken from Table 4. Schematic of Concordia Station by C. Davrinche.</figcaption></figure>

on the influence of katabatic winds on the surface isotopic signal in summer. In contrast, the influence of advection on the depletion of the vapor $\delta^{18}$O shows no strong diurnal cycle in the oceanic grid cell, with a depletion around -10‰ during day and night, but with substantial sub-diurnal variations. Shallow convection occurs predominantly during the day and contributes to the depletion of the $\delta^{18}$O signal by -6.8‰ in the continental grid cell and by -8.0‰ in the oceanic grid cell. Atmospheric condensation does not influence the surface vapor $\delta^{18}$O signal at Dumont d'Urville (Figures 12d and 12f, Figures S6d and S6f in Supporting Information S1, Table 4).

**5. Discussion and Conclusions**

We have shown that in the LMDZ6iso atmospheric model, the isotopic composition of surface snow, precipitation and water vapor in Antarctica is strongly dependent on the parameterization of supersaturation used to compute fractionation at condensation, in agreement with previous studies (Cauquoin & Werner, 2021; Dütsch et al., 2019; Risi et al., 2010). A synthesis of the evaluation of LMDZ6iso is shown in Figures 13a and 13b. With LMDZ6iso, a low supersaturation ($\lambda = 0.002 K^{-1}$) yields excellent results for surface snow $\delta^{18}$O across the continent, but a too large d-excess, while a high supersaturation ($\lambda = 0.006 K^{-1}$) generates a positive bias in surface snow $\delta^{18}$O but a more accurate d-excess (Figure S2 in Supporting Information S1). Following the comparison with surface snow observations, we found that $\lambda = 0.004 K^{-1}$ was the best compromise by reducing the bias in $\delta^{18}$O and d-excess in surface snow for low temperatures (+2.6‰ and +6.6‰ respectively at Concordia station). With this choice of parameter, the multi-year average (1980–2023) of the isotopic composition of surface across the continent snow
shows no mean bias in δ¹⁸O (0.0‰) and a positive bias of +4.9‰ in d-excess. Although the choice of parameter λ = 0.004 K⁻¹ slightly degrades surface snow δ¹⁸O for very cold temperatures (Figure S2 in Supporting Information S1), the bias introduced remains small compared with uncertainties on δ¹⁸O related to other processes, such as the crude supersaturation parametrization. Although this tuning of the λ parameter produces d-excess values consistent with observations, this outcome can result from compensating underlying errors in the model's representation of isotopes in both precipitation and surface snow. For the modeled d-excess to be reliably used as a paleoclimatic proxy, improvements in the modeled processes governing d-excess will be necessary.

Several hypotheses could explain the differences between the model and the observations. Our main hypothesis relates to the implementation of isotope physics in the model. Indeed, there is still considerable uncertainty about equilibrium fractionation coefficients during solid-vapor exchanges at very low temperature, as well as about the kinetic fractionation during supersaturation conditions. Secondly, the difference between models and observations could be due to cloud physics, since isotopes are sensitive to the latter, particularly during supersaturation. Thirdly, post-deposition processes that are not currently included in the model could also play a role in the model isotopic biases. Finally, since our simulation is guided by a reanalysis, we think that biases in large-scale moisture transport might be secondary compared to the other ones. Future studies should first focus on better constraining isotopic physics, in particular the fractionation coefficients, in order to use isotopic constraints to derive cloud processes and improve cloud physics.

Water isotope observations are crucial to evaluate isotope-enabled atmospheric models, and model evaluation would benefit from standardized sample collection protocols across the continent. The Masson-Delmotte et al. (2008) database, which serves as a reference for many isotopic model evaluations in Antarctica (Cauquoin & Werner, 2021; Masson-Delmotte et al., 2008; Risi et al., 2010; Werner et al., 2011), contains a wide variety of different sampling protocols: the surface snow isotopic samples in this database come from snow precipitation (Masson-Delmotte et al., 2008), surface snow cores (Masson-Delmotte et al., 2003), snow pits (Ekaykin et al., 2002) or surface snow collected along transects (BecAGLi et al., 2004; Magand et al., 2004; Petit et al., 1991), rendering it challenging to ascertain meaningful comparisons with model outputs. A first order improvement of the data set would be to systematically include the period covered by the sampling in the database.

Inland climate is well represented in the low resolution LMDZ6iso simulations while our simulation presents limitations for the study of coastal regions. Notably, the Dumont d'Urville station is located at the boundary of an oceanic grid cell and a continental grid cell, which complicates the evaluation of the model and its use to interpret observations. Consequently, studying water isotopes at the ice sheet margins would greatly benefit from regional or global simulations at higher resolutions, such as those performed with the COSMOiso regional atmospheric model with a horizontal resolution of 0.125° in Thurnherr et al. (2020), but which were centered over the Southern Ocean. When the thermodynamic fields are correctly represented by the model, the decomposition of the specific humidity and vapor δ¹⁸O tendencies into contribution of the different processes implemented in the model is a very powerful tool for understanding processes which control the observed signal. However, it is important to note that this approach assumes that the model processes correspond to the observed processes. This is why an individual assessment of each process, for example, turbulent fluxes or clouds, would enable us to better constrain their relative contributions to the observed isotopic signal.

The decomposition of the isotopic signal into processes is also a very useful tool for improving model param-
eterizations. At Concordia, we highlighted the importance of surface sublimation in the isotopic composition of
water vapor during clear sky days in summer, which is driven by the isotopic composition of the surface snow
(Figure 13c). Currently, the sublimating snow layer in LMDZ6iso corresponds to the snow bucket, which is
calculated as the accumulation of precipitation since the start of the simulation minus snow sublimation. Future
developments will aim at using a thinner bucket of surface snow for evaporation, the thickness of which remains
to be determined, and which would allow sublimation to be more sensitive to recent precipitation events. In
addition, our results indicate that the amplitude of δ¹⁸O in vapor is higher in the model than observed. Tendency
analysis suggests that this discrepancy may arise from an overly strong surface evaporation flux during the day
and excessive condensation at night. Since temperature and specific humidity are accurately represented by the
model, our primary hypothesis is that this issue may be linked to unresolved or badly resolved air-snow isotopic
exchanges in the model. At Dumont d'Urville, the observed daily cycle is better represented in the LMDZ
continental grid cell, in which depletion at night is driven by horizontal advection from the continent (katabatic
winds) as shown in Bréant et al. (2019). In the nearest LMDZ oceanic grid cell, the isotopic daily cycle in phase opposition compared to observation is surprising. Process analysis revealed the absence of daily cycles in the contribution of surface sublimation to changes in vapor $\delta^{18}$O, which after investigation revealed a likely inappropriate isotopic flux during ocean evaporation, and will be further studied in the future.

The continental station of Concordia, where the effect of parameterized supersaturation is particularly pronounced (Figure 3 and Figure S2 in Supporting Information S1), is an ideal location to further assess isotopic fractionation at condensation for low temperature. The study of the $\delta D$-d-excess relationship revealed that for daily precipitation at Concordia, the optimal model-observation agreement was obtained for intermediate supersaturation strength ($\lambda = 0.004 \text{ K}^{-1}$), whereas for surface snow, the best agreement were obtained for higher supersaturation ($\lambda = 0.006 \text{ K}^{-1}$, Figures 2 and 4). Post-deposition processes may account for some of the discrepancy between the assessment based on surface snow and that based on precipitation, as shown by Steen-Larsen et al. (2014); Casado et al. (2018); Wahl et al. (2022); Dietrich et al. (2023); Ollivier et al. (2024). Several recent studies confirmed the existence of fractionation during net surface sublimation (Hughes et al., 2021; Wahl et al., 2021, 2022), although its microphysical origin during snow metamorphism remains debated, whether it arises from fractionating sublimation or from non-fractionating sublimation with fractionating deposition at the snow particle scale (Wahl et al., 2024). It would be of considerable interest to implement post-deposition processes in the model, at least fractionation at sublimation as shown in Casado et al. (2016); Wahl et al. (2022); Dietrich et al. (2023), for more relevant model-data evaluation with surface snow, and for improving the vapor isotopic signal in the model, as it is affected by the isotopic composition of surface snow. While the inclusion of these processes would initially increase the positive bias in surface snow $\delta^{18}$O, it would simultaneously reduce the bias in d-excess, thus requiring an adjustment in the $\lambda$ parameter tuning. In order to elucidate the respective influence of equilibrium fractionation formulation, of post-deposition processes implementation and of supersaturation, we are planning to develop a test case based on isotopic diurnal cycles in Concordia, constrained by observations of supersaturation, evaporation fluxes, meteorological data and isotopic composition of the surface snow.

In the Alps, Aemisegger et al. (2022) demonstrated the variability of post-deposition effects, showing that these effects are minimal during the cold weeks of winter (dry snow metamorphism) but significantly more pronounced during warmer winter periods with wet snow metamorphism. For a more accurate constraint on cloud processes from isotopic data, it is essential to distinguish post-deposition effects within the firm from the cloud processes recorded in the snow. Thus, it would also be valuable to couple LMDZiso with a higher complexity snow scheme to better capture the variability of post-deposition effects in the coastal regions of Antarctica.

Although it is possible to modify the empirical supersaturation equation to enhance the capacity of the model to represent the d-excess (Cauquoin & Werner, 2021; Dütsch et al., 2019), these changes remain highly model dependant (Risi et al., 2010; J. C. Tindall et al., 2009; Okazaki & Yoshimura, 2019b). The study of fog at Concordia has revealed that high supersaturation can occur very frequently, during summer nights and during the polar night, under temperature conditions similar to those of cirrus (Genthon et al., 2017; Vignon et al., 2022). It would be valuable to evaluate the current supersaturation parameterization in Antarctica against these observations of supersaturation, and to constrain the model by the observed supersaturation to evaluate the isotopic fractionation at condensation for low temperatures. Finally, we want to highlight that beyond uncertainty on supersaturation, $^{18}$O equilibrium fractionation coefficient estimates for vapor-ice are very uncertain, as experimental determination strongly disagree for temperature below -20°C (Ellehoj et al., 2013; Hillaire-Marcel et al., 2021; Lamb et al., 2017; Majoube, 1971a; Merlivat & Nief, 1967), which is a major limitation for the study of water isotopes in polar regions.

## Appendix A: Computation of the Temporal Variation of the Isotopic Delta

In order to compute the temporal variation of the isotopic delta, we use the definition of $\delta = (R/R_{VSMOW} - 1) \cdot 1000$. In the LMDZ model, water variables are defined by the specific humidity of total water $q$ (in kg kg$^{-1}$) and the specific humidity of each water isotopes $q^{iso}$ (in kg kg$^{-1}$). Using these variables, the isotopic ratio $R$ in mol mol$^{-1}$ can be computed as follows:
$$R = \frac{q^{iso}/M^{iso}}{q/M^{16}} = \frac{q^{iso}}{q} \cdot \frac{M^{16}}{M^{iso}}, \qquad (A1)$$

where $M^{iso}$ is the molar mass of the water isotope (in kg mol⁻¹), and $M^{16}$ is the molar mass of H₂¹⁶O (in kg mol⁻¹). In this equation, we approximated the specific humidity of H₂¹⁶O, $q^{16}$, by the specific humidity of total water q.

Using this framework, the $δ$ of water vapor becomes:

$$\delta = \left( \frac{q^{iso}}{q} \cdot \frac{M^{16}}{M^{iso} \cdot R_{VSMOW}} - 1 \right) \cdot 1000. \qquad (A2)$$

In the LMDZ model, the change in time of each water isotope is saved for each process affecting the water cycle:

$$dq/dt = \sum_{process} dq/dt|_{process} \qquad (A3)$$

$$dq^{iso}/dt = \sum_{process} dq^{iso}/dt|_{process} \qquad (A4)$$

Where $dq/dt|_{process}$ is the specific humidity tendency associated to each process (in kg kg⁻¹ s⁻¹), and $dq^{iso}/dt|_{process}$ is the isotopic specific humidity tendency associated to each process (in kg kg⁻¹ s⁻¹).

We define $δ_{process}$ as the isotopic composition of the specific humidity tendency associated to a process at each time step (in ‰):

$$\delta_{process} = \left( \frac{dq^{iso}/dt|_{process}}{dq/dt|_{process}} \cdot \frac{M^{16}}{M^{iso} \cdot R_{VSMOW}} - 1 \right) \cdot 1000 \qquad (A5)$$

Finally, the $dδ/dt$ tendency, that is, the change in time of the water vapor $δ$ for a specific water isotope (in ‰ s⁻¹), can be derived as a sum of process contributions $dδ/dt|_{process}$ as follows:

$$\begin{align*} d\delta/dt &= \frac{d}{dt}\left(\frac{q^{iso}}{q} \cdot \frac{M^{16}}{M^{iso} \cdot R_{VSMOW}} - 1\right) \cdot 1000 \\ d\delta/dt &= \frac{1000 \cdot M^{16}}{M^{iso} \cdot R_{VSMOW}} \cdot \frac{d}{dt}\left(\frac{q^{iso}}{q}\right) \\ d\delta/dt &= \frac{1000 \cdot M^{16}}{M^{iso} \cdot R_{VSMOW}} \cdot \left(\frac{dq^{iso}/dt}{q} - q^{iso} \frac{dq/dt}{q^2}\right) \\ d\delta/dt &= \frac{1000 \cdot M^{16}}{M^{iso} \cdot R_{VSMOW}} \cdot \frac{1}{q} \cdot \left(dq^{iso}/dt - \frac{q^{iso}}{q} \cdot dq/dt\right) \tag{A6} \\ d\delta/dt &= \frac{1000 \cdot M^{16}}{M^{iso} \cdot R_{VSMOW}} \cdot \sum_{process} \frac{1}{q} \cdot \left(dq^{iso}/dt|_{process} - \frac{q^{iso}}{q} \cdot dq/dt|_{process}\right) \\ d\delta/dt &= \frac{1000 \cdot M^{16}}{M^{iso} \cdot R_{VSMOW}} \cdot \sum_{process} \frac{dq/dt|_{process}}{q} \cdot \left(\frac{dq^{iso}/dt|_{process}}{dq/dt|_{process}} - \frac{q^{iso}}{q}\right) \\ d\delta/dt &= \sum_{process} \underbrace{\frac{dq/dt|_{process}}{q} \cdot (\delta_{process} - \delta)}_{d\delta/dt|_{process}} \end{align*}$$

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.
## Data Availability Statement

The python scripts written to generate the analyses and figures for this study are available at Dutrievoz and Agosta (2024). The LMDZ6iso model outputs generated for this study are available at Dutrievoz et al. (2024).

## Acknowledgments

The LMDZ-iso simulations were performed thanks to granted access to the HPC resources of IDRIS under the allocations 2022-AD010107632R1, 2023-AD010114000R1 and A15-A0150102212 made by GENCI. We thank Cécile Davrache for her graphic interpretation of the Concordia station (Figure 13). This work is part of the AWACA project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 951596). We acknowledge support from the Agence Nationale de la Recherche projects ANR-20-CE01-0013 (ARCA). Isotopic observations at Concordia and at Dumont d'Urville were supported by the IPEV ADELISE and IPEV NIVO projects and the LEFE program ADELISE. Research leading to these observations has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 306045 (COMBINISO) and funding from the Prince Albert II of Monaco Foundation "ANTARCTIC-SNOW" program. The precipitation collection at Dome C as well as the isotopic analysis have been conducted in the framework the projects PNRA 2013/AC3.05 (PRE-REC) and PNRA18_00031 (WHETSTONE) of the Italian National Antarctic Research Program "Programma Nazionale di Ricerche in Antartide" (PNRA) funded by MIUR (now MUR).

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