# Identifying the provenance and quantifying the contribution of dust sources in EPICA Dronning Maud Land ice core (Antarctica) over the last deglaciation (7–27 kyr BP): A high-resolution, quantitative record from a new Rare Earth Element mixing model

Aubry Vanderstraeten, Nadine Mattielli, Goulven Laruelle, Stefania Gili, Aloys Bory, Paolo Gabrielli, Sibylle Boxho, Jean-Louis Tison, Steeve Bonneville

► To cite this version:

Aubry Vanderstraeten, Nadine Mattielli, Goulven Laruelle, Stefania Gili, Aloys Bory, et al. Identifying the provenance and quantifying the contribution of dust sources in EPICA Dronning Maud Land ice core (Antarctica) over the last deglaciation (7–27 kyr BP): A high-resolution, quantitative record from a new Rare Earth Element mixing model. Science of the Total Environment, 2023, 881, pp.163450. 10.1016/j.scitotenv.2023.163450. hal-04323160

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1 **Identifying the provenance and quantifying the contribution of dust sources in EPICA Dronning Maud Land ice core (Antarctica) over the last deglaciation (7-27 kyr BP): a high-resolution, quantitative record from a new Rare Earth Element mixing model**

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6 Aubry Vanderstraeten¹,², Nadine Mattielli¹, Goulven G. Laruelle³, Stefania Gili⁴, Aloys Bory², Paolo

7 Gabrielli⁵, Sibylle Boxho¹, Jean-Louis Tison⁶ and Steeve Bonneville³*

8 ¹Laboratoire G-Time, Département Géosciences, Environnement et Société (DGES), Université Libre de Bruxelles (ULB), Av. F. Roosevelt, 50 (CP 160/02), Brussels, 1050, Belgium.

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10 ²Laboratoire d'Océanologie et de Géosciences UMR 8187 - LOG, Univ. Lille, CNRS, Univ. Littoral Côte d'Opale, IRD, F-59000 Lille, France

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12 ³Biogéochimie et Modélisation du Système Terre, Département Géosciences, Environnement et Société (DGES), Université Libre de Bruxelles (ULB), Brussels, 1050, Belgium.

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14 ⁴Department of Geosciences, Princeton University, Princeton, NJ 08544, United States of America.

15 ⁵Italian Glaciological Committee, c/o University of Turin, Turin, Italy

16 ⁶Laboratoire de Glaciologie, Département Géosciences, Environnement et Société (DGES), Université Libre de Bruxelles (ULB), Av. F. Roosevelt, 50 (CP 160/02), Brussels, 1050, Belgium.

18 *corresponding author

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Antarctic ice cores have revealed the interplay between dust and climate in the Southern Hemisphere.

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Yet, so far, no continuous record of dust provenance has been established through the last deglaciation.

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Here, using a new database of 207 Rare Earth Element (REE) patterns measured in dust and sediments/soils from well-known potential source areas (PSA) of the Southern Hemisphere, we developed a statistical model combining those inputs to provide the best fit to the REE patterns measured in EPICA Dronning Maud Land (EDML) ice core (E. Antarctica). Out of 398 samples measured in the EDML core, 386 samples have been un-mixed with statistical significance. Combined with the total atmospheric deposition, we quantified the dust flux from each PSA to EDML between 7-

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27 kyr BP. Our results reveal that the dust composition was relatively uniform up until 14.5 kyr BP despite a large drop in atmospheric deposition at ~18 kyr with a large contribution from Patagonia yielding ~68% of total dust deposition. The remaining dust was supplied from Australia (14-15%),

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Southern Africa (~9%), New Zealand (~3-4%) and Puna-Altiplano (~2-3%). The most striking change occurred ~14.5 kyr BP when Patagonia dropped below 50% on average while low-latitude PSA

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increased their contributions to 21-23% for Southern Africa, 13-21% for Australia and ~4-10% for Puna-Altiplano. We argue that this shift is linked to long-lasting changes in the hydrology of Patagonian rivers and to sudden acceleration of the submersion of Patagonian shelf at 14.5 kyr BP, highlighting a relationship between dust composition and eustatic sea level. Early Holocene dust composition is highly variable, with Patagonian contribution being still prevalent, at ~50% on average. Provided a good coverage of local and distal PSA, our statistical model based on REE pattern offers a straightforward and cost-effective method to trace dust source in ice cores.

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# 1. Introduction

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Ice cores collected in Antarctica are invaluable, direct and highly-resolved archives of the past atmosphere and of the Earth's climate (Lambert et al., 2008). Together with deep-sea sediment drillings in the Southern Ocean, Antarctic ice cores have revealed the close connection between dust deposition rates and export productivity, possibly relating the emergence of deep glaciations with dust-laden atmosphere in the Southern Hemisphere (SH) via the impact of dust on phytoplankton growth in the HNLC (High Nitrate Low Chlorophyll) waters of the Southern Ocean (Martínez-García et al., 2014).

Using Antarctic ice cores, Lambert et al., (2008) suggested that the location of the dust source regions and the lifetime of dust in the atmosphere are the main factors controlling the high dust flux during glacial periods. As such, the stimulation of ocean surface biological activity through the enhanced delivery of Fe by dust during glacial times may have lowered the atmospheric pCO₂ by ~20 ppmv (Lambert et al., 2015), which represents about a quarter to a third of the difference in pCO₂ between interglacial and glacial periods. More recently, Lambert et al., (2021) confirmed that the Southern Ocean was the most sensitive oceanic area to iron fertilization, with the Atlantic and Pacific sectors accounting for ~57% of the total CO₂ reduction from iron fertilization globally. Yet, those estimates of pCO₂ drawdown suffer from large uncertainties stemming from the lack of knowledge about dust emission from each individual PSA during the last glacial-interglacial transition. Indeed, Patagonian dust and Southern African dust for instance have distinct iron contents (Qu, 2016) and iron solubility in general has been shown to vary according to the weathering intensity in topsoil source (Shi et al., 2011). Quantifying the contribution of each PSA in aeolian deposits such as Antarctic ice cores is thus instrumental not only to reconstruct paleoclimates in the SH continental source regions but also to improve estimates of the dust climate feedback through iron fertilization. Establishing a record of dust provenance would also help to better constrain shifts in the position and strength of the SH westerly winds, which is key to our understanding of atmospheric and oceanic circulation changes during the last glacial cycle (Engelbrecht et al., 2019). As Antarctic ice cores accumulate continuously, are well dated and receive dust from long-distance transport (especially drill-site located inland at high altitude), they provide a valuable record of dust deposition. Recently, Coppo et al., (2022) estimated the average
76 provenance of dust reaching East Antarctica plateau over the LGM and Holocene using isotopic
77 fingerprint. Yet, to our best knowledge, no quantitative, high-resolution (centennial to decade) and
78 continuous provenance record of dust source apportionment has been established from Antarctic ice
79 cores throughout the Last Glaciation Interglacial Transition (LGIT).

80 The favored method to trace dust consists in matching the geochemical fingerprint of PSA with those
81 of dust preserved in eolian deposits. Isotopic ratios of Nd, Sr or Pb and the pattern of Rare Earth
82 Elements (REE) concentrations (Marx et al., 2005) have proved to be the most reliable indicators for
83 provenance purposes (Grousset and Biscaye, 2005). Using isotopic signatures, Patagonia (PAT) was
84 identified as the main supplier of dust to Antarctica during the Last Glacial Maximum (LGM) (Grousset
85 et al., 1992; Basile et al., 1997; Gaiero, 2007; Gaiero et al., 2013; Gili et al., 2017), yet its specific
86 contribution could not be quantified. It is also still unclear which were the other PSA supplying dust to
87 Antarctica during the LGM. Several PSA have been proposed, such as Australia (De Deckker et al.,
88 2010), New Zealand (Neff and Bertler, 2015; Koffman et al., 2021), and other Southern South American
89 sources such as Southern Central West Argentina or Puna-Altiplano Plateaus (Gili et al., 2017; Gaiero,
90 2007; Gaiero et al., 2004). As for the interglacial periods, because of the drop in atmospheric deposition
91 in Antarctica, obtaining reliable isotopic fingerprint requires a much larger amount of ice - up to 60-90
92 cm of ice core per measurement (Delmonte et al., 2017) which reduces the temporal resolution of the
93 record. Independently of analytical limitations, interpreting dust isotopic fingerprints in terms of
94 provenance is complex as most PSA have overlapping isotopic 'domains' (Gaiero et al., 2004; Gili et
95 al., 2017; Delmonte et al., 2017; Delmonte et al., 2020). The matter is further complexified by the
96 mixing of dust coming from multiple sources during atmospheric transport and déposition.

97 To overcome those limitations, the use of REE patterns to trace dust is promising: REE analyses are
98 less time-consuming, sensitive and precise and can thus be done on a lesser amount of dust compared
99 to isotopes, allowing for a higher (spatio)-temporal resolution. Gabrielli et al., (2010) and Wegner et
100 al., (2012) reached a multi-decadal temporal resolution in their analyses of dust REE concentrations in
101 EPICA Dronning Maud Land (EDML) and EPICA Dome C (EDC) ice cores. Yet, those studies could
102 not decipher the provenance of dust from their REE pattern. Here, we developed the first apportionment
model based on REE pattern fitting that is able to disentangle multiple dust inputs from a large number
of dust sources across the Southern Hemisphere. We applied this model to the REE pattern dataset from
(Wegner et al., 2012) in order to provide the first quantitative, continuous, high-resolution
reconstruction of dust provenance at EDML, East Antarctica throughout the Last Glacial Interglacial
Transition (LGIT).

## 2. Statistical analysis of EDML REE patterns

The EDML REE dataset, comprising 398 samples from (21 - data are available at
https://doi.org/10.1594/PANGAEA.756834), covers the last glacial-interglacial transition from 26.568
kyr to 7.554 kyr at an average temporal resolution of ~48 yr (varying from 18 to 171 yr) between
adjacent samples. To identify and quantify dust source contributions to E. Antarctica, we developed a
method based on a numerical algorithm that solves a set of linear equations using a least-squares method
while only allowing positive coefficients in order to provide the best fit of the dust REE patterns
measured in EDML by combining REE patterns from well-known PSAs across the Southern
Hemisphere. The details of our approach are summarized below.

Out of 398 samples from the original EDML REE dataset, two samples were removed as they missed
concentration data for one or more of the 14 different REE required for our model. The remaining 396
EDML REE samples were normalized to the Upper Continental Crust (UCC) composition (Rudnick
and Gao, 2003) and by an element-specific correction factor (cf - Table S1) resulting from the analytical
procedure detailed in (Gabrielli et al., 2010). Those elemental-specific cf values account for the fact that
the REE concentrations of EDML dust were measured via an acid-leaching method (Wegner et al.,
2012) implying analysis of samples containing partially dissolved dust particles that may not be fully
atomized in the plasma (and detected) by Inductively Coupled Plasma Sector Field Mass Spectrometry.
Thus, in order to transform those ‘partial’ dissolution REE concentrations into total bulk REE
concentrations allowing for comparison with REE patterns from PSA (which are measured after full
acid digestion), we applied an elemental-specific correction factors (cf, Table S1):
$$128 \qquad c_{fi} = \frac{[REE]_{i \text{ full digestion}}}{[REE]_{i \text{ acid-leaching}}} \qquad (\text{Eq. 1})$$

where [REE]i denotes the concentration of a given REE i measured via a full digestion and in partial acid-leaching method for glacial dust in EDC ice core (Gabrielli et al., 2010). As the methodology applied at EDC and EDML sites is the same, and that dust from those two sites were shown to have similar dust composition (25; 26), we applied the elemental-specific cfi derived from EDC dust to the EDML dataset (Dusti in ng/L) from (Wegner et al., 2012) and define the normalized concentration of a given REE i in the Dust Ice Core samples (DICi) as follows:

$$135 \qquad DIC_i = \frac{Dust_i \times c_{fi}}{UCC_i} \qquad (\text{Eq. 2})$$

with UCCi, the Upper Crust Concentration for the element i. Depending on the element, the cf values ranges from 1.621 to 2.801 (Gabrielli et al., 2010) are presented in Table S1 (as well as their standard deviation values - 1SD).

For the PSA, we assembled a large database consisting of 207 REE patterns spanning across well-known PSAs of the SH comprising: Patagonia (PAT - 39 Dust Source -DS-), Central Western Argentina (CWA - 5 DS), Puna-Altiplano Plateau (PAP - 14 DS), Southern Africa (SAF - 17 DS), Australia (AUS - 107 DS, focused mostly in S-E) and New Zealand (NZ - 25 DS) (see Fig. S1 for maps; Table S2 for localization of DS, physical characteristics, REE chemical composition; Fig S2 for REE patterns). In addition, as for DIC, all DS are normalized to UCC (Rudnick and Gao, 2003). The idea of the database is to sample the geochemical variability of atmospheric dust within each PSA, thus we prioritized the local mineral dust or the fine fraction (<5 µm) of loess, sediment or soils, however when unavailable, coarser sediment/topsoil samples (<63 µm and <90 µm for South Altiplano and Australia, respectively) were incorporated into the DS. We also analyzed REE content of the fine fraction (<5 µm) in samples collected in Southern Africa especially from the dry riverbed sediments from Namib desert coast (see Supplementary Information and Gili et al., 2022). Antarctic dust is not considered as dust source in our model as (Wegner et al., 2012) has shown that Antarctic dust sources (mostly from Dry Valleys) have a distinctive enrichment in light REE that is not consistent with the REE patterns of EDML dust. Model
runs with Antarctic sources including DS from the Dry Valley region (Diaz et al., 2020) and cryoconites collected near the Sør Rondane Mountains confirmed this observation, showing very low inputs (<5% in contribution in 7 samples – data not shown). Local sources of dust generate coarser grain size distribution with a significant amount of 5-10 µm particles and a poor size sorting as observed in Talos Dome ice core (Delmonte et al., 2010) and in Berkner Island surface snow (Bory et al., 2010). The latter features are absent in EDML grain size distribution (Wegner et al., 2015). Indeed, the grain size mode in EDML (2892 m.a.s.l.) range between 1.8 and 2.6 µm and is similar to EPICA Dome C dust size which is known to receive only atmospheric deposition from long-range intercontinental transport (Wegner et al., 2015). All in all, we posit that local and Antarctic dust do not influence significantly the REE fingerprint in EDML.

The fitting of dust REE pattern can be defined as a constrained least squares problem where the coefficients (i.e., the respective contributions of each DS - i.e., CDS¹...²⁰⁷- in a given DIC sample) are not allowed to be negative (lsqnonneg function from Matlab). Thus, the goal of our fitting approach is to minimize the following expression:

$$ \left[ \begin{bmatrix} DS_{La}^1 & \cdots & DS_{La}^{207} \\ \vdots & \ddots & \vdots \\ DS_{Lu}^1 & \cdots & DS_{Lu}^{207} \end{bmatrix} \times \begin{pmatrix} CDS^1 \\ \vdots \\ CDS^{207} \end{pmatrix} - \begin{pmatrix} DIC_{La} \\ \vdots \\ DIC_{Lu} \end{pmatrix} \right]_2^2 \qquad (\text{Eq. 3}) $$

That is, the 'input' matrix (14 rows × 207 columns) formed by the 14 UCC-normalized REE concentrations of the 207 DS is multiplied by a vector of 207 components (i.e., CDS¹...²⁰⁷), which corresponds to the contributions of each DS to match the vector represented by the 14 REE concentrations in a given dust ice core sample (DICLa-Lu). The selected set of CDS values is the one that minimizes the difference between the product of the input matrix and the CDS vector on one hand, and the DIC vector on the other hand. As such in Eq. 3, only a fraction of the 207 DS participates to the fit of a given DIC, i.e., the DS with CDS value > 0 (the DS with CDS value equal to zero do not contribute to the fit).
To quantify the impact of uncertainties (from the DIC REE concentration analyses and from cf values) on the results of our model, we performed Monte Carlo (MC) simulations for each ice dust sample. The analytical uncertainty for Dustᵢ concentration data from the acid-leaching method was quantified in (Dick et al., 2008) and amounts to a relative standard deviation ranging between 4.5 % and 7.8 % (1 RSD) depending on the considered REE element (Table S1). For each MC simulation, the algorithm randomly assigns a value of cfᵢ and Dustᵢ following a normal distribution (function normrnd from Matlab) constrained by their respective analytic uncertainties. Then, for each MC simulation, the model calculates the best fit of CDS values to minimize Eq. 3 (Fig. S3). Cumulated over 5000 MC simulations, the results of CDS (% contributions) provides a probability density function per DS for each time step (Fig. S4). All the DS probability density function that belongs to a given PSA are then aggregated (i.e., added) to form a probability density function per PSA. We then calculate the median of each PSA probability density functions to quantify the contribution of every PSA (in % contribution of total dust deposition) in each DIC sample (Fig. S4). In addition, to the contributions, we used the total dust influx to EDML via non-sea-salt Ca flux - nssCa²⁺ in µg m⁻² yr⁻¹ from (Fischer et al., 2007) to derive the dust flux coming from each PSA to EDML (Fig. S5).

To evaluate the goodness of the selected fit to a given DIC vector (the 14 REE concentrations of dust ice core), we calculated the correlation coefficient (R) for each MC iteration. Cumulated over 5000 MC simulations per DIC sample, those R values form a distribution from which we use the median R value to evaluate the overall goodness of the selected fits proposed by our model (Fig S3 and S5). To know whether there is a statistically significant relationship between the selected fit and a given DIC and determine the lower limit of R value admissible in our statistical analysis, we calculated the t-statistic values considering the overall number of observation pairs (i.e., 14 in this case, so n=14):

$$t = \frac{R \times \sqrt{n-2}}{\sqrt{(1-R^2)}} \quad (\text{Eq. 4})$$

Subsequently, from the evolution of the corresponding p-value (two-tailed test, degree of freedom of 12) as a function of the value of R, we could determine that p-value <0.01 (99% confidence to reject the null hypothesis) corresponds to a R>0.67. In other words, when R>0.67, there is sufficient evidence
to conclude that there is a significant linear relationship between the selected fit and the REE pattern of
the “target” dust sample. Out of the 396 samples of the dataset, only 6 samples (at 8.556, 13.642, 14.056,
15.991, 19.456 and 24.017 kyr BP) failed this threshold and were removed from our provenance
reconstruction. Overall, 92% of the fits we calculated had a median R values >0.8 with their respective
DIC (Fig. S5B). In addition, 4 more samples (at 14.785, 14.809, 14.833 and 14.858 kyr BP) were
removed as they were impacted by a large deposition of volcanic/ash materials evidenced by a peak in
sulfate concentration (Wegner et al., 2012). As *lsqnonneg* routine uses an iterative algorithm to solve
Eq. 3, we also tested the influence of initialization conditions (i.e., the order of DS in the database) on
the output of the model (See Supplementary Information and Fig. S7). Those additional results show
that our results were independent of the initialization conditions of the input REE database.

## 3. Results

The provenance of 386 samples could be determined with sufficient statistical confidence to reconstruct
the dust provenance record during the last glacial-interglacial transition in EDML, from 26.568 to 7.554
kyr BP. Figures 1 and 2 (Table S3 for detailed values) present the contributions we obtained expressed
in percentage and flux of each PSA in the Southern Hemisphere to the dust deposited at EDML during
the last glacial-interglacial transition.

### 3.1. LGM (26.568 to ~18 kyr BP)

Patagonia is the foremost dust source with an average contribution of ~68% of total dust deposition
over the LGM (Fig. 1). An average of 104 different DS (out of the 207 DS available) was selected to fit
the dust deposition at EDML (Fig. S7). For PAT, our model points out a northern DS (41°19′S
69°31′W) located within a large closed-basin in the ‘North Patagonian Massif Plateau’ – a 10⁵ km²
basaltic plateau culminating at 1200 m. The secondary sources reaching EDML are, in decreasing order
of importance: Australia (DS from Murray-Darling basins), SAF (mostly DS from Sua and Etosha pans)
and NZ with, respectively, ~14%, 9% and 5% of total deposition respectively. Despite the large number
of DS involved, the dust assemblage seems homogeneous and exhibits limited variability over the LGM
period, except for the period between 19-20.5 kyr BP (Fig. 1). During this 1.5 kyr interval, Australia
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and to a lesser extent SAF and PAP contributions peaked while contributions from Patagonia and NZ
decreased sharply.

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3.2. *Heinrich Stadial 1 (Antarctic Isotope Maximum 1) - HS1 (18-14.7 kyr BP)*

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The HS1 interval marks a rise in δ¹⁸O that is concomitant with a drastic fall of total dust fluxes (Fig. 2B). Despite this drop, the composition of the dust assemblage remained relatively stable along the HS1 when compared to the LGM, with an HS1 average provenance of: PAT ~68%, AUS ~15%, SAF ~8%, NZ 5% and PAP 4% with an average of ~98 different DS combined to fit the dust REE patterns (Fig S7). From ~15.2 kyr BP onwards, PAT contributions fell repeatedly below 50% for the first time since 20.5-19 kyr BP period while SAF and AUS contributions were rising significantly.

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3.3. *Antarctic Cold Reversal - ACR (14.7 - 12.9 kyr)*

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3.4. *Younger Dryas (YD, 12.9 - 11.7 kyr)*

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The HS1-ACR transition is a turning point in terms of dust composition. SAF and AUS contributions increased greatly (up to 74% and 91%, respectively at 13.671 kyr BP and 13.729 kyr BP) while the contributions of NZ and PAT dropped at their minimum level (i.e., 22 out of 43 samples are <50% of total dust for PAT). As such, the ACR dust is composed of, on average, ~50% PAT, 23% SAF, 21% AUS, 4% PAP and ~3% of dust from NZ with ~72 DS (Fig. S7) combined to fit the dust REE patterns from EDML. In contrast to LGM and HS1 where dust assemblage was relatively homogeneous over time, the provenance of ACR dust (and so as the rest of the ice core) is much more variable (Fig. 1).

The dust assemblage of YD consists of dust from PAT 44%, SAF 23%, 19% AUS, 8% PAP and 7% NZ with 56 DS involved in the dust mixture on average. Patagonian contributions declined continuously over YD with the 7 steps-mobile average contribution at a minimum (~25% at ~11.5-11.7 kyr BP) corresponding to average flux of 37 µg m⁻² yr⁻¹. After 14.5 kyr BP up (until ~11 kyr BP), SAF and AUS contributions are comparable, on average ~19-23% (~30 µg m⁻² yr⁻¹) however their evolutions seem anti-phased to one another.
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257 **3.5. Early Holocene (11.7 to 7.5 kyr)**

258 The period is characterized by the lowest total dust flux and large variability over short-timescale in
259 those PSA contributions. The average dust assemblage of the Holocene has 50% PAT, 22% SAF, 13%
260 AUS, 10% PAP and 5% NZ with an average number of 57 DS involved in the Holocene dust assemblage
261 (Fig. S7). Patagonian contributions tend to increase till ~10 kyr BP, drop abruptly between 9.6-9.8 kyr
262 BP and increase again at 9.5 kyr BP (~70% contribution on average) before declining to ~45% at 7.5
263 kyr BP. The early Holocene is clearly the period of the LGIT where PAP is the most active with
264 contributions up to 71% at 7.951 kyr BP (Fig. 1).

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266 **3.6. Comparison between REE and isotopic for dust provenance**

267 In Fig. 3, we compiled the most recent Nd and Sr dataset from the different PSA (grain size < 5µm)
268 across the Southern Hemisphere in order to validate our REE model. Combining the % contributions of
269 each PSA determined by our approach with the average Sr or Nd concentration and isotopic composition
270 values for each of the 6 main PSAs, we calculated the Nd-Sr isotopic ratios for the EDML dust
271 throughout the LGIT (Fig. 3). The idea is to compare those calculated isotopic signatures with the
272 isotopic signatures measured in the E. Antarctic ice cores. Direct isotope measurements for EDML dust
273 are not available, nonetheless our calculated Nd-Sr signatures for EDML dust match well with those of
274 other coring sites in E. Antarctic dust (i.e., EDC, Dome B, Komsomolskaya, old Dome C and Vostok)
275 both for the LGM and the LGIT (Fig. 3). Our calculated isotopic values reproduce well the shift in Nd-
276 Sr signatures observed between LGM and Holocene dust in East Antarctica. This isotopic shift reflects
277 a drop in PAT contributions at the HS1-ACR transition (insert Fig. 3), which is concomitant with an
278 increase of SAF and AUS contributions inducing lower εNd values and more radiogenic ⁸⁷Sr/⁸⁶Sr. The
279 agreement between our calculated Nd-Sr isotopic signatures for EDML and those measured in dust
280 from other sites across E. Antarctica indicates that our model can disentangle the dust PSA contributions
281 throughout the LGIT.
282 **4. Discussion**

283 We have developed a novel statistical model based on the fitting of the REE pattern of dust in ice core
284 using a combination of 207 different REE patterns from well-known PSA across the SH. Applying this
285 method to the REE dataset collected in the EDML ice core, we provide the first, continuous and
286 quantitative record of dust provenance in Antarctica over the LGIT at a decadal to centennial resolution
287 (Figs. 1 and 2). Our provenance record shows that the LGM dust was largely made of Patagonian
288 materials complemented by S-E AUS, SAF and NZ dust (in decreasing order of importance). This dust
289 composition remained roughly stable throughout HS1 despite a massive drop in total deposition, likely
290 caused by an intensification of the ‘rainout process’ during long-range transport. At ~14.5 kyr BP, the
291 composition of EDML dust abruptly changed with sharp increase in SAF and AUS contributions at the
292 expense of Patagonia. In what follows, we present evidences showing that our dust provenance record
293 is not only in agreement with the existing literature on the dust provenance in E. Antarctica but that it
294 is also coherent with the regional and hemispheric climate evolution of the last interglacial transition.
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4.1. LGM (>18 kyr BP): prevalence of high-latitude dust sources (PAT and NZ)

297 The prevalence of Patagonian dust in EDML during LGM was expected considering that PAT is the
298 closest landmass to E. Antarctica and that EDML is directly downwind to Southern South America
299 sources (Basile et al., 1997; Gaiero, 2007; Gili et al., 2017). Our reconstruction of PAT flux is consistent
300 with those inferred from sediment cores in Laguna Potrok Aike in Southern Patagonia (Haberzettl et
301 al., 2009) and from the Scotia Sea (Weber et al., 2012), that is midway between PAT and EDML (Fig.
302 4). Sediments from Scotia Sea are of terrigenous origin and exhibit a homogeneous REE patterns, very
303 similar to Patagonian dust (Gaiero et al., 2013). According to our results, the PAT contribution during
304 LGM comes - in large part - from a volcanic/basaltic source. Those observations are coherent with the
305 presence of an extensive Patagonian Ice Sheet covering most of the Andes (2090 km long and 350 km
306 in width at its maximum at 29.4-34 kyr BP - Davies et al., 2020). NZ also developed an ice sheet in the
307 South Island as early as ~33.4 kyr BP (Williams et al., 2015). Up until the onset of deglaciation (Moreno
308 et al., 2015; García et al., 2019; Davies et al., 2020), those ice sheet fed extensive outwash plains on
309 their eastern flanks with high loads of glacial sediment, rich in fine-grained particles. Outwash plains
are well-known sources of dust due the constant lateral migration of braided river channels that expose unconsolidated sediments to aeolian deflation (Sugden et al., 2009; Bullard et al., 2016). The eastern outwash plains in PAT and NZ represented also a much larger surface than present-day as sea level was ~135 m below present-day level (Lambeck et al., 2014). Combining sea level change throughout the LGIT with the detailed bathymetry of the Patagonian shelf (Tozer et al., 2019), we calculated that the enlarged continental surface represented an extra 763.10³ km² compared to present-day PAT (~1060.10³ km²) and 62.10³ km² for NZ (~150.10³ km²) (Figs. 8 and S7 for maps). This shelf expansion probably accentuated the arid nature of the climate on the eastern side of the Andes and in the South Alps and permitted the development of a large networks of braided rivers that spread glacial sediments (Bullard et al., 2016; Sugden et al., 2009). The role of the PAT shelf as a dust supplier to Antarctica during LGM has been discussed previously (e.g., Basile et al., 1997; Gaiero et al., 2003; Gaiero, 2007). While early works presented paleontological evidences (Burckle et al., 1988; Ram et al., 1988), more recent studies provided mineralogical indications pointing towards major contributions from the PAT shelf to E. Antarctica during LGM (Delmonte et al., 2017). The implication of the PAT shelf source is also supported by the persistent presence of leaf wax-derived n-alkanes (C₂₅ to C₃₅) in the sedimentary record of the Southern Ocean which suggests a low-altitude source area rather than a higher altitude source glacial/periglacial source (Maher et al., 2010). In terms of geochemistry, the Argentinian shelf shows an isotopic signature (i.e., -2 < εNd < -4 and 0.7052 < ⁸⁷Sr/⁸⁶Sr < 0.7073 - (Basile et al., 1997)) and REE patterns that cannot be distinguished from sedimentary and volcanic materials of the PAT continental surface (Gaiero et al., 2003; Gaiero et al., 2004; de Mahiques et al., 2008). That is, the contributions of those emerged shelves are most likely accounted for the contributions of adjacent continents (i.e., PAT, NZ) and we considered the Argentinean shelf as being part of the overall PAT dust signature.

Despite the glacial conditions, pollen-inferred vegetation changes indicate millennial-scale reversions from glacial to cold/temperate conditions during the LGM (Figs. 4 and 5). Stratotype of NZ climate documents either two interstadials between 25.6-24.5 kyr BP and 22.6-21.7 kyr BP (Barrell et al., 2013 and references therein) or a single, longer episode of cold/temperate conditions between ~25-23 kyr BP (Augustinus et al., 2011). As for PAT, the only sites east of the Andes providing pollen records of the LGM are posterior to 17 kyr BP (Markgraf et al., 2002). Older pollen records only exist west of the
Andes (i.e., Isla Grande de Chiloé in Fig. 4A; Moreno et al., 2015). Although caution is required to extrapolate those pollen records east of the Andes, Markgraf et al., (2002) showed similar climate trends on both sides of the Andes. As such, episodes of milder climate are described (evidenced by the lowering of Poaceae pollen and an increase of arboreal pollen (Moreno et al., 2015) between 23.4-22.6 kyr BP and later 22-19.3 kyr BP that coincide with two periods of low dust flux from PAT and NZ (Figs. 4 and 5) and less complex dust assemblages (Fig. S5A). Those two dust events are unlikely to be related to changes in intensity or in latitude of Southern Westerly Wind (SWW) as pollen records from northwestern PAT and lake-level reconstructions in Lake Cardiel (48.9°S, 71.3°W) have shown a strong and permanent influence of SWW up until 17.5 kyr BP (Moreno et al., 2015; Moreno et al., 2018; Quade and Kaplan, 2017). In our view, the two periods of low dust fluxes - between 22.7-23.7 kyr BP and 19.3-20.5 kyr BP in the PAT (and NZ) records are distinct (Fig. 1). The oldest event is not related to a specific decline of contributions from PAT (i.e., dust assemblage remains unchanged during the interval, Fig. 1) but rather to a drop in total atmospheric deposition in EDML (Fig. 2B) caused by a short-lived episode of milder climate between 23-23.7 kyr BP evidenced by a slight increase of δ¹⁸O in EDML core (Fig. 2B) and a warming of sea surface temperature (Fig. 4E). The lifetime of dust in the atmosphere is constrained by wet deposition (and thus by the water content and temperature - Lambert et al., 2008; Markle et al., 2018). The milder climate between ~22.7-23.7 kyr BP may thus have reduced the efficiency of long-range dust transport to E. Antarctica without changing the dust assemblage composition, yet this warmer period was short-lived and did not affect the extent of ice-sheet and glacier lobes supplying glaciogenic sediment. In contrast, the 19.3-20.5 kyr BP event of low dust emission comes within a longer interval of milder climate starting ~22 kyr BP in PAT (Fig. 4A) and is characterized by a large shift in dust provenance - i.e., the PAT and NZ contributions dropped while those of SAF and AUS increased (Fig. 1) - without large changes in total atmospheric deposition (Fig. 2B). This drop in PAT contributions is corroborated by a decline in dust flux between 19-20 kyr BP in the Laguna Potrok Aike magnetic susceptibility record (Fig. 4C) and by a warming of the Southern Ocean of ~2°C at ~19.5-22 kyr BP (Fig 4E, (Barker et al., 2009; García et al., 2019; Lamy et al., 2004) leading to the first Antarctic Ice Discharge (AID8 - Fig 8B). Concomitant to this event, there is substantial evidence that ice retreat was underway before 19 kyr BP on a range of Patagonian glaciers
366 (Markgraf et al., 2007; García et al., 2019; Henríquez et al., 2017). As a result, proglacial (paleo)lakes 367 formed such as the Lago CP (Henríquez et al., 2017) or expanded greatly e.g., Lake Epuyen (42°S), 368 Lake Cholia (42.5°S), Lakes Cisnes-Nirehuao (45°S), Lake Frio (45.7°S) and lakes Balmaceda, 369 Tehuelche and Magellan ((García et al., 2019); (Davies et al., 2020) and references therein). According 370 to Davies et al., (2020)(Davies et al., 2020), the surface of proglacial lakes in Patagonia doubled 371 between 25 kyr BP and 20 kyr BP. Those proglacial lakes might have efficiently trapped glaciogenic 372 sediment, and stalled dust emission from PAT (Sugden et al., 2009). The early onset of deglaciation 373 starting ~22 kyr BP culminating between 20.5-19.3 kyr BP affected primarily the high-latitude PSA. 374 The sudden return of colder conditions between 18-19 kyr BP (Fig 4A, (Barker et al., 2009), (Lamy et 375 al., 2004)) marked the last glacial read advance before the LGM termination documented in large 376 glaciers/lobes in PAT (Bendle et al., 2017; Davies et al., 2020; Kaplan et al., 2008) - but also in south- 377 NZ (Denton et al., 2021). Those glacial readvances, coupled with cooler temperature likely reinitiated 378 the generation of dust from the outwash in high-latitude PSA and the efficient long-range transport to 379 EDML as evidenced in our reconstructions (Fig. 1 and 2) and in Potrok Aike Lake record (Fig. 4C). 380

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381 4.2 LGM: low-latitude PSA as auxiliary sources of dust (SAF, AUS, PAP)

382 Dust emission from low latitude PSAs correlate with aridity events and the resulting drying of riverbeds, 383 alluvial fans and large lake systems. According to our results, major low-latitude PSA include, in 384 decreasing order of importance: the Murray and Darling Basins in southeast AUS, the Makgadikgadi 385 complex in Botswana, the Etosha pan and ephemeral coastal rivers in Namibia in SAF and the Puna- 386 Altiplano Plateau (PAP). Climate during LGM over most of southern-west Africa was generally wetter 387 than today with 4-6°C and 8-10°C cooler temperatures in summer and in winter than present-day, 388 respectively (Engelbrecht et al., 2019; Chase and Meadows, 2007). As a result of a northward migration 389 of SWW, an equatorward expansion of the winter rainfall zone was modeled during LGM, with southern 390 Namibia and Botswana being wetter than today, while the present-day summer rainfall regions (south 391 of 22°S) are also projected to have received more rainfall in winter during the LGM (Engelbrecht et al., 392 2019). This allowed for a northern expansion of the vegetation cover (Lim et al., 2016), resulting in 393 better stabilization of regolith/soils. At first glance, those conditions should have induced a limitation
of aeolian deflation and thus lower contributions of SAF dust. Yet, our record shows that SAF experienced two millennial-scale periods of high dust emission between 26.5-25 kyr BP and between 19.3-20.5 kyr BP (Figs. 2 and 6E). We pinpoint the dust of those high-emission periods to originate mostly from the Makgadikgadi lake complex and Etosha pans. Interestingly, the catchments of those two closed-basins are situated in tropical latitudes, i.e., from the Angolan highlands (~12°S) to the southern margins for the Makgadikgadi depression at 24°S and from 15°S to 19°S for the Etosha pan. This means that those basins were mostly unaffected by the increased rainfall brought by the northward shift/expansion of SWW during the LGM. The chronology of lake occupancy in Makgadikgadi complex indicates dry phases at ~23 and ~20 kyr BP (Burrough et al., 2009), also reported in paleo-lake Tsodilo near the Okavango delta between 19-22 kyr BP (Thomas et al., 2003) which corresponds well with millennial-scale aridity phases in Southern Namib desert (Lim et al., 2016) (Fig 6B and 6F). In the Etosha pan, lake level record reveals an intermittent lake with several millennial-scale periods of low level - possibly dry - between 13-28 kyr (Hipondoka et al., 2014). We believe those arid conditions together with dry phases of the Makgadikgadi and Etosha pans were the triggers of an important influx of SAF dust to SH and EDML between 19-20.5 kyr BP (Fig. 6). This assertion is supported by winds intensity proxies and pollen influx measured in sediment cores on Namibian shelf (~26°S and 23°S respectively) and is also in agreement with distal dust flux reported in the Mfabeni Peatland (28°S, N-E of South Africa) both showing respective maxima between 19-21 kyr BP (Figs. 6A, C and D - Humphries et al., 2017).

As for AUS dust (Fig. 7), its record also shows a very noticeable peak between 19-21 kyr BP and a secondary peak between 25-27 kyr BP. Our provenance results pinpoint that most of LGM dust from AUS originated from Darling Basin which experienced an arid climate between 28-18 kyr BP (Builth et al., 2008; Petherick et al., 2008). In Lake Surprise, which is located downwind from Darling Basin in SE-Australia, this aridity is reflected by low value of $\delta^{13}C_{\text{organic matter}}$ (Fig. 7A) throughout LGM and particularly between 18.5 and 21 kyr BP, concomitant with an increased flux of aeolian deposition (Fig. 7B; Falster et al., 2018). Interestingly, the period of increased effective moisture centered ~24.8 kyr BP in Lake Surprise, which corresponds to a climate amelioration for both NZ (Fig. 5) and AUS (Petherick et al., 2008) and with a warming event in the Southern Ocean to the south of Australia (Calvo et al.,
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2007) is also marked by a period of lower dust flux from AUS to EDML (Fig. 7C). Overall, dust records from Lake Surprise and those from other locations from south-eastern and eastern AUS (Fig. 7B, De Deckker et al., 2012); Petherick et al., 2009) match well with our record of AUS contributions and flux in EDML.

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The Puna - Altiplano Plateau (PAP) is distinct from the other low latitude PSA as it is located at high altitude (~3600-3800 m.a.s.l.) and bounded by the Western and Eastern cordilleras. PAP is also characterized by several large, interconnected endorheic basins fed by rainfall and thus sensitive to climate transitions. Proxy data from the Central Andes during the LGM suggest cold conditions with an estimated decrease in temperature of 5-8°C (Colinvaux et al., 2000) and dry conditions up until ~25 kyr BP, followed by a slight increase in rainfall (Fornace et al., 2014) allowing for the formation of large paleolakes between ~25 and ~20 kyr BP covering ~21 000 km² the so-called 'Sajsi lake' ((Blard et al., 2011); (Placzek et al., 2006)). Our LGM record (Fig. 4F) for PAP dust seems to be directly correlated with the absence/low-stand of paleolakes. Indeed, large fluxes of PAP dust are observed between ~26.5 and 25 kyr BP that correspond to a dry period, followed by a period of minimum dust flux during the 'Sajsi lake cycle' and finally a noticeable emission peak between 20-18 kyr BP when the Sajsi lake regressed (Fig. 4F). Thus, we argue that dust emission from PAP to E. Antarctica is essentially controlled by aridity events at millennial-scale in the central Andes.

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## 4.3 Heinrich Stadial 1 (HS1, 18 - 14.7 kyr BP): warming and stalling of dust flux to E. Antarctica

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The warming during HS1 is documented across the SH between ~19 to 17.5 kyr BP by multiple lines of evidence: (i) the continuous increase in δ¹⁸O values at EDML (Fig. 2B), (ii) the warming in the south Atlantic (Fig. 4E) and in the southeast Pacific (Lamy et al., 2004) (iii) the rise in temperature of ~8°C in South Patagonia (Pendall et al., 2001). Yet, this increase in temperature in Patagonia was not accompanied by a change in effective precipitation that remained markedly lower than today, following deglaciation (Markgraf et al., 2007; Quade and Kaplan, 2017). Even though the arboreal vegetation expanded largely between ~18.6-17.8 kyr BP on the west side of the Andes (Fig. 4A - (Moreno et al., 2015)), it only spreads eastward through the Andes much later, i.e., between 16-14 kyr BP and was restricted to the piedmont of the Andes (Markgraf et al., 2007; Villa-Martínez and Moreno, 2021). The

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vegetation of eastern Patagonia remained largely unchanged during HS1 with respect to LGM i.e.,
dominated by steppe taxa and Poaceae (Markgraf et al., 2007). Conversely, the record of Lago Cardiel
(48°S - PAT) water level during the HS1 also denotes dry conditions in Patagonia (Quade and Kaplan,
2017). Thus, changes in vegetation type and/or moisture can hardly be invoked to explain the fall of
dust emission flux from PAT observed during HS1 (Fig 4B). Nonetheless, the HS1 warming triggered
a rapid deglaciation between 18-16.5 kyr BP along the Andes, (García et al., 2019; Davies et al., 2020)
and in South-NZ after ~18-17.5 kyr BP (Barrell et al., 2019; Shulmeister et al., 2018) causing an
enlargement of proglacial lakes in NZ (Sutherland et al., 2019) and a doubling of glacial lake area in
PAT between 20 kyr BP and 15 kyr BP - (Davies et al., 2020; Thorndycraft et al., 2019). As discussed
above, proglacial lakes potentially affect dust emission solely in high latitude PSAs - PAT and NZ.
Yet, here we observe a drastic fall in total dust deposition at EDML after 17.5 kyr BP (Fig. 2B) affecting
all PSAs indiscriminately. Indeed, the composition of the HS1 dust remains roughly stable compared
to LGM up until ~15 kyr BP (Fig 1). The most logical explanation for this stability in the dust
composition is that the lifetime of airborne particles was shorter in the wetter/warmer atmosphere of
the HS1 because of a stronger “rainout process” centered in the mid-latitudes acting as a barrier
preventing dust to reach the polar regions (Markle et al., 2018). Dust deposits across the Southern
Hemisphere tend to support this view showing abrupt decreases in the deposition (in Potrok Aike bog -
Fig. 4C; in Scotia Sea - Fig. 4D; in the Namibia continental shelf - Fig. 6A and 6D). This wet period is
also evidenced by high-stands between 18-15 kyr BP in the Makgadikgadi complex (Fig. 6F - (Burrough
et al., 2009)). A similar declining trend can be observed in AUS dust deposits between 18 and 15 kyr
BP (Figs. 7B and 7C) which can be related to wetter conditions (Fig. 7A) in S-E Australia after ~18 kyr
BP (Falster et al., 2018; Builth et al., 2008). Our provenance record during HS1 supports the idea that
the scavenging of dust by the condensation of water from the atmosphere is a first-order control of the
variability of dust deposition in Antarctic ice cores at the millennial timescales.

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475 4.4. Antarctic Cold Reversal (14.7-12.9 kyr BP - ACR): Shift in dust provenance induced by sea level
476 rise and hydrological rearrangement in Patagonia?
The ACR is a millennial-scale cooling event expressed in broad regions of the Southern Ocean, the South Atlantic and land sectors south of 40°S (Pedro et al., 2016). During this interval, SWW shifted southward compared to LGM and were centered ~49°S spanning from 41°S to 58°S in PAT (Quade and Kaplan, 2017). Some glacier advances or stabilization were also documented in Patagonia (McCulloch et al., 2000; Moreno et al., 2009; García et al., 2012; Davies et al., 2020). In terms of vegetation, between 14 and 13 kyr BP, records document a replacement of steppe vegetation by *Nothofagus* woodland in the eastern piedmont of the Andes (Markgraf et al., 2007), however, most of the Patagonian lowlands were still dominated by steppe vegetation with pollen records showing continued aridity throughout the period (Markgraf et al., 2003; Markgraf et al., 2007; Mancini et al., 2013). Lago Cardiel was at its lowest level by 13 kyr BP denoting of very arid conditions at 49°S in PAT (Quade and Kaplan, 2017). All those elements should have played favorably in terms of dust emission from PAT, yet the HS1-ACR transition is marked by a major change in provenance where EDML dust contains less material from PAT and more from lower-latitude PSA (Fig. 1).

Interestingly, the shift at ~14.5 kyr BP (Fig. 1) is not in phase with the massive drop in dust flux ~18 kyr BP (Fig. 2). However, it is coeval with the largest Antarctic Iceberg Discharge (AID6 - Fig. 8B), the onset of the Bølling interstadial in the Northern Hemisphere (14.6 kyr BP) and with a rapid sea-level rise referred to as Meltwater Pulse (MWP1A) occurring 14.65–14.3 kyr BP (Weber et al., 2014) - Fig. 8C). This transgression submerged a large proportion of the surface that PAT gained during the glacial low-stand (as discussed in section 4.2) and we argue here that it played a major role in the shift of dust composition observed in EDML during the ACR (as well as in EDC ice core – (Gabrielli et al., 2010)). Combining the sea level change over the LGIT (Lambeck et al., 2014) and the bathymetry of NZ and PAT shelves, we inferred the evolution of emerged shelves throughout the LGIT (Fig 8C and S5 for maps). From ~25 to ~16 kyr BP, the NZ and PAT emerged shelves were close to their LGM maximum extension. Crucially, this low stand interval corresponds with the period where the contributions of the high-latitude PSA to EDML were the largest and where the dust assemblage was the most complex but also very much uniform in its composition (Fig. S7A). Between 14.5-14.0 kyr BP, the sea level raised rapidly inducing a fast-paced submersion possibly reaching ~385 km² yr⁻¹ in PAT (Lambeck et al., 2014). By ~12.9 kyr BP, the sea level reached -70 m and the PAT shelf had shrunk
by 70% from to its maximum glacial expansion, with most of the PAT shelf south of 40°S submerged (Fig. S7). We argue that this ACR transgression reduced the area available for aeolian deflation in PAT limiting the contributions of PAT dust to EDML ~14.5 kyr BP. It is important here to note that dust record in the Falklands islands (Monteath et al., 2022) – directly downwind to PAT – also shows a drop in dust flux between 15.5 – 14 kyr BP.

Yet, another ‘delayed’ consequence of the HS1 warming may have contributed to the abrupt decline of PAT contributions to EDML. The deglaciation opened new drainage pathways westward of the Andes for Patagonian rivers (Davies et al., 2020 and refs. therein). Atlantic-to-Pacific drainage reversals and the eastward shift in the continental water divide had a clear impact on the sediment supply of the Patagonian outwash plains. For instance, the reconfiguration of the Baker and Pascua river catchments that drain to the Pacific involved the capture of ~41 000 km² (i.e., the areas surrounding the Lago Pueyrredon/Cochrane and the Lago General Carrera/Buenos Aires) from the eastward-flowing Deseado river catchment (Thorndycraft et al., 2019). The Deseado river catchment is just one of many river basins in PAT that underwent drainage reversal during the LGIT. Caldenius (1932)(Caldenius, 1932) reported drainage reversals from the Chubut drainage basin in northern Patagonia (41–44 °S) down to the Gallegos basin at 52°S. The loss of drainage area in the high Andes (where most of precipitation and erosion occur) induced (i) a weakening of sediment transport to Patagonian outwash plains (Gaiero et al., 2003) but also (ii) a reduction in water discharge that caused, in turn, a shift from braided to meandering river planform (Skirrow et al., 2021). Both of those processes are detrimental to aeolian deflation (Bullard et al., 2016). For Rio Deseado, drainage reversal has been dated to ~15-14 kyr BP (Thorndycraft et al., 2019) but it occurred later (up to ~11 kyr) for other catchments (Glasser et al., 2016; García et al., 2019). For the Rio Chubut, the waning of braided planform was loosely constrained between 12.3±1.0 and 9.4 ± 0.8 kyr BP (Skirrow et al., 2021). The timing of drainage reversals and the ensuing ‘deactivation’ of Patagonian rivers as sediment conveyors for aeolian deflation suggest a diachronous pattern depending on local to regional scale relief and topography. Overall the conjunction of drainage reversals, shift in river planform and the submersion of PAT and NZ shelves altered durably the flux and also the composition of the dust assemblage reaching EDML. The decline of PAT contributions led to a long-lasting increase of the contributions of low-latitude PSA after 14.5 kyr BP
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(Fig. 1) throughout the ACR and YD. The decline of PAT contribution may explain the high-frequency variability of the composition of the dust observed since HS1-ACR transition as low-latitude PSA dust are more prone to rain-out processes during atmospheric transport leading to a more variable composition.

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## 4.5. Younger Dryas (YD - 12.9-11.7 kyr BP)

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Compared to ACR, the YD in SH is marked by milder conditions evidenced by rising values of the δ¹⁸O record in EDML (Fig. 2B), glacier retreat (Kaplan et al., 2010) and by paleo-vegetation records indicating the onset of warm interglacial conditions after 11.5 kyr BP (Moreno et al., 2009). Yet, several pollen sequences and lake-level reconstruction (e.g., Lago Cardiel) indicate the persistence of arid conditions in the Patagonian plains with precipitation >200 mm during YD (Gilli et al., 2001; Mancini et al., 2013; Quade and Kaplan, 2017). Our YD record shows a continuous decline in PAT contribution and an increase in SAF contribution (Fig. 1) indicating that the processes responsible for the reduction of dust emission from PAT during ACR (see section 4.3) were probably still active. Those phenomena are further compounded by a concomitant shift/weakening of the SWW core southward from 49°S at ~13 kyr BP to ~58°S by ~11 ka (Quade and Kaplan, 2017). The near absence of NZ dust in EDML (Fig. 1) match well with a Late glacial cooler and wetter episode between 13.6-12.4 kyr BP (NZce-3, Barrell et al., 2013). Our record also shows an anti-phase between SAF and AUS contributions starting ~15.8 kyr BP up until ~11 kyr BP with alternating millennial-scale events of high and low contributions (Fig. 1). Similar 500-year-long fluctuations have been reported in deuterium records - δD, a proxy for paleotemperature - in East Antarctica at a pace of ~ 1.0-1.4 kyr during Holocene (Masson et al., 2000). Some of those contribution peaks for AUS and SAF can be related to increased dust emissivity in those regions. For instance, our record shows a sharp increase in AUS contribution between 14.3-13.7 and 13-12.6 kyr BP which could also be noted in Lake Surprise in S-E AUS (in Fig. 7B, (Falster et al., 2018)) and in sediment core MD03-2611 (only the 13-12.6 kyr BP peak) located south of Australia (De Deckker et al., 2012).

Regarding SAF, while dust records are scarce, very arid periods were reported between 14.4-12.5 kyr BP and 10.9-9.3 kyr BP in marine sediment off Namibia (Shi et al., 1998), which might explain the
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elevated SAF contributions observed in those intervals in our record (Fig. 1). Similarly, the
Makgadikgadi pan complex exhibits evidence of drying out at 12.2-11.9 kyr (Burrough et al., 2009) and
more broadly, there is evidence of drier conditions in central Southern Africa between 13 to 10 kyr BP
compared to ACR and HS1 (Thomas and Shaw, 2002). At this stage, the cause(s) of this AUS-SAF
alternance in dust contribution to Antarctica during the 15.8-11 kyr BP interval observed in our
provenance record is unclear and warrant further research.

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## 4.6. Early Holocene (11.7-7.5 kyr): prime period for PAP and SAF contributions

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Up until ~9 kyr BP, pollen records and lake level reconstructions in eastern PAT document wetter and warmer conditions with a southward shift (possibly between 52°-63°S - (Anderson et al., 2009)) of the SWW over Patagonia ((Mancini et al., 2013); (Quade and Kaplan, 2017); (Villa-Martínez and Moreno, 2021)). This early Holocene optimum is also expressed across E. Antarctica between 11.5 to 9 kyr BP (Masson et al., 2000). Yet, deuterium excess record at EDML shows that this climatic optimum is interrupted by two cold events at ~11.6-11.5 and 9.8-9.6 kyr BP (Stenni et al., 2010). Interestingly, those two cold episodes are well marked by minima (maxima) in PAT (SAF and PAP) contributions and in total dust deposition (Fig. 1 and 2). Stenni et al., (2010) (Stenni et al., 2010) interpreted the ~11.6-11.5 and 9.8-9.6 kyr BP cold events at EDML as reflecting abrupt changes in the air/moisture source areas reaching EDML - possibly towards higher latitude (as opposed to the midlatitude storm track (Reboita et al., 2019)). This interpretation is consistent with the observed sudden drop in PAT contribution at EDML during those cold events.

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Apart from the latter two events, our record shows a rise of PAT contributions culminating at 93% at 10.04 kyr BP (Fig.1), mirrored by an increasing trend of atmospheric dust deposition peaking sharply at 10.167-10.147 kyr BP and 10.514-10.411 kyr BP (Fig. 2). The abrupt nature of those peaks is suggestive of large volcanic eruptions. Six volcanoes had major explosive eruptions in Patagonia (Volcanic Explosivity Index of 5) in relative “quick” succession: Mt Burney (9.45±0.64 kyr BP), Chaiten (9.92±0.13 kyr BP), Llaima (10.12±0.1 kyr BP), Yanteles (10.34±0.18 kyr BP), Corcovado (10.34±0.18 kyr BP), Calbuco (10.39±0.1) (Fontijn et al., 2014). There is no record of non-sea-salt sulfate concentration in EDML for the early Holocene, however the timing of those eruptions match

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SO₄ peaks in EDC during the 10-11 kyr BP interval (Kurbatov et al., 2006). As EDML is downwind and closer to PAT than EDC, volcanic ash likely reached EDML, hence contributing to the relatively high atmospheric deposition and the increased PAT contributions. The early Holocene also witnessed a shift in fire regime. As early as 12 kyr BP, charcoal records throughout the mid-and high-latitudes of Patagonia (south of 40°S) register a major increase in fire-episode frequency and in their magnitude (Markgraf et al., 2007; Huber et al., 2004). Fire activity was already greater than present at ~12 kyr BP and increased further and became widespread throughout Southern South America at 9.5 kyr BP (Whitlock et al., 2007). Fires co-emit mineral soil-dust particles (Wagner et al., 2021), but also consume the soil-protecting vegetation and induce the breakdown of larger soil aggregates into finer particles favoring aeolian deflation (Dukes et al., 2018). The interval of high fire frequency ended at different times throughout Patagonia: south of 50°S it lasted until 6 kyr BP (Huber et al., 2004), at ~45°S the reduction in fire occurred between 7-8 kyr BP (Haberle and Bennett, 2004) while, at mid-latitudes (40-42°S), fires were essentially absent after 10 kyr BP (Whitlock et al., 2007). This increased fire activity and the sustained volcanic activity may explain the elevated contribution from PAT throughout the early Holocene (apart from the two cold events at EDML).

Besides PAT, the 11.7-7.5 kyr BP interval is also a prime period for PAP contributions to EDML (Fig. 4F). As discussed above, PAP dust emission is correlated with aridity phases denoted by the drying-out of lakes in the Altiplano. After the Tauca lake phase (~17.5-14.5 kyr) during which a large paleolake formed, the Coipasa lake transgression occurred before 13.3 kyr BP, culminated by ~12.5 kyr BP and regressed completely before 11.5 kyr BP (Blard et al., 2011; Placzek et al., 2006). The 'Coipasa' and 'Tauca' high-stands correspond well with periods of low dust emission from PAP (Fig. 1 and 4F). In contrast, after 11.5 kyr BP, as no further transgressive-regressive cycles occurred and conditions on the Altiplano were persistently dry (Condom et al., 2004), our record shows an increase of dust contributions. As such, our observations are in line with those of Gili et al., (2017) and Torre et al., (2022) who highlight the role of the Altiplano as a dust supplier during interglacial periods on top of Patagonia's contribution.

## 5. Conclusion
Overall, our dust provenance record in EDML suggests a relationship between eustatic sea level, high-latitude PSA hydrology and the composition of atmospheric deposition in the E. Antarctica during LGIT. As such, this study thus opens a new perspective on the use of deep ice cores (and possibly of other type of dust deposits such as loess, peat or sediment) as archives of dust provenance. Our approach nonetheless has some limitations that could potentially impact the reconstructed provenance record.

Our statistical approach is empirically constrained by a REE pattern database covering the major PSA in the SH implying that our result could potentially be modified by the addition of missing sources or more representative samples in each PSA. Our model would also benefit from a better geochemical characterization of Australian dust (that is, its fine fractions) or a larger cover of Southern Africa dust/topsoil (in particular, from paleolakes areas). At the other end of our statistical analysis, the precision of REE patterns in ice cores could be improved by analyzing total REE concentrations - at least in some portions - instead of relying on partial dissolution. To correct for partial dust dissolution of dust, we applied *cf* values which were determined in EDC (on a limited set of samples) to the EDML REE dataset. Although dust deposition EDC and EDML are close in terms of composition, *cf* values would need to be determined in EDML on a representative set of samples to refine further our provenance record. With this study, we hope to encourage future studies to place greater emphasis on the use of REE to trace dust provenance over time.

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640

641
642 Acknowledgments and funding

643 S. Bonneville acknowledges the support of the Fonds National de la Recherche Scientifique (FRS-
644 FNRS CDR-J.0017.22). A. Vanderstraeten was supported at the LOG by the CaPPA project (Chemical
645 and Physical Properties of the Atmosphere) funded by ANR (ANR-ll-LABX-0005-01), and the CNES
646 (TOSCA/DOC project). N. Mattielli acknowledges the BRAIN-be funding for the CHASE Antarctica
647 research program (BR/175/A2/Chase). The CESAM chamber team is acknowledged for their help to
648 obtain fine fraction of SAF samples. This CNRS-INSU facility received funding from the European
649 Union's Horizon 2020 research and innovation program through the EUROCHAMP-2020
650 Infrastructure Activity (No. 730997). Sample collection was supported by the Natural Science and
651 Engineering Research Council of Canada (Discovery Grant RGPIN-2016-05417). Fieldwork in
652 Namibia was supported through permits issued by the Ministry of the Environment and Transport
653 (#2255/2017) and the National Commission on Research, Science, & Technology (#RPIV00272018).
654 We are grateful to J. King, M. Hipondoka, G.Maggs-Kölling, and the Gobabeb Research and Training
655 Centre for the collection of the field samples.

656 Data availability

657 All data needed to evaluate the conclusions in the paper are present in the paper and/or the
658 Supplementary Materials. Additional data related to the paper may be requested from the authors.
659 Correspondence and requests should be addressed to S.B.

660 Contributions

661 St.B. directed the research and wrote the manuscript. St.B, A.V. and G.L. developed together the model.
662 Sy.B, A.B. and A.V. performed the size fractionation of Namibian samples and their analysis. A.V. did
663 the isotopic back-calculations. All authors discussed the results and commented on the manuscript.
664

665

666 Competing Interest: The authors declare no competing interests.

667
<figure><img src="image_3.png" /></figure>
669

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673

Figure 1: Evolution of contributions to EDML from PSA between 7.55 and 26.55 kyr (% of total dust deposition). Bold lines denote the mobile average over 7 steps (~289 years). Gray vertical zones delineate the various epochs of the transition from LGM to Holocene. Central-Western-Argentina is not shown as its contribution is very low, between 0.03 to 0.3 % of the total deposition in EDML. 95% confidence interval for each time-step is plotted in Fig S5 of Supplementary Materials.
<figure><img src="image_4.png" /><figcaption>Figure 2: (A) Reconstruction of fluxes from large PSA to EDML (5-steps mobile average) based on the contribution of large PSA (Fig. 1) and the nssCa²⁺ flux to EDML from (Fischer et al., 2007) shown in (B) together with the record of δ¹⁸O in EDML (Barbante et al., 2006).</figcaption></figure>
<figure><img src="image_5.png" /></figure>

Figure 3 and insert: Compilation of $^{87}$Sr/$^{86}$Sr and $\varepsilon_{Nd}$ isotopic fingerprint representative of PSA, dust in ice core (red and black squares for Holocene and LGM samples respectively) and calculated isotopic values using REE contributions from EDML (this study - colored dots). Colored-frame areas denote the domains of isotopic values of respective PSA used for the isotope calculation, i.e., involved in the dust assemblage (data from Grousset et al., 1992; Delmonte et al., 2004; Gingele and De Deckker, 2005; De Deckker et al., 2014; Revel-Rolland et al., 2006; Gaiero, 2007; Gili et al., 2017; Gili et al., 2022; Li et al., 2020; Koffman et al., 2021). Note that the SAF domain has an angular frame at high radiogenic values as $\varepsilon_{Nd}$ of -25 are published without the corresponding $^{87}$Sr/$^{86}$Sr ratio (Li et al., 2020). Note also that both SAF and AUS domains expands beyond the limit of the graph with dust samples $^{87}$Sr/$^{86}$Sr > 0.730 (not shown here). Black squares represent LGM and glacial dust samples from EDC
691 (stage 2, 4 and 6), Vostok (4 to 12), Dome B, Komsomolskaya and old Dome C (Grousset et al., 1992;

692 Basile et al., 1997 ; Delmonte et al., 2004 ; Delmonte et al., 2010). Red squares correspond to Holocene

693 dust isotopic signature measured in EDC and Vostok dust (Delmonte et al., 2007). REE-based

694 calculated isotopic fingerprints (blue to red dots corresponding to the 5 epochs among LGIT) were

695 obtained from (i) the % contributions of PSA (Table S3 – data shown here are composition averaged

696 over 500-year periods) and (ii) the isotopic fingerprint and elemental concentration of the PSAs end-

697 members (Table S4).

698
<figure><img src="image_6.png" /></figure>
Figure 4: (A) Light blue and orange lines represent respectively the record of Poaceae (a proxy for dryer conditions) and arboreal pollens (a proxy for wetter, moist climate) from Canal de la Puntilla and Huelmo sites (41-42°S) in Chiloe Island in northwestern Patagonia (data from Moreno et al., 2015). The vertical blue bars represent extreme glacial conditions, green bars represent cold-temperate conditions, red bars represent warm climatic conditions based on changes of pollen-inferred vegetation in (Moreno et al., 2015). (B) Reconstruction of dust flux in EDML from Patagonia (dark blue line) between 7.55 and 26.5 kyr. Triangles denote major ice advances ~27 kyr, ~18 kyr and 13 kyr in the Southern Andes at 37°S (Davies et al., 2020; García et al., 2019). (C and D) Magnetic susceptibility records used as a proxy of dust flux from PAT in sites MD07-3134 (in Scotia Sea at 57°26′S 43°27′W) and Potrok Aike lake located in southern PAT at 51°57S 70°22′W (data from Weber et al., (2012) and Haberzettl et al., 2009). (E) Percentage of polar foraminiferal species in South Atlantic at TNO57-21 site 41°08′S 7°49′E (data from Barker et al., 2009). (F) Evolution of dust flux in EDML from Puna-Altiplano Plateau (PAP) with paleolake level reconstruction from shoreline dating in the southern basin of the Altiplano from (Blard et al., 2011) and (Placzek et al., 2006).

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721
<figure><img src="image_7.png" /></figure>

Figure 5: (A) Evolution of dust flux in EDML from New Zealand (green line) between 7.55 and 26.5 kyr. Orange and dark blue lines denote tall forest tree and herbfield pollens from Okarito Lagoon, Westland, S-NZ (data from Williams et al., 2015). The vertical blue bars represent extreme glacial conditions, green bars represent cold/temperate conditions, red bars represent warm conditions according to NZ-INTIMATE climate event stratigraphy (Barrell et al., 2013). Time interval for proglacial lake formation in SNZ was compiled from ((Sutherland et al., 2019) while major ice advances are reported at ~27 kyr, 23-22 kyr, ~20 kyr, ~18 kyr and later ~13 kyr at the transition ACR/YD. (Denton et al., 2021; Kaplan et al., 2010).
<figure><img src="image_8.png" /><figcaption>Figure 6: Evolution of dust flux in EDML from Southern Africa (E) compared with marine and terrestrial proxies of regional climate. (A) Total pollen flux (indicator of trade wind intensity) in site GeoB1711-4 located on Namibia continental slope (data from Shi et al., 2001). (B) Aridity Index based on vegetation pollen records found near Pella, N-W South Africa (data from Lim et al., 2016). (C) Distal aeolian dust flux to Mfabeni Peatland - East coast of Southern Africa (data from Humphries et al., 2017). (D) Coarse-grained dust diameters (wind strength proxy) recorded in MD962087 core on the Namibian continental slope (25°6′S, 13°38′E) (Pichevin et al., 2005). (E) Highstands (blue bars) in the Makgadikgadi lake, Ngami lake and Mababe depression (i.e., Paleo-megalake Makgadikgadi system)</figcaption></figure>
741 and Etosha systems. Orange bars refer to periods where there is evidence for very low or absent lakes

742 (Burrough et al., 2009).

743
<figure><img src="image_9.png" /></figure>

Figure 7: (D) Evolution of dust flux from AUS to EDML between 7-27 kyr compared to δ¹³C of organic matter (A) and Si flux (B) in Lake Surprise (south-eastern Australia - 38.06°S 141.91°W) respectively (data from (Falster et al., 2018)). (C) denote the % of aeolian content in Native Companion Lagoon in Eastern Australia (27°30′S 153°30′E, data from (Petherick et al., 2009)).
<figure><img src="image_10.png" /><figcaption>Figure 8: (A) Contributions of PAT to EDML during the last glacial-interglacial transition (% contributions in pale blue line and 7-steps mobile average in bold blue line). (B) Record of iceberg-rafted debris flux in Scotia Sea showing eight phases of Antarctic Iceberg Discharge events (AID1 to AID8) at approximately 20–19 kyr ago, 17–16 kyr ago, 15–14 kyr ago, 13.5 kyr ago, 13 kyr ago, 12 kyr ago, 11 kyr ago and 10–9 kyr ago (vertical shading data from (Weber et al., 2014)). (C) Sea level (in m) between 7-27 kyr BP (open gray symbols - data from (Lambeck et al., 2014)) and associated decrease of continental shelf surface area in PAT (open blue symbols) and south-NZ (open green symbols) with respect to maximum glacial low-stand. (D) Derivative of sea level between 7-27 kyr BP highlighting a major submersion between 14-15 kyr BP.</figcaption></figure>
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1193 https://doi.org/10.1016/j.palaeo.2021.110459

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1195 Fires—Theoretical Estimates From Two Conceptually Different Dust Emission

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1197 https://doi.org/10.1029/2020JD034355

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1219

1220

1221
1222 **List of Supplementary materials**

1223 Supplementary text: *Laboratory method and analyses*

1224 Supplementary text: *Effect of initialization conditions (i.e. order of DS) in the REE input database on*

1225 **% contributions**

1226 Fig S1: Localization of dust sources (DS) used in our apportionment model.

1227 Fig. S2: REE patterns of all the Dust Source (DS) assembled by PSA

1228 Fig S3: Examples of modelled fit to REE pattern in EDML ice core.

1229 Fig S4: Example of distributions of % contributions of DS “PataGi4”.

1230 Fig S5: Aggregated density probability function per PSA.

1231 Fig S6: Number of DS involved in the dust assemblage through time and histogram of R median

1232 values.

1233 Fig S7: Variability of % PSA contributions for successive runs of model and for different order of DS

1234 in the REE database.

1235 Fig S8: Maps of emerged shelves in PAT and S-NZ over time through the LGIT.

1236 Table S1: Correction factor (cf) and error applied to EDML samples.

1237 Table S2: Description of Dust Sources (DS) included in the apportionment model.

1238 Table S3: Model outputs for the 396 samples.

1239 Table S4: Isotopic composition and Sr and Nd concentrations of PSA.

1240 **Supplementary Information**

1241 *Laboratory method and analyses*

1242 Regarding PSA samples compilation, our study relies mostly on literature data ((Gaiero et al., 2004);

1243 (Marx et al., 2005); (McGowan et al., 2005); (Marx and Kamber, 2010); (Gili et al., 2017); (Li et al.,

1244 2020); (Koffman et al., 2021)). Yet, data from Southern Africa (SAF) are sparse, only six aerosol

1245 samples were available (Li et al., 2020). Thus, we analyzed the REE contents in 11 additional topsoil

1246 samples collected in (Gili et al., 2022) from the Namibian coast at Kuiseb, Omaruru and Huab river bed
sediments. The fine fraction (i.e., < 5µm particles) was extracted and REE analysis were conducted at the G-Time laboratory at ULB.

To avoid contamination, the sample preparation and chemical analysis were performed in a laminar flow hood (Class 100) within a Class 1000 laboratory. The reagents i.e., 6 mol L⁻¹ HCl, 14 mol L⁻¹ HNO₃ and 23 mol L⁻¹ HF (Merck®, pro-analis) - were systematically purified via distillation and sub-boiling before use. MilliQ water (18.2 M Ω.cm) was also used at all steps. All analytical procedures took place under the cleanest conditions using acid-cleaned Savillex® or Teflon vials, acid-cleaned uncoloured tips and powder-free gloves to prevent any contamination.

After careful weighting, the < 5µm fraction was dissolved in 1.5 mL of 14 M HNO₃ and 0.5 mL of 23 M HF on a hot plate at 120 °C for 48h. The solution was dried down on a hot plate at 90 °C before addition of 2 mL of 6 M HCl and re-dissolved for 24 hours at 120 °C. Samples were dried down again before appropriate dilution for ICP-MS analysis. The REE content was determined on an Agilent 7700 ICP-mass spectrometer. Following sample digestion and addition of indium (In) as an internal standard and adequate dilution, REE (from La to Lu) were measured. Quantification took place against an external calibration curve, while oxide production was evaluated and corrected using particular combinations of single-element standard solutions. The blank levels had no significant effect on the calculated sample concentrations. Next to the samples, replicates, blanks, international reference basalt BHVO-2 and andesite AGV-2 of the U.S. Geological Survey with certified compositions were processed in the same way and used to evaluate the accuracy and precision of the analytical procedure. Based on repeated measurements of these rock standards, the analytical precision of the procedure outlined above is estimated to be below 3.5% relative standard deviation (%RSD). The obtained REE concentrations for these reference materials agree within uncertainty (1 standard deviation) with those certified, in other words, show a relative difference >1%.

*Effect of initialization conditions of the REE input database on the model output.*

Initialization conditions (i.e., the order of 207 DS in the input matrix in Eq.3) have been reported in the literature to have an influence on the solution and convergence rate of iterative algorithms such as
the one at the basis of the *lsqnonneg* routine (Fathi Hafshejani and Moaberfard, 2022). A potential issue would come from the fact that the algorithm might find a local minimum after a given number of iterations regardless of the “fitting value” of the DS used. Thus, for a given order of DS in the input matrix, this artefact would create a positive bias towards a certain set of DS unrepresentative of their actual contributions to the REE mix but only as a calculation artefact. To investigate this issue (i.e. the sensibility of our results to the set-up of our input matrix), we performed (i) a run of the model with an input matrix for which the order of DS was permuted and (ii) two successive runs of the model with the same, non-permuted matrix. All model runs were done with 5000 Monte Carlo simulations.

The non-permutated matrix is the one used originally in the model to produce the results presented here in which DS are ranked as follow: PAT (DS₁ to DS₃₉), CWA (DS₄₀ to DS₄₄), PAP (DS₄₅ to DS₅₈), SAF (DS₅₉ to DS₇₅), AUS (DS₇₆ to DS₁₈₂), NZ (DS₁₈₃ to DS₂₀₇). The permuted matrix has the DS of PAT permuted to the last position of the input matrix as described here: CWA (DS₁ to DS₄), PAP (DS₅ to DS₁₈), SAF (DS₁₉ to DS₃₅), AUS (DS₃₆ to DS₁₄₂), NZ (DS₁₄₃ to DS₁₆₇), PAT (DS₁₆₈ to DS₂₀₇).

This experiment allows (i) to visualize the variability in the outputs between two successive runs of the model set-up with the same initialization conditions and (ii) to compare it with the variability of the model outputs induced by changes in the initial conditions. Fig. R25 (bottom) shows that the variability between successive runs of model with the non-permuted input matrix is very limited especially considering the 5000 Monte Carlo simulations. With respect to the output variability caused by the initialization conditions, Fig. S7 (top) clearly demonstrates that the order of DS of the input matrix has no significant effect on the results of the model. In addition, there is no increase in the residuals (i.e. difference between the contributions of a given DS for a given timestep) between the two sets of simulations (i.e., using permuted or non-permuted matrices), showing that initial conditions do not influence our results or create bias in favor of certain DS based on their position in the input matrix.

Similarly, we also investigated whether the number of DS per PSA has an influence on their output.

We have tested this potential bias along the development of the database of REE patterns for New Zealand and Australia. In a first stage, our input database for New Zealand was limited to 6 DS from
1300 Marx et al., (2005), which were complemented at a later stage with an extra 19 DS patterns from
1301 Koffman et al. (2021). Yet, the outputs of the model for New Zealand with 6 and 25 REE patterns
1302 were close. For Australia, we started with 51 REE patterns (mostly from Darling Basin and Lake
1303 Eyre) and later added Murray Basin and other nearby areas from Marx and Kamber, (2010) for a total
1304 of 107 REE patterns. Again, this increase did not change much Australian contributions. This shows
1305 that our database includes REE pattern representative of the source of dust to EDML and that the %
1306 contributions of a given PSA does not depend much on the number of DS associated to this particular
1307 PSA.

1308
<figure><img src="image_11.png" /></figure>

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

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

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

Central Western
Argentina (CWA)

<figure><img src="image_16.png" /></figure>

Southern
Africa (SAF)

<figure><img src="image_17.png" /></figure>

New-
Zealand (NZ)

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

Puna AltiPlano
(PAP)

<figure><img src="image_19.png" /></figure>

Patagonia

<figure><img src="image_20.png" /></figure>

Australia
Figure S1: Localization of dust sources (DS) used in our apportionment model in South America (including Patagonia, Central Western Argentina and Puna-Altiplano), Southern Africa, South-Eastern Australia and South-Island in New Zealand (see Table S2 for details).
2

3

4

5

6

7

8

<figure><img src="image_21.png" /><figcaption>Figure S2: REE patterns of all the Dust Source (DS) assembled by PSA. All the concentrations are normalized to UCC (Rodnick et al. 2003).</figcaption></figure>
<figure><img src="image_22.png" /></figure>

Figure S3: (A), (B), (C), (D), (E) illustrates some examples of modelled fits (over 5000 MC simulations) compared to their corresponding Dust Ice Core REE pattern (see Table S2) and the associated correlation factor R (inserts). (E) illustrates of the cumulated individual contributions of the DS for the modelled fit to the DIC at 26400 kyr BP. Note that all those individual DS contributions are later aggregated by PSA for each time-step.
15

<figure><img src="image_23.png" /></figure>

Fig S4: Illustration of distributions of % contributions of DS “PataGi4” (see Table S2) between 18955 (step 286) and 20 532 yr BP (step 304) for 1000 MC iterations. n denotes the number of iterations for which the DS appears at a given % contribution in the dust mixture.

16

17

18

19

20

21
<figure><img src="image_24.png" /></figure>
Fig S5: Aggregated density probability function per PSA. From top to bottom: Patagonia, Australia, Southern Africa, New-Zealand (South Island), Puna-Altiplano and coefficient of correlation R (Eq. 4). Vertical light blue dots are the results of the 5000 MC iterations for each time-step aggregated per PSA. Those MC iterations form a distribution (see Fig. S2) from which we extract the 95% confidence interval (vertical dark blue dots). The median of the latter interval is used then as a quantification of the % contribution of the PSA for this timestep to EDML total deposition. Here, the median values are represented by the 9-timesteps mobile average (dark blue line). The black dots denote the total dust flux (as nss Ca²⁺) to EDML (data from Fischer et al. 2007). The pink line represents the flux of dust (9-timesteps mobile average) deposited at EDML in the provenance of the given PSA (i.e., the product of the % contribution and total dust flux).
<figure><img src="image_25.png" /><figcaption>Figure S6: (A) Number of DS (out of 207) involved in the fit of each dust ice core (DIC) sample over the last glacial-interglacial transition. (B) Histogram of R median value for the 396 EDM1 samples. Note that for our reconstruction we rejected the samples that had R median values < 0.67 (99% confidence interval, p-value <0.01).</figcaption></figure>
<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /><figcaption>Figure S7 (Top) % PSA contribution in model run with DS in permuted order vs. % PSA contribution in model run with DS in non-permuted order of DS (i.e. order DS of the original model run). (Bottom) shows the two successive runs of the model with the non-permuted order of DS in the input matrice. Solid line denotes the 1:1 line. The different color refers to the various PSA. Insert graphs shows the residuals plot of the regressions for each PSA.</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>Fig. S8: (A and B) Maps of emerged shelves in PAT and S-NZ over time through the LGIT. Black line in the -500 m contour lines. Different colors denote the shelves submerged from the maximum glacial low-stand up until 16.2 kyr (black), from 16.2 to 14.3 kyr, (purple), from 14.3 to 12.8 kyr (orange) and</figcaption></figure>

58

59

60

61
62 later than 12.8 kyr (yellow). Results of extended shelf surface area are similar to (17) for PAT and (16)

63 for S-NZ.

64